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Hiremath A, Corredor G, Li L, Leo P, Magi-Galluzzi C, Elliott R, Purysko A, Shiradkar R, Madabhushi A. An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings. Heliyon 2024; 10:e29602. [PMID: 38665576 PMCID: PMC11044050 DOI: 10.1016/j.heliyon.2024.e29602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Objectives To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE). Methods Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC). Results Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2). Conclusions Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).
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Affiliation(s)
| | - Germán Corredor
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Lin Li
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Patrick Leo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | | | - Robin Elliott
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Andrei Purysko
- Department of Radiology and Nuclear Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA
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2
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Duan L, Liu Z, Wan F, Dai B. Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects. BMC Cancer 2024; 24:448. [PMID: 38605339 PMCID: PMC11007899 DOI: 10.1186/s12885-024-12071-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 02/29/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Whole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects. METHODS An extensive PubMed search was conducted until February 26, 2023, with the search term "prostate", "whole-mount", "large format histology", which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding. RESULTS WMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region. CONCLUSIONS WMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.
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Affiliation(s)
- Lewei Duan
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China
| | - Zheng Liu
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China
| | - Fangning Wan
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China.
| | - Bo Dai
- Department of Urology, Fudan University Shanghai Cancer Center, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
- Shanghai Genitourinary Cancer Institute, 200032, Shanghai, China.
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3
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Sun Z, Wu P, Cui Y, Liu X, Wang K, Gao G, Wang H, Zhang X, Wang X. Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI. J Magn Reson Imaging 2023; 58:1067-1081. [PMID: 36825823 DOI: 10.1002/jmri.28608] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE Retrospective. POPULATION One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China
| | - Yingpu Cui
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xiang Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Huihui Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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Hu L, Fu C, Song X, Grimm R, von Busch H, Benkert T, Kamen A, Lou B, Huisman H, Tong A, Penzkofer T, Choi MH, Shabunin I, Winkel D, Xing P, Szolar D, Coakley F, Shea S, Szurowska E, Guo JY, Li L, Li YH, Zhao JG. Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique. Cancer Imaging 2023; 23:6. [PMID: 36647150 PMCID: PMC9843860 DOI: 10.1186/s40644-023-00527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.
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Affiliation(s)
- Lei Hu
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen magnetic Resonance Ltd., Shenzhen, China
| | - Xinyang Song
- grid.443573.20000 0004 1799 2448Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, 441000 China
| | - Robert Grimm
- grid.5406.7000000012178835XMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Heinrich von Busch
- grid.5406.7000000012178835XInnovation Owner Artificial Intelligence for Oncology, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- grid.5406.7000000012178835XMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Ali Kamen
- grid.415886.60000 0004 0546 1113Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA
| | - Bin Lou
- grid.415886.60000 0004 0546 1113Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA
| | - Henkjan Huisman
- grid.10417.330000 0004 0444 9382Radboud University Medical Center, Nijmegen, Netherlands
| | - Angela Tong
- grid.137628.90000 0004 1936 8753New York University, New York City, NY USA
| | - Tobias Penzkofer
- grid.6363.00000 0001 2218 4662Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Moon Hyung Choi
- grid.411947.e0000 0004 0470 4224Eunpyeong St. Mary’s Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | | | - David Winkel
- grid.410567.1Universitätsspital Basel, Basel, Switzerland
| | - Pengyi Xing
- grid.411525.60000 0004 0369 1599Changhai Hospital, Shanghai, China
| | | | - Fergus Coakley
- grid.5288.70000 0000 9758 5690Oregon Health and Science University, Portland, OR USA
| | - Steven Shea
