Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models. J Gastrointest Surg 2023 Jan;27(1):122-130
Date
10/23/2022Pubmed ID
36271199DOI
10.1007/s11605-022-05477-9Scopus ID
2-s2.0-85140356180 (requires institutional sign-in at Scopus site) 1 CitationAbstract
BACKGROUND: Radiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear.
METHODS: A retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed. Clinical and radiomic features were extracted from electronic medical record and pre-treatment magnetic resonance imaging, respectively. Machine learning models were created and assessed for complete response and positive treatment effect using the area under the receiver operating curves.
RESULTS: Of 131 rectal cancer patients evaluated, 68 (51.9%) were identified to have a positive treatment effect and 35 (26.7%) had a complete response. On univariate analysis, clinical T-stage (OR 0.46, p = 0.02), lymphovascular/perineural invasion (OR 0.11, p = 0.03), and statin use (OR 2.45, p = 0.049) were associated with a complete response. Clinical T-stage (OR 0.37, p = 0.01), lymphovascular/perineural invasion (OR 0.16, p = 0.001), and abnormal carcinoembryonic antigen level (OR 0.28, p = 0.002) were significantly associated with a positive treatment effect. The clinical model was the strongest individual predictor of both positive treatment effect (AUC = 0.64) and complete response (AUC = 0.69). The predictive ability of a positive treatment effect increased by adding tumor and mesorectal radiomic features to the clinical model (AUC = 0.73).
CONCLUSIONS: The use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.
Author List
Peterson KJ, Simpson MT, Drezdzon MK, Szabo A, Ausman RA, Nencka AS, Knechtges PM, Peterson CY, Ludwig KA, Ridolfi TJAuthors
Kirk A. Ludwig MD Chief, Professor in the Surgery department at Medical College of WisconsinAndrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of Wisconsin
Carrie Peterson MD, MS, FACS, FASCRS Associate Professor in the Surgery department at Medical College of Wisconsin
Timothy J. Ridolfi MD, MS, FACS Associate Professor in the Surgery department at Medical College of Wisconsin
Aniko Szabo PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
HumansMachine Learning
Magnetic Resonance Imaging
Neoadjuvant Therapy
Rectal Neoplasms
Retrospective Studies
Treatment Outcome
