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

TL;DR

This work curates TumorCoT, a large-scale dataset of 1.5M CoT-labeled VQA instructions paired with 3D CT scans, and proposes TumorChain, a multimodal interleaved reasoning framework that tightly couples 3D imaging encoders, clinical text understanding, and organ-level vision-language alignment.

Abstract

Accurate tumor analysis is central to clinical radiology and precision oncology, where early detection, reliable lesion characterization, and pathology-level risk assessment guide diagnosis and treatment planning. Chain-of-Thought (CoT) reasoning is particularly important in this setting because it enables step-by-step interpretation from imaging findings to clinical impressions and pathology conclusions, improving 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 trajectory from findings to impression to pathology, enabling evaluation of both answer 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. Experiments show consistent improvements over strong baselines in lesion detection, impression generation, and pathology classification, and demonstrate strong generalization on the DeepTumorVQA benchmark. These results highlight the potential of multimodal reasoning for reliable and interpretable tumor analysis in clinical practice. Detailed information about our project can be found on our project homepage at https://github.com/ZJU4HealthCare/TumorChain.

Figures (10)

• Figure 1: (a) TumorChain focuses on tumor analysis of the five major organs in the digestive system. (b) We establish a corresponding medical reasoning logic chain from radiology findings to radiology impressions to pathological conclusions. (c) The system tackles four diverse tasks across 1.5 million multimodal VQA instructions, enabling advanced clinical reasoning.
• Figure 2: TumorCoT-1.5M statistics.
• Figure 3: The overview of the interactive-validated CoT Data Engine, which is a system of multi-agent collaboration, including the procedure of raw data processing, diagnostic knowledge graph importing, and CoT data building and validation.
• Figure 4: The overview of TumorChain. Given CT volumes and task prompts, TumorChain analyzes the task requirements and performs iterative interleaved reasoning by integrating a segmentation expert model for ROI localization, auxiliary classification for local feature enhancement, and organ-guided iterative reasoning for clinical tumor analysis.
• Figure 5: (a) Ablation on classification loss weight $\alpha$. (b) Model performance on five organs of TumorCoT. (c) Workflow of reasoning chains extraction and evaluation for task 4.