Publications by authors named "Michael D Kuo"

66 Publications

Tissue clearing techniques for three-dimensional optical imaging of intact human prostate and correlations with multi-parametric MRI.

Prostate 2021 Jun 20;81(9):521-529. Epub 2021 Apr 20.

Medical Artificial Intelligence Laboratory Program, The University of Hong Kong, Hong Kong, Hong Kong SAR.

Background: Tissue clearing technologies have enabled remarkable advancements for in situ characterization of tissues and exploration of the three-dimensional (3D) relationships between cells, however, these studies have predominantly been performed in non-human tissues and correlative assessment with clinical imaging has yet to be explored. We sought to evaluate the feasibility of tissue clearing technologies for 3D imaging of intact human prostate and the mapping of structurally and molecularly preserved pathology data with multi-parametric volumetric MR imaging (mpMRI).

Methods: Whole-mount prostates were processed with either hydrogel-based CLARITY or solvent-based iDISCO. The samples were stained with a nuclear dye or fluorescently labeled with antibodies against androgen receptor, alpha-methylacyl coenzyme-A racemase, or p63, and then imaged with 3D confocal microscopy. The apparent diffusion coefficient and K maps were computed from preoperative mpMRI.

Results: Quantitative analysis of cleared normal and tumor prostate tissue volumes displayed differences in 3D tissue architecture, marker-specific cell staining, and cell densities that were significantly correlated with mpMRI measurements in this initial, pilot cohort.

Conclusions: 3D imaging of human prostate volumes following tissue clearing is a feasible technique for quantitative radiology-pathology correlation analysis with mpMRI and provides an opportunity to explore functional relationships between cellular structures and cross-sectional clinical imaging.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/pros.24129DOI Listing
June 2021

Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review.

Radiol Cardiothorac Imaging 2020 Feb 13;2(1):e200034. Epub 2020 Feb 13.

Department of Diagnostic Radiology (M.Y.N., E.Y.P.L., P.L.K., M.D.K.), Department of Medicine (M.M.S.L., C.K.M.H.), and Medical Artificial Intelligence Lab Program (M.D.K.), University of Hong Kong, Hong Kong; Department of Diagnostic Radiology (M.Y.N.), Department of Clinical Microbiology and Infection Control (J.Y., K.Y.Y.), and Department of Medicine (F.Y., X.L., H.W.), University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China; Department of Radiology, Queen Mary Hospital, Hong Kong (C.S.Y.L.); and Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong (B.L.).

Purpose: To present the findings of 21 coronavirus disease 2019 (COVID-19) cases from two Chinese centers with CT and chest radiographic findings, as well as follow-up imaging in five cases.

Materials And Methods: This was a retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on radiologic features of COVID-19 infection was conducted.

Results: The predominant imaging pattern was of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrated evolution of the ground-glass opacities into consolidation and subsequent resolution of the airspace changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiography, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified four other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding at CT chest examinations.

Conclusion: Pulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with four previous publications from other sites.© RSNA, 2020See editorial by Kay and Abbara in this issue.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/ryct.2020200034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233595PMC
February 2020

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

J Thorac Imaging 2020 Nov;35(6):369-376

Medical Artificial Intelligence Laboratory Program (MAIL), Department of Diagnostic Radiology, LKS Faculty of Medicine.

Purpose: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).

Materials And Methods: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC).

Results: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test).

Conclusions: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/RTI.0000000000000559DOI Listing
November 2020

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.

Int J Infect Dis 2020 Dec 15;101:74-82. Epub 2020 Sep 15.

Department of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Division of Respiratory & Critical Care Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.

Objectives: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.

Methods: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.

Conclusion: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ijid.2020.09.022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7491462PMC
December 2020

Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19.

Radiology 2020 08 27;296(2):E72-E78. Epub 2020 Mar 27.

From the Departments of Radiology (H.Y.F.W., H.Y.S.L., C.S.Y.L., T.P.W.L.), Medicine (M.M.S.L., I.F.N.H.), and Microbiology (T.W.H.C.), Queen Mary Hospital, Hong Kong; Departments of Diagnostic Radiology (A.H.T.F., K.W.H.C., E.Y.P.L., M.D.K., M.Y.N.), Family Medicine and Primary Care (E.Y.F.W.), Pharmacology and Pharmacy (E.Y.F.W.), and Medicine (I.F.N.H.), University of Hong Kong, Room 406, Block K, Hong Kong; Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong (S.T.L.); Ruttonjee and Tang Shiu Kin Hospitals, Hong Kong (S.T.L.); Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong (T.W.Y.C., J.C.Y.L.) and Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China (M.Y.N.).