- grid.411451.40000 0001 2215 0876Loyola University Medical Center, Maywood, IL USA
| | - Edyta Szurowska
- grid.11451.300000 0001 0531 3426Medical University of Gdansk, Gdansk, Poland
| | - Jing-yi Guo
- grid.16821.3c0000 0004 0368 8293Clinical Research Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233 China
| | - Liang Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060 China
| | - Yue-hua Li
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Jun-gong Zhao
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
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Lemay A, Hoebel K, Bridge CP, Befano B, De Sanjosé S, Egemen D, Rodriguez AC, Schiffman M, Campbell JP, Kalpathy-Cramer J. Improving the repeatability of deep learning models with Monte Carlo dropout. NPJ Digit Med 2022; 5:174. [PMID: 36400939 PMCID: PMC9674698 DOI: 10.1038/s41746-022-00709-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 10/10/2022] [Indexed: 11/19/2022] Open
Abstract
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Ideal repeatable models output predictions without variation during independent tests carried out under similar conditions. However, slight variations, though not ideal, may be unavoidable and acceptable in practice. During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice. In this work, we evaluate the repeatability of four model types (binary classification, multi-class classification, ordinal classification, and regression) on images that were acquired from the same patient during the same visit. We study the each model's performance on four medical image classification tasks from public and private datasets: knee osteoarthritis, cervical cancer screening, breast density estimation, and retinopathy of prematurity. Repeatability is measured and compared on ResNet and DenseNet architectures. Moreover, we assess the impact of sampling Monte Carlo dropout predictions at test time on classification performance and repeatability. Leveraging Monte Carlo predictions significantly increases repeatability, in particular at the class boundaries, for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 16% points and of the class disagreement rate by 7% points. The classification accuracy improves in most settings along with the repeatability. Our results suggest that beyond about 20 Monte Carlo iterations, there is no further gain in repeatability. In addition to the higher test-retest agreement, Monte Carlo predictions are better calibrated which leads to output probabilities reflecting more accurately the true likelihood of being correctly classified.
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Affiliation(s)
- Andreanne Lemay
- Martinos Center for Biomedical Imaging, Boston, MA, USA
- NeuroPoly, Polytechnique Montreal, Montreal, QC, Canada
| | - Katharina Hoebel
- Martinos Center for Biomedical Imaging, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christopher P Bridge
- Martinos Center for Biomedical Imaging, Boston, MA, USA
- MGH & BWH Center for Clinical Data Science, Boston, MA, USA
| | - Brian Befano
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Silvia De Sanjosé
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA
| | - Didem Egemen
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA
| | - Ana Cecilia Rodriguez
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA
| | - Mark Schiffman
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD, USA
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Zhang KS, Schelb P, Netzer N, Tavakoli AA, Keymling M, Wehrse E, Hog R, Rotkopf LT, Wennmann M, Glemser PA, Thierjung H, von Knebel Doeberitz N, Kleesiek J, Görtz M, Schütz V, Hielscher T, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein K, Bonekamp D. Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration. Invest Radiol 2022; 57:601-612. [PMID: 35467572 DOI: 10.1097/rli.0000000000000878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages. RESULTS A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8% and specificity of 54.8%, not statistically different from the current clinical routine PI-RADS ≥4 assessment at 90.9% and 54.8%, respectively ( P = 0.30/ P = 1.0). In general, residents achieved similar sensitivity and specificity before and after CNN review. On a prostate sextant basis, clinical assessment possessed the highest ROC area under the curve of 0.82, higher than CNN (AUC = 0.76, P = 0.21) and significantly higher than resident performance before and after CNN review (AUC = 0.76 / 0.76, P ≤ 0.03). The resident survey indicated CNN to be helpful and clinically useful. CONCLUSIONS Pseudoprospective paraclinical integration of fully automated CNN-based detection of suspicious lesions on prostate multiparametric MRI was demonstrated and showed good acceptance among residents, whereas no significant improvement in resident performance was found. General CNN performance was preserved despite an observed shift in CNN calibration, identifying the requirement for continuous quality control and recalibration.
