964 results match your criteria radiomic model


Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases.

J Neurosurg Spine 2021 Sep 24:1-9. Epub 2021 Sep 24.

1Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore.

Objective: In the treatment of spinal metastases with stereotactic body radiation therapy (SBRT), vertebral compression fracture (VCF) is a common and potentially morbid complication. Better methods to identify patients at high risk of radiation-induced VCF are needed to evaluate prophylactic measures. Radiomic features from pretreatment imaging may be employed to more accurately predict VCF. Read More

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September 2021

Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study.

Eur Radiol 2021 Sep 24. Epub 2021 Sep 24.

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland.

Objectives: To investigate, in patients with metastatic prostate cancer, whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone using  Ga-PSMA PET imaging as reference standard.

Methods: In this IRB-approved retrospective study, 67 patients (mean age 71 ± 7 years; range: 55-84 years) showing a total of 205  Ga-PSMA-positive prostate cancer bone metastases in the thoraco-lumbar spine and pelvic bone being invisible in CT were included. Metastases and 86  Ga-PSMA-negative bone volumes in the same body region were segmented and further post-processed. Read More

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September 2021

Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis.

Front Oncol 2021 7;11:744250. Epub 2021 Sep 7.

Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States.

Purpose: There is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV-) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV- and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients.

Experimental Design: This retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as S) with HPV+ OPSCC. Read More

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September 2021

Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings.

J Magn Reson Imaging 2021 Sep 22. Epub 2021 Sep 22.

School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.

Background: Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning.

Purpose: To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA.

Study Type: Retrospective. Read More

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September 2021

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.

J Digit Imaging 2021 Sep 20. Epub 2021 Sep 20.

Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA, 90033, USA.

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. Read More

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September 2021

Multi-level multi-modality (PET and CT) fusion radiomics: Prognostic modeling for non-small cell lung carcinoma.

Phys Med Biol 2021 Sep 20. Epub 2021 Sep 20.

Division of Nuclear Medicine, University Hospital of Geneva, 24 Rue Micheli-du-Crest, 1211 Geneva 4, Geneva, SWITZERLAND.

We developed multi-modality radiomic models by integrating information extracted from 18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. Read More

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September 2021

Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer.

Breast 2021 Sep 11;60:90-97. Epub 2021 Sep 11.

Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China. Electronic address:

Background: One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance.

Objective: To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. Read More

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September 2021

A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis.

AJNR Am J Neuroradiol 2021 Sep 16. Epub 2021 Sep 16.

From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.).

Background And Purpose: Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images.

Materials And Methods: In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. Read More

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September 2021

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases.

Front Oncol 2021 30;11:732704. Epub 2021 Aug 30.

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Background: The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.

Methods: One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set ( = 80) or validation set ( = 20). Read More

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Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples.

Phys Med 2021 Sep 11;90:13-22. Epub 2021 Sep 11.

Physics and Chemistry Department "Emilio Segrè", University of Palermo, Palermo, Italy; National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy.

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). Read More

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September 2021

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

Br J Radiol 2021 Oct 14;94(1126):20210221. Epub 2021 Sep 14.

Keck School of Medicine, University of Southern California, CA, USA.

Objectives: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes.

Methods: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Read More

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October 2021

Preliminary Computed Tomography Radiomics Model for Predicting Pretreatment CD8+ T-Cell Infiltration Status for Primary Head and Neck Squamous Cell Carcinoma.

J Comput Assist Tomogr 2021 Jul-Aug 01;45(4):629-636

Department of Radiology, The University of Chicago, Chicago, IL.

Purpose: Immunotherapy has emerged as a treatment option for head and neck squamous cell carcinoma (HNSCC), with tumor response being linked to the CD8+ T-cell inflammation. The purpose of this study is to assess whether computed tomography (CT) radiomic analysis can predict CD8+ T-cell enrichment in HNSCC primary tumors.

Methods: This retrospective study included 71 patients from a head and neck cancer genomics cohort with CD8+ T-cell enrichment status. Read More

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September 2021

Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI.

Math Biosci Eng 2021 07;18(5):6198-6215

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. Read More

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Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.

Brainlesion 2021 27;12658:157-167. Epub 2021 Mar 27.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Glioblastoma ( ) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Read More

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Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.

J Med Imaging (Bellingham) 2021 Sep 7;8(5):054501. Epub 2021 Sep 7.

Mayo Clinic Arizona, Department of Radiology, Phoenix, Arizona, United States.