Background Current coronavirus disease 2019 (COVID-19) radiologic literature is dominated by CT, and a detailed description of chest radiography appearances in relation to the disease time course is lacking. Purpose To describe the time course and severity of findings of COVID-19 at chest radiography and correlate these with real-time reverse transcription polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, nucleic acid. Materials and Methods This is a retrospective study of patients with COVID-19 confirmed by using RT-PCR and chest radiographic examinations who were admitted across four hospitals and evaluated between January and March 2020. Baseline and serial chest radiographs ( = 255) were reviewed with RT-PCR. Correlation with concurrent CT examinations ( = 28) was performed when available. Two radiologists scored each chest radiograph in consensus for consolidation, ground-glass opacity, location, and pleural fluid. A severity index was determined for each lung. The lung scores were summed to produce the final severity score. Results The study was composed of 64 patients (26 men; mean age, 56 years ± 19 [standard deviation]). Of these, 58 patients had initial positive findings with RT-PCR (91%; 95% confidence interval: 81%, 96%), 44 patients had abnormal findings at baseline chest radiography (69%; 95% confidence interval: 56%, 80%), and 38 patients had initial positive findings with RT-PCR testing and abnormal findings at baseline chest radiography (59%; 95% confidence interval: 46%, 71%). Six patients (9%) showed abnormalities at chest radiography before eventually testing positive for COVID-19 with RT-PCR. Sensitivity of initial RT-PCR (91%; 95% confidence interval: 83%, 97%) was higher than that of baseline chest radiography (69%; 95% confidence interval: 56%, 80%) ( = .009). Radiographic recovery (mean, 6 days ± 5) and virologic recovery (mean, 8 days ± 6) were not significantly different ( = .33). Consolidation was the most common finding (30 of 64; 47%) followed by ground-glass opacities (21 of 64; 33%). Abnormalities at chest radiography had a peripheral distribution (26 of 64; 41%) and lower zone distribution (32 of 64; 50%) with bilateral involvement (32 of 64; 50%). Pleural effusion was uncommon (two of 64; 3%). The severity of findings at chest radiography peaked at 10-12 days from the date of symptom onset. Conclusion Findings at chest radiography in patients with coronavirus disease 2019 frequently showed bilateral lower zone consolidation, which peaked at 10-12 days from symptom onset. © RSNA, 2020.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2020201160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233401PMC
August 2020

State of the Art: Toward Improving Outcomes of Lung and Liver Tumor Biopsies in Clinical Trials-A Multidisciplinary Approach.

J Clin Oncol 2020 05 5;38(14):1633-1640. Epub 2020 Mar 5.

Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD.

Purpose: National Cancer Institute (NCI)-sponsored clinical trial network studies frequently require biopsy specimens for pharmacodynamic and molecular biomarker analyses, including paired pre- and post-treatment samples. The purpose of this meeting of NCI-sponsored investigators was to identify local institutional standard procedures found to ensure quantitative and qualitative specimen adequacy.

Methods: NCI convened a conference on best biopsy practices, focusing on the clinical research community. Topics discussed were (1) criteria for specimen adequacy in the personalized medicine era, (2) team-based approaches to ensure specimen adequacy and quality control, and (3) risk considerations relevant to academic and community practitioners and their patients.

Results And Recommendations: Key recommendations from the convened consensus panel included (1) establishment of infrastructure for multidisciplinary biopsy teams with a formalized information capture process, (2) maintenance of standard operating procedures with regular team review, (3) optimization of tissue collection and yield methodology, (4) incorporation of needle aspiration and other newer techniques, and (5) commitment of stakeholders to use of guideline documents to increase awareness of best biopsy practices, with the goal of universally improving tumor biopsy practices.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/JCO.19.02322DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351328PMC
May 2020

Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests.

PLoS One 2020 10;15(1):e0226634. Epub 2020 Jan 10.

Department of Radiology, The University of Hong Kong, Hong Kong, China.

Purpose: The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer.

