Project description
Improving lung cancer outcomes via an automated tumour characterisation approach
Fast and accurate diagnosis is crucial for the effective treatment of all types of cancer. Unfortunately, the advanced detection, segmentation and characterisation of tumours hinges on the laborious manual or semi-manual processes used, restricting treatment accuracy and treatment response monitoring. The EU-funded AUTO.DISTINCT project will introduce, demonstrate and evaluate a groundbreaking, fully automated software for the fast, accurate, observer-independent and reproducible detection and volumetric segmentation of lung tumours and metastases on CT images. The project's work will radically improve tumour characterisation in the case of lung cancer patients by refining the detection of lesions on CT images, with significant impacts on patient outcomes and radiotherapy treatment accuracy.
Objective
The inaccuracy and inconsistency of state-of-the-art tumour volume detection and segmentation has an adverse influence on patient outcomes. Accurately determining the exact location and volume of tumours is a prerequisite for the detection, segmentation, characterisation and therapy response monitoring for any type of cancer. Today, tumour segmentation is performed manually or semi-automatically in a laborious and time-consuming process that exhibits low accuracy and inconsistency. This compromises quality of care by limiting the certainty of lesion detection on medical images, hindering the effectivity of radiotherapy and restricting the accuracy of treatment response monitoring.
In this ERC PoC project, we introduce fully automated software for fast, accurate, observer independent and reproducible detection and volumetric segmentation of (lung) tumours and metastases on CT images. Through a unique three-step approach, our software demonstrates superior speed, accuracy and robustness of tumour segmentation over both the state-of-the-art as well as published competing solutions for automated tumour segmentation. Hence, our software has the potential to drastically reduce the adverse impact that inaccurate tumour detection and segmentation currently has on (lung) cancer patient outcomes by: improving the detection of lesions on CT images, increasing the accuracy of radiotherapy treatment to reduce the occurrence of geometric misses, and advance the evaluation of tumour response to treatments through volumetric treatment monitoring.
In AUTO.DISTINCT we will provide technical and commercial proof-of-concept for our novel software. We will solve the remaining technical challenges and develop a user-friendly prototype that can be validated with end users. Moreover, we will develop a business strategy that incorporates all technical, commercial, IPR and regulatory aspects of our invention to ensure successful commercialisation.
Fields of science
Programme(s)
Funding Scheme
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotHost institution
6200 MD Maastricht
Netherlands