Publications by authors named "Mannudeep K Kalra"

230 Publications

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.

J Digit Imaging 2021 Feb 25. Epub 2021 Feb 25.

Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.
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http://dx.doi.org/10.1007/s10278-021-00430-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906242PMC
February 2021

Reply to "Quality Control of Radiomics Study to Differentiate Benign and Malignant Hepatic Lesions".

AJR Am J Roentgenol 2021 03;216(3):W13

Massachusetts General Hospital, Harvard Medical School, Boston, MA.

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http://dx.doi.org/10.2214/AJR.20.24741DOI Listing
March 2021

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.

NPJ Digit Med 2021 Feb 18;4(1):29. Epub 2021 Feb 18.

Health Informatics Lab, Metropolitan College, Boston University, Boston, USA.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
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http://dx.doi.org/10.1038/s41746-021-00399-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7893172PMC
February 2021

Use of radiomics to differentiate left atrial appendage thrombi and mixing artifacts on single-phase CT angiography.

Int J Cardiovasc Imaging 2021 Feb 5. Epub 2021 Feb 5.

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

To assess if radiomics can differentiate left atrial appendage (LAA) contrast-mixing artifacts and thrombi on early-phase CT angiography without the need for late-phase images. Our study included 111 patients who underwent early- and late-phase, contrast-enhanced cardiac CT. Of these, 79 patients had LAA filling defects from thrombus (n = 46, mean age: 72  ±  12 years, M:F 26:20) or contrast-mixing artifact (n = 33, mean age: 71  ±  13 years, M:F 21:12) on early-contrast-enhanced phase. The remaining 32 patients (mean age: 66  ±  10 years, M:F 19:13) had homogeneous LAA opacification without filling defects. The entire LAA volume on early-phase CT images was manually segmented to obtain radiomic features (Frontier, Siemens). A radiologist assessed for the presence of LAA filling defects and recorded the size and mean CT attenuation (HU) of filling defects and normal LAA. The data were analyzed using multiple logistic regression with receiver operating characteristics area under the curve (AUC) as an output. The radiologist correctly identified all 32 patients without LAA filling defects, 42/46 LAA with thrombi, and 23/33 contrast mixing artifacts. Although HU of LAA thrombi and contrast mixing artifacts was significantly different, with the lowest AUC (0.66), it was inferior to both radiologist assessment and radiomics (p = 0.05). Combination of radiologist assessment and radiomics (AUC 0.92) was superior to HU (0.66), radiomics (0.85), and radiologist (0.80) alone (p < 0.008). Radiomics can differentiate between LAA filling defects from thrombi and contrast mixing artifacts on early-phase contrast-enhanced CT images without the need for late-phase CT.
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http://dx.doi.org/10.1007/s10554-021-02178-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863854PMC
February 2021

Association of AI quantified COVID-19 chest CT and patient outcome.

Int J Comput Assist Radiol Surg 2021 Jan 23. Epub 2021 Jan 23.

Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

Purpose: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome.

Methods: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients).

Results: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets.

Conclusions: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.
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http://dx.doi.org/10.1007/s11548-020-02299-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822756PMC
January 2021

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

Sci Rep 2021 01 13;11(1):858. Epub 2021 Jan 13.

Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r = 0.79-0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients' age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90-0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87-0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.
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http://dx.doi.org/10.1038/s41598-020-79470-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807029PMC
January 2021

Quantifying and leveraging predictive uncertainty for medical image assessment.

Med Image Anal 2021 02 14;68:101855. Epub 2020 Oct 14.

Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ, USA.

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy. Finally, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.
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http://dx.doi.org/10.1016/j.media.2020.101855DOI Listing
February 2021

Artificial intelligence in image reconstruction: The change is here.

Phys Med 2020 Nov 24;79:113-125. Epub 2020 Nov 24.

Department of Radiology, Division of Thoracic Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address:

Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)-based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.
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http://dx.doi.org/10.1016/j.ejmp.2020.11.012DOI Listing
November 2020

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study.

Abdom Radiol (NY) 2020 Nov 26. Epub 2020 Nov 26.

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Court, Room 248, Boston, MA, 02114, USA.

Purpose: To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi.

Methods: The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output.

Results: Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85-0.92). Higher-order radiomics (gray-level size zone matrix - GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89-0.92).

Conclusion: Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.
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http://dx.doi.org/10.1007/s00261-020-02865-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690335PMC
November 2020

Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 Countries in the IAEA Study.

Radiology 2021 03 10;298(3):E141-E151. Epub 2020 Nov 10.