Materials And Methods: This retrospective, single-institution analysis of 219 patients involved two cohorts using one of two FDA approved transcriptome-based tests that were performed as part of the clinical care of breast cancer patients at Harbor-UCLA Medical Center between April 2008 and January 2013. BI-RADS descriptive terminology was collected from the corresponding ultrasound reports for each patient in conjunction with transcriptomic test results. Recursive partitioning and regression trees were used to test and validate classification of the two cohorts.

Results: The area under the curve (AUC) of the receiver operator curves (ROC) for the regression classifier between the two FDA approved tests and ultrasound features were 0.77 and 0.65, respectively; they employed the 'margins', 'retrotumoral', and 'internal echoes' feature descriptors. Notably, the 'retrotumoral' and mass 'margins' features were used in both classification trees. The identification of sonographic correlates of gene tests provides added value to the ultrasound exam without incurring additional procedures or testing.

Conclusions: The predictive capability using structured language from diagnostic ultrasound reports (BI-RADS) was moderate for the two tests, and provides added value from ultrasound imaging without incurring any additional costs. Incorporation of additional measures, such as ultrasound contrast enhancement, with validation in larger, prospective studies may further substantiate these results and potentially demonstrate even greater predictive utility.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226634PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953781PMC
April 2020

Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation.

Cancers (Basel) 2019 May 9;11(5). Epub 2019 May 9.

Department of Diagnostic Radiology, University of Hong Kong, Hong Kong 999077, China.

Chromosomal instability (CIN) of gastric cancer is correlated with distinct outcomes. This study aimed to investigate the role of computed tomography (CT) imaging traits in predicting the CIN status of gastric cancer. We screened 443 patients in the Cancer Genome Atlas gastric cancer cohort to filter 40 patients with complete CT imaging and genomic data as the training cohort. CT imaging traits were subjected to logistic regression to select independent predictors for the CIN status. For the validation cohort, we prospectively enrolled 18 gastric cancer patients for CT and tumor genomic analysis. The imaging predictors were tested in the validation cohort using receiver operating characteristic curve (ROC) analysis. Thirty patients (75%) in the training cohort and 9 patients (50%) in the validation cohort had CIN subtype gastric cancers. Smaller tumor diameter ( = 0.017) and acute tumor transition angle ( = 0.045) independently predict CIN status in the training cohort. In the validation cohort, acute tumor transition angle demonstrated the highest accuracy, sensitivity, and specificity of 88.9%, 88.9%, and 88.9%, respectively, and areas under ROC curve of 0.89. In conclusion, this pilot study showed acute tumor transition angle on CT images may predict the CIN status of gastric cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/cancers11050641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562475PMC
May 2019

Receptor-based Surrogate Subtypes and Discrepancies with Breast Cancer Intrinsic Subtypes: Implications for Image Biomarker Development.

Radiology 2018 10 24;289(1):210-217. Epub 2018 Jul 24.

From the Department of Radiology, University of California-Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, Calif (N.J., S.Y.); College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu Taiwan (S.Y.); Department of Biomathematics, University of California-Los Angeles, Los Angeles, Calif (J.G.); and Department of Diagnostic Radiology, University of Hong Kong, Room 406, Block K, Queen Mary Hospital, 102 Pok Fu Lam Rd, Hong Kong (M.D.K.).

Purpose To determine the concordance and accuracy of imaging surrogates of immunohistochemical (IHC) markers and the molecular classification of breast cancer. Materials and Methods A total of 3050 patients from 17 public breast cancer data sets containing IHC marker receptor status (estrogen receptor/progesterone receptor/human epidermal growth factor receptor 2 [HER2]) and their molecular classification (basal-like, HER2-enriched, luminal A or B) were analyzed. Diagnostic accuracy and concordance as measured with the κ statistic were calculated between the IHC and molecular classifications. Simulations were performed to assess the relationship between accuracy of imaging-based IHC markers to predict molecular classification. A simulation was performed to examine effects of misclassification of molecular type on patient survival. Results Accuracies of intrinsic subtypes based on IHC subtype were 71.7% (luminal A), 53.7% (luminal B), 64.8% (HER2-enriched), and 81.7% (basal-like). The κ agreement was fair (κ = 0.36) for luminal A and HER2-enriched subtypes, good (κ = 0.65) for the basal-like subtype, and poor (κ = 0.09) for the luminal B subtypes. Introduction of image misclassification by simulation lowered image-true subtype accuracies and κ values. Simulation analysis showed that misclassification caused survival differences between luminal A and basal-like subtypes to decrease. Conclusion There is poor concordance between triple-receptor status and intrinsic molecular subtype in breast cancer, arguing against their use in the design of prognostic genomic-based image biomarkers. © RSNA, 2018 Online supplemental material is available for this article.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2018171118DOI Listing
October 2018

Lung Cancer Radiogenomics: The Increasing Value of Imaging in Personalized Management of Lung Cancer Patients.

