Artificial intelligence for enhanced diagnostic precision of prostate cancer
DOI:
https://doi.org/10.13181/mji.oa.258312Keywords:
artificial intelligence, computer-assisted diagnosis, computer-assisted image interpretation, pathology, prostatic neoplasmsAbstract
BACKGROUND Accurate diagnosis and grading of prostate cancer are essential for treatment planning. The role of artificial intelligence in prostate cancer intervention and diagnosis (RAPID) is a study aimed at developing artificial intelligence (AI) models to enhance diagnostic precision in prostate cancer by distinguishing malignant from non-cancerous histopathological findings.
METHODS Histopathological images were collected between 2023 and 2024 at the Department of Anatomical Pathology, Faculty of Medicine, Universitas Indonesia. The dataset included benign prostatic hyperplasia and prostate cancer cases. All slides were digitized and manually annotated by pathologists. Patch-based classification was performed using convolutional neural network and transformer-based models to differentiate malignant from non-malignant tissues.
RESULTS A total of 529 whole-slide images were processed, yielding 26,418 image patches for model training and testing. Deep learning models achieved strong performance in classification. Architectures including EfficientNetV2B0, Xception, ConvNeXt-Tiny, and Vision Transformer (ViT) achieved near-perfect classification outcomes. EfficientNetV2B0 reached an AUC of 1.00 (95% CI: 1.00–1.00), sensitivity 0.99 (95% CI: 0.99–1.00), and specificity 1.00 (95% CI: 1.00–1.00). Xception and ConvNeXt-Tiny both achieved AUC 1.00 (95% CI: 1.00–1.00) with sensitivity and specificity of 1.00 (95% CI: 1.00–1.00). ViT performed strongly with AUC 0.999 (95% CI: 0.99–1.00), sensitivity 0.99 (95% CI: 0.99–0.99), and specificity 0.99 (95% CI: 0.99–0.99).
CONCLUSIONS RAPID demonstrated high potential as an AI-based diagnostic tool for prostate cancer, showing excellent accuracy in histopathological classification using the Indonesian dataset. These findings highlight the feasibility of deploying deep learning models to support diagnostic decision-making in clinical practice.
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