KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Health & Wellbeing — Medical Devices & Health Technology, Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

The KOAL (Knowledge-Driven Ordinal-Aware Learning) framework offers a novel approach for non-invasive Gleason Grade Group (GGG) prediction in prostate cancer using multiparametric MRI (mpMRI). It addresses limitations of existing methods by integrating non-image clinical information like age and prostate-specific antigen (PSA), and by accounting for GGG's intrinsic hierarchical structure. KOAL comprises three modules: Clinical-Context Modulation (CCM) dynamically adjusts image representations with clinical variables; Knowledge-Guided Prototype Alignment (KGPA) uses an LLM to extract expert knowledge from radiology reports, creating semantic anchors for pathology-aligned representation learning; and Hierarchical Ordinal-aware Constraints (HOC) decouples primary and secondary Gleason pattern prediction, mapping outputs to GGG via a Differentiable Bio-logic Mapping Layer (DBML) for pathological consistency. Experiments on public PI-CAI and in-house datasets show KOAL outperforms state-of-the-art methods.

Key takeaway

For AI Scientists developing diagnostic tools for prostate cancer, this work highlights the importance of integrating diverse data types. You should consider incorporating clinical context and expert knowledge, potentially via LLMs, to enhance mpMRI-based Gleason Grade Group prediction. Furthermore, explicitly modeling the hierarchical nature of GGG with mechanisms like a Differentiable Bio-logic Mapping Layer can significantly improve pathological consistency and diagnostic accuracy in your models.

Key insights

Integrating clinical context, expert knowledge, and hierarchical ordinal awareness improves prostate cancer Gleason Grade Group prediction.

Principles

• Non-image clinical data enhances image representation learning.
• LLMs can extract expert knowledge for semantic alignment.
• Decoupling hierarchical ordinal predictions ensures consistency.

Method

KOAL modulates image representations with clinical context, aligns patient-specific mpMRI with LLM-extracted expert knowledge anchors, and ensures pathological consistency by decoupling primary/secondary Gleason pattern prediction via a Differentiable Bio-logic Mapping Layer.

In practice

• Use age and PSA to modulate mpMRI features.
• Extract grade-specific radiological findings with LLMs.
• Implement Differentiable Bio-logic Mapping for GGG consistency.

Topics

• Prostate Cancer Grading
• Gleason Grade Group
• Multiparametric MRI
• Knowledge-Driven AI
• Ordinal-Aware Learning
• Large Language Models
• Medical Imaging

Code references

• Gother-GZ/KOAL

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.