J Thorac Imaging 2018 Jan;33(1):17-25

Department of Computer and Electrical Engineering, National Chiao Tung University, HsinChu, Taiwan.

Radiogenomics provide a large-scale data analytical framework that aims to understand the broad multiscale relationships between the complex information encoded in medical images (including computational, quantitative, and semantic image features) and their underlying clinical, therapeutic, and biological associations. As such it is a powerful and increasingly important tool for both clinicians and researchers involved in the imaging, evaluation, understanding, and management of lung cancers. Herein we provide an overview of the growing field of lung cancer radiogenomics and its applications.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/RTI.0000000000000312DOI Listing
January 2018

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.

Korean J Radiol 2017 May-Jun;18(3):498-509. Epub 2017 Apr 3.

Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Seoul National University, Suwon 16229, Korea.

Objective: The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software.

Materials And Methods: MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic.

Results: Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant.

Conclusion: The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3348/kjr.2017.18.3.498DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390619PMC
October 2017

Multiregional Radiogenomic Assessment of Prostate Microenvironments with Multiparametric MR Imaging and DNA Whole-Exome Sequencing of Prostate Glands with Adenocarcinoma.

Radiology 2017 07 28;284(1):109-119. Epub 2017 Apr 28.

From the Departments of Radiological Sciences (N.J., S.R., M.D.K.) and Urology (R.E.R.), University of California, Los Angeles-David Geffen School of Medicine, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721; Department of Radiology, Weill Cornell Imaging, New York-Presbyterian Hospital, New York, NY (D.J.M.); Department of Pathology, Duke University School of Medicine, Durham, NC (J.H.); and College of Electrical and Computer Engineering, National Chiao Tung University, HsinChu, Taiwan (M.D.K.).

Purpose To assess the underlying genomic variation of prostate gland microenvironments of patients with prostate adenocarcinoma in the context of colocalized multiparametric magnetic resonance (MR) imaging and histopathologic assessment of normal and abnormal regions by using whole-exome sequencing. Materials and Methods Six patients with prostate adenocarcinoma who underwent robotic prostatectomy with whole-mount preservation of the prostate were identified, which enabled spatial mapping between preoperative multiparametric MR imaging and the gland. Four regions of interest were identified within each gland, including regions found to be normal and abnormal via histopathologic analysis. Whole-exome DNA sequencing (>50 times coverage) was performed on each of these spatially targeted regions. Radiogenomic analysis of imaging and mutation data were performed with hierarchical clustering, phylogenetic analysis, and principal component analysis. Results Radiogenomic multiparametric MR imaging and whole-exome spatial characterization in six patients with prostate adenocarcinoma (three patients, Gleason score of 3 + 4; and three patients, Gleason score of 4 + 5) was performed across 23 spatially distinct regions. Hierarchical clustering separated histopathologic analysis-proven high-grade lesions from the normal regions, and this reflected concordance between multiparametric MR imaging and resultant histopathologic analysis in all patients. Seventy-seven mutations involving 29 cancer-associated genes across the 23 spatially distinct prostate samples were identified. There was no significant difference in mutation load in cancer-associated genes between regions that were proven to be normal via histopathologic analysis (34 mutations per sample ± 19), mildly suspicious via multiparametric MR imaging (37 mutations per sample ± 21), intermediately suspicious via multiparametric MR imaging (31 mutations per sample ± 15), and high-grade cancer (33 mutations per sample ± 18) (P = .30). Principal component analysis resolved samples from different patients and further classified samples (regardless of histopathologic status) from prostate glands with Gleason score 3 + 4 versus 4 + 5 samples. Conclusion Multiregion spatial multiparametric MR imaging and whole-exome radiogenomic analysis of prostate glands with adenocarcinoma shows a continuum of mutations across regions that were found via histologic analysis to be high grade and normal. RSNA, 2017 Online supplemental material is available for this article.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2017162827DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197054PMC
July 2017

Genomic Adequacy from Solid Tumor Core Needle Biopsies of ex Vivo Tissue and in Vivo Lung Masses: Prospective Study.

