#radiomics

A pioneering pilot study explores the power of CT radiomics in distinguishing Small Cell and Non-Small-Cell Lung Cancer within the Moscow population 🌆. This innovative step toward a virtual biopsy transforms cancer diagnostics 🧬—merging precision imaging, AI intelligence 🤖, and clinical potential for early, non-invasive tumor identification.

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New radiomics model uses immunohistochemistry to predict thyroid nodules — ScienceDaily

According to an ahead-of-print article published in the December issue of the American Journal of Roentgenology (AJR), researchers have validated a first-of-its-kind machine learning-based model to evaluate immunohistochemical (IHC) characteristics in patients with suspected thyroid nodules, achieving “excellent performance” for individualized noninvasive prediction of the presence of cytokeratin…

Authored by G Lohith

Tumors are spatially and temporally heterogeneous, requiring biopsies or invasive surgeries to extract and analyze what are generally small portions of tumor tissue, which do not allow for a complete characterization of the tumor. Imaging provides a comprehensive view of the entire tumor and can be used on an ongoing basis to dynamically monitor the development and progression of the tumor or its response to therapy. Radiomics based quantitative imaging is a new dynamic field which could be co-related with large amount of available data of clinical parameters, gene expression profiles and other prognostic and predictive markers to further validate the optimum value of Precision and personalized medicine in cancer therapy. In this study, we explored radiomic features for extracting reliable information from CT (Computed Tomography) images used for biopsy guidance in adenocarcinomas of the lung, Leading to development of noninvasive biomarkers in predicting EGFR mutations at the time of biopsy and hence moving to a new process of proactive medicine.

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