Philip Heesen, Sebastian M Christ, Olga Ciobanu-Caraus, Abdullah Kahraman, Georg Schelling, Gabriela Studer, Beata Bode-Lesniewska, Bruno Fuchs
{"title":"Clinical prognostic models for sarcomas: a systematic review and critical appraisal of development and validation studies.","authors":"Philip Heesen, Sebastian M Christ, Olga Ciobanu-Caraus, Abdullah Kahraman, Georg Schelling, Gabriela Studer, Beata Bode-Lesniewska, Bruno Fuchs","doi":"10.1186/s41512-025-00186-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Current clinical guidelines recommend the use of clinical prognostic models (CPMs) for therapeutic decision-making in sarcoma patients. However, the number and quality of developed and externally validated CPMs is unknown. Therefore, we aimed to describe and critically assess CPMs for sarcomas.</p><p><strong>Methods: </strong>We performed a systematic review including all studies describing the development and/or external validation of a CPM for sarcomas. We searched the databases MEDLINE, EMBASE, Cochrane Central, and Scopus from inception until June 7th, 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST).</p><p><strong>Results: </strong>Seven thousand six hundred fifty-six records were screened, of which 145 studies were eventually included, developing 182 and externally validating 59 CPMs. The most frequently modeled type of sarcoma was osteosarcoma (43/182; 23.6%), and the most frequently predicted outcome was overall survival (81/182; 44.5%). The most used predictors were the patient's age (133/182; 73.1%) and tumor size (116/182; 63.7%). Univariable screening was used in 137 (75.3%) CPMs, and only 7 (3.9%) CPMs were developed using pre-specified predictors based on clinical knowledge or literature. The median c-statistic on the development dataset was 0.74 (interquartile range [IQR] 0.71, 0.78). Calibration was reported for 142 CPMs (142/182; 78.0%). The median c-statistic of external validations was 0.72 (IQR 0.68-0.75). Calibration was reported for 46 out of 59 (78.0%) externally validated CPMs. We found 169 out of 241 (70.1%) CPMs to be at high risk of bias, mostly due to the high risk of bias in the analysis domain.</p><p><strong>Discussion: </strong>While various CPMs for sarcomas have been developed, the clinical utility of most of them is hindered by a high risk of bias and limited external validation. Future research should prioritise validating and updating existing well-developed CPMs over developing new ones to ensure reliable prognostic tools.</p><p><strong>Trial registration: </strong>PROSPERO CRD42022335222.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"7"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974052/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostic and prognostic research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41512-025-00186-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Current clinical guidelines recommend the use of clinical prognostic models (CPMs) for therapeutic decision-making in sarcoma patients. However, the number and quality of developed and externally validated CPMs is unknown. Therefore, we aimed to describe and critically assess CPMs for sarcomas.
Methods: We performed a systematic review including all studies describing the development and/or external validation of a CPM for sarcomas. We searched the databases MEDLINE, EMBASE, Cochrane Central, and Scopus from inception until June 7th, 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST).
Results: Seven thousand six hundred fifty-six records were screened, of which 145 studies were eventually included, developing 182 and externally validating 59 CPMs. The most frequently modeled type of sarcoma was osteosarcoma (43/182; 23.6%), and the most frequently predicted outcome was overall survival (81/182; 44.5%). The most used predictors were the patient's age (133/182; 73.1%) and tumor size (116/182; 63.7%). Univariable screening was used in 137 (75.3%) CPMs, and only 7 (3.9%) CPMs were developed using pre-specified predictors based on clinical knowledge or literature. The median c-statistic on the development dataset was 0.74 (interquartile range [IQR] 0.71, 0.78). Calibration was reported for 142 CPMs (142/182; 78.0%). The median c-statistic of external validations was 0.72 (IQR 0.68-0.75). Calibration was reported for 46 out of 59 (78.0%) externally validated CPMs. We found 169 out of 241 (70.1%) CPMs to be at high risk of bias, mostly due to the high risk of bias in the analysis domain.
Discussion: While various CPMs for sarcomas have been developed, the clinical utility of most of them is hindered by a high risk of bias and limited external validation. Future research should prioritise validating and updating existing well-developed CPMs over developing new ones to ensure reliable prognostic tools.