From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., M.K.K.); Radiation Protection of Patients Unit, International Atomic Energy Agency, Vienna, Austria (O.H., J.V.); The Royal Hospital, Muscat, Oman (R.A.U.); Alfa Scan Radiology Center, Cairo, Egypt (S.A.); Department of Radiology, Lithuanian University of Health Sciences, Kaunas, Lithuania (A.B.); Institute of Physics, University of São Paulo, São Paulo, Brazil (P.R.C.); Hamad Medical Corporation, Doha, Qatar (A.D.); Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Skopje, North Macedonia (V.G.); Tartu University Hospital, University of Tartu, Institute of Clinical Medicine, Department of Radiology, Tartu, Estonia (P.I.); Aleksandrovska University Hospital, Sofia, Bulgaria (D.K.L.); Institute of Radioprotection and Dosimetry, National Nuclear Energy Commission, Rio de Janeiro, Brazil (S.K.R.); Radiology Department, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran (I.M.); Medical Physics Unit, A.O.U. Città della Salute e della Scienza di Torino, Turin, Italy (O.R.); Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau, Moldova (N.R.); Radiological and Medical Sciences Research Institute, Ghana Atomic Energy Commission, Accra, Ghana (I.S.); University Hospital, Lomonosov Moscow State University, Moscow, Russian Federation (V.S.); University Hospital Osijek, Faculty of Medicine, J.J. Strossmayer University of Osijek, Osijek, Croatia (T.T.); and Department of Radiology, Hôpital Européen Georges Pompidou, Paris, France (C.V.N.T.).

Background There is lack of guidance on specific CT protocols for imaging patients with coronavirus disease 2019 (COVID-19) pneumonia. Purpose To assess international variations in CT utilization, protocols, and radiation doses in patients with COVID-19 pneumonia. Materials and Methods In this retrospective data collection study, the International Atomic Energy Agency coordinated a survey between May and July 2020 regarding CT utilization, protocols, and radiation doses from 62 health care sites in 34 countries across five continents for CT examinations performed in patients with COVID-19 pneumonia. The questionnaire obtained information on local prevalence, method of diagnosis, most frequent imaging, indications for CT, and specific policies on use of CT in COVID-19 pneumonia. Collected data included general information (patient age, weight, clinical indication), CT equipment (CT make and model, year of installation, number of detector rows), scan protocols (body region, scan phases, tube current and potential), and radiation dose descriptors (CT dose index and dose length product). Descriptive statistics and generalized estimating equations were performed. Results Data from 782 patients (median age, 59 years [interquartile range, 15 years]) from 54 health care sites in 28 countries were evaluated. Less than one-half of the health care sites used CT for initial diagnosis of COVID-19 pneumonia and three-fourths used CT for assessing disease severity. CT dose index varied based on CT vendors (7-11 mGy; < .001), number of detector rows (8-9 mGy; < .001), year of CT installation (7-10 mGy; = .006), and reconstruction techniques (7-10 mGy; = .03). Multiphase chest CT examinations performed at 20% of sites (11 of 54) were associated with higher dose length product compared with single-phase chest CT examinations performed in 80% of sites (43 of 54) ( = .008). Conclusion CT use, scan protocols, and radiation doses in patients with coronavirus disease 2019 pneumonia showed wide variation across health care sites within the same and between different countries. Many patients were imaged multiple times and/or with multiphase CT scan protocols. © RSNA, 2021 See also the editorial by Lee in this issue.
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http://dx.doi.org/10.1148/radiol.2020203453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673104PMC
March 2021

Integrative analysis for COVID-19 patient outcome prediction.

Med Image Anal 2021 01 13;67:101844. Epub 2020 Oct 13.

Department of Biomedical Engineering and the Center for Biotechnology and Interdisciplinary Studies at Rensselaer Polytechnic Institute, Troy, NY 12180, USA. Electronic address:

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.
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http://dx.doi.org/10.1016/j.media.2020.101844DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553063PMC
January 2021

Low contrast volume dual-energy CT of the chest: Quantitative and qualitative assessment.

Clin Imaging 2021 Jan 6;69:305-310. Epub 2020 Oct 6.

Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America.

Purpose: To evaluate the image quality of chest CT performed on dual-energy scanners using low contrast volume for routine chest (DECT-R) and pulmonary angiography (DECTPA) protocols.

Materials And Methods: This retrospective study included dual-energy CT scans of chest performed with low contrast volume in 84 adults (34M:50F; Age 69 ± 16 years: Weight 71 ± 16kg). There were 42 patients with DECT-R and 42 patients with DECT-PA protocols. Images were reviewed by two thoracic radiologists. Qualitative assessment was done on a four-point scale, for subjective assessment of contrast enhancement and artifacts (1 = Excellent, 2 = optimal, 3 = suboptimal, and 4 = Limited) in the pulmonary arteries and thoracic aorta, on virtual monoenergetic and material decomposition iodine (MDI) images. Quantitative assessment was performed by measuring the CT (Hounsfield) units in aorta and pulmonary arteries. The estimated glomerular filtration rate (eGFR) was calculated before and after CT scans. Two tailed student's t-test was performed to assess the significance of findings, and strength of correlation between readers was determined by Cohen's kappa test.

Results: DECT-PA and DECT-R demonstrated excellent/adequate contrast density within the pulmonary arteries (up to segmental branch), and aorta. There was no suboptimal or limited examination. There was strong interobserver agreement for arterial enhancement in pulmonary arteries (kappa = 0.62-0.89) and for thoracic aorta (kappa = 0.62-0.94). Pulmonary emboli were seen in 3/42(7%) in DECT-R and in 5/42(12%) in DECT-PA. There was no significant change in eGFR before and after IV contrast injection (p = 0.46-0.52).

