{"title":"MRI-based radiomics features for prediction of pathological deterioration upgrading in rectal tumor.","authors":"Yongping Hong,Xingxing Chen,Wei Sun,Guofeng Li","doi":"10.1016/j.acra.2024.08.057","DOIUrl":null,"url":null,"abstract":"PURPOSE\r\nOur aim is to develop and validate an MRI-based diagnostic model for predicting pathological deterioration upgrading in rectal tumor.\r\n\r\nMETHODS\r\nThis retrospective study included 158 eligible patients from January 2017 to November 2023. The patients were divided into a training group (n = 110) and a validation group (n = 48). Radiomics features were extracted from T2-weighted images to create a radiomics score model. Significant factors identified through multifactor analysis were used to develop the final clinical feature model. By combining these two models, an combined radiomics-clinical model was established. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the ROC Curve (AUC).\r\n\r\nRESULTS\r\nA total of 1197 features were extracted, with 11 features selected for calculating the radiomics score to establish the radiomics model. This model demonstrated good predictive performance for pathological upgrading in both the training and validation groups (AUC of 0.863 and 0.861, respectively). Clinical factors such as chief complaint and differential carcinoembryonic antigen levels showed statistical significance (P < 0.05). The clinical model, incorporating these factors, yielded AUC values of 0.669 and 0.651 for the training and validation groups, respectively. Furthermore, the radiomics-clinical combined model outperformed the individual models in predicting preoperative pathological upgrading in both the training and validation groups (AUC of 0.932 and 0.907, respectively).\r\n\r\nCONCLUSIONS\r\nA radiomics-clinical model, which combines clinical features with radiomics features based on MRI, can predict pathological deterioration upgrading in patients with rectal tumor and provide valuable insights for personalized treatment strategies.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.acra.2024.08.057","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
PURPOSE
Our aim is to develop and validate an MRI-based diagnostic model for predicting pathological deterioration upgrading in rectal tumor.
METHODS
This retrospective study included 158 eligible patients from January 2017 to November 2023. The patients were divided into a training group (n = 110) and a validation group (n = 48). Radiomics features were extracted from T2-weighted images to create a radiomics score model. Significant factors identified through multifactor analysis were used to develop the final clinical feature model. By combining these two models, an combined radiomics-clinical model was established. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the ROC Curve (AUC).
RESULTS
A total of 1197 features were extracted, with 11 features selected for calculating the radiomics score to establish the radiomics model. This model demonstrated good predictive performance for pathological upgrading in both the training and validation groups (AUC of 0.863 and 0.861, respectively). Clinical factors such as chief complaint and differential carcinoembryonic antigen levels showed statistical significance (P < 0.05). The clinical model, incorporating these factors, yielded AUC values of 0.669 and 0.651 for the training and validation groups, respectively. Furthermore, the radiomics-clinical combined model outperformed the individual models in predicting preoperative pathological upgrading in both the training and validation groups (AUC of 0.932 and 0.907, respectively).
CONCLUSIONS
A radiomics-clinical model, which combines clinical features with radiomics features based on MRI, can predict pathological deterioration upgrading in patients with rectal tumor and provide valuable insights for personalized treatment strategies.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.