Poster Thu, Apr 23, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 4 P4-#3416

TumorChain: Interleaved Multimodal Chain-of-Thought Reasoning for Traceable Clinical Tumor Analysis

Sijing Li ⋅ Zhongwei Qiu ⋅ Jiang Liu ⋅ Wenqiao Zhang ⋅ Tianwei Lin ⋅ Yihan Xie ⋅ Jianxiang An ⋅ Boxiang Yun ⋅ Chenglin Yang ⋅ Jun Xiao ⋅ Guangyu Guo ⋅ Jiawen Yao ⋅ Wei Liu ⋅ Yuan gao ⋅ Ke Yan ⋅ Weiwei Cao ⋅ Zhilin Zheng ⋅ Tony C. W. MOK ⋅ Kai Cao ⋅ Yu Shi ⋅ Jiuyu Zhang ⋅ Jian Zhou ⋅ Beng Chin Ooi ⋅ Yingda Xia ⋅ Ling Zhang

Project Page

Abstract

Accurate tumor analysis is central to clinical radiology and precision oncology, where early detection, reliable lesion characterization, and pathology-level risk assessment directly guide diagnosis, staging, and treatment planning. Chain-of-Thought (CoT) reasoning is particularly critical in this setting, as it enables stepwise interpretation from imaging findings to clinical impressions and pathology-level conclusions, ensuring traceability and reducing diagnostic errors. Here, we target the clinical tumor analysis task and build a large-scale benchmark that operationalizes a multimodal reasoning pipeline, spanning findings, impressions, and pathology predictions. We curate TumorCoT, a large-scale dataset of 1.5M CoT-labeled VQA instructions paired with 3D CT scans, with step-aligned rationales and cross-modal alignments along the “findings → impression → pathology” trajectory, enabling standardized evaluation of both final accuracy and reasoning consistency. We further propose TumorChain, a multimodal interleaved reasoning framework that tightly couples 3D imaging encoders, clinical text understanding, and organ-level vision-language alignment. Through cross-modal alignment and iterative interleaved causal reasoning, TumorChain grounds visual evidence, aggregates conclusions, and issues pathology predictions after multiple rounds of self-refinement, improving traceability and reducing hallucination risk. TumorChain demonstrates consistent gains over strong unimodal and pipeline baselines in lesion detection, impression quality, and pathology classification, and successfully generalizes to the public DeepTumorVQA benchmark. Ablations validate the key contributions of interleaved reasoning and clinical CoT. Clinically, these advances lay the groundwork for reliable, interpretable tumor assessment to support real-world decision-making. To advance safe, explainable, and reproducible multimodal reasoning for high-stakes tumor analysis, detailed information about our project can be found on our project homepage at https://github.com/ZJU4HealthCare/TumorChain.