التفاصيل البيبلوغرافية
| العنوان: |
Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models. |
| المؤلفون: |
Kokkinakis, Stamatios1 (AUTHOR) stamatioskokkinakis@gmail.com, Ziogas, Ioannis A.2 (AUTHOR) ioannis.ziogas@cuanschutz.edu, Llaque Salazar, Jose D.2 (AUTHOR) jose.llaquesalazar@cuanschutz.edu, Moris, Dimitrios P.3 (AUTHOR) dimitrios.moris@duke.edu, Tsoulfas, Georgios4 (AUTHOR) tsoulfasg@auth.gr |
| المصدر: |
Cancers. May2024, Vol. 16 Issue 9, p1645. 27p. |
| مصطلحات موضوعية: |
*LIVER tumors, *RISK assessment, *PREDICTION models, *ARTIFICIAL intelligence, *COLORECTAL cancer, *METASTASIS, *SURGICAL complications, *PROGRESSION-free survival, *DISEASE relapse, *MACHINE learning, *OVERALL survival |
| مستخلص: |
Simple Summary: Interest in stratification of prognosis for patients with colorectal liver metastases is growing. Numerous clinical prediction models have been developed for this purpose in recent years, either with the aid of traditional statistical methods or by using the aid of artificial intelligence techniques. We herein provide an overview of relevant studies discussing the different types of predictors proven to be of importance and critically assess the variable model development and validation techniques as well as the performance of the reported models. Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of "high-risk" patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework. [ABSTRACT FROM AUTHOR] |
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