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Ten quick tips for computational analysis of medical images

PLoS computational biology, 2023-01, Vol.19 (1), p.e1010778-e1010778

2023

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Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

Journal of magnetic resonance imaging, 2018-12, Vol.48 (6), p.1626-1636

2018

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Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

Chinese journal of cancer research, 2021-10, Vol.33 (5), p.563-573

2021

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An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

The Lancet. Digital health, 2021-07, Vol.3 (7), p.e445-e454

2021

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Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings

Journal of magnetic resonance imaging, 2018-09, Vol.48 (3), p.818-828

2018

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“Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation

Journal of the American College of Radiology, 2023-09, Vol.20 (9), p.842-851

2023

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T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

European radiology, 2021-03, Vol.31 (3), p.1336-1346

2021

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Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps

European radiology, 2021-01, Vol.31 (1), p.379-391

2021

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Editorial for “Multiparametric MRI‐Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer”

Journal of magnetic resonance imaging, 2021-10, Vol.54 (4), p.1231-1232

Shiradkar, Rakesh

2021

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Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Cancers, 2020-08, Vol.12 (8), p.2200

2020

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Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings

Frontiers in oncology, 2023-09, Vol.13, p.1166047-1166047

2023

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Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

Frontiers in oncology, 2022-05, Vol.12, p.841801

2022

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Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

European radiology, 2021-04, Vol.31 (4), p.2644-2644

2021

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A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

EBioMedicine, 2021-01, Vol.63, p.103163-103163, Article 103163

2021

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Ten quick tips for computational analysis of medical images

PLoS computational biology, 2023-01, Vol.19 (1), p.e1010778

2023

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Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

European journal of radiology Open, 2023-01, Vol.10, p.100496-100496, Article 100496

2023

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PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI)

The Journal of urology, 2017-04, Vol.197 (4), p.e1269-e1269

2017

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PD50-11 POPULATION SPECIFIC RADIOMICS MODEL IMPROVES PROSTATE CANCER DIAGNOSTIC RISK STRATIFICATION AT MRI IN AFRICAN AMERICAN MEN

The Journal of urology, 2024-05, Vol.211 (5S)

2024

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Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

Radiation oncology (London, England), 2016-11, Vol.11 (1), p.148-148, Article 148

2016

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MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS

The Journal of urology, 2020-04, Vol.203 (Supplement 4)

2020

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Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

Magnetic resonance in medicine, 2020-06, Vol.83 (6), p.2293-2309

2020

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A combination of computer extracted measurements of prostate capsule shape and tumor texture on MRI to predict biochemical recurrence post treatment

Journal of clinical oncology, 2017-05, Vol.35 (15_suppl), p.e16554-e16554

2017

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Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study

Computers in biology and medicine, 2024-07, Vol.177, p.108643, Article 108643

2024

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Abstract 14084: Computationally Identified Shape Differences In The Left Atrium On Pre-ablation Ct Scans Appear To Be Associated With Recurrence Of Atrial Fibrillation

Circulation (New York, N.Y.), 2021-11, Vol.144 (Suppl_1)

2021

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Ten quick tips for computational analysis of medical images

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings

“Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps

Editorial for “Multiparametric MRI‐Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer”

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings

Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

Ten quick tips for computational analysis of medical images

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI)

PD50-11 POPULATION SPECIFIC RADIOMICS MODEL IMPROVES PROSTATE CANCER DIAGNOSTIC RISK STRATIFICATION AT MRI IN AFRICAN AMERICAN MEN

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS

Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

A combination of computer extracted measurements of prostate capsule shape and tumor texture on MRI to predict biochemical recurrence post treatment

Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study

Abstract 14084: Computationally Identified Shape Differences In The Left Atrium On Pre-ablation Ct Scans Appear To Be Associated With Recurrence Of Atrial Fibrillation

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