Medicine and Health

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

M. Karabacak, P. Jagtiani, et al.

This innovative research by Mert Karabacak, Pemla Jagtiani, Alejandro Carrasquilla, Isabelle M. Germano, and Konstantinos Margetis harnesses machine learning to predict survival outcomes for glioma patients, transforming clinical decision-making with personalized analytics integrated into a user-friendly web application. The predictive models, utilizing LightGBM and Random Forest, demonstrate impressive accuracy, ensuring that neuro-oncology practices are more data-driven and tailored to individual patient needs.

00:00

Playback language: English

Index

Abstract

This research utilizes machine learning (ML) models to predict survival outcomes (12, 24, 36, and 60 months) for patients diagnosed with WHO grade II and III gliomas. Data from the National Cancer Database (NCDB) encompassing 10,001 grade II and 11,456 grade III gliomas were analyzed. LightGBM and Random Forest algorithms yielded the best predictive models, achieving AUROC values ranging from 0.813 to 0.896 for grade II and 0.855 to 0.878 for grade III gliomas. These models are integrated into a user-friendly web application for individualized survival predictions, enhanced by SHAP explanations for improved interpretability. The application aims to integrate predictive analytics into neuro-oncology clinical practice, facilitating personalized prognostication and clinical decision-making.

Publisher

npj Digital Medicine

Published On

Oct 26, 2023

Authors

Mert Karabacak, Pemla Jagtiani, Alejandro Carrasquilla, Isabelle M. Germano, Konstantinos Margetis

Related Publications

Explore these studies to deepen your understanding of the subject.

Medicine and Health

Efficacy of early PET-CT directed switch to carboplatin and paclitaxel based definitive chemoradiotherapy in patients with oesophageal cancer who have a poor early response to induction cisplatin and capecitabine in the UK: a multi-centre randomised controlled phase II trial

S. Mukherjee, C. N. Hurt, et al.

Medicine and Health

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

Y. Iwatate, I. Hoshino, et al.

Physics

Ground-based and JWST Observations of SN 2022pul: II. Evidence from Nebular Spectroscopy for a Violent Merger in a Peculiar Type Ia Supernova

Kw, Jo, et al.

Medicine and Health

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

A. Mcdowell, J. Kang, et al.

Listen, Learn & Level Up

Over 10,000 hours of research content in 25+ fields, available in 12+ languages.

No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.

listen to research audio papers with researchbunny