Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.
Authors
Affiliations (8)
Affiliations (8)
- College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore.
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.
- Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore.
- School of Physical and Mathematical Science, Nanyang Technological University, Singapore, Singapore.
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore.
- School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore. [email protected].
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. [email protected].
- Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore. [email protected].
Abstract
This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer Vision models to accurately predict neoadjuvant rectal (NAR) score for locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation (NACRT). The NAR score is a validated surrogate endpoint for LARC. 160 CT scans of patients were used in this study, along with 4 different types of radiology reports, 2 generated from CT scans and other 2 from MRI scans, both before and after NACRT. For CT scans, two different approaches with convolutional neural network were utilized to tackle the 3D scan entirely or tackle it slice by slice. For radiology reports, an encoder architecture LLM was used. The performance of the approaches was quantified by the Area under the Receiver Operating Characteristic curve (AUC). The two different approaches for CT scans yielded [Formula: see text] and [Formula: see text] while the LLM trained on post NACRT MRI reports showed the most predictive potential at [Formula: see text] and a statistical improvement, p = 0.03, over the baseline clinical approach (from [Formula: see text] to [Formula: see text])). This study showcases the potential of Large Language Models and the inadequacies of CT scans in predicting NAR values. Clinical trial number Not applicable.