Background: Combined hepatocellular carcinoma (HCC) and cholangiocarcinoma (cHCC-CCA) is defined as a single nodule showing differentiation into HCC and intrahepatic cholangiocarcinoma and has a poor prognosis.
Aim: To develop a radiomics nomogram for predicting post-resection survival of patients with cHCC-CCA.
Methods: Patients with pathologically diagnosed cHCC-CCA were randomly divided into training and validation sets. Radiomics features were extracted from portal venous phase computed tomography (CT) images using the least absolute shrinkage and selection operator Cox regression and random forest analysis. A nomogram integrating the radiomics score and clinical factors was developed using univariate analysis and multivariate Cox regression. Nomogram performance was assessed in terms of the C-index as well as calibration, decision, and survival curves.
Results: CT and clinical data of 118 patients were included in the study. The radiomics score, vascular invasion, anatomical resection, total bilirubin level, and satellite lesions were found to be independent predictors of overall survival (OS) and were therefore included in an integrative nomogram. The nomogram was more strongly associated with OS (hazard ratio: 8.155, 95% confidence interval: 4.498-14.785, < 0.001) than a model based on the radiomics score or only clinical factors. The area under the curve values for 1-year and 3-year OS in the training set were 0.878 and 0.875, respectively. Patients stratified as being at high risk of poor prognosis showed a significantly shorter median OS than those stratified as being at low risk (6.1 81.6 mo, < 0.001).
Conclusion: This nomogram may predict survival of cHCC-CCA patients after hepatectomy and therefore help identify those more likely to benefit from surgery.
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http://dx.doi.org/10.3748/wjg.v27.i41.7173 | DOI Listing |
Front Oncol
January 2025
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Purpose: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.
Material And Methods: In this multicenter diagnostic study, we retrospectively collected basic information and CT images about the patients concerned from three hospitals. An interactive deep learning-based bladder cancer image segmentation framework was constructed using the Swin UNETR algorithm for further features extraction.
J Pain Res
January 2025
Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Purpose: To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP).
Patients And Methods: For this retrospective study, a total of 95 clinically definite LBLP patients (40 cognitively impaired patients and 45 cognitively preserved patients) were included, and all patients underwent functional MRI and clinical assessments. After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively.
J Med Imaging (Bellingham)
January 2025
Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Purpose: We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images.
View Article and Find Full Text PDFUltrason Imaging
January 2025
Department of Ultrasound, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
This study aims to establish and validate an ultrasound radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) before operation. A retrospective analysis conducted on ultrasonic images and clinical features derived from 288 PTMC patients, who were divided into training cohorts ( = 201) and validating cohorts ( = 87) in a ratio of 7:3 base on the principle of random allocation. Radiomics features were extracted from the PTMC patients after ultrasonic examination, followed by dimension reduction and characteristic selection to construct the radiomics score (Radscore) using LASSO regression analysis.
View Article and Find Full Text PDFInt J Med Inform
January 2025
School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, PR China. Electronic address:
Background: In the context of routine breast cancer diagnosis, the precise discrimination between benign and malignant breast masses holds utmost significance. Notably, few prior investigations have concurrently explored the integration of imaging histology features, deep learning characteristics, and clinical parameters. The primary objective of this retrospective study was to pioneer a multimodal feature fusion model tailored for the prediction of breast tumor malignancy, harnessing the potential of ultrasound images.
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