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Abstract #2675

Predicting treatment outcome and prognosis in locally advanced rectal cancer using pretreatment MRI and pathology based machine learning models

Luu-Ngoc Do1, Ilwoo Park1,2,3, and Suk Hee Heo1,4
1Radiology, Chonnam National Univeristy, Gwangju, Korea, Republic of, 2Radiology, Chonnam National University Hospital, Gwangju, Korea, Republic of, 3Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea, Republic of, 4Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea, Republic of

Synopsis

Keywords: Pelvis, Pelvis

Motivation: Monitoring and predicting treatment outcome in locally advanced rectal cancer is of clinical importance.

Goal(s): To use machine learning models with the integration of complementary information from MRI-derived radiomics, deep learning, and pathology for the early prediction of treatment outcomes.

Approach: Pretreatment T2 MRI, ADC and pathology-derived machine learning and deep learning models as well as a fusion model were developed for the prediction of pathologic complete response and local recurrence/distant metastasis.

Results: The proposed models demonstrated high levels of performances for the prediction of early treatment outcomes.

Impact: The results highlight the potential of machine learning models using MRI and pathology to enhance non-invasive prediction of rectal cancer treatment outcomes. This could lead to earlier, more tailored interventions and stimulate research into multimodal integration methods.

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Keywords