ArticleJournal of inflammation research2024
Preoperative Albumin to Alkaline Phosphatase Ratio and Inflammatory Burden Index for Rectal Cancer Prognostic Nomogram-Construction: Based on Multiple Machine Learning.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Employment of the Albumin to Alkaline Phosphatase Ratio to Predict All-cause Mortality and Cardiovascular and Cerebrovascular Mortality in Adults Aged At Least 60 Years: An Analysis From NHANES 2003-2018.Reviews in cardiovascular medicine · 2026Article
- The role of prognostic scoring systems in determining mortality and morbidity in patients with Fournier's gangrene.BMC surgery · 2026Article
- Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.Journal of cellular and molecular medicine · 2025Article
- Association and Predictive Ability between Inflammatory Burden Index and Fever Following Endobronchial Forceps Biopsy in Lung Cancer Patients.Journal of inflammation research · 2025Article
- Prognostic value of albumin-to-alkaline phosphatase ratio and CONUT score in rectal cancer patients undergoing XELOX-based chemotherapy: development of a nomogram-based predictive model.American journal of cancer research · 2025Article
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Authors and funding
6 authors.
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Abstract
Purpose: Preoperative albumin to alkaline phosphatase ratio (AAPR) and inflammatory burden index (IBI) are prognostic indicators for a multitude of cancers, and our study focuses on evaluating the prognostic significance of the AAPR and the IBI on rectal cancer (RC) patients to provide a more accurate guideline for patient prognosis. Patients and Methods: This study enrolled patients who underwent laparoscopic rectal cancer surgery from January 2016 to January 2021. We utilized three machine learning approaches to select variables most relevant to prognosis in the training cohort. Finally, based on the screened variables, a nomogram was established to predict RC patients' overall survival (OS). The improvement in predictive ability and clinical benefit was assessed through the concordance index (C-index), receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). Results: A total of 356 patients were enrolled and they were randomly divided into a training cohort (60%, n=214) and a validation cohort (40%, n=143). Overall survival (OS) was worse for patients in either the low AAPR or the high AAPR group, whereas patients in the low AAPR with both high IBI group had the lowest OS (P<0.001). Finally, five variables were obtained after screening the best variables by three machine learning, and the nomogram was constructed. In both the development and validation cohorts, the C-index values exceeded 0.85, indicating that the predictive model has a strong predictive performance in terms of overall survival. The calibration curves and the decision curve analysis (DCA) showed that the nomogram demonstrated a superior benefit. Conclusion: Preoperative AAPR and IBI can serve as effective indicators for predicting the OS of RC patients. We have developed a nomogram for predicting the OS of patients who underwent laparoscopic rectal cancer surgery.
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