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Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes ( < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features. Pediatrics, MR Imaging, Oncology, Radiomics, Reproducibility, Repeatability, Neuroblastic Tumors © RSNA, 2024 See also the commentary by Safdar and Galaria in this issue.
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http://dx.doi.org/10.1148/ryai.230208 | DOI Listing |
Front Oncol
August 2025
Department of Radiology, The Affiliated Panyu Central Hospital, Guangzhou Medical University, Guangzhou, China.
Objectives: Lymph node metastasis (LNM) is an important factor affecting the stage and prognosis of patients with lung adenocarcinoma. The purpose of this study is to explore the predictive value of the stacking ensemble learning model based on F-FDG PET/CT radiomic features and clinical risk factors for LNM in lung adenocarcinoma, and elucidate the biological basis of predictive features through pathological analysis.
Methods: Ninety patients diagnosed with lung adenocarcinoma who underwent PET/CT were retrospectively analyzed and randomly divided into the training and testing sets in a 7:3 ratio.
Front Oncol
August 2025
Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
Purpose: Identifying radiomics features that help predict whether glioblastoma patients are prone to developing epilepsy may contribute to an improvement of preventive treatment and a better understanding of the underlying pathophysiology.
Materials And Methods: In this retrospective study, 3-T MRI data of 451 pretreatment glioblastoma patients (mean age: 61.2 ± 11.
J Magn Reson Imaging
September 2025
Key Laboratory of Intelligent Medical Imaging of Wenzhou, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored.
Purpose: To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer.
Curr Med Imaging
May 2025
Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
Background: Predicting the recurrence risk of NMIBC after TURBT is crucial for individualized clinical treatment.
Objective: The objective of this study is to evaluate the ability of radiomic feature analysis of intratumoral and peritumoral regions based on computed tomography (CT) imaging to predict recurrence in non-muscle-invasive bladder cancer (NMIBC) patients who underwent transurethral resection of bladder tumor (TURBT).
Methods: A total of 233 patients with NMIBC who underwent TURBT were retrospectively analyzed.
J Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.