Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.

ABSTRACT: Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294

SUBMITTER: Lombardo E 

PROVIDER: S-EPMC7979766 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature