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Objectives: This study aimed to develop a clinical-radiomics nomogram to predict the long-term outcomes of patients with classical trigeminal neuralgia (CTN) following microvascular decompression (MVD).
Materials And Methods: This retrospective study included 455 patients with CTN who underwent MVD from three independent institutions A total of 2030 radiomics features from the cistern segment of the trigeminal nerve were extracted computationally from the three-dimensional steady-state free precession and three-dimensional time-of-flight magnetic resonance angiography sequences. Using the least absolute shrinkage and selection operator regression, 16 features were chosen to develop radiomics signatures. A clinical-radiomics nomogram was subsequently developed in the development cohort of 279 patients via multivariate Cox regression. The predictive performance and clinical application of the nomogram were assessed in an external cohort consisting of 176 patients.
Results: Sixteen highly outcome-related radiomics features extracted from multisequence images were used to construct the radiomics model, with concordance indices (C-index) of 0.804 and 0.796 in the development and test cohorts, respectively. Additionally, a clinical-radiomics nomogram was developed by incorporating both radiomics features and clinical characteristics (i.e., pain type and degree of neurovascular compression) and yielded higher C-indices of 0.865 and 0.834 in the development and test cohorts, respectively. K‒M survival analysis indicated that the nomogram successfully stratified patients with CTN into high-risk and low-risk groups for poor outcomes (hazard ratio: 37.18, p < 0.001).
Conclusion: Our study findings indicated that the clinical-radiomics nomogram exhibited promising performance in accurately predicting long-term pain outcomes following MVD.
Clinical Relevance Statement: This model had the potential to aid clinicians in making well-informed decisions regarding the treatment of patients with CTN.
Key Points: Trigeminal neuralgia recurs in about one-third of patients after undergoing MVD. The clinical-radiomics nomogram stratified patients into high- and low-risk groups for poor surgical outcomes. Using this nomogram could better inform patients of recurrence risk and allow for discussion of alternative treatments.
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http://dx.doi.org/10.1007/s00330-024-10775-8 | DOI Listing |
Int J Chron Obstruct Pulmon Dis
September 2025
Department of Cardiovascular Center, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People's Republic of China.
Objective: This study aimed to develop and validate a deep learning radiomics (DLR) nomogram for individualized CHD risk assessment in the COPD population.
Methods: This retrospective study included 543 COPD patients from two different centers. Comprehensive clinical and imaging data were collected for all participants.
J Ultrasound Med
August 2025
Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Objectives: This study aims to develop a noninvasive preoperative predictive model utilizing ultrasound radiomics combined with clinical characteristics to differentiate uterine sarcoma from leiomyoma.
Methods: This study included 212 patients with uterine mesenchymal lesions (102 sarcomas and 110 leiomyomas). Clinical characteristics were systematically selected through both univariate and multivariate logistic regression analyses.
Acad Radiol
August 2025
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China (Q-y.W., Y.L., Y-c.W., C-z.Y., Y-y.Y., J-y.L.); Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021,
Rationale And Objectives: To develop a nomogram integrating clinical and multimodal MRI features for non-invasive prediction of microsatellite instability (MSI) in endometrial cancer (EC), and to evaluate its diagnostic performance.
Materials And Methods: This retrospective multicenter study included 216 EC patients (mean age, 54.68 ± 8.
Cancer Imaging
August 2025
Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, China. tjl
Background: Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preoperative work-up. The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients.
View Article and Find Full Text PDFFront Neurosci
August 2025
Department of Neurology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Objective: This study aimed to develop a multi-omics nomogram that combines clinical parameters, radiomics, and deep transfer learning (DTL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict functional outcomes at discharge.
Methods: This study enrolled 246 patients with HIM who underwent MT. Patients were randomly assigned to a training cohort ( = 197, 80%) and a validation cohort ( = 49, 20%), with an additional internal prospective test cohort ( = 57).