Xinran Zhong1,2, Ruiming Cao3,4, Sepideh Shakeri3, Fabien Scalzo5, Yeejin Lee3, Dieter R Enzmann3, Holden H Wu3,6, Steven S Raman3, Kyunghyun Sung3,6. 1. Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. XZhong@mednet.ucla.edu. 2. Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. XZhong@mednet.ucla.edu. 3. Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. 4. Department of Computer Science, School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA. 5. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. 6. Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Abstract
PURPOSE: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation. METHODS: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC. RESULTS: After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set. CONCLUSION: The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).
PURPOSE: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation. METHODS: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC. RESULTS: After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set. CONCLUSION: The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).
Authors: Elena Bertelli; Laura Mercatelli; Chiara Marzi; Eva Pachetti; Michela Baccini; Andrea Barucci; Sara Colantonio; Luca Gherardini; Lorenzo Lattavo; Maria Antonietta Pascali; Simone Agostini; Vittorio Miele Journal: Front Oncol Date: 2022-01-13 Impact factor: 6.244
Authors: Yongkai Liu; Haoxin Zheng; Zhengrong Liang; Qi Miao; Wayne G Brisbane; Leonard S Marks; Steven S Raman; Robert E Reiter; Guang Yang; Kyunghyun Sung Journal: Diagnostics (Basel) Date: 2021-09-28
Authors: Nikhil J Dhinagar; William Speier; Karthik V Sarma; Alex Raman; Adam Kinnaird; Steven S Raman; Leonard S Marks; Corey W Arnold Journal: J Med Imaging (Bellingham) Date: 2020-12-29