Conclusion: DECT-R and DECT-PA performed with low contrast volume provide diagnostic quality opacification of the pulmonary vessels and aorta vessels.
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http://dx.doi.org/10.1016/j.clinimag.2020.10.006DOI Listing
January 2021

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

IEEE J Biomed Health Inform 2020 12 4;24(12):3529-3538. Epub 2020 Dec 4.

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.
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http://dx.doi.org/10.1109/JBHI.2020.3030224DOI Listing
December 2020

Optimization of paranasal sinus CT procedure: Ultra-low dose CT as a roadmap for pre-functional endoscopic sinus surgery.

Phys Med 2020 Oct 7;78:195-200. Epub 2020 Oct 7.

Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

Objective: To assess image quality and radiation dose associated with ultra-low dose CT protocol for patients with benign paranasal sinus diseases undergoing functional endoscopic surgery (FESS).

Methods: We scanned the head portion of Alderson RANDO phantom on a second generation, dual-source, multidetector-row CT scanner (Siemens Definition Flash) using standard-dose and five low-dose protocols. Two radiologists assessed the image quality for each protocol to determine best ultra-low-dose protocols for imaging patients with benign paranasal sinus diseases undergoing FESS. The ultra-low-dose CT protocols were then used for scanning. Thereafter, 40 adult patients (age range 18-54 years, M:F 23:17) were scanned with the four low dose scanning protocols (10 patients per protocol). On both transverse and coronal reformatted CT images, two radiologists assessed visibility of key anatomic landmarks for FESS on a 2-point scale (1 = clear and complete visualization; 2 = suboptimal visualization). Data were analyzed with descriptive statistics and Cohen's kappa coefficient for interobserver agreement.

Results: In phantom study, the lowest dose scan protocol (CTDI 2.1 mGy, 70 kV, 75 mAs) was unacceptable due to poor image quality. For patient studies, both radiologists gave acceptable image quality scores for ultra-low-dose scan protocol with axial scan mode, automatic tube potential selection and tube current modulation (CTDI 2.2 mGy; DLP 22.9 mGy.cm) with up to 60% lower dose compared to prior standard-dose CT (CTDI 5.3 mGy; DLP 73.5 mGy.cm).

Conclusions: Ultra-low-dose CT protocol provides sufficient image quality for scanning patients undergoing functional endoscopic surgery for benign paranasal sinus diseases.
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http://dx.doi.org/10.1016/j.ejmp.2020.09.014DOI Listing
October 2020

Radiation dose monitoring in computed tomography: Status, options and limitations.

Phys Med 2020 Nov 25;79:1-15. Epub 2020 Sep 25.

Webster Center for Quality and Safety, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. Electronic address:

In the last few years there has been an increasing interest on radiation dose to patients undergoing various diagnostic or therapeutic procedures with the use of ionizing radiation. Especially for CT examinations and interventional procedures, where it is known that patient doses are much higher than conventional radiography, new norms have been published that require to have appropriate radiation dose indices registered in the patient medical record. Because of these demands, dose monitoring has been recommended and adopted into many clinical practices as a routine procedure for every patient and every examination. Dedicated dose monitoring systems (DMS) that facilitate data collection and processing, statistical comparisons, reporting and management of radiation dose related information have been devised and are being used worldwide. In this review paper, a brief flashback to the reasons that necessitated dose monitoring in radiology will be first presented. Furthermore, since the focus of this manuscript is on CT, the CT dosimetry principles and metrics will be summarized. The limitations of these metrics will be also discussed, so that DMS users are aware of the semantics of the parameters shown in the DMS reports. The operation of DMS systems will be outlined to make users aware of functions, limitations, and available options of DMS systems. Furthermore, the usefulness of DMS systems as an optimization tool will be presented and discussed. Finally, information about the DMS solutions available in the market and relevant links will be presented.
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http://dx.doi.org/10.1016/j.ejmp.2020.08.020DOI Listing
November 2020

Clinical and imaging features predict mortality in COVID-19 infection in Iran.

PLoS One 2020 24;15(9):e0239519. Epub 2020 Sep 24.

Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.

The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239519PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514030PMC
October 2020

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.

JAMA Netw Open 2020 09 1;3(9):e2017135. Epub 2020 Sep 1.

Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston.

Importance: The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer.

Objective: To assess the performance of a deep learning-based nodule detection algorithm for the detection of lung cancer on chest radiographs from participants in the National Lung Screening Trial (NLST).

Design, Setting, And Participants: This diagnostic study used data from participants in the NLST ro assess the performance of a deep learning-based artificial intelligence (AI) algorithm for the detection of pulmonary nodules and lung cancer on chest radiographs using separate training (in-house) and validation (NLST) data sets. Baseline (T0) posteroanterior chest radiographs from 5485 participants (full T0 data set) were used to assess lung cancer detection performance, and a subset of 577 of these images (nodule data set) were used to assess nodule detection performance. Participants aged 55 to 74 years who currently or formerly (ie, quit within the past 15 years) smoked cigarettes for 30 pack-years or more were enrolled in the NLST at 23 US centers between August 2002 and April 2004. Information on lung cancer diagnoses was collected through December 31, 2009. Analyses were performed between August 20, 2019, and February 14, 2020.

Exposures: Abnormality scores produced by the AI algorithm.