Radiology 2017 03 18;282(3):903-912. Epub 2016 Oct 18.

From the Departments of Radiological Sciences (N.J., D.H., F.G.A., C.T.L., S.T.K., R.D.S., S.Y., D.R.E., M.D.K.) and Pathology (S.Y., K.D., S.D., S.B., M.D.K.), David Geffen School of Medicine at UCLA, 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721.

Purpose To identify the variables and factors that affect the quantity and quality of nucleic acid yields from imaging-guided core needle biopsy. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA. The authors prospectively obtained 232 biopsy specimens from 74 patients (177 ex vivo biopsy samples from surgically resected masses were obtained from 49 patients and 55 in vivo lung biopsy samples from computed tomographic [CT]-guided lung biopsies were obtained from 25 patients) and quantitatively measured DNA and RNA yields with respect to needle gauge, number of needle passes, and percentage of the needle core. RNA quality was also assessed. Significance of correlations among variables was assessed with analysis of variance followed by linear regression. Conditional probabilities were calculated for projected sample yields. Results The total nucleic acid yield increased with an increase in the number of needle passes or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both). However, contrary to calculated differences in volume yields, the effect of needle gauge was markedly greater than the number of passes. For example, the use of an 18-gauge versus a 20-gauge biopsy needle resulted in a 4.8-5.7 times greater yield, whereas a double versus a single pass resulted in a 2.4-2.8 times greater yield for 18- versus 20-gauge needles, respectively. Ninety-eight of 184 samples (53%) had an RNA integrity number of at least 7 (out of a possible score of 10). Conclusion With regard to optimizing nucleic acid yields in CT-guided lung core needle biopsies used for genomic analysis, there should be a preference for using lower gauge needles over higher gauge needles with more passes. RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 21, 2016.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2016132230DOI Listing
March 2017

Transcriptome profiling reveals novel gene expression signatures and regulating transcription factors of TGFβ-induced epithelial-to-mesenchymal transition.

Cancer Med 2016 08 18;5(8):1962-72. Epub 2016 Jun 18.

Department of Radiology, The David Geffen School of Medicine at UCLA, Los Angeles, California, 90095.

Dysregulated epithelial to mesenchymal transition (EMT) in cancer cells endows invasive and metastatic properties upon cancer cells that favor successful colonization of distal target organs and therefore play a critical role in transforming early-stage carcinomas into invasive malignancies. EMT has also been associated with tumor recurrence and drug resistance and cancer stem cell initiation. Therefore, better understanding of the mechanisms behind EMT could ultimately contribute to the development of novel prognostic approaches and individualized therapies that specifically target EMT processes. As an effort to characterize the central transcriptome changes during EMT, we have developed a Transforming growth factor (TGF)-beta-based in vitro EMT model and used it to profile EMT-related gene transcriptional changes in two different cell lines, a non-small cell lung cancer cell line H358, and a breast cell line MCF10a. After 7 days of TGF-beta/Oncostatin M (OSM) treatment, changes in cell morphology to a mesenchymal phenotype were observed as well as concordant EMT-associated changes in mRNA and protein expression. Further, increased motility was noted and flow cytometry confirmed enrichment in cancer stem cell-like populations. Microarray-based differential expression analysis identified an EMT-associated gene expression signature which was confirmed by RT-qPCR and which significantly overlapped with a previously published EMT core signature. Finally, two novel EMT-regulating transcription factors, IRF5 and LMCD1, were identified and independently validated.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cam4.719DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4971924PMC
August 2016

Reply.

Hepatology 2016 08 23;64(2):692-3. Epub 2016 Jun 23.

David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/hep.28618DOI Listing
August 2016

Radiogenomic Analysis Demonstrates Associations between (18)F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer.

Radiology 2016 07 15;280(1):261-70. Epub 2016 Apr 15.

From the Department of Radiology, The David Geffen School of Medicine at University of California-Los Angeles (UCLA), 10833 LeConte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., D.H., L.D., N.J., B.L.B., M.D.K.); Department of Bioengineering, UCLA, Los Angeles, Calif (M.D.K.); and Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.).

