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Purpose: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).
Materials And Methods: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.
Results: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.
Conclusion: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
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http://dx.doi.org/10.1007/s11547-024-01859-y | DOI Listing |
Child Care Health Dev
September 2025
Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden.
Objective: To describe the self-report instruments used to measure well-being in children with disabilities, investigate their psychometric quality, cognitive accessibility and alignment with Keyes's operationalization of well-being, including emotional, psychological and social aspects.
Methods: MEDLINE, ProQuest, PubMed and CINAHL were searched for articles published from 2011 to March 2023, identifying 724 studies. Synonyms provided by thesaurus on the main constructs: 'children', 'measure', 'disability' and 'mental health' were employed in the search strategy.
J Imaging Inform Med
September 2025
Department of Biomedical Engineering, Gachon University, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.
To develop and validate a deep-learning-based algorithm for automatic identification of anatomical landmarks and calculating femoral and tibial version angles (FTT angles) on lower-extremity CT scans. In this IRB-approved, retrospective study, lower-extremity CT scans from 270 adult patients (median age, 69 years; female to male ratio, 235:35) were analyzed. CT data were preprocessed using contrast-limited adaptive histogram equalization and RGB superposition to enhance tissue boundary distinction.
View Article and Find Full Text PDFJ Robot Surg
September 2025
Department of Pediatric Surgery, Affiliated Hospital of North Sichuan Medical College (Wenhua Road Campus), No. 57, Section 2 of Wenhua Road, Shunqing District, Nanchong City, 637000, Sichuan Province, People's Republic of China.
This study aims to systematically assess the therapeutic effectiveness of TiRobot-assisted percutaneous kyphoplasty or vertebroplasty in managing osteoporotic thoracolumbar compression fractures. Previous studies have suggested that TiRobot-assisted techniques outperform conventional manual procedures in treating this condition, but relevant conclusions remain controversial. A thorough literature retrieval was carried out across 4 major databases: PubMed, Embase, the Cochrane Library, and Web of Science.
View Article and Find Full Text PDFNeotrop Entomol
September 2025
Dept of Entomology, Federal Univ of Viçosa, Viçosa, MG, Brazil.
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A.
View Article and Find Full Text PDFLancet Digit Health
September 2025
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; NIHR Biomedical Research Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Background: New-onset atrial fibrillation, a condition associated with adverse outcomes in the short and long term, is common in patients admitted to intensive care units (ICUs). Identifying patients at high risk could inform trials of preventive interventions and help to target such interventions. We aimed to develop and externally validate a prediction model for new-onset atrial fibrillation in patients admitted to ICUs.
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