Main Outcomes And Measures: The performance of an AI algorithm for the detection of lung nodules and lung cancer on radiographs, with lung cancer incidence and mortality as primary end points.

Results: A total of 5485 participants (mean [SD] age, 61.7 [5.0] years; 3030 men [55.2%]) were included, with a median follow-up duration of 6.5 years (interquartile range, 6.1-6.9 years). For the nodule data set, the sensitivity and specificity of the AI algorithm for the detection of pulmonary nodules were 86.2% (95% CI, 77.8%-94.6%) and 85.0% (95% CI, 81.9%-88.1%), respectively. For the detection of all cancers, the sensitivity was 75.0% (95% CI, 62.8%-87.2%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.8% (95% CI, 2.6%-5.0%), and the negative predictive value was 99.8% (95% CI, 99.6%-99.9%). For the detection of malignant pulmonary nodules in all images of the full T0 data set, the sensitivity was 94.1% (95% CI, 86.2%-100.0%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.4% (95% CI, 2.2%-4.5%), and the negative predictive value was 100.0% (95% CI, 99.9%-100.0%). In digital radiographs of the nodule data set, the AI algorithm had higher sensitivity (96.0% [95% CI, 88.3%-100.0%] vs 88.0% [95% CI, 75.3%-100.0%]; P = .32) and higher specificity (93.2% [95% CI, 89.9%-96.5%] vs 82.8% [95% CI, 77.8%-87.8%]; P = .001) for nodule detection compared with the NLST radiologists. For malignant pulmonary nodule detection on digital radiographs of the full T0 data set, the sensitivity of the AI algorithm was higher (100.0% [95% CI, 100.0%-100.0%] vs 94.1% [95% CI, 82.9%-100.0%]; P = .32) compared with the NLST radiologists, and the specificity (90.9% [95% CI, 89.6%-92.1%] vs 91.0% [95% CI, 89.7%-92.2%]; P = .91), positive predictive value (8.2% [95% CI, 4.4%-11.9%] vs 7.8% [95% CI, 4.1%-11.5%]; P = .65), and negative predictive value (100.0% [95% CI, 100.0%-100.0%] vs 99.9% [95% CI, 99.8%-100.0%]; P = .32) were similar to those of NLST radiologists.

Conclusions And Relevance: In this study, the AI algorithm performed better than NLST radiologists for the detection of pulmonary nodules on digital radiographs. When used as a second reader, the AI algorithm may help to detect lung cancer.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.17135DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516603PMC
September 2020

Radiomic features of primary tumor by lung cancer stage: analysis in mutated non-small cell lung cancer.

Transl Lung Cancer Res 2020 Aug;9(4):1441-1451

Division of Thoracic Imaging, Massachusetts General Hospital, Boston, MA, USA.

Background: The clinical features and traditional semantic imaging characteristics of -mutated non-small cell lung cancer (NSCLC) have been previously reported. The radiomic features of -mutated NSCLC and their role in predicting cancer stage, however, have yet to be investigated. This study's goal is to assess the differences in CT radiomic features of primary NSCLC driven by mutation and stratified by tumor-node-metastasis (TNM) staging.

Methods: Our IRB approved study included 62 patients with mutations (V600 in 27 and non-V600 in 35 patients), who underwent contrast-enhanced chest CT. Tumor stage was determined based on the 8 edition of TNM staging. Two thoracic radiologists assessed the primary tumor imaging features such, including tumor size (maximum and minimum dimensions) and density (Hounsfield units, HU). De-identified transverse CT images (DICOM) were processed with 3D slicer (Version 4.7) for manual lesion segmentation and estimation of radiomic features. Descriptive statistics, multivariate logistic regression, and receiver operating characteristics (ROC) were performed.

Results: There were significant differences in the radiomic features based on cancer stages I-IV with the most significant differences between stage IV and stage I lesions [AUC 0.94 (95% CI: 0.86-0.99), P<0.04]. There were also significant differences in radiomic features between stage IV and combined stages I-III [40/113 radiomic features; AUC 0.71 (95% CI: 0.59-0.85); P<0.04-0.0001]. None of the clinical (0/6) or imaging (0/3) features were significantly different between stage IV and combined stages I-III.

Conclusions: The radiomic features of primary tumor in driven NSCLC significantly vary with cancer stage, independent of standard imaging and clinical features.
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http://dx.doi.org/10.21037/tlcr-20-347DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481629PMC
August 2020

Cause determination of missed lung nodules and impact of reader training and education: Simulation study with nodule insertion software.

J Cancer Res Ther 2020 Jul-Sep;16(4):780-787

Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.

Background: There are "blind spots" on chest computed tomography (CT) where pulmonary nodules can easily be overlooked. The number of missed pulmonary nodules can be minimized by instituting a training program with particular focus on the depiction of nodules at blind spots.

Purpose: The purpose of this study was to assess the variation in lung nodule detection in chest CT based on location, attenuation characteristics, and reader experience.