Purpose To investigate whether non-small cell lung cancer (NSCLC) tumors that express high normalized maximum standardized uptake value (SUVmax) are associated with a more epithelial-mesenchymal transition (EMT)-like phenotype. Materials and Methods In this institutional review board-approved study, a public NSCLC data set that contained fluorine 18 ((18)F) fluoro-2-deoxyglucose positron emission tomography (PET) and messenger RNA expression profile data (n = 26) was obtained, and patients were categorized on the basis of measured normalized SUVmax values. Significance analysis of microarrays was then used to create a radiogenomic signature. The prognostic ability of this signature was assessed in a second independent data set that consisted of clinical and messenger RNA expression data (n = 166). Signature concordance with EMT was evaluated by means of validation in a publicly available cell line data set. Finally, by establishing an in vitro EMT lung cancer cell line model, an attempt was made to substantiate the radiogenomic signature with quantitative polymerase chain reaction, and functional assays were performed, including Western blot, cell migration, glucose transporter, and hexokinase assays (paired t test), as well as pharmacologic assays against chemotherapeutic agents (half-maximal effective concentration). Results Differential expression analysis yielded a 14-gene radiogenomic signature (P < .05, false discovery rate [FDR] < 0.20), which was confirmed to have differences in disease-specific survival (log-rank test, P = .01). This signature also significantly overlapped with published EMT cell line gene expression data (P < .05, FDR < 0.20). Finally, an EMT cell line model was established, and cells that had undergone EMT differentially expressed this signature and had significantly different EMT protein expression (P < .05, FDR < 0.20), cell migration, glucose uptake, and hexokinase activity (paired t test, P < .05). Cells that had undergone EMT also had enhanced chemotherapeutic resistance, with a higher half-maximal effective concentration than that of cells that had not undergone EMT (P < .05). Conclusion Integrative radiogenomic analysis demonstrates an association between increased normalized (18)F fluoro-2-deoxyglucose PET SUVmax, outcome, and EMT in NSCLC. (©) RSNA, 2016 Online supplemental material is available for this article.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2016160259DOI Listing
July 2016

Diversity of Gene Expression in Hepatocellular Carcinoma Cells.

Genomics Proteomics Bioinformatics 2015 Dec 11;13(6):377-82. Epub 2016 Jan 11.

Department of Radiological Sciences, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, CA 90095, USA. Electronic address:

Understanding tumor diversity has been a long-lasting and challenging question for researchers in the field of cancer heterogeneity or tumor evolution. Studies have reported that compared to normal cells, there is a higher genetic diversity in tumor cells, while higher genetic diversity is associated with higher progression risks of tumor. We thus hypothesized that tumor diversity also holds true at the gene expression level. To test this hypothesis, we used t-test to compare the means of Simpson's diversity index for gene expression (SDIG) between tumor and non-tumor samples. We found that the mean SDIG in tumor tissues is significantly higher than that in the non-tumor or normal tissues (P<0.05) for most datasets. We also combined microarrays and next-generation sequencing data for validation. This cross-platform and cross-experimental validation greatly increased the reliability of our results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.gpb.2015.07.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747664PMC
December 2015

The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial.

Eur Radiol 2016 Aug 11;26(8):2798-807. Epub 2015 Nov 11.

Department of Radiological Sciences, University of California-Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA.

Objectives: To characterize a radiogenomic risk score (RRS), a previously defined biomarker, and to evaluate its potential for stratifying radiological progression-free survival (rPFS) in patients with metastatic renal cell carcinoma (mRCC) undergoing pre-surgical treatment with bevacizumab.

Methodology: In this IRB-approved study, prospective imaging analysis of the RRS was performed on phase II clinical trial data of mRCC patients (n = 41) evaluating whether patient stratification according to the RRS resulted in groups more or less likely to have a rPFS to pre-surgical bevacizumab prior to cytoreductive nephrectomy. Survival times of RRS subgroups were analyzed using Kaplan-Meier survival analysis.

Results: The RRS is enriched in diverse molecular processes including drug response, stress response, protein kinase regulation, and signal transduction pathways (P < 0.05). The RRS successfully stratified rPFS to bevacizumab based on pre-treatment computed tomography imaging with a median progression-free survival of 6 versus >25 months (P = 0.005) and overall survival of 25 versus >37 months in the high and low RRS groups (P = 0.03), respectively. Conventional prognostic predictors including the Motzer and Heng criteria were not predictive in this cohort (P > 0.05).

Conclusions: The RRS stratifies rPFS to bevacizumab in patients from a phase II clinical trial with mRCC undergoing cytoreductive nephrectomy and pre-surgical bevacizumab.

