Descrizione del progetto
Migliorare gli esiti del cancro del polmone tramite un approccio automatizzato di caratterizzazione del tumore
Una diagnosi rapida e accurata è fondamentale per il trattamento efficace di tutti i tipi di cancro. Sfortunatamente, il rilevamento, la segmentazione e la caratterizzazione avanzati dei tumori dipendono dai laboriosi processi manuali o semi-manuali utilizzati, il che limita l’accuratezza del trattamento e il monitoraggio della risposta al trattamento. Il progetto AUTO.DISTINCT finanziato dall’UE, introdurrà, dimostrerà e valuterà un software rivoluzionario e completamente automatizzato per il rilevamento rapido, accurato, riproducibile e indipendente