Survival of a patient with five recurrences of retroperitoneal liposarcoma over a period of 13 years: A case report and review

Qiu, Miao Zhen; Lin, De-Rong; Xiong, Zheqiang

doi:10.3892/ol.2023.13953

Cited by 1 publication

(2 citation statements)

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“…34,35 Previous studies have evaluated risk factors affecting the prognosis of RLPS patients, mainly through case reports and small sample studies. 34,36 Li et al evaluated the prognosis of RLPS patients by using information from the SEER database and employing a Cox regression model. 37 However, the model only included five prognostic risk factors, and the AUC for 1, 3, and 5-year OS were all below 0.80.…”

Section: Discussionmentioning

confidence: 99%

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Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on theSEERdatabase and external validation in China

Wang,

Li,

Zeng

et al. 2024

Cancer Medicine

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ObjectiveWe have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.MethodsWe collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component‐wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C‐index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox–Snell residual plot. We also used time‐dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model‐agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model.ResultsThe final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS.ConclusionsThe ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

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Section: Discussionmentioning

confidence: 99%

“…The prognosis of patients with RLPS has traditionally been associated with surgical treatment and histological subtypes of the tumor 34,35 . Previous studies have evaluated risk factors affecting the prognosis of RLPS patients, mainly through case reports and small sample studies 34,36 . Li et al.…”

Section: Discussionmentioning

confidence: 99%