Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach

Maryamalsadat Mahootiha; Hemin Ali Qadir; Davit Aghayan; Åsmund Avdem Fretland; Bjørn von Gohren Edwin; Ilangko Balasingham

doi:10.1016/j.heliyon.2024.e24374

Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach

Heliyon. 2024 Jan 12;10(2):e24374. doi: 10.1016/j.heliyon.2024.e24374. eCollection 2024 Jan 30.

Authors

Maryamalsadat Mahootiha^{1

2}, Hemin Ali Qadir¹, Davit Aghayan¹, Åsmund Avdem Fretland¹, Bjørn von Gohren Edwin^{1

2}, Ilangko Balasingham^{1

3}

Affiliations

¹ The Intervention Centre, Oslo University Hospital, Oslo, 0372, Norway.
² Faculty of Medicine, University of Oslo, Oslo, 0372, Norway.
³ Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway.

Abstract

This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed approach consists of two networks: a classifier- and a survival- network. The classifier attempts to extract features from 3D CT scans to predict the ISUP grade of Renal cell carcinoma (RCC) tumors, as defined by the International Society of Urological Pathology (ISUP). Our classifier is a 3D convolutional neural network to avoid losing crucial information on the interconnection of slides in 3D images. We employ multiple procedures, including image augmentation, preprocessing, and concatenation, to improve the performance of the classifier. Given the strong correlation between ISUP grading and renal cancer prognosis in the clinical context, we use the ISUP grading features extracted by the classifier as the input to the survival network. By leveraging this clinical association and the classifier network, we are able to model our survival analysis using a simple DL-based network. We adopt a discrete LogisticHazard-based loss to extract intrinsic survival characteristics of RCC tumors from CT images. This allows us to build a completely parametric survival model that varies with patients' tumor characteristics and predicts non-proportional survival probability curves for different patients. Our results demonstrated that the proposed method could predict the future course of renal cancer with reasonable accuracy from the CT scans. The proposed method obtained an average concordance index of 0.72, an integrated Brier score of 0.15, and an area under the curve value of 0.71 on the test cohorts.

Keywords: Cancer prognosis; Deep learning; Imaging biomarkers; Kidney tumor grading; Personalized prognosis; Radiomics; Renal cell carcinoma; Survival analysis.