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Related Experiment Video

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WiNGPT-32B: An Open-Source, Locally Deployable LLM for RECIST Assessment via Chained Task Execution Using Radiology

Lingyun Wang1, Lu Zhang1, Yaping Zhang1

  • 1Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd. 100, Shanghai 200080, China.

Diagnostics (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

A new large language model (LLM), WiNGPT-32B, was developed for evaluating tumor response using radiology reports. It shows strong performance in detecting disease progression, outperforming GPT-4 in RECIST classification.

Keywords:
Response Evaluation Criteria in Solid Tumors (RECIST)large language modelmedical reasoningradiology reports

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate tumor response assessment is crucial for cancer treatment efficacy.
  • Longitudinal radiology reports contain valuable data for RECIST assessment.
  • Existing LLMs may require specialized adaptation for medical tasks.

Purpose of the Study:

  • To develop an open-source large language model (LLM) for Response Evaluation Criteria in Solid Tumors (RECIST) assessment.
  • To utilize exclusively longitudinal radiology report text for training the LLM.
  • To evaluate the performance of the developed LLM against GPT-4 and human radiologists.

Main Methods:

  • Developed WiNGPT-32B, an open-source LLM, using knowledge distillation from GPT-4.
  • Employed a Chained Task Execution (CTE) framework for modular RECIST assessment.
  • Benchmarked WiNGPT-32B against GPT-4 and radiologists using a consensus standard.

Main Results:

  • WiNGPT-32B achieved a 0.934 lesion extraction rate, surpassing GPT-4.
  • For RECIST classification, WiNGPT-32B reached 0.805 accuracy, significantly outperforming GPT-4 (0.699).
  • WiNGPT-32B demonstrated a superior F1 score (0.841) for progressive disease detection compared to GPT-4 (0.755).

Conclusions:

  • WiNGPT-32B demonstrates the feasibility of text-only LLMs for longitudinal RECIST assessment.
  • The CTE framework enables structured and modular RECIST evaluation.
  • WiNGPT-32B shows promising performance, particularly in identifying disease progression.