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Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts.
- Source :
-
Scientific reports [Sci Rep] 2021 Mar 19; Vol. 11 (1), pp. 6418. Date of Electronic Publication: 2021 Mar 19. - Publication Year :
- 2021
-
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 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation [Harrell's concordance indices (HCIs) of 0.78, 0.74 and 0.80] in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation.
- Subjects :
- Aged
Biomarkers, Tumor
Female
Follow-Up Studies
Head and Neck Neoplasms epidemiology
Humans
Italy epidemiology
Lymphatic Metastasis pathology
Male
Middle Aged
Neck
Netherlands epidemiology
Probability
Prognosis
Quebec epidemiology
Reproducibility of Results
Risk Assessment
Time Factors
Tumor Burden
Deep Learning
Head and Neck Neoplasms pathology
Image Processing, Computer-Assisted methods
Lymph Nodes pathology
Lymphatic Metastasis diagnosis
Subjects
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 33742070
- Full Text :
- https://doi.org/10.1038/s41598-021-85671-y