REFINED-CNN framework for survival prediction with high-dimensional features.

iScience

Modeling & Simulation/Clinical Pharmacology, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA.

Published: September 2023

Robust and accurate survival prediction of clinical trials using high-throughput genomics data is a fundamental challenge in pharmacogenomics. Current machine learning tools often provide limited predictive performance and model interpretation in these settings. In the present study, we extend the application of REFINED-CNN from regression tasks to making survival predictions, by mapping high-dimensional RNA sequencing data into REFINED images which are conducive to CNN modeling. We show that the REFINED-CNN survival model can be easily adapted to new tasks of a similar nature (e.g., predicting on new cancer types) using transfer learning with a low number of patients. Furthermore, the model can also be interpreted both locally and globally through risk score back propagation that quantifies each feature (e.g., gene) importance in survival prediction task for the patient or cancer type of interest.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474067PMC
http://dx.doi.org/10.1016/j.isci.2023.107627DOI Listing

Publication Analysis

Top Keywords

survival prediction
12
survival
5
refined-cnn framework
4
framework survival
4
prediction high-dimensional
4
high-dimensional features
4
features robust
4
robust accurate
4
accurate survival
4
prediction clinical
4

Similar Publications

Pulmonary arterial hypertension (PAH) is a chronic progressive exacerbation of cardiopulmonary vascular disease. The patients' exercise endurance decreased progressively and the survival rate was low. Current basic therapy and targeted drug therapy can improve the quality of life (QoL) of PAH patients, but the long-term efficacy and prognosis are not good.

View Article and Find Full Text PDF

Background: For esophageal squamous cell carcinoma (ESCC), universally accepted pathological criteria for classification by differentiation degree are lacking. Tumor budding, single-cell invasion, and nuclear grade, recognized as prognostic factors in other carcinomas, have rarely been investigated for their correlation with differentiation and prognosis in ESCC. This study aims to determine if pathological findings can predict differentiation degree and prognosis in ESCC.

View Article and Find Full Text PDF

Background: The limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC.

View Article and Find Full Text PDF

Background: The emergence of the Omicron variant of severe acute respiratory syndrome coronavirus-2 has significantly altered the clinical features and severity of coronavirus disease 2019 (COVID-19).

Objective: This study aims to evaluate whether the clinical factors that previously predicted COVID-19 remain valid following the emergence of the Omicron variant.

Methods: This cross-sectional study was conducted at Showa University Fujigaoka Hospital from April 2022 to March 2023.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!