Fangyu Hao, Jiayu Yang, Yifan Zhu, Zijun Yu, Qicen Wu, Yunlong Wang, Jiawei Li, Yulin Liu, Xu Zeng, Guanting Chen, Shihao Li, Zhonghong Ou, Meina Song, Mengyang Sun, Haoran Luo, Yu Shi, Yingyi Wang
6 min
Abstract
Lung cancer clinical decision support demands precise reasoning across complex, multi-stage oncological workflows. Existing multimodal large language models (MLLMs) fail to handle guideline-constrained staging and treatment reasoning. We formalize three oncological precision treatment (OPT) tasks for lung cancer, spanning TNM staging, treatment recommendation, and end-to-end clinical decision support. We introduce LungCURE, the first standardized multimodal benchmark built from 1,000 real-world, clinician-labeled cases across more than 10 hospitals. We further propose LCAgent, a multi-agent framework that ensures guideline-compliant lung cancer clinical decision-making by suppressing cascading reasoning errors across the clinical pathway. Experiments reveal large differences across various large language models (LLMs) in their capabilities for complex medical reasoning, when given precise treatment requirements. We further verify that LCAgent, as a simple yet effective plugin, enhances the reasoning performance of LLMs in real-world medical scenarios.
Alex: Before those T, N, and M agents get their checklists, how do they start with messy reports like scans and notes—and turn them into something usable?
Sam: It begins with cleaning and organizing raw reports—like sorting puzzle pieces by color before assembling, so no wrong fits later. For a CT scan, it splits into what the doctor saw and a summary impression, keeping exact sizes and uncertain words like "might be spread." It breaks combined phrases, such as nodes in multiple spots, into separate items. The paper stresses preserving doubts exactly as written.
Alex: So that creates a neutral list each agent can use, without guessing too early?
Sam: Yes. A filter grabs only tumor clues—like size or pushing into chest wall—for the T agent, ignoring the rest. Picture handing a builder just the foundation blueprint. The T agent checks rules step by step: size brackets like under 3 cm, then invasions, picking the highest match. A dispatch step sorts node clues into regional or distant pools, keeping them pure. The N agent scans for confirmed spread: nearby nodes make N1, deeper chest N2. For M, it checks chest spread first, then distant organs. All output structured facts for the final combo using AJCC tables—like a calculator for scores. It flags uncertainties too, alerting doctors.
Alex: So narrow views plus rule checklists stop one bad read from tainting others.
Sam: That's the point. This cuts interference, like a tumor hint fooling node judgment, leading to cleaner stages that feed accurate treatments.
Alex: Once staging is set, how does it handle picking treatments without drifting off guidelines?
Sam: A step pulls key details—like cancer cell type, fitness, gene changes—from reports and staging into an organized list. It's like filling a standard form with exact categories. This list routes to the right specialist agent—for post-surgery cases, pre-surgery planning, or advanced stages by gene type. Each gets only matching guidelines, like a narrow playbook. A post-surgery agent sticks to rules for adding chemo after tumor removal. An advanced agent uses PD-L1 levels for immunotherapy versus chemo. Driver-positive ones match genes to targeted therapies. This keeps outputs tied to evidence from proven trials.
Alex: Are there spots where it still leans on the base model's limits?
Sam: Yes. It depends on the model's staging accuracy, around 60 to 75 percent—stronger with text than images. Gains are meaningful but tied to that base. It hasn't been checked against guidelines after 2025, so tweaks might be needed. Still, the paper suggests it's a practical bridge, turning shaky outputs into guideline-faithful steps.
Alex: Yeah... a grounded way to harness these models without overpromising. Thanks for breaking it down, Sam—that clarifies the full picture.
Sam: My pleasure, Alex. Thanks for listening to ResearchPod.