An approach for cancer outcomes modelling using a comprehensive synthetic dataset

Limited patient data availability presents a challenge for efficient machine learning (ML) model development. Recent studies have proposed methods to generate synthetic medical images but lack the corresponding prognostic information required for predicting outcomes. We present a cancer outcomes mod...

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Bibliographic Details
Published in:	Physical and engineering sciences in medicine Vol. 48; no. 3; pp. 1473 - 1483
Main Authors:	Tu, Lorna, Choi, Hervé H. F., Clark, Haley, Lloyd, Samantha A. M.
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.09.2025
Subjects:	Aged AI Notes Algorithms Biological and Medical Physics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Carcinoma, Non-Small-Cell Lung - diagnostic imaging Female Humans Lung Neoplasms - diagnostic imaging Machine Learning Male Medical and Radiation Physics Middle Aged Prognosis Support Vector Machine Tomography, X-Ray Computed Treatment Outcome Data augmentation Overall survival Outcomes prediction Non-small cell lung cancer Synthetic data Radiomics
ISSN:	2662-4729, 2662-4737, 2662-4737
Online Access:	Get full text
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