AUTHOR=Zhuang Junlong , Kan Yansheng , Wang Yuwen , Marquis Alessandro , Qiu Xuefeng , Oderda Marco , Huang Haifeng , Gatti Marco , Zhang Fan , Gontero Paolo , Xu Linfeng , Calleris Giorgio , Fu Yao , Zhang Bing , Marra Giancarlo , Guo Hongqian 

TITLE=Machine Learning-Based Prediction of Pathological Upgrade From Combined Transperineal Systematic and MRI-Targeted Prostate Biopsy to Final Pathology: A Multicenter Retrospective Study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.785684

DOI=10.3389/fonc.2022.785684

ISSN=2234-943X

ABSTRACT=Objective: 
To evaluate the pathological concordance from combined systematic and MRI-targeted prostate biopsy to final pathology, and to verify the effectiveness of machine learning-based model with targeted biopsy (TB) features in predicting pathological upgrade.
Materials and methods: 
All patients in this study underwent prostate mpMRI, transperineal systematic plus transperineal targeted prostate biopsy under local anesthesia and robot-assisted laparoscopic radical prostatectomy (RARP) for prostate cancer (PCa) sequentially from October 2016 to February 2020 in two referral centers. For cores with cancer, grade group (GG) and Gleason score were determined by using the 2014 International Society of Urologic Pathology (ISUP) guidelines. Four supervised machine learning methods were employed, including two base classifiers and two ensemble-learning–based classifiers. In all classifiers, the training set was 395 of 565 (70%) patients and test set was the rest 170 patients. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The Gini index was used to evaluate the importance of all features and figured out the most contributed features. A nomogram was established to visually predict the risk of upgrading. Predicted probability was a prevalence rate calculated by a proposed nomogram. 
Results: 
A total of 515 patients were included in our cohort. Combined biopsy had better concordance of postoperative histopathology than systematic biopsy (SB) only (48.15% vs 40.19%, P=0.012). Combined biopsy could significant reduce the upgrading rate of postoperative pathology, in comparison to SB only (23.30% vs 39.61%, P<0.0001) or TB only (23.30% vs 40.19%, P<0.0001). The most common pathological upgrade occurred in ISUP GG1and GG2, accounting for 53.28% and 20.42%. All machine learning methods had satisfactory predictive efficacy. The overall accuracy was 0.703, 0.768, 0.794 and 0.761 for logistic regression, random forest, eXtreme Gradient Boosting and support vector machine, respectively. TB related features were among the most contributed features of prediction model for upgrade prediction.
Conclusion: 
The combined effect of SB plus TB led to better pathological concordance rate and to less upgrading from biopsy to RP. Machine learning models with features of TB to predict PCa GG upgrading have a satisfactory predictive efficacy.