From Radiomics to Deep Learning: Leveraging Gramian Matrix Features in CNNs for NSCLC Survival Analysis

Deep learning has demonstrated effectiveness in PET-CT lesion detection [1] and interpreting complex glycotic uptake distributions [2]. Our prior work [3] pioneered the use of Gramian matrices to characterize and synthesize PET tumour heterogeneity. This study employs our “Titanium” features derived from the activation functions within the pretrained VGG-19 convolutional neural network (CNN). From this diverse feature set, we present our survival prediction model and validate the superior efficacy of “Titanium” features in enhancing predictive modeling of 2-year survival for non-small-cell lung cancer (NSCLC) patients compared to traditional IBSI Radiomic Features. The model was evaluated on 381 NSCLC patients, expertly segmented and split into 80% training, $10 \%$ validation, and $10 \%$ testing sets. “Titanium” features were computed from activation functions across five CNN layers on three orthogonal tumor slices, providing a comprehensive 2.5D representation. This yielded 150 complex textural descriptors. Contrastingly, 203 traditional IBSI radiomic features were derived. Preprocessing included standard scaling, polynomial expansion, and L1-regularized logistic regression for feature selection. Data transformations and feature selection were determined solely from training the set to prevent data leakage. Prediction involved optimizing a multilayer perceptron on the validation set using cross-validation followed by independent testing on the held out set. Our results revealed that the “Titanium” model achieved a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of 0.83, significantly outperforming the traditional radiomic approach, which only achieved an AUC of 0.54. This $53.7 \%$ improvement in predictive power highlights the effectiveness of “Titanium” features in capturing more nuanced and predictive information and presents a sophisticated analytical technique for clinical predictive modelling of oncological outcomes.