Assessing breast cancer chemotherapy response in radiology and pathology reports via a large language model

Abstract

A wealth of medical knowledge is used to make clinical decisions, yet treatment or disease outcomes are challenging to assess without clinical trials. However, clinical trials take time, are expensive, and are impossible to perform for every decision. One approach to systematically assess treatment outcomes involves the retrospective analysis of clinical notes, e.g., radiology and pathology reports. While such studies are often performed by clinicians who manually review the notes and other information, such retrospective analysis can benefit from the automated parsing of radiology and pathology reports to provide systematic framework to extract outcome information. In this study, we used a large language model, i.e., ChatGPT (GPT-3.5), to parse 267 radiology and pathology reports and extract information related to response to neoadjuvant chemotherapy in patients with breast cancer. Our study includes a heterogeneous group of 89 women who underwent neoadjuvant therapy and underwent two MRI exams, pre- and post-therapy, followed by surgery (lumpectomy or mastectomy). We assessed the treatment response based on clinical reports from the post-therapy surgical excision. From the reports, we extracted the number of lesions, their anatomic location, and size. Our study provides insight into neoadjuvant chemotherapy response, indicating that even cases with complete MRI response can still have residual invasive breast carcinoma (1/3 of subjects), and, on the other hand, even those with reduced MRI response (⪅30% reduction in tumor size) can have no residual tumor at surgery, indicating that when cancer responds to treatment, it may not be captured by the MRI. The large language model achieved sensitivities of 84-94% in extracting the information from radiology reports, but had lower performance in the pathology reports, 72-87%, where more information is provided in free format. While this study is preliminary and performed in a small cohort, it illustrates the complexity of outcome prediction using radiology images.

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