VILLEJUIF, France–(BUSINESS WIRE)–A study published in The Lancet Oncology establishes for the first time that artificial intelligence can process medical images to extract biological and clinical information. By designing an algorithm and developing it to analyse CT scan images, medical researchers at Gustave Roussy, CentraleSupélec, Inserm, Paris-Sud University and TheraPanacea (spin-off from CentraleSupélec specialising in artificial intelligence in oncology-radiotherapy and precision medicine) have created a so-called radiomic signature. This signature defines the level of lymphocyte infiltration of a tumour and provides a predictive score for the efficacy of immunotherapy in the patient.
In the future, physicians might thus be able to use imaging to identify biological phenomena in a tumour located in any part of the body without having to perform a biopsy.
Up to now, no marker can accurately identify those patients who will respond to anti-PD-1/PD-L1 immunotherapy in a situation where only 15 to 30% of patients do respond to such treatment. It is known that the richer the tumour environment is immunologically (presence of lymphocytes) the greater the chance that immunotherapy will be effective, so the researchers have tried to characterise this environment using imaging and correlate this with the patients’ clinical response. Such is the objective of the radiomic signature designed and validated in the study published in The Lancet Oncology.
In this retrospective study, the radiomic signature was captured, developed and validated in 500 patients with solid tumours (all sites) from four independent cohorts. It was validated genomically, histologically and clinically, making it particularly robust.
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