Objectives: To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B.
Methods: Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).
Results: The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis.
Conclusions: Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis.
Key Points: • Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.
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http://dx.doi.org/10.1007/s00330-022-09137-z | DOI Listing |
Front Oncol
November 2024
Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Purpose: This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC).
Methods: A total of 301 patients (training cohort, n = 210; testing cohort, n = 91) with GC were retrospectively collected. Relevant clinical predictors were determined through the application of univariate and multivariate logistic regression analyses.
Front Oncol
December 2024
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Objectives: The accurate assessment of lymph node metastasis (LNM) can facilitate clinical decision-making on radiotherapy or radical hysterectomy (RH) in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC). This study aims to develop a deep learning radiomics nomogram (DLRN) to preoperatively evaluate LNM in cervical AC/ASC.
Materials And Methods: A total of 652 patients from a multicenter were enrolled and randomly allocated into primary, internal, and external validation cohorts.
Acad Radiol
December 2024
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China (B.W., X.H., Z.Z., Z.L., S.L.). Electronic address:
Rationale And Objectives: To develop and validate a radiomics signature, utilizing baseline and restaging CT, for preoperatively predicting progression-free survival (PFS) after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).
Methods: A total of 316 patients with LAGC who received NAC followed by gastrectomy were retrospectively included in this single-center study; these patients were split into two cohorts, one for training (n = 243) and the other for validation (n = 73), based on the different districts of our hospital. A total of 1316 radiomics features were extracted from the volume of interest of the gastric-cancer lesion on venous phase CT images.
Acad Radiol
December 2024
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China (Y.T., Y.W., Y.Y., X.Q., Y.H., J.L.); Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi Zhuang Autonomous Region, PR China (J.L.). Electronic address:
Rationale And Objectives: To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC).
Materials And Methods: We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n=155), a test set (n=67), and an external validation set (n=86).
Eur J Radiol
December 2024
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China. Electronic address:
Purpose: Microvascular invasion (MVI) serves as a significant predictor of poor prognosis in intrahepatic cholangiocarcinoma (ICC). This study aims to establish a comprehensive model utilizing MR radiomics for preoperative MVI status stratification and outcome prediction in ICC patients.
Materials And Methods: A total of 249 ICC patients were randomly assigned to training and validation cohorts (174:75), along with a time-independent test cohort consisting of 47 ICC patients.
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