Prognostic and Prediction Modelling with Radiomics for Non-Small Cell Lung Cancer

Ravindra B. Patil

Research output: ThesisDoctoral ThesisExternal prepared

164 Downloads (Pure)

Abstract

With advancements in Artificial Intelligence (AI) improvements in cancer care can be achieved. In this work, AI models for lung cancer were built to enhance the accuracy and automation of end-to-end clinical decision support systems.
The lung auto-segmentation and deep learning tumour detection model can be used by clinicians to rapidly improve disease diagnosis and treatment in cancer care.
The newly developed radiomic models such as survival models, automatic classification of tumour histopathology and fractal analysis for non-small lung cancer, are currently being verified and validated.
A cloud-based platform for image analytics can help connect experienced radiologists practicing in the large cities to physicians in remote villages and towns. Furthermore, cloud-based clinical decision support systems can empower physicians and healthcare workers in primary care to improve their diagnosis, treatment strategies and throughput.
Original languageEnglish
Awarding Institution
  • Maastricht University
Supervisors/Advisors
  • Dekker, Andre, Supervisor
  • Wee, Leonard, Co-Supervisor
Award date6 Oct 2020
Place of PublicationMaastricht
Publisher
DOIs
Publication statusPublished - 2020

Keywords

  • Radiomics
  • virtual biopsy
  • deep learning
  • auto-segmentation

Cite this