Prediction of Pathologic Complete Response for Rectal Cancer Based on Pre-treatment Factors Using Machine Learning

Abstract

BACKGROUND: Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated nonoperatively in the future. Existing models for predicting pathologic complete response in the pre-treatment setting are limited by small datasets and low accuracy. OBJECTIVE: We sought to use machine learning to develop a more generalizable predictive model for pathologic complete response for locally advanced rectal cancer. DESIGN: Patients with locally advanced rectal cancer who underwent neoadjuvant therapy followed by surgical resection were identified in the National Cancer Database from years 2010-2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve. SETTINGS: This study used a national, multicenter dataset. PATIENTS: Patients with locally advanced rectal cancer who underwent neoadjuvant therapy and proctectomy. MAIN OUTCOME MEASURES: Pathologic complete response defined as T0/xN0/x. RESULTS: The dataset included 53,684 patients. 22.9% of patients experienced pathologic complete response. Gradient boosting showed the best performance with area under the receiver operating characteristic curve of 0.777 (95% CI, 0.773 - 0.781), compared with 0.684 (95% CI, 0.68 - 0.688) for logistic regression. The strongest predictors of pathologic complete response were no lymphovascular invasion, no perineural invasion, lower CEA, smaller size of tumor, and microsatellite stability. A concise model including the top 5 variables showed preserved performance. LIMITATIONS: The models were not externally validated. CONCLUSIONS: Machine learning techniques can be used to accurately predict pathologic complete response for locally advanced rectal cancer in the pretreatment setting. After fine-tuning on a dataset including patients treated nonoperatively, these models could help clinicians identify the appropriate candidates for a watch and wait strategy