"We already knew that radiomic algorithms have strong clinical importance; however, the biological basis for these observations remained unknown. This study now answers this key question for the first time by defining and independently validating the driving biological pathways of radiomic phenotypes" said Hugo Aerts, Ph.D., director of the Computational Imaging and Bioinformatics Laboratory and associate professor of Radiation Oncology at Harvard Medical School.

Radiomics has several advantages over other commonly used techniques that guide precision medicine. Currently, biological markers are routinely analyzed with tissue biopsies that are invasive, collected only at the beginning of care, and may not accurately reflect the biology of the entire tumor. In contrast, imaging techniques are noninvasive and can provide information about the entire tumor throughout the entire course of treatment and response. Additionally, the majority of cancer patients routinely have images taken for diagnostic purposes already, making radiomics a cost-effective approach.


"This study advances the molecular knowledge of radiomic characterization of tumors, information currently not used clinically. This may provide opportunities to improve decision-support in all patients as imaging is routinely used in clinical practice as standard of care," said Gillies.

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