Explainable Multi-Modal Deep Learning for Early-Stage Pan-Cancer Detection and Survival Prognostication: Empirical Benchmarking of Gigapixel Whole-Slide Histopathology and Genomic Biomarkers Across Multi-Center Clinical Cohorts

Abstract

Early and accurate cancer diagnosis combined with robust survival risk prognostication remains the paramount determinant of therapeutic success in precision oncology. While routine clinical workflows evaluate hematoxylin and eosin (H&E) stained gigapixel Whole Slide Images (WSI) for morphological staging and transcriptomic sequencing (RNA-Seq) for molecular phenotyping, these diagnostic modalities are conventionally analyzed in clinical silos. Monomodal computational pathology algorithms often fail to detect early occult micro-metastases and cannot infer underlying driver genomic pathways, while bulk genomic profiling lacks spatial micro-architectural context and sub-clonal geographic orientation. To resolve this multi-modal diagnostic barrier, this paper presents PathoGen-Net, an explainable, end-to-end multi-modal deep learning architecture that fuses gigapixel computational histopathology with bulk transcriptomics and somatic mutation profiles. Utilizing self-supervised foundation vision transformers (DINOv2 / CONCH) pre-trained on over 1.2 million histological patches, PathoGen-Net implements hierarchical weakly-supervised Multiple Instance Learning (MIL) coupled with a bidirectional cross-attention co-pooling mechanism. The model was trained, rigorously validated, and multi-center benchmarked on 12,450 gigapixel WSIs and 8,920 matched genomic profiles across five prominent solid tumor types (invasive breast carcinoma [TCGA-BRCA], lung adenocarcinoma [TCGA-LUAD], colon adenocarcinoma [TCGA-COAD], pancreatic ductal adenocarcinoma [TCGA-PAAD], and prostate adenocarcinoma [TCGA-PRAD]) from The Cancer Genome Atlas (TCGA), supplemented by four external, multi-national hospital validation cohorts (Mount Sinai, Mayo Clinic, UK Biobank, and AIIMS Oncology; total N = 3,840 patients). Key Empirical Findings: • PathoGen-Net achieves state-of-the-art pan-cancer diagnostic classification (AUROC = 0.964 ± 0.008; AUPRC = 0.951 ± 0.011), significantly outperforming unimodal WSI models (CLAM-SB AUROC = 0.892, TransMIL AUROC = 0.906) and genomic-only classifiers (MLP AUROC = 0.865; DeLong test p < 0.0001). • In 5-year overall survival (OS) stratification, PathoGen-Net achieves a Concordance Index (C-index) of 0.782 ± 0.015 and a statistically significant Hazard Ratio (HR) of 2.48 (95% CI: 2.12–2.91, log-rank p = 1.4 × 10^-12), effectively separating high-risk from low-risk patient populations across all five solid tumor malignancies. • Blinded concordance evaluations with six board-certified surgical pathologists demonstrated an 89.5% diagnostic agreement rate on slide-level attention heatmaps, successfully localizing tumor-infiltrating lymphocytes (TILs), desmoplastic stroma, and invasive tumor margins. • The pipeline delivers sub-1.5 second whole-slide inference on standard hospital workstation GPUs, satisfying intraoperative frozen-section consultation requirements. Finally, we formalize an open-source clinical deployment architecture and interpretability evaluation protocol to accelerate the clinical translation of multi-modal AI in diagnostic oncology.