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Prediction of preoperative peritoneal cancer index for pseudomyxoma peritonei by multiple linear regression analysis
Mingjian Bai1, Jing Feng1, Jie Liu1
1Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China.
Background:
The aim of the present study was to establish a predictive model to predict the peritoneal cancer index (PCI) preoperatively in patients with pseudomyxoma peritonei (PMP).
Methods:
A total of 372 PMP patients were consecutively included from a prospective follow-up database between 1 June 2013 and 1 June 2023. Nine potential variables, namely, gender, age, Barthel Index (BAI), hemoglobin (Hb), albumin (Alb), D-dimer, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA 125), and CA 19-9, were estimated using multiple linear regression (MLR) analysis with a stepwise selection procedure. The established MLR model was internally validated using K-fold cross-validation. The agreement between the predicted and surgical PCI was assessed using Bland-Altman plots and intraclass correlation (ICC). A p-value of less than 0.05 was considered statistically significant.
Results:
Six independent predictors were confirmed by the stepwise MLR analysis with an R value of 0.570. The predicted PCI formula was represented as follows: PCI = 19.567 + 2.091 * Gender (male = 1, female = 0) - 0.643 * Alb +4.201 * Lg (D-dimer) + 2.938 * Lg (CEA) + 5.441 * Lg (CA 125) + 1.802 * Lg (CA 19-9). The agreement between predicted and surgical PCI was assessed using Bland-Altman plots, showing a limit of agreement (LoA) between -15.847 (95%CI: -17.2646 to -14.4292) and +15.847 (95%CI: 14.4292-17.2646).
Conclusion:
This study represents the first attempt to use an MLR model for the preoperative prediction of PCI in PMP patients. Nevertheless, the MLR model did not perform well enough in predicting preoperative PCI. In the future, more advanced statistical techniques and a radiomics-based CT-PCI-participated MLR model will be developed, which may enhance the predictive ability of PCI.
Insights
This study developed a multiple linear regression model to predict the peritoneal cancer index (PCI) in pseudomyxoma peritonei (PMP) patients. The model showed moderate predictive ability, suggesting future research with advanced techniques for improved accuracy.
Area of Science:
- Oncology
- Surgical Oncology
- Medical Statistics
Background:
- Pseudomyxoma peritonei (PMP) is a rare malignancy.
- Accurate preoperative assessment of the Peritoneal Cancer Index (PCI) is crucial for PMP patient management.
- Current methods for PCI assessment are primarily intraoperative.
Purpose of the Study:
- To develop and validate a predictive model for preoperative Peritoneal Cancer Index (PCI) in patients with Pseudomyxoma Peritonei (PMP).
- To identify key clinical and biochemical variables that can predict PCI.
- To establish a formula for estimating PCI before surgery.
Main Methods:
- A cohort of 372 PMP patients was analyzed from a prospective database.
- Multiple linear regression (MLR) with stepwise selection was used to build the predictive model.
- Internal validation was performed using K-fold cross-validation; agreement was assessed with Bland-Altman plots and ICC.
Main Results:
- Six independent predictors (gender, albumin, D-dimer, CEA, CA 125, CA 19-9) were identified, explaining 57.0% of the variance in PCI (R²=0.570).
- A predictive formula was established: PCI = 19.567 + 2.091*Gender - 0.643*Alb + 4.201*Lg(D-dimer) + 2.938*Lg(CEA) + 5.441*Lg(CA 125) + 1.802*Lg(CA 19-9).
- Bland-Altman analysis showed limits of agreement between -15.847 and +15.847.
Conclusions:
- This study is the first to propose an MLR model for preoperative PCI prediction in PMP.
- The developed MLR model demonstrated moderate predictive performance for preoperative PCI.
- Future research should explore advanced statistical methods and radiomics integration for enhanced PCI prediction accuracy.