: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). : In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. Read More

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September 2021

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics.

Front Cell Dev Biol 2021 26;9:710461. Epub 2021 Aug 26.

Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Background: Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.

Purpose: To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses. Read More

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Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma.

Front Oncol 2021 27;11:704994. Epub 2021 Aug 27.

Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China.

Objective: We aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.

Methods: 170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Read More

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MRI-based Delta-Radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.

Radiother Oncol 2021 Sep 7. Epub 2021 Sep 7.

Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich.

Purpose: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR).

Methods: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). Read More

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September 2021

Radiomic signature of DWI-FLAIR mismatch in large vessel occlusion stroke.

J Neuroimaging 2021 Sep 10. Epub 2021 Sep 10.

Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.

Background And Purpose: Ischemic diffusion-weighted imaging-fluid-attenuated inversion recovery (DWI-FLAIR) mismatch may be useful in guiding acute stroke treatment decisions given its relationship to onset time and parenchymal viability; however, it relies on subjective grading. Radiomics is an emerging image quantification methodology that may objectively represent continuous image characteristics. We propose a novel radiomics approach to characterize DWI-FLAIR mismatch. Read More

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September 2021

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

Sci Rep 2021 09 9;11(1):17885. Epub 2021 Sep 9.

Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Read More

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September 2021

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

BMC Infect Dis 2021 Sep 8;21(1):931. Epub 2021 Sep 8.

Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China.

Background: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).

Methods: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. Read More

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September 2021

CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.

Radiology 2021 Sep 7:210699. Epub 2021 Sep 7.

From the Departments of Radiology (F.R., K.J.L., M.M., P.L., Y.D., F.R.S., E.S., D.M.) and Radiation Oncology (K.J.L.), Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 27710; Multi-Dimensional Image Processing Laboratory, Duke Radiology, Duke University School of Medicine, Durham, NC (F.R., M.M., P.L., Y.D., F.R.S., D.M.); progettoDiventerò, Bracco Foundation, Milan, Italy (F.R.); Carl E. Ravin Advanced Imaging Laboratories (J.H., E.S.), Department of Biostatistics and Bioinformatics (R.L., S.L.), and Duke Electrical and Computer Engineering (K.J.L.), Duke University, Durham, NC; Department of Biostatistics, Yale University, New Haven, Conn (C.L.); Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany (M.M.); Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China (P.L.); Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (Y.D.); Duke Cancer Center, Duke Health, Durham, NC (N.B.M., S.Z.); and Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (D.E.M.).

Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. Read More

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September 2021

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [F]FET PET radiomics.

Eur J Nucl Med Mol Imaging 2021 Sep 7. Epub 2021 Sep 7.

Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.

Purpose: To evaluate radiomic features extracted from standard static images (20-40 min p.i.), early summation images (5-15 min p. Read More

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September 2021

An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum.

Transl Psychiatry 2021 Sep 6;11(1):462. Epub 2021 Sep 6.

Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea.

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Read More

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September 2021

Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases.

J Transl Med 2021 09 6;19(1):377. Epub 2021 Sep 6.

Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xuanwu District, Beijing, 100053, China.

Background: Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO. Read More

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September 2021

Machine Learning Approach to Differentiation of Peripheral Schwannomas and Neurofibromas: A Multi-Center Study.

Neuro Oncol 2021 Sep 6. Epub 2021 Sep 6.

Department of Neurosurgery, Stanford University, Stanford, CA, USA.

Background: Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas.

Methods: We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Read More

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September 2021

Spleen Radiomics Signature: A Potential Biomarker for Prediction of Early and Late Recurrences of Hepatocellular Carcinoma After Resection.

Front Oncol 2021 13;11:716849. Epub 2021 Aug 13.

The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.

Objectives: To explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection.

Methods: This retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Read More

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Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.

Front Oncol 2021 16;11:706043. Epub 2021 Aug 16.

Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.

Objectives: Accurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer.

Methods: This retrospective study included 106 patients with cervical cancer (FIGO stage IB1-IVa) between October 2017 and May 2019. Read More

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Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.

Sci Rep 2021 Sep 3;11(1):17633. Epub 2021 Sep 3.

Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA.

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Read More

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September 2021

Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study.

Br J Radiol 2021 Oct 3;94(1126):20210188. Epub 2021 Sep 3.

Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Objectives: To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC).

Methods And Materials: A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort ( = 216), as well as the internal validation cohort ( = 108) and external validation cohort ( = 162). Read More

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October 2021