Deep-SEA: a deep learning based patient specific multi-modality post-cancer survival estimation architecture

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 21; pp. 10640 - 10652
• Main Authors: Ahmad, Ibtihaj, Riaz, Saleem
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2024; Springer Nature B.V
• Subjects: Ablation; Artificial Intelligence; Artificial neural networks; Cancer; Censored data (mathematics); Computer Science; Deep learning; Deep sea environments; Histology; Machine learning; Machines; Manufacturing; Mechanical Engineering; Parameter estimation; Processes; State-of-the-art reviews; Statistical methods; Survival; Radiology features; Time to event; Survival prediction; Cancer survival estimation; Clinical features
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-024-05794-3

Cancer survival estimation is essential for post-cancer patient care, cancer management policy building, and the development of tailored treatment plans. Existing survival estimation methods use censored data; therefore, standard machine learning methods can not be used directly. Some censoring-based semi-machine learning methods have recently been proposed; however, these methods pose challenges. They are less patient-specific and non-linear. Furthermore, they rely on single-modality features. These drawbacks result in lower survival estimation performance. To address these issues, this work proposes a framework named Deep-SEA. Compared to the state-of-the-art, Deep-SEA uses multi-modality features, i.e., clinical, radiology, and histology features. These features are analyzed with statistical methods, and only significant features are selected. Then, the baseline hazard of the Cox model is estimated using Breslow’s estimator, which is optimized using stochastic gradient descent. Finally, the risk function, i.e., the parameters of our model, are estimated via an ANN with time as additional input. ANN makes it non-linear while training on the patient-specific features makes it more patient-specific than the state-of-the-art. We train and evaluate Deep-SEA on five datasets, including head, neck, and colorectal-liver cancer. We have achieved a Concordance-index (C-index) score of 0.7181, the highest compared to the state-of-the-art. Results and ablation studies on Deep-SEA suggest that the proposed method improves cancer survival estimation and can be applied to other estimations, such as cancer recurrence estimation.