Clinical-Grade Interpretable Artificial Intelligence Tool for Automated Detection of Lymph Node Metastasis in Prostate Cancer

Abstract

Lymph node metastasis (LNM) is a critical prognostic factor for prostate cancer and is associated with increased mortality and poor clinical outcomes, necessitating modifications to therapeutic strategies. Manual histopathological evaluation of lymphatic tissue on glass slides is labor intensive, subject to interobserver variability, and prone to error. Deep learning approaches offer substantial promise in enhancing the accuracy and efficiency of LNM detection; however, their efficacy is contingent upon the availability of extensive annotated data sets. In this study, we developed a novel artificial intelligence (AI)-driven model leveraging a limited data set of annotated samples. By identifying and incorporating the most informative instances from unlabeled data into the training process, the model optimizes its learning trajectory through iterative error correction. Validation was performed on independent data sets from 3 academic medical centers, comprising 787 whole slide images and >2000 lymph node tissues. On a combined test set of 165 positive and 622 negative cases, the model achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.92-0.96), with slide-level sensitivity and specificity of 96% (95% CI, 92%-99%) and 92% (95% CI, 89%-94%), respectively. Importantly, the AI algorithm identified micrometastases in 17 cases that were initially missed by pathologists. Although pathologists exhibited a 9% miss rate in micrometastasis detection, the AI model demonstrated a significantly lower miss rate of 3% using the institutional data set, highlighting its potential for clinical deployment. This fully autonomous and reproducible method also significantly reduced slide examination times compared with both general and genitourinary pathologists (P < .001). The proposed method demonstrated interpretability by identifying metastasis regions on whole slide images labeled as positive. Ablation studies substantiate the robustness of the proposed methodology for LNM detection.

Keywords: computational pathology; computer-assisted diagnosis; deep learning; digital pathology; lymph node metastases; prostate cancer.