Imaging Biomarker in Oncology

Dear Colleagues,

Cancers affect a large percentage of chronic and fragile patients, and their workup and therapeutic options depend on timing of diagnosis, staging, and tumor aggressiveness. In such a scenario, imaging has been acquiring a pivotal role in oncology by providing some relevant non-invasive biomarkers extracted from medical images. Conventional radiological evaluation, based on qualitative and subjective assessment of images, is the recognized imaging approach to study cancer patients. However, the main drawbacks of subjective images assessment are represented by their subjective nature and the difficulty to reproduce the measures. Today, imaging is shifting from a qualitative to a quantitative approach, especially in tumor diagnosis, prognosis prediction, and assessment of response to therapy. In such a scenario, radiomics has been overcoming conventional imaging using dedicated software having the ability to extract high dimensional data having the expectancy to provide objective and quantitative non-invasive imaging biomarkers in cancer patients. Medical images could narrow some quantitative data reflecting microenvironmental tumor heterogeneity, neoplasm phenotypes and heterogeneity, usually correlated with tumor aggressiveness and patient prognosis. Then, radiomic data could be extracted, analyzed, and integrated with clinical data using the strengths of Artificial Intelligence, helpful to overcome the main limitations of traditional tumor management, starting from conventional lesion biopsy, often affected by bias in tumor sampling, lack of repeatability, and possible procedure complications.

In the new era of target therapy, imaging is expected to become a supporting tool for clinicians by providing non-invasive biomarkers. In this manner, imaging could have the ability to outline the profile of tumors based on several features extracted from medical images, assessed with both qualitative and quantitative approaches.

Dr. Damiano Caruso
Collection Editor

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