Key Points: • The RRS SOMA stratifies patient outcomes in a phase II clinical trial. • RRS stratifies subjects into prognostic groups in a discrete or continuous fashion. • RRS is biologically enriched in diverse processes including drug response programs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00330-015-4082-8DOI Listing
August 2016

The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma.

Radiology 2015 Oct 19;277(1):114-23. Epub 2015 Aug 19.

From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.).

Purpose: To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image.

Materials And Methods: In this institutional review board approved study, gene expression profile data and contrast material-enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression-based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis.

Results: Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57, P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001).

Conclusion: A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2015150800DOI Listing
October 2015

Renal Denervation: A Novel Therapy at the Crossroads of Imaging, Intervention, and Innovation.

J Lab Autom 2016 Apr 17;21(2):312-6. Epub 2015 Sep 17.

David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.

Hypertension (HTN) is one of the most significant medical problems affecting society today. The estimated 76 million Americans with hypertension represent a significant public health problem, contributing to cardiac, vascular, renal, and neurovascular morbidity and mortality. HTN is the most common indication for lifelong pharmacologic treatment, mainly because of the incontrovertible reductions in cardiovascular events with blood pressure (BP) reduction and control. However, despite the availability and potency of multiple different antihypertensive drugs, up to half of American patients have BPs above the recommended target. Given the overwhelming evidence of both the cost to society of HTN and the benefits that are accrued from improved BP control, alternatives or adjuncts to current management options have been sought to aid in treatment of these patients. Over the past few years, a device-based approach involving modulation of the autonomic nervous system, termed renal denervation, has evolved to meet this challenge. With early trials showing startlingly good results, with few side effects, multiple devices were fast-tracked to clinical trials and hence to the market. However, larger trials have shone an unfavorable light on the field, with concerns about the short- and long-term effectiveness, diverting attention back to operational and procedural details. Despite this, image-guided manipulation of the sympathetic nervous system to treat HTN remains a fertile area of laboratory and clinical research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/2211068215605838DOI Listing
April 2016

A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma.

Hepatology 2015 Sep 1;62(3):792-800. Epub 2015 Jul 1.

Department of Radiology, University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA.

Unlabelled: Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is an independent predictor of poor outcomes subsequent to surgical resection or liver transplantation (LT); however, MVI currently cannot be adequately determined preoperatively. Radiogenomic venous invasion (RVI) is a contrast-enhanced computed tomography (CECT) biomarker of MVI derived from a 91-gene HCC "venous invasion" gene expression signature. Preoperative CECTs of 157 HCC patients who underwent surgical resection (N = 72) or LT (N = 85) between 2000 and 2009 at three institutions were evaluated for the presence or absence of RVI. RVI was assessed for its ability to predict MVI and outcomes. Interobserver agreement for scoring RVI was substantial among five radiologists (κ = 0.705; P < 0.001). The diagnostic accuracy, sensitivity, and specificity of RVI in predicting MVI was 89%, 76%, and 94%, respectively. Positive RVI score was associated with lower overall survival (OS) than negative RVI score in the overall cohort (P < 0.001; 48 vs. >147 months), American Joint Committee on Cancer tumor-node-metastasis stage II (P < 0.001; 34 vs. >147 months), and in LT patients within Milan criteria (P < 0.001; 69 vs. >147 months). Positive RVI score also portended lower recurrence-free survival at 3 years versus negative RVI score (P = 0.001; 27% vs. 62%).

Conclusion: RVI is a noninvasive radiogenomic biomarker that accurately predicts histological MVI in HCC surgical candidates. Its presence on preoperative CECT is associated with early disease recurrence and poor OS and may be useful for identifying patients less likely to derive a durable benefit from surgical treatment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/hep.27877DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4654334PMC
September 2015

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis.

Radiology 2015 May 26;275(2):384-92. Epub 2015 Feb 26.

From the Department of Radiological Sciences, UCLA School of Medicine, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., L.D., N.J., D.H., M.D.K.); and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea (W.H., Y.K., J.H.K.).

Purpose: To perform a radiogenomic analysis of women with breast cancer to study the multiscale relationships among quantitative computer vision-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging phenotypes, early metastasis, and long noncoding RNA (lncRNA) expression determined by means of high-resolution next-generation RNA sequencing.

