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Meta learning optimized TabNet for small sample repeat prostate biopsy prediction
Jienv Lou1, Jiahan Xu2, Dan Mao2
1Department of Ultrasound, The Affiliated People's Hospital of Ningbo University, No. 251, Baizhang East Road, Yinzhou County, Ningbo, Zhejiang, China. 815131765@qq.com.
Purpose:
Repeat prostate biopsy prediction remains limited by small patient cohorts that constrain artificial intelligence application despite theoretical advantages in capturing complex clinical patterns. This study develops and validates a meta-learning optimized TabNet framework using readily available clinical parameters to overcome sample size constraints and enhance repeat biopsy (RB) prediction accuracy through knowledge transfer from larger initial biopsy (IB) cohorts, with particular applicability to resource-limited settings where mpMRI remains unavailable. Meta-learning enables rapid model adaptation by leveraging knowledge from related tasks with minimal training examples.
Methods:
This retrospective study analyzed 2,087 initial prostate biopsies and 139 subsequent RBs without mpMRI data. A two-stage training paradigm implemented Model-Agnostic Meta-Learning for pre-training on IB data, followed by fine-tuning on the RB cohort. Performance evaluation included discrimination analysis, calibration assessment, and decision curve analysis compared to original TabNet and conventional machine learning approaches, with classification performance benchmarked against established clinical risk calculators.
Results:
Among 139 RB patients, cancer was detected in 40 cases (28.8%), including 31 clinically significant cancers (75.5%). On the independent testing set of 42 patients, meta-learning TabNet achieved superior discriminative performance (AUROC 0.872) compared to XGBoost (0.808), original TabNet (0.800), and conventional approaches. The model demonstrated optimal calibration (Brier score 0.068, ECE 0.100) and high specificity (90.0%) with only three false positives, substantially outperforming ERSPC and PCPT calculators.
Conclusion:
Meta-learning optimization successfully addresses sample size limitations in repeat prostate biopsy prediction without requiring advanced imaging. This provides an evidence-based decision support tool enhancing diagnostic accuracy while minimizing unnecessary procedures.