Materials And Methods: We selected 18 noncalcified lung nodules (6-8 mm) suspicious of primary and metastatic lung cancer with solid (n = 7), pure ground-glass (6), and part-solid ground-glass (5) attenuation from 12 chest CT scans. These nodules were randomly inserted in chest CT of 34 patients in lung hila, 1 costochondral junction, branching vessels, paramediastinal lungs, lung apices, juxta-diaphragm, and middle and outer thirds of the lungs. Two residents and two chest imaging clinical fellows evaluated the CT images twice, over a 4-month interval. Before the second reading session, the readers were trained and made aware of the potential blind spots. Chi-square test was used to assess statistical significance.

Results: Pretraining session: Fellows detected significantly more part-solid ground-glass nodules compared to residents (P = 0.008). A substantial number of nodules adjacent to branching vessels and posterior mediastinum were missed. Posttraining session: There was a significant increase in detectability independent of attenuation and location of nodules for all readers (P < 0.0008).

Conclusion: Dedicated chest CT training improves detection of lung nodules, especially the part-solid ground-glass nodules. Detection of nodules adjacent to branching vessels and the posterior mediastinal lungs is difficult even for fellowship-trained radiologists.
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http://dx.doi.org/10.4103/jcrt.JCRT_312_17DOI Listing
November 2020

Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia.

J Comput Assist Tomogr 2020 Sep/Oct;44(5):640-646

From the Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA.

Purpose: This study aimed to assess if computed tomography (CT) radiomics can predict the severity and outcome of patients with coronavirus disease 2019 (COVID-19) pneumonia.

Methods: This institutional ethical board-approved study included 92 patients (mean age, 59 ± 17 years; 57 men, 35 women) with positive reverse transcription polymerase chain reaction assay for COVID-19 infection who underwent noncontrast chest CT. Two radiologists evaluated all chest CT examinations and recorded opacity type, distribution, and extent of lobar involvement. Information on symptom duration before hospital admission, the period of hospital admission, presence of comorbid conditions, laboratory data, and outcomes (recovery or death) was obtained from the medical records. The entire lung volume was segmented on thin-section Digital Imaging and Communication in Medicine images to derive whole-lung radiomics. Data were analyzed using multiple logistic regression with receiver operator characteristic area under the curve (AUC) as the output.

Results: Computed tomography radiomics (AUC, 0.99) outperformed clinical variables (AUC, 0.89) for prediction of the extent of pulmonary opacities related to COVID-19 pneumonia. Type of pulmonary opacities could be predicted with CT radiomics (AUC, 0.77) but not with clinical or laboratory data (AUC, <0.56; P > 0.05). Prediction of patient outcome with radiomics (AUC, 0.85) improved to an AUC of 0.90 with the addition of clinical variables (patient age and duration of presenting symptoms before admission). Among clinical variables, the combination of peripheral capillary oxygen saturation on hospital admission, duration of symptoms, platelet counts, and patient age provided an AUC of 0.81 for predicting patient outcomes.

Conclusions: Radiomics from noncontrast CT reliably predict disease severity (AUC, 0.99) and outcome (AUC, 0.85) in patients with COVID-19 pneumonia.
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http://dx.doi.org/10.1097/RCT.0000000000001094DOI Listing
October 2020

Integrative Analysis for COVID-19 Patient Outcome Prediction.

ArXiv 2020 Jul 20. Epub 2020 Jul 20.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386508PMC
July 2020

Chest CT practice and protocols for COVID-19 from radiation dose management perspective.

Eur Radiol 2020 Dec 3;30(12):6554-6560. Epub 2020 Jul 3.

International Atomic Energy Agency, Vienna, Austria.

The global pandemic of coronavirus disease 2019 (COVID-19) has upended the world with over 6.6 million infections and over 391,000 deaths worldwide. Reverse-transcription polymerase chain reaction (RT-PCR) assay is the preferred method of diagnosis of COVID-19 infection. Yet, chest CT is often used in patients with known or suspected COVID-19 due to regional preferences, lack of availability of PCR assays, and false-negative PCR assays, as well as for monitoring of disease progression, complications, and treatment response. The International Atomic Energy Agency (IAEA) organized a webinar to discuss CT practice and protocol optimization from a radiation protection perspective on April 9, 2020, and surveyed participants from five continents. We review important aspects of CT in COVID-19 infection from the justification of its use to specific scan protocols for optimizing radiation dose and diagnostic information.Key Points• Chest CT provides useful information in patients with moderate to severe COVID-19 pneumonia.• When indicated, chest CT in most patients with COVID-19 pneumonia must be performed with non-contrast, low-dose protocol.• Although chest CT has high sensitivity for diagnosis of COVID-19 pneumonia, CT findings are non-specific and overlap with other viral infections including influenza and H1N1.
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http://dx.doi.org/10.1007/s00330-020-07034-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332743PMC
December 2020

Relation between age and CT radiation doses: Dose trends in 705 pediatric head CT.

Eur J Radiol 2020 Sep 21;130:109138. Epub 2020 Jun 21.

Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Purpose: To evaluate the relationship between patient age and radiation doses associated with routine pediatric head CT performed with automatic tube potential selection and tube current modulation techniques.

