Dr. Wei Mu
Researchers developed deep learning models that could accurately predict a patient’s PD-L1 and EGFR mutation status without the need for a biopsy. If these models are validated in prospective trials, they could guide treatment decisions in patients with NSCLC, according to the researchers.
Wei Mu, PhD, of Moffitt Cancer Center and Research Institute in Tampa, Fla., described this research at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (abstract PR-03).
Rationale
Guidelines from the National Comprehensive Cancer Network (NCCN) endorse tailored treatment for patients with NSCLC; namely, immune checkpoint inhibitors for those with PD-L1-positive tumors and EGFR tyrosine kinase inhibitors for patients with tumors harboring a mutation in EGFR.
However, the conventional approach to ascertaining tumor status for these biomarkers has disadvantages, Dr. Mu noted.
“Both require biopsy, which may fail due to insufficient quality of the tissue and, particularly for NSCLC, may increase the chance of morbidity,” Dr. Mu said.
In addition, there is room for improvement in the rigor of the biomarker assays, and there can be substantial wait times for results.
To address these issues, Dr. Mu and colleagues explored an AI radiomics approach using PET/CT scans.
“We know that EGFR mutation and positive PD-L1 expression may change the metabolism of the peritumor and intratumor microenvironment,” Dr. Mu explained. “Therefore, we had the hypothesis that they can be captured by the FDG-PET/CT images.”
Results
The investigators used FDG-PET/CT images from 837 patients with advanced NSCLC treated at four institutions. The team developed AI deep learning models that generated one score for PD-L1 positivity and another score for presence of an EGFR mutation, as well as an associated algorithm that would direct patients to the appropriate treatments depending on the scores.
Results for the PD-L1 deep learning score showed good accuracy in predicting positivity for this ligand, with an area under the curve of 0.89 in the training cohort, 0.84 in the validation cohort, and 0.82 in an external test cohort, Dr. Mu reported. All exceeded the corresponding areas under the curve for maximal standardized uptake values.
Moreover, the score was prognostic and statistically indistinguishable from PD-L1 status determined by immunohistochemistry in predicting progression-free survival.
Similarly, the EGFR deep learning score showed good accuracy in predicting mutational status, with an area under the curve of 0.86 in the training cohort, 0.83 in the validation cohort, and 0.81 in an external test cohort. It outperformed a clinical score based on sex, smoking status, tumor histology, and maximal standardized uptake value in each cohort.
The EGFR deep learning score was prognostic and statistically indistinguishable from EGFR mutational status determined by polymerase chain reaction in predicting progression-free survival.
The models showed good stability when size of the input region of interest was varied, and when different radiologists delineated the region of interest, with an intraclass correlation coefficient of 0.91.
“We developed deep learning models to predict PD-L1 status and EGFR mutation with high accuracy. Using the generated deep learning scores, we obtained a noninvasive treatment decision support tool, which may be useful as a clinical decision support tool pending validation of its clinical utility in a large prospective trial,” Dr. Mu summarized. “Using our tool, NSCLC patients could be directly offered a treatment decision without the need of biopsy.”
“In the future, we will perform a prospective observational trial to compare the results of our noninvasive treatment decision tool with molecular biomarker–based NCCN guidelines,” she said.
The investigators plan to add ALK rearrangement status and prediction of serious adverse events and cachexia to the decision support tool.
Dr. Mu disclosed no conflicts of interest. The study did not have specific funding.