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Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice.
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http://dx.doi.org/10.3390/diagnostics11060979 | DOI Listing |
J Cancer Res Clin Oncol
September 2025
Department of Radiology, Guizhou Provincial People's Hospital, No. 83 East Zhongshan Road, Guiyang, 550002, Guizhou, China.
Purpose: Targeted therapy with lenvatinib is a preferred option for advanced hepatocellular carcinoma, however, predicting its efficacy remains challenging. This study aimed to build a nomogram integrating clinicoradiological indicators and radiomics features to predict the response to lenvatinib in patients with hepatocellular carcinoma.
Methods: This study included 211 patients with hepatocellular carcinoma from two centers, who were allocated into the training (107 patients), internal test (46 patients) and external test set(58 patients).
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.
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