News | Radiomics | September 21, 2021

Peer-reviewed research published in Cancers demonstrates radiomics ability to pinpoint lung adenocarcinoma patients who would benefit from immunotherapy

HealthMyne, a pioneer in applied radiomics, announced today that peer-reviewed research recently published in the journal Cancers has demonstrated the ability of its radiomics technology to identify biomarkers that predict whether patients with lung adenocarcinoma would benefit from immunotherapy.

Semi-automatic lesion identification: (A) Manual ROI indication. In blue, it is possible to observe the axes that cross the lesion manually delineated by the radiologist on a plane of the MPR. The intensity of the lesion boundary (estimated) is represented with a red outline. (B) Additional axes can be dragged onto other orthogonal MPR views. From left to right, it is possible to observe the initial long axis outlined by the radiologist and the 2D contours on the axial, coronal and sagittal views of the lesion used as a starting point for the RPM algorithms. (C) Resulting 3D contour of the lesion (in blue).


September 21, 2021 —  HealthMyne, a pioneer in applied radiomics, announced today that peer-reviewed research recently published in the journal Cancers has demonstrated the ability of its radiomics technology to identify biomarkers that predict whether patients with lung adenocarcinoma would benefit from immunotherapy.

In the Cancers article, researchers led by Vincenza Granata evaluated HealthMyne’s technology as a quantitative imaging decision support tool for radiomic analysis of lung adenocarcinoma in chest computed tomography (CT) scans. Researchers analyzed radiomic biomarkers to predict Overall Survival (OS) and Progression Free Survival (PFS) time.

To perform the study, researchers selected 74 patients with histologically confirmed lung cancer who underwent immunotherapy and compared them with 50 patients with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy.

Researchers segmented each patient’s lesion leveraging HealthMyne’s advanced imaging analytics solution to extract 573 radiometric metrics from the cohort images to predict OS and PFS time. Researchers found that 19 radiomic features were significant for predicting OS and 108 radiomic features for predicting PFS time.

Researchers concluded that the study demonstrated the relationship between radiomics and immunotherapeutic response and that specific radiomic features can be used to select patients with lung adenocarcinoma who would benefit from immunotherapy.

“To maximize the value of research and development investments, drug developers need an accurate and efficient means of selecting and stratifying patients for clinical studies,” said Rose Higgins, CEO, HealthMyne. “Numerous examples of peer-reviewed research have shown that radiomics and precision image analysis identifies biomarkers that drive greater personalization of treatment and provides new insights for better decisions. At HealthMyne, we strive to develop innovative radiomic solutions that contribute to the advancement of this critical body of evidence.”

For more information: www.healthmyne.com

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