Researchers have used Artificial Intelligence (AI) to train algorithms and predict tumour sensitivity in three advanced non-small cell lung cancer therapies which can help predict more accurate treatment efficacy at an early stage of the disease.
The researchers at Columbia University's Irving Medical Center analysed CT images from 92 patients receiving drug agent nivolumab in two trials; 50 patients receiving docetaxel in one trial, and 46 patients receiving gefitinib in one trial.
To develop the model, the researchers used the CT images taken at baseline and on first-treatment assessment.
"The purpose of this study was to train cutting-edge AI technologies to predict patients' responses to treatment, allowing radiologists to deliver more accurate and reproducible predictions of treatment efficacy at an early stage of the disease," explained Laurent Dercle, associate research scientist at the Columbia University Irving Medical Center.
Radiologists currently quantify changes in tumour size and the appearance of new tumour lesions.
However, this type of evaluation can be limited, especially in patients treated with immunotherapy, who can display atypical patterns of response and progression.
"Newer systemic therapies prompt the need for alternative metrics for response assessment, which can shape therapeutic decision-making," Dercle said in a paper appeared in the journal Clinical Cancer Research.
The researchers used machine learning to develop a model to predict treatment sensitivity in the training cohort.
Each model could predict a score ranging from zero (highest treatment sensitivity) to one (highest treatment insensitivity) based on the change of the largest measurable lung lesion identified at baseline.
"We observed that similar radiomics features predicted three different drug responses in patients with advanced non-small cell lung cancer (NSCLC)," Dercle said.
"With AI, cancer imaging can move from an inherently subjective tool to a quantitative and objective asset for precision medicine approaches," he added.