Methods: We obtained patient demographics, scan parameters, and radiation dose descriptors (CT dose index volume -CTDIvol and dose length product -DLP) associated with consecutive routine head CT in 705 children (mean age 6.9 ± 5 years). Children were scanned on one of the three multidetector-row CTs (64-128 slices, Siemens) over 6 months period in a tertiary hospital. All head CT exams were performed in helical scan mode using automatic tube potential selection (Care kV) and automatic tube current modulation (Care Dose 4D) techniques. The information was obtained from a radiation dose monitoring software. Data were analyzed using linear correlation and analysis of variance.

Results: Most age-wise median CTDIvol (9-27 mGy; 703/705 pediatric head CT, >99 %) from our institution were lower than the European Diagnostic Reference Levels (EDRL, CTDIvol 24-50 mGy) but median DLP (151-586 mGy cm) from 201/705 children (28 %) was higher than the EDRL (DLP 300-650 mGy cm). Unlike the age-stratified EDRL, a combination of automatic tube potential selection and tube current modulation for pediatric head results in a significant linear correlation between radiation doses and patient age (r2 = 0.66, p < 0.001).

Conclusions: Radiation doses for head CT change linearly with children's age. Despite lower CTDIvol and DLP for most children, longer scan length resulted in higher DLP for some pediatric head CT compared to the corresponding EDRL; this result underscores the need to promote clear guidelines for technologists operating CT.
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http://dx.doi.org/10.1016/j.ejrad.2020.109138DOI Listing
September 2020

Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT.

Int J Comput Assist Radiol Surg 2020 Oct 26;15(10):1727-1736. Epub 2020 Jun 26.

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Court, Room 248, Boston, MA, 02114, USA.

Purpose: Radiomics help move cross-sectional imaging into the domain of quantitative imaging to assess the lesions, their stoma as well as in their temporal monitoring. We applied and assessed the accuracy of radiomics for differentiating healthy liver from diffuse liver diseases (cirrhosis, steatosis, amiodarone deposition, and iron overload) on non-contrast abdomen CT images in an institutional-reviewed board-approved, retrospective study.

Methods: Our study included 300 adult patients (mean age 63 ± 16 years; 171 men, 129 women) who underwent non-contrast abdomen CT and had either a healthy liver (n = 100 patients) or an evidence of diffuse liver disease (n = 200). The diffuse liver diseases included steatosis (n = 50), cirrhosis (n = 50), hyperdense liver due to amiodarone deposition (n = 50), or iron overload (n = 50). We manually segmented the liver in one section at the level of the porta hepatis (all 300 patients) and then over the entire liver volume (50 patients). Radiomics were estimated for the liver, and statistical comparison was performed with multiple logistic regression and random forest classifier.

Results: With random forest classifier, the AUC for radiomics ranged between 0.72 (iron overload vs. healthy liver) and 0.98 (hepatic steatosis vs. healthy liver) for differentiating diffuse liver disease from the healthy liver. Combined root mean square and gray-level co-occurrence matrix had the highest AUC (AUC:0.99, p < 0.01) for differentiating healthy liver from steatosis. Radiomics were more accurate for differentiating healthy liver from amiodarone (AUC:0.93) than from iron overload (AUC:0.79).

Conclusion: Radiomics enable differentiation of healthy liver from hepatic steatosis, cirrhosis, amiodarone deposition, and iron overload from a single section of non-contrast abdominal CT. The high accuracy of radiomics coupled with rapid segmentation of the region of interest, radiomics estimation, and statistical analyses within the same prototype makes a compelling case for bringing radiomics to clinical use for improving reporting in evaluation of healthy liver and diffuse liver diseases.
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http://dx.doi.org/10.1007/s11548-020-02212-0DOI Listing
October 2020

Semiautomatic Segmentation and Radiomics for Dual-Energy CT: A Pilot Study to Differentiate Benign and Malignant Hepatic Lesions.

AJR Am J Roentgenol 2020 08 14;215(2):398-405. Epub 2020 May 14.

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Ct, Rm 248, Boston, MA 02114.

This study assessed a machine learning-based dual-energy CT (DECT) tumor analysis prototype for semiautomatic segmentation and radiomic analysis of benign and malignant liver lesions seen on contrast-enhanced DECT. This institutional review board-approved study included 103 adult patients (mean age, 65 ± 15 [SD] years; 53 men, 50 women) with benign (60/103) or malignant (43/103) hepatic lesions on contrast-enhanced dual-source DECT. Most malignant lesions were histologically proven; benign lesions were either stable on follow-up CT or had characteristic benign features on MRI. Low- and high-kilovoltage datasets were deidentified, exported offline, and processed with the DECT tumor analysis for semiautomatic segmentation of the volume and rim of each liver lesion. For each segmentation, contrast enhancement and iodine concentrations as well as radiomic features were derived for different DECT image series. Statistical analyses were performed to determine if DECT tumor analysis and radiomics can differentiate benign from malignant liver lesions. Normalized iodine concentration and mean iodine concentration in the benign and malignant lesions were significantly different ( < 0.0001-0.0084; AUC, 0.695-0.856). Iodine quantification and radiomic features from lesion rims (AUC, ≤ 0.877) had higher accuracy for differentiating liver lesions compared with the values from lesion volumes (AUC, ≤ 0.856). There was no difference in the accuracies of DECT iodine quantification (AUC, 0.91) and radiomics (AUC, 0.90) for characterizing liver lesions. DECT radiomics were more accurate than iodine quantification for differentiating solid benign and malignant hepatic lesions.
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http://dx.doi.org/10.2214/AJR.19.22164DOI Listing
August 2020

Multiplatform, Non-Breath-Hold Fast Scanning Protocols: Should We Stop Giving Breath-Hold Instructions for Routine Chest CT?

