Multimodal AI for Predicting Biochemical Recurrence of Prostate Cancer: Precision Medicine Practice Integrating Pathological Images, MRI, and Clinical Data

This article introduces a project for predicting biochemical recurrence (BCR) of prostate cancer based on multimodal data fusion, integrating H&E pathological images, multiparametric MRI, and clinical information. Originating from the CHIMERA international challenge, it improves prediction accuracy through three-modal feature extraction and cross-modal fusion strategies, discusses clinical significance and technical challenges, and provides decision support for precision medicine.

Prostate cancer is a common malignant tumor in men worldwide. Predicting postoperative BCR (PSA elevation) is a core clinical challenge. Traditional methods rely on indicators such as preoperative PSA and Gleason score, but they struggle to capture tumor heterogeneity and microfeatures, leading to limited prediction accuracy. AI and multimodal fusion provide a new direction for precise prediction.

The project originates from the CHIMERA Grand Challenge, an international competition focusing on computational histopathology and multimodal fusion. Its goal is to develop BCR prediction models, with data covering clinical information, high-resolution pathological images, and MRI, which is close to the scenario of comprehensive judgment by clinicians.

Three key data sources are used: 1. H&E whole-slide images (WSI): the gold standard for pathology, providing microstructural features of tumors; 2. Multiparametric MRI (mpMRI): including T2-weighted, DWI, and DCE sequences, depicting anatomical and physiological features; 3. Clinical information: traditional prognostic indicators such as age and preoperative PSA, providing prior knowledge.

Modal feature extraction: WSI uses patch extraction + aggregation network; mpMRI uses 3D convolution or multi-sequence fusion; clinical data uses fully connected/tree models. Cross-modal fusion mechanisms include early fusion (feature concatenation), late fusion (prediction integration), intermediate fusion (attention/gating), and Transformer fusion (global dependency learning).

Clinical value: 1. Personalized treatment decisions: active treatment for high-risk patients, avoiding over-treatment for low-risk patients; 2. Pathological auxiliary tools: automatically identify high-risk areas, improving assessment consistency; 3. Discovery of new biomarkers: reveal cancer progression mechanisms, helping to develop therapeutic targets.

Challenges faced: data alignment and registration, data scarcity, model interpretability, external validation. Future directions: transfer learning/semi-supervised learning to alleviate data issues, improve model interpretability, and conduct multi-center validation studies.

This project demonstrates the innovative application of AI in tumor prognosis prediction. Multimodal fusion captures complex patterns that are difficult to find with a single modality. With technological progress and data sharing, such tools are expected to become standard clinical configurations, improving patients' treatment outcomes and quality of life.