Authors : Manjima Sunil; Hamlin Joseph Antony; Gowtham Rajkumar

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/hxpezkm5

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DOI : https://doi.org/10.38124/ijisrt/25nov515

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Abstract : Radiomics has rapidly evolved as a transformative approach in medical imaging, enabling the extraction of high- dimensional quantitative features from routine scans to identify imaging patterns beyond human perception[1]. By converting standard medical images into mineable data, radiomics bridges the gap between imaging science and personalized medicine[13][14]. Recent advances in machine and deep learning have further expanded its potential, allowing for the decoding of complex tumor phenotypes and facilitating non-invasive prediction of treatment response, prognosis, and clinical outcomes[3]. Radiomic models have demonstrated strong predictive and prognostic value across various malignancies, including lung, glioblastoma, and prostate cancers[9]. However, multicenter investigations have emphasized the necessity for methodological standardization and reproducibility in image processing, feature extraction, and model validation[15]. Despite significant progress, persistent challenges remain concerning data harmonization, feature stability, and model interpretability[20]. Initiatives such as the Image Biomarker Standardisation Initiative and ComBat harmonization have been instrumental in improving cross-platform and cross-center reliability[16]. Looking ahead, the integration of radiomics with genomic, molecular, and clinical data—so-called radiogenomics—is expected to advance precision oncology, enabling more accurate, patient-specific therapeutic strategies[10]. Radiomics thus represents a crucial step toward realizing the promise of data-driven, personalized medicine.

Keywords : Radiomics; Medical Imaging; Machine Learning; Deep Learning; Precision Oncology; Feature Extraction; Data Harmonization; Model Interpretability; Radiogenomics; Personalized Medicine.

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