Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI

Xinran Zhong, Ruiming Cao, Sepideh Shakeri, Fabien Scalzo, Yeejin Lee, Dieter R. Enzmann, Holden H. Wu, Steven S. Raman, Kyunghyun Sung

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

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).

Original languageEnglish (US)
Pages (from-to)2030-2039
Number of pages10
JournalAbdominal Radiology
Volume44
Issue number6
DOIs
StatePublished - Jun 15 2019
Externally publishedYes

Keywords

  • Clinically significant lesion classification
  • Deep learning
  • Multi-parametric MRI
  • PIRADS v2 score
  • Prostate cancer
  • Whole-mount histopathology

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Gastroenterology
  • Urology

Fingerprint

Dive into the research topics of 'Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI'. Together they form a unique fingerprint.

Cite this