![]() However, the patterns of tumor regression after neoadjuvant CRT vary widely, ranging from a pathological complete response (pCR) to disease progression. Neoadjuvant chemoradiotherapy (CRT) has benefited patients with locally advanced rectal cancer (LARC) with specific respect to improvements in local control, disease-free survival, and sphincter preservation rates 1, 2, 3. The predictors of pCR can be used to identify which patients with LARC can benefit from watch-and-wait approaches. The ANN model was a more accurate pCR predictor than other conventional prediction models. Our results revealed that the post-CRT carcinoembryonic antigen is the most influential pCR predictor, followed by intervals between CRT and surgery, chemotherapy regimens, clinical nodal stage, and clinical tumor stage. The ANN model significantly outperformed the KNN, SVM, NBC, and MLR models in pCR prediction. ![]() We trained the model with an estimation data set and evaluated model performance with a validation data set. Data from two hundred seventy patients with LARC were used to compare the efficacy of the forecasting models. Predictive accuracy was compared between the ANN, k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes classifier (NBC), and multiple logistic regression (MLR) models. We evaluated the performance of an artificial neural network (ANN) model in pCR prediction in patients with LARC. However, no reliable prediction model is presently available. For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal prognosis.
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