Can Assoc Radiol J 2020 May 4:846537120920530. Epub 2020 May 4.

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Objective: We assessed if non-breath-hold (NBH) fast scanning protocol can provide respiratory motion-free images for interpretation of chest computed tomography (CT).

Materials And Methods: In our 2-phase project, we first collected baseline data on frequency of respiratory motion artifacts on breath-hold chest CT in 826 adult patients. The second phase included 62 patients (mean age 66 ± 15 years; 21 females, 41 males) who underwent an NBH chest CT on either single-source (n = 32) or dual-source (n = 30) multidetector-row CT scanners. Clinical indications for chest CT, reason for using NBH CT, scanner type, scan duration, and radiation dose (CT dose index volume, dose length product) were recorded. Two thoracic radiologists (R1 and R2) independently graded respiratory motion artifacts (1 = no respiratory motion artifacts with unrestricted evaluation; 2 = minor motion artifacts limited to one lung lobe or less with good diagnostic quality; 3 = moderate motion artifacts limited to 2 to 3 lung lobes but adequate for clinical diagnosis; 4 = poor evaluability or unevaluable from severe motion artifacts; and 5 = limited quality due to other causes like high noise, beam hardening, or metallic artifacts), and recorded pulmonary and mediastinal findings. Descriptive analyses, Cohen κ test for interobserver agreement, and Student test were performed for statistical analysis.

Results: No NBH chest CT were deemed uninterpretable by either radiologist; most NBH CT (R1-59 of 62, 95%; R2-62 of 62, 100%) had no or minimal motion artifacts. Only 3 of 62 (R1) NBH chest CT had motion artifacts limiting diagnostic evaluation for lungs but not in the mediastinum.

Conclusion: Non-breath-hold fast protocol enables acquisition of diagnostic quality chest CT free of respiratory motion artifacts in patients who cannot hold their breath.
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http://dx.doi.org/10.1177/0846537120920530DOI Listing
May 2020

Reducing Radiation Dose and Contrast Medium Volume With Application of Dual-Energy CT in Children and Young Adults.

AJR Am J Roentgenol 2020 06 14;214(6):1199-1205. Epub 2020 Apr 14.

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Ste 248, Boston, MA 02114.

The purpose of this study was to assess if dual-source dual-energy CT (DS-DECT) can be used with lower radiation doses and contrast material volumes than single-energy CT (SECT) in children and young adults. This retrospective study included 85 consecutive children and young adults (age range, 1 month old to 19 years old; 81 male, 70 female) who underwent contrast-enhanced DS-DECT of the chest ( = 41) or the abdomen and pelvis ( = 44) on second- or third-generation dual-source CT scanners (Somatom Flash or Force, Siemens Healthineers) for clinically indicated reasons. We included 66 age-, sex-, body region-, and weight-matched patients who underwent SECT on the same scanner. Patients were scanned with either SECT (with automatic exposure control using both CARE kV [Siemens Healthineers] and CARE Dose 4D [Siemens Healthineers]) or DS-DECT (with CARE Dose 4D). Two pediatric radiologists assessed clinical indications, radiologic findings, image quality, and any study limitations (noise or artifacts). Patient demographics (age, sex, weight), scan parameters (tube voltage, tube current-time product, pitch, section thickness), CT dose descriptors (volume CT dose index, dose-length product, size-specific dose estimate [SSDE]), and contrast material volume were recorded. Descriptive statistics, paired test, and Cohen kappa test were performed. Mean patient ages and weights ± SD in DS-DECT (10 ± 6 years old, 38 ± 23 kg) and SECT (11 ± 7 years old, 43 ± 29 kg) groups were not significantly different ( > 0.05). Respective SSDEs for chest DS-DECT (4.0 ± 2.1 mGy), chest SECT (6.1 ± 4.4 mGy), abdomen-pelvis DS-DECT (5.0 ± 5.0 mGy), and abdomen-pelvis SECT (8.3 ± 4.0 mGy) were significantly different ( = 0.003-0.005). Contrast material volume for DS-DECT examinations was 19-22% lower compared with the weight- and body region-matched scans obtained with SECT. Image quality of DECT was acceptable in all patients. In children and young adults, chest and abdomen-pelvis DS-DECT enables substantial radiation dose and contrast volume reductions compared with weight- and region-matched SECT.
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http://dx.doi.org/10.2214/AJR.19.22231DOI Listing
June 2020

Can Dual-Energy Computed Tomography Quantitative Analysis and Radiomics Differentiate Normal Liver From Hepatic Steatosis and Cirrhosis?

J Comput Assist Tomogr 2020 Mar/Apr;44(2):223-229

Siemens Medical Solutions USA Inc, Malvern, PA.

Objectives: This study aimed to assess if dual-energy computed tomography (DECT) quantitative analysis and radiomics can differentiate normal liver, hepatic steatosis, and cirrhosis.

