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MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study
by
Jia-Cheng Liu
1,†,
Xiao-Hao Ruan
1,†,
Tsun-Tsun Chun
2,
Chi Yao
2,
Da Huang
1,
Hoi-Lung Wong
3,
Chun-Ting Lai
3,
Chiu-Fung Tsang
3,
Sze-Ho Ho
3,
Tsui-Lin Ng
3,
Dan-Feng Xu
1,* and
Rong Na
2,3,* 1
Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
2
Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
3
Department of Surgery, Queen Mary Hospital, Hong Kong, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2024, 16(17), 2944; https://doi.org/10.3390/cancers16172944
Submission received: 26 July 2024
/
Revised: 16 August 2024
/
Accepted: 19 August 2024
/
Published: 23 August 2024
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
Simple Summary
Prostate mpMRI is currently the most widely used image diagnosis approach to detect prostate cancer, while the PI-RADS system was developed to standardize and improve the accuracy of suspicious lesion identification on MRI. However, there still remain several limitations including inter-individual inconsistencies and naked-eye insufficiency. This study aims to apply AI technology to image interpretation to enhance diagnostic efficiency and explore the use of T2-weighted image-based stimulated biopsy in predicting prostate cancer (PCa). Using 820 lesions from The Cancer Imaging Archive database and 83 lesions from Hong Kong Queen Mary Hospital, we constructed 18 machine-learning models based on three algorithms and conducted both internal and external validation. We found that the logistic regression-based model provides additional diagnostic value to the PI-RADS in predicting PCa.
Abstract
Background: Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. Method: A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. Results: All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. Conclusions: The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.