Materials And Methods: In this institutional review board-approved study, an automated image analysis platform extracted 47 computational quantitative features from DCE MR imaging data in a training set (n = 19) to screen for MR imaging biomarkers indicative of poor metastasis-free survival (MFS). The lncRNA molecular landscape of the candidate feature was defined by using an RNA sequencing-specific negative binomial distribution differential expression analysis. Then, this radiogenomic biomarker was applied prospectively to a validation set (n = 42) to allow prediction of MFS and lncRNA expression by using quantitative polymerase chain reaction analysis.

Results: The quantitative MR imaging feature, enhancing rim fraction score, was predictive of MFS in the training set (P = .007). RNA sequencing analysis yielded an average of 55.7 × 10(6) reads per sample and identified 14 880 lncRNAs from a background of 189 883 transcripts per sample. Radiogenomic analysis allowed identification of three previously uncharacterized and five named lncRNAs significantly associated with high enhancing rim fraction, including Homeobox transcript antisense intergenic RNA (HOTAIR) (P < .05), a known predictor of poor MFS in patients with breast cancer. Independent validation confirmed the association of the enhancing rim fraction phenotype with both MFS (P = .002) and expression of four of the top five differentially expressed lncRNAs (P < .05), including HOTAIR.

Conclusion: The enhancing rim fraction score, a quantitative DCE MR imaging lncRNA radiogenomic biomarker, is associated with early metastasis and expression of the known predictor of metastatic progression, HOTAIR.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.15142698DOI Listing
May 2015

Drs. Chan et al respond.

J Vasc Interv Radiol 2014 Aug;25(8):1315-6

Department of Radiology, University of California, Los Angeles, Los Angeles, California.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jvir.2014.04.006DOI Listing
August 2014

ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization.

Radiology 2014 Aug 2;272(2):568-76. Epub 2014 Jun 2.

From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 10833 Le Conte Ave, Box 951721, CHS 17-135, Los Angeles, CA 90095-1721 (S.Y., C.M., M.D.K.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.L.K.); Scottsdale Healthcare, Scottsdale, Ariz (R.L.K.); Departments of Vascular Interventional Radiology (R.O.) and Pathology (A.J.I.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Mayo Clinic, Phoenix, Ariz (M.B.G.); Department of Radiology, Mayo Clinic, Scottsdale, Ariz (M.B.G.); Cancer Treatment Centers of America, Goodyear, Ariz (G.J.W.); Translational Genomics Research Institute, Phoenix, Ariz (G.J.W.); and Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea (D.W.K.).

Purpose: To present a radiogenomic computed tomographic (CT) characterization of anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) (ALK+).

Materials And Methods: In this HIPAA-compliant institutional review board-approved retrospective study, CT studies, ALK status, and clinical-pathologic data in 172 patients with NSCLC from three institutions were analyzed. A screen of 24 CT image traits was performed in a training set of 59 patients, followed by random forest variable selection incorporating 24 CT traits plus six clinical-pathologic covariates to identify a radiogenomic predictor of ALK+ status. This predictor was then validated in an independent cohort (n = 113). Test-for-accuracy and subset analyses were performed. A similar analysis was performed to identify a biomarker associated with shorter progression-free survival (PFS) after therapy with the ALK inhibitor crizotinib.

Results: ALK+ status was associated with central tumor location, absence of pleural tail, and large pleural effusion. An ALK+ radiogenomic CT status biomarker consisting of these three imaging traits with patient age of younger than 60 years showed strong discriminatory power for ALK+ status, with a sensitivity of 83.3% (15 of 18), a specificity of 77.9% (74 of 95), and an accuracy of 78.8% (89 of 113) in independent testing. The discriminatory power was particularly strong in patients with operable disease (stage IIIA or lower), with a sensitivity of 100.0% (five of five), a specificity of 88.1% (37 of 42), and an accuracy of 89.4% (42 of 47). Tumors with a disorganized vessel pattern had a shorter PFS with crizotinib therapy than tumors without this trait (11.4 vs 20.2 months, P = .041).

Conclusion: ALK+ NSCLC has distinct characteristics at CT imaging that, when combined with clinical covariates, discriminate ALK+ from non-ALK tumors and can potentially identify patients with a shorter durable response to crizotinib.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.14140789DOI Listing
August 2014

Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations.

Radiology 2014 Feb;270(2):320-5

From the Department of Radiological Sciences, University of California-Los Angeles, CHS-17-135, 10833 Le Conte Ave, Los Angeles, CA 90095-1721.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.13132195DOI Listing
February 2014