{"slug": "oncotriad-qa-a-patient-level-radiology-pathology-genomics-benchmark-for-pan", "title": "OncoTriad-QA: A Patient-Level Radiology-Pathology-Genomics Benchmark for Pan-Cancer Reasoning", "summary": "Researchers introduced OncoTriad-QA, a patient-level radiology-pathology-genomics benchmark for pan-cancer question answering, containing 86.1k semantic questions across 9,281 TCGA patient cases from 32 cancer cohorts. The accompanying OncoVLM model, after fine-tuning on the benchmark, exceeded MedGemma-4B by an average of 10.7 points in MCQ accuracy and BERTScore-F1, demonstrating improved integrated cancer reasoning.", "body_md": "arXiv:2608.02615v1 Announce Type: new\nAbstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata. However, most medical large language model (LLM) and vision-language model (VLM) benchmarks focus on isolated modalities or narrow image-text tasks, leaving patient-level oncology assessment across multiple evidence streams largely untested. We introduce OncoTriad-QA, a patient-level radiology-pathology-genomics benchmark for pan-cancer question answering. OncoTriad-QA contains 86.1k semantic questions across 9,281 TCGA patient cases from 32 cancer cohorts, aligning CT/MRI radiology, whole-slide histopathology, somatic mutations, copy-number alterations, DNA methylation, bulk RNA-seq, and clinical metadata. Case-specific annotations are constructed through a source-grounded LLM-assisted pipeline using curated labels, diagnostic reports, molecular profiles, and modality-derived evidence as primary sources of truth, with automated consistency checks and clinician review. We also introduce OncoVLM, a reference multimodal model that maps modality-native radiology, pathology, DNA methylation, and RNA-seq evidence into an LLM interface through learned projectors. Experiments show that existing general-purpose and medical LLMs remain limited on comprehensive pan-cancer QA, especially when questions require integrating imaging findings, tumor morphology, and molecular evidence. After fine-tuning on OncoTriad-QA, OncoVLM exceeds MedGemma-4B by an average of 10.7 points when using MCQ accuracy and BERTScore-F1, with consistent gains across multiple-choice and open-ended questions under radiology-only, pathology-only, and all-available settings. These results demonstrate the benchmark's value for training and evaluating models for integrated cancer question answering.", "url": "https://wpnews.pro/news/oncotriad-qa-a-patient-level-radiology-pathology-genomics-benchmark-for-pan", "canonical_source": "https://arxiv.org/abs/2608.02615", "published_at": "2026-08-05 04:00:00+00:00", "updated_at": "2026-08-05 04:03:03.496522+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["OncoTriad-QA", "OncoVLM", "MedGemma-4B", "TCGA"], "alternates": {"html": "https://wpnews.pro/news/oncotriad-qa-a-patient-level-radiology-pathology-genomics-benchmark-for-pan", "markdown": "https://wpnews.pro/news/oncotriad-qa-a-patient-level-radiology-pathology-genomics-benchmark-for-pan.md", "text": "https://wpnews.pro/news/oncotriad-qa-a-patient-level-radiology-pathology-genomics-benchmark-for-pan.txt", "jsonld": "https://wpnews.pro/news/oncotriad-qa-a-patient-level-radiology-pathology-genomics-benchmark-for-pan.jsonld"}}