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To evaluate the performance of a T2-weighted image (T2WI)-based radiomics signature for differentiating between seminomas and nonseminomas. In this retrospective study, 39 patients with testicular germ-cell tumors (TGCTs) confirmed by radical orchiectomy were enrolled, including 19 cases of seminomas and 20 cases of nonseminomas. All patients underwent 3T magnetic resonance imaging (MRI) before radical orchiectomy. Eight hundred fifty-one radiomics features were extracted from the T2WI of each patient. Intra- and interclass correlation coefficients were used to select the features with excellent stability and repeatability. Then, we used the minimum-redundancy maximum-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms for feature selection and radiomics signature development. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the radiomics signature. Five features were selected to build the radiomics signature. The radiomics signature was significantly different between the seminomas and nonseminomas ( < 0.01). The area under the curve (AUC), sensitivity, and specificity of the radiomics signature for discriminating between seminomas and nonseminomas were 0.979 (95% CI: 0.873-1.000), 90.00 (95% CI: 68.3-98.8), and 100.00 (95% CI: 82.4-100.0), respectively. The T2WI-based radiomics signature has the potential to non-invasively discriminate between seminomas and nonseminomas.
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http://dx.doi.org/10.3389/fonc.2019.01330 | DOI Listing |
Front Oncol
August 2025
Department of Spinal Surgery, No. 1 Orthopedics Hospital of Chengdu, Chengdu, China.
Primary bone tumours remain among the most challenging indications in radiation oncology-not because of anatomical size or distribution, but because curative intent demands ablative dosing alongside stringent normal-tissue preservation. Over the past decade, the therapeutic landscape has shifted markedly. Proton and carbon-ion centres now report durable local control with acceptable late toxicity in unresectable sarcomas.
View Article and Find Full Text PDFMed Phys
August 2025
Department of Nuclear Medicine, Affiliated Hospital of Nantong University, Nantong, JiangSu, China.
Background: Super-resolution (SR) reconstruction-based positron emission tomography (PET) imaging has been widely applied in the field of computer vision. However, their definitive clinical benefits have yet to be validated. Radiomics-based modeling provides an effective approach to evaluate the clinical utility of SRPET imaging.
View Article and Find Full Text PDFCurr Opin Immunol
September 2025
Center for Interstitial and Rare Lung Diseases, Pneumology Department, University Hospital Essen, Ruhrlandklinik, Essen, Germany.
Purpose Of Review: Diagnosing sarcoidosis remains challenging. Histology findings and a variable clinical presentation can mimic other infectious, malignant, and autoimmune diseases. This review synthesizes current evidence on histopathology, sampling techniques, imaging modalities, and biomarkers and explores how emerging 'omics' and artificial intelligence tools may sharpen diagnostic accuracy.
View Article and Find Full Text PDFWorld J Hepatol
August 2025
Department of Radiology, Third Affiliated Hospital of Soochow University: Changzhou First People's Hospital, Changzhou 213003, Jiangsu Province, China.
Background: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening cancer with increasing incidence worldwide. High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC. However, the performance of radiomic and deep transfer learning (DTL) models derived from biparametric magnetic resonance imaging (bpMRI) in predicting Ki-67 risk stratification and recurrence-free survival (RFS) in patients with HCC remains limited.
View Article and Find Full Text PDFWorld J Radiol
August 2025
Department of Radiology, Huizhou Central People's Hospital, Huizhou 516001, Guangdong Province, China.
Background: Esophageal cancer (EC) is one of the most prevalent malignant gastrointestinal tumors; accurate prediction of EC staging has high significance before treatment.
Aim: To explore a rational radiomic approach for predicting preoperative staging of EC based on magnetic resonance imaging (MRI).
Methods: This retrospective study included 210 patients with pathologically confirmed EC, randomly divided into a primary cohort ( = 147) and a validation cohort ( = 63) in a ratio of 7:3.