Feature Engineering Assessment of Tumor Infiltrating Lymphocytes in Lung Adenocarcinoma.

Tumor-Infiltrating Lymphocytes (TIL) are emerging as immunotherapy prognostic markers. Currently, TIL are assessed on hematoxylin and eosin (H&E)-stained slides of tumor tissue by pathologists. This approach is time-consuming, and subjected to inter-observer variability. The aim of this study is to propose a machine learning-based algorithm, called Feature Engineering TIL Assessment (FTA), for the automatic TIL assessment by using adenocarcinoma metadata (i.e., anamnestic, clinical and pathological data). The algorithm is an Elastic Net, tuned by Bayesian Optimization and validated by Leave-One-Subject-Out cross validation. Obtained coefficients were used for feature ranking. Results confirms the goodness of performance of FTA, with an overall Mean Absolute Error of 2.1%, Concordance Correlation Coefficient equal to 0.71 and difference in the Bland- Altman plot equal to -0.001. The obtained feature ranking revealed the key role of gender, as confirmed by the clinical literature. In conclusion, FTA is the first image-independent automatic TIL assessment procedure, having the potential to address challenges associated with inter-observer variability and the time-consuming nature of classical procedures.