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Affiliation(s)
- Kevin Sun Zhang
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Myriam Keymling
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Eckhard Wehrse
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Robert Hog
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Markus Wennmann
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Heidi Thierjung
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center
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7
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Verma R, Hill VB, Statsevych V, Bera K, Correa R, Leo P, Ahluwalia M, Madabhushi A, Tiwari P. Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study. AJNR Am J Neuroradiol 2022; 43:1115-1123. [PMID: 36920774 PMCID: PMC9575418 DOI: 10.3174/ajnr.a7591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/13/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Glioblastoma is an aggressive brain tumor, with no validated prognostic biomarkers for survival before surgical resection. Although recent approaches have demonstrated the prognostic ability of tumor habitat (constituting necrotic core, enhancing lesion, T2/FLAIR hyperintensity subcompartments) derived radiomic features for glioblastoma survival on treatment-naive MR imaging scans, radiomic features are known to be sensitive to MR imaging acquisitions across sites and scanners. In this study, we sought to identify the radiomic features that are both stable across sites and discriminatory of poor and improved progression-free survival in glioblastoma tumors. MATERIALS AND METHODS We used 150 treatment-naive glioblastoma MR imaging scans (Gadolinium-T1w, T2w, FLAIR) obtained from 5 sites. For every tumor subcompartment (enhancing tumor, peritumoral FLAIR-hyperintensities, necrosis), a total of 316 three-dimensional radiomic features were extracted. The training cohort constituted studies from 4 sites (n = 93) to select the most stable and discriminatory radiomic features for every tumor subcompartment. These features were used on a hold-out cohort (n = 57) to evaluate their ability to discriminate patients with poor survival from those with improved survival. RESULTS Incorporating the most stable and discriminatory features within a linear discriminant analysis classifier yielded areas under the curve of 0.71, 0.73, and 0.76 on the test set for distinguishing poor and improved survival compared with discriminatory features alone (areas under the curve of 0.65, 0.54, 0.62) from the necrotic core, enhancing tumor, and peritumoral T2/FLAIR hyperintensity, respectively. CONCLUSIONS Incorporating stable and discriminatory radiomic features extracted from tumors and associated habitats across multisite MR imaging sequences may yield robust prognostic classifiers of patient survival in glioblastoma tumors.
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Affiliation(s)
- R Verma
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio .,Alberta Machine Intelligence Institute (R.V.), Edmonton, Alberta
| | - V B Hill
- Department of Neuroradiology (V.B.H.), Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - V Statsevych
- Brain Tumor and Neuro-Oncology Center (V.S.), Cleveland Clinic, Cleveland, Ohio
| | - K Bera
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio
| | - R Correa
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio
| | - P Leo
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio
| | - M Ahluwalia
- Miami Cancer Institute (M.A.), Miami, FL and Herbert Wertheim College of Medicine, Florida International University, Florida
| | - A Madabhushi
- Department of Biomedical Engineering (A.M.), Emory University, Atlanta Veterans Administration Medical Center
| | - P Tiwari
- Departments of Radiology and Biomedical Engineering (P.T.), University of Wisconsin Madison, Wisconsin
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8
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Naik N, Tokas T, Shetty DK, Hameed BZ, Shastri S, Shah MJ, Ibrahim S, Rai BP, Chłosta P, Somani BK. Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review. J Clin Med 2022; 11:jcm11133575. [PMID: 35806859 PMCID: PMC9267773 DOI: 10.3390/jcm11133575] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/07/2022] [Accepted: 06/18/2022] [Indexed: 11/16/2022] Open
Abstract
This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility.
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Affiliation(s)
- Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Krnataka, India;
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India; (M.J.S.); (S.I.); (B.P.R.); (B.K.S.)
| | - Theodoros Tokas
- Department of Urology and Andrology, General Hospital Hall i.T., Milser Str. 10, 6060 Hall in Tirol, Austria;
| | - Dasharathraj K. Shetty
- Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
- Correspondence: (D.K.S.); (B.M.Z.H.)
| | - B.M. Zeeshan Hameed
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India; (M.J.S.); (S.I.); (B.P.R.); (B.K.S.)
- Department of Urology, Father Muller Medical College, Mangalore 575002, Karnataka, India
- Correspondence: (D.K.S.); (B.M.Z.H.)
| | - Sarthak Shastri
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Milap J. Shah
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India; (M.J.S.); (S.I.); (B.P.R.); (B.K.S.)
