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Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7594896 | PMC |
http://dx.doi.org/10.1259/bjro.20190046 | 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).
J Cardiovasc Comput Tomogr
September 2025
Depertament of Cardiology, Hitit University, Faculty of Medicine, Çorum, Turkey.
J Immunother Cancer
September 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility.
View Article and Find Full Text PDFArch Dis Child Educ Pract Ed
September 2025
Department of Cancer and Genomic Sciences, University of Birmingham, Birmingham, England, UK
Radiother Oncol
September 2025
Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China. Electronic address:
Background And Purpose: Determining the appropriate sample size for developing robust radiomics-based binary outcome prediction models and identifying the maximum number of predictors safely allowable within a fixed dataset size remain critical yet challenging tasks. This study aims to propose and demonstrate a structured method for addressing these issues, enhancing methodological rigor and practicality in radiomics research.
Materials And Methods: We introduce a comprehensive sample size calculation framework for binary outcome prediction models in radiomic studies.