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ORIGINAL RESEARCH article

Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Volume 11 - 2024 | doi: 10.3389/fmolb.2024.1512937
This article is part of the Research Topic Clinical Molecular Biological Characteristics of Malignant Tumors View all articles

Preoperative peritoneal cancer index prediction for pseudomyxoma peritonei by multiple linear regression analysis

Provisionally accepted
Mingjian Bai Mingjian Bai 1Jing Feng Jing Feng 1*Jie Liu Jie Liu 1*Yunxiang Li Yunxiang Li 1*Yueming Xu Yueming Xu 2*Fucun Ma Fucun Ma 1Ruiqing Ma Ruiqing Ma 3*Xuekai Liu Xuekai Liu 1*Guowei Liang Guowei Liang 1*Na Zhao Na Zhao 4*
  • 1 Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China
  • 2 Department of Literature and Science, University of Wisconsin-Madison, Madison Wisconsin 50155, United States, Madison Wisconsin, United States
  • 3 Department of Myxoma, Aerospace Center Hospital, Beijing 100049, China., Beijing, China
  • 4 Department of Nephrology, Aerospace Center Hospital, Beijing 100049, China, Beijing, China

The final, formatted version of the article will be published soon.

    The aim of the present study was to establish a predictive model to predict peritoneal cancer index (PCI) preoperatively for pseudomyxoma peritonei (PMP) patients.: A total of 372 PMP patients were consecutively included from a prospective follow-up database between June 1, 2013 and June 1, 2023. Nine potential variables (including gender, age, Barthel Index (BAI), hemoglobin (Hb), albumin (Alb), D-dimer, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA 125), and CA 19-9) have been appraised for multiple linear regression (MLR) analysis with a stepwise selection procedure. The established MLR model was internally validated through Kfold cross validation. The agreement between predicted and surgical PCI were assessed using Bland-Altman plots and intraclass correlation (ICC). A p-value less than 0.05 was considered statistically significant. Results: Six independent predictors were confirmed by stepwise MLR analysis with an R 2 of 0.570. The predicted PCI formula was 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 to 17.2646).This study represents the first attempt to use a MLR model to preoperative prediction of PCI for PMP patients. Nevertheless, the MLR model did not perform well enough in predicting preoperative PCI. In the future, more advanced statistical techniques and radiomics-based CT-PCI participated MLR model will be developed, which may enhance the predictive ability for PCI.

    Keywords: Pseudomyxoma Peritonei, Peritoneal cancer index, prediction, multiple linear regression, Surgery

    Received: 17 Oct 2024; Accepted: 26 Nov 2024.

    Copyright: © 2024 Bai, Feng, Liu, Li, Xu, Ma, Ma, Liu, Liang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jing Feng, Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China
    Jie Liu, Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China
    Yunxiang Li, Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China
    Yueming Xu, Department of Literature and Science, University of Wisconsin-Madison, Madison Wisconsin 50155, United States, Madison Wisconsin, United States
    Ruiqing Ma, Department of Myxoma, Aerospace Center Hospital, Beijing 100049, China., Beijing, China
    Xuekai Liu, Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China
    Guowei Liang, Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China
    Na Zhao, Department of Nephrology, Aerospace Center Hospital, Beijing 100049, China, Beijing, China

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