- Robotics and Urooncology, Max Hospital and Max Institute of Cancer Care, New Delhi 110024, India
| | - Sufyan Ibrahim
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India; (M.J.S.); (S.I.); (B.P.R.); (B.K.S.)
- Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Bhavan Prasad Rai
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India; (M.J.S.); (S.I.); (B.P.R.); (B.K.S.)
- Department of Urology, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK
| | - Piotr Chłosta
- Department of Urology, Jagiellonian University in Krakow, Gołębia 24, 31-007 Kraków, Poland;
| | - Bhaskar K. Somani
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India; (M.J.S.); (S.I.); (B.P.R.); (B.K.S.)
- Department of Urology, University Hospital Southampton NHS Trust, Southampton SO16 6YD, UK
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9
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Shiradkar R, Ghose S, Mahran A, Li L, Hubbard I, Fu P, Tirumani SH, Ponsky L, Purysko A, Madabhushi A. Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings. Front Oncol 2022; 12:841801. [PMID: 35669420 PMCID: PMC9163353 DOI: 10.3389/fonc.2022.841801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/13/2022] [Indexed: 11/25/2022] Open
Abstract
Objective To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa. Methods This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (CS) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (CR) was trained for BCR. An integrated classifier CS+R was then trained using predictions from CS and CR. These models were trained on D1 (N = 71, 27 BCR+) and evaluated on independent hold-out set D2 (N = 62, 12 BCR+). CS+R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years. Results CS+R resulted in a higher AUC (0.75) compared to CR (0.70, p = 0.04) and CS (0.69, p = 0.01) on D2 in predicting BCR. On univariable analysis, CS+R achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. CS+R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). CS+R resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07). Conclusions Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.
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Affiliation(s)
- Rakesh Shiradkar
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- *Correspondence: Rakesh Shiradkar,
| | - Soumya Ghose
- GE Global Research, Niskayuna, NY, United States
| | - Amr Mahran
- Department of Urology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Lin Li
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Isaac Hubbard
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Sree Harsha Tirumani
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Lee Ponsky
- Department of Urology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Andrei Purysko
- Department of Abdominal Imaging and Nuclear Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
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10
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Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040799. [PMID: 35453847 PMCID: PMC9027206 DOI: 10.3390/diagnostics12040799] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 02/04/2023] Open
Abstract
Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
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11
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Cunha GM, Fowler KJ. Automated Liver Segmentation for Quantitative MRI Analysis. Radiology 2021; 302:355-356. [PMID: 34783598 DOI: 10.1148/radiol.2021212306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Guilherme Moura Cunha
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Seattle, WA 98195-0005 (G.M.C.); and Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, Calif (K.J.F.)
| | - Kathryn J Fowler
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Seattle, WA 98195-0005 (G.M.C.); and Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, Calif (K.J.F.)
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12
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Bevilacqua A, Mottola M, Ferroni F, Rossi A, Gavelli G, Barone D. The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer. Diagnostics (Basel) 2021; 11:diagnostics11050739. [PMID: 33919299 PMCID: PMC8143289 DOI: 10.3390/diagnostics11050739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 12/04/2022] Open
Abstract
Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.
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Affiliation(s)
- Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Via Toffano 2/2, I-40125 Bologna, Italy;
- Correspondence: ; Tel.: +39-051-209-5409
| | - Margherita Mottola
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Via Toffano 2/2, I-40125 Bologna, Italy;
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Viale Risorgimento 2, I-40136 Bologna, Italy
| | - Fabio Ferroni
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Via Piero Maroncelli 40, I-47014 Meldola, Italy; (F.F.); (A.R.); (G.G.); (D.B.)
| | - Alice Rossi
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Via Piero Maroncelli 40, I-47014 Meldola, Italy; (F.F.); (A.R.); (G.G.); (D.B.)
| | - Giampaolo Gavelli
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Via Piero Maroncelli 40, I-47014 Meldola, Italy; (F.F.); (A.R.); (G.G.); (D.B.)
| | - Domenico Barone
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Via Piero Maroncelli 40, I-47014 Meldola, Italy; (F.F.); (A.R.); (G.G.); (D.B.)
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