Qiang Wang, Torkel B Brismar, Dennis Björk, Erik Baubeta, Gert Lindell, Bergthor Björnsson, Ernesto Sparrelid
{"title":"用于预测门静脉栓塞后肝残余肥厚不足的临床-放射学联合模型的建立和外部验证。","authors":"Qiang Wang, Torkel B Brismar, Dennis Björk, Erik Baubeta, Gert Lindell, Bergthor Björnsson, Ernesto Sparrelid","doi":"10.1245/s10434-024-16592-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and externally validate a model for predicting insufficient future liver remnant (FLR) hypertrophy after portal vein embolization (PVE) based on clinical factors and radiomics of pretreatment computed tomography (CT) PATIENTS AND METHODS: Clinical information and CT scans of 241 consecutive patients from three Swedish centers were retrospectively collected. One center (120 patients) was applied for model development, and the other two (59 and 62 patients) as test cohorts. Logistic regression analysis was adopted for clinical model development. A FLR radiomics signature was constructed from the CT images using the support vector machine. A model combining clinical factors and FLR radiomics signature was developed. Area under the curve (AUC) was adopted for predictive performance evaluation RESULTS: Three independent clinical factors were identified for model construction: pretreatment standardized FLR (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.20), alanine transaminase (ALT) level (OR: 0.98, 95% CI: 0.97-0.99), and PVE material (OR: 0.27, 95% CI: 0.08-0.87). This clinical model showed an AUC of 0.75, 0.71, and 0.68 in the three cohorts, respectively. A total of 833 radiomics features were extracted, and after feature dimension reduction, 16 features were selected for FLR radiomics signature construction. When adding it to the clinical model, the AUC of the combined model increased to 0.80, 0.76, and 0.72, respectively. However, the increase was not significant.</p><p><strong>Conclusions: </strong>Pretreatment CT radiomics showed added value to the clinical model for predicting FLR hypertrophy following PVE. Although not reaching statistically significant, the evolving radiomics holds a potential to supplement traditional predictors of FLR hypertrophy.</p>","PeriodicalId":8229,"journal":{"name":"Annals of Surgical Oncology","volume":" ","pages":"1795-1807"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811440/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and External Validation of a Combined Clinical-Radiomic Model for Predicting Insufficient Hypertrophy of the Future Liver Remnant following Portal Vein Embolization.\",\"authors\":\"Qiang Wang, Torkel B Brismar, Dennis Björk, Erik Baubeta, Gert Lindell, Bergthor Björnsson, Ernesto Sparrelid\",\"doi\":\"10.1245/s10434-024-16592-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This study aimed to develop and externally validate a model for predicting insufficient future liver remnant (FLR) hypertrophy after portal vein embolization (PVE) based on clinical factors and radiomics of pretreatment computed tomography (CT) PATIENTS AND METHODS: Clinical information and CT scans of 241 consecutive patients from three Swedish centers were retrospectively collected. One center (120 patients) was applied for model development, and the other two (59 and 62 patients) as test cohorts. Logistic regression analysis was adopted for clinical model development. A FLR radiomics signature was constructed from the CT images using the support vector machine. A model combining clinical factors and FLR radiomics signature was developed. Area under the curve (AUC) was adopted for predictive performance evaluation RESULTS: Three independent clinical factors were identified for model construction: pretreatment standardized FLR (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.20), alanine transaminase (ALT) level (OR: 0.98, 95% CI: 0.97-0.99), and PVE material (OR: 0.27, 95% CI: 0.08-0.87). This clinical model showed an AUC of 0.75, 0.71, and 0.68 in the three cohorts, respectively. A total of 833 radiomics features were extracted, and after feature dimension reduction, 16 features were selected for FLR radiomics signature construction. When adding it to the clinical model, the AUC of the combined model increased to 0.80, 0.76, and 0.72, respectively. However, the increase was not significant.</p><p><strong>Conclusions: </strong>Pretreatment CT radiomics showed added value to the clinical model for predicting FLR hypertrophy following PVE. Although not reaching statistically significant, the evolving radiomics holds a potential to supplement traditional predictors of FLR hypertrophy.</p>\",\"PeriodicalId\":8229,\"journal\":{\"name\":\"Annals of Surgical Oncology\",\"volume\":\" \",\"pages\":\"1795-1807\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811440/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Surgical Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1245/s10434-024-16592-z\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/10 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Surgical Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1245/s10434-024-16592-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/10 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Development and External Validation of a Combined Clinical-Radiomic Model for Predicting Insufficient Hypertrophy of the Future Liver Remnant following Portal Vein Embolization.
Objectives: This study aimed to develop and externally validate a model for predicting insufficient future liver remnant (FLR) hypertrophy after portal vein embolization (PVE) based on clinical factors and radiomics of pretreatment computed tomography (CT) PATIENTS AND METHODS: Clinical information and CT scans of 241 consecutive patients from three Swedish centers were retrospectively collected. One center (120 patients) was applied for model development, and the other two (59 and 62 patients) as test cohorts. Logistic regression analysis was adopted for clinical model development. A FLR radiomics signature was constructed from the CT images using the support vector machine. A model combining clinical factors and FLR radiomics signature was developed. Area under the curve (AUC) was adopted for predictive performance evaluation RESULTS: Three independent clinical factors were identified for model construction: pretreatment standardized FLR (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.20), alanine transaminase (ALT) level (OR: 0.98, 95% CI: 0.97-0.99), and PVE material (OR: 0.27, 95% CI: 0.08-0.87). This clinical model showed an AUC of 0.75, 0.71, and 0.68 in the three cohorts, respectively. A total of 833 radiomics features were extracted, and after feature dimension reduction, 16 features were selected for FLR radiomics signature construction. When adding it to the clinical model, the AUC of the combined model increased to 0.80, 0.76, and 0.72, respectively. However, the increase was not significant.
Conclusions: Pretreatment CT radiomics showed added value to the clinical model for predicting FLR hypertrophy following PVE. Although not reaching statistically significant, the evolving radiomics holds a potential to supplement traditional predictors of FLR hypertrophy.
期刊介绍:
The Annals of Surgical Oncology is the official journal of The Society of Surgical Oncology and is published for the Society by Springer. The Annals publishes original and educational manuscripts about oncology for surgeons from all specialities in academic and community settings.