Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer

TL;DR

This study analyzes risk factors for second primary cancers in rectal cancer survivors to improve personalized treatment strategies.

Contribution

The study introduces a predictive model using clinical data and decision trees to assess risk factors for second primary cancers.

Findings

Chemotherapy is identified as the most important prognostic risk factor for second primary cancers.

The proposed model achieved a high AUC of 84.88% and acceptable testing accuracy of 80.85%.

A decision tree was developed to assess the effectiveness of combined risk factors for personalized treatment.

Abstract

Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.

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