Materials And Methods: Our retrospective study included 75 adult patients (mean age, 54 ± 16 years) who underwent contrast-enhanced, dual-source DECT of the abdomen. We used Dual-Energy Tumor Analysis prototype for semiautomatic liver segmentation and DECT and radiomic features. The data were analyzed with multiple logistic regression and random forest classifier to determine area under the curve (AUC).

Results: Iodine quantification (AUC, 0.95) and radiomic features (AUC, 0.97) differentiate between healthy and abnormal liver. Combined fat ratio percent and mean mixed CT values (AUC, 0.99) were the strongest differentiators of healthy and steatotic liver. The most accurate differentiating parameters of normal liver and cirrhosis were a combination of first-order statistics (90th percentile), gray-level run length matrix (short-run low gray-level emphasis), and gray-level size zone matrix (gray-level nonuniformity normalized; AUC, 0.99).

Conclusion: Dual-energy computed tomography iodine quantification and radiomics accurately differentiate normal liver from steatosis and cirrhosis from single-section analyses.
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http://dx.doi.org/10.1097/RCT.0000000000000989DOI Listing
April 2020

Comparison of Baseline, Bone-Subtracted, and Enhanced Chest Radiographs for Detection of Pneumothorax.

Can Assoc Radiol J 2020 Mar 18:846537120908852. Epub 2020 Mar 18.

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Purpose: To assess and compare detectability of pneumothorax on unprocessed baseline, single-energy, bone-subtracted, and enhanced frontal chest radiographs (chest X-ray, CXR).

Method And Materials: Our retrospective institutional review board-approved study included 202 patients (mean age 53 ± 24 years; 132 men, 70 women) who underwent frontal CXR and had trace, moderate, large, or tension pneumothorax. All patients (except those with tension pneumothorax) had concurrent chest computed tomography (CT). Two radiologists reviewed the CXR and chest CT for pneumothorax on baseline CXR (ground truth). All baseline CXR were processed to generate bone-subtracted and enhanced images (ClearRead X-ray). Four radiologists (R1-R4) assessed the baseline, bone-subtracted, and enhanced images and recorded the presence of pneumothorax (side, size, and confidence for detection) for each image type. Area under the curve (AUC) was calculated with receiver operating characteristic analyses to determine the accuracy of pneumothorax detection.

Results: Bone-subtracted images (AUC: 0.89-0.97) had the lowest accuracy for detection of pneumothorax compared to the baseline (AUC: 0.94-0.97) and enhanced (AUC: 0.96-0.99) radiographs ( < .01). Most false-positive and false-negative pneumothoraces were detected on the bone-subtracted images and the least numbers on the enhanced radiographs. Highest detection rates and confidence were noted for the enhanced images (empiric AUC for R1-R4 0.96-0.99).

Conclusion: Enhanced CXRs are superior to bone-subtracted and unprocessed radiographs for detection of pneumothorax.

Clinical Relevance/application: Enhanced CXRs improve detection of pneumothorax over unprocessed images; bone-subtracted images must be cautiously reviewed to avoid false negatives.
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http://dx.doi.org/10.1177/0846537120908852DOI Listing
March 2020

CT protocols and radiation doses for hematuria and urinary stones: Comparing practices in 20 countries.

Eur J Radiol 2020 May 29;126:108923. Epub 2020 Feb 29.

Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, Massachusetts, USA.

Purpose: Patients with hematuria and renal colic often undergo CT scanning. The purpose of our study was to assess variations in CT protocols and radiation doses for evaluation of hematuria and urinary stones in 20 countries.

Method: The International Atomic Energy Agency (IAEA) surveyed practices in 51 hospitals from 20 countries in the European region according to the IAEA Technical cooperation classification and obtained following information for three CT protocols (urography, urinary stones, and routine abdomen-pelvis CT) for 1276 patients: patient information (weight, clinical indication), scanner information (scan vendor, scanner name, number of detector rows), scan parameters (such as number of phases, scan start and end locations, mA, kV), and radiation dose descriptors (CTDI, DLP). Two radiologists assessed the appropriateness of clinical indications and number of scan phases using the ESR Referral Guidelines and ACR Appropriateness Criteria. Descriptive statistics and Student's t tests were performed.

Results: Most institutions use 3-6 phase CT urography protocols (80 %, median DLP 1793-3618 mGy.cm) which were associated with 2.4-4.9-fold higher dose compared to 2-phase protocol (20 %, 740 mGy.cm) (p < 0.0001). Likewise, 52 % patients underwent 3-5 phase routine abdomen- pelvis CT (1574-2945 mGy.cm) as opposed to 37 % scanned with a single-phase routine CT (676 mGy.cm). The median DLP for urinary stones CT (516 mGy.cm) were significantly lower than the median DLP for the other two CT protocols (p < 0.0001).

Conclusions: Few institutions (4/13) use low dose CT for urinary stones. There are substantial variations in CT urography and routine abdomen-pelvis CT protocols result in massive radiation doses (up to 2945-3618 mGy.cm).
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http://dx.doi.org/10.1016/j.ejrad.2020.108923DOI Listing
May 2020