Validation of Platelet-index based Score (PIBS) for Early Prediction of the Pathological Stage in Colorectal Cancer

Abstract

INTRODUCTION: Accurate staging is essential in colorectal cancer (CRC) management. Platelet indices have emerged as promising biomarkers of tumor burden and progression. This study aimed to validate the Platelet Index-Based Score (PIBS-CRC), a machine learning-based model using platelet parameters, for predicting the pathological TNM stage of CRC. MATERIALS AND METHODS: A cross-sectional study was conducted involving 116 patients with histologically confirmed CRC. Preoperative platelet indices platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), and plateletcrit (PCT) were input into the PIBS-CRC model. The model's prediction was then compared with the final pathological TNM stage (AJCC 8th edition). Diagnostic performance was assessed using confusion matrix and classification metrics. RESULTS: The PIBS-CRC score showed strong performance, with an overall accuracy of 87%, precision of 87%, F1-score of 88%, and Matthews Correlation Coefficient (MCC) of 0.81. The model accurately predicted stages I, III, and IV, though some misclassification occurred between stages II and III. PLT and PCT were significantly associated with advancing TNM stage (p < 0.001). CONCLUSION: The PIBS-CRC model is a reliable, non-invasive tool for predicting pathological CRC stages using routine blood tests. It is particularly useful for early-stage detection and may serve as a practical adjunct in clinical settings with limited diagnostic resources.

Access to Document

Other files and links

• Link to publication in Scopus
• Open Access Version Available

Fingerprint