From radiomics to deep learning: Leveraging gramian matrix features in CNNs for NSCLC survival analysis

Smith, R. L., Al-Battat, K., John, R., Li, M., Ackerley, I., Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813, Wells, K., Morley, N. and Marshall, C. 2024. From radiomics to deep learning: Leveraging gramian matrix features in CNNs for NSCLC survival analysis. Presented at: 2024 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, Tampa, FL, USA, 26 October - 2 November 2024. 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, pp. 1-2. 10.1109/nss/mic/rtsd57108.2024.10657697

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9798350388169
ISSN:	1082-3654
Date of First Compliant Deposit:	18 October 2024
Last Modified:	07 Nov 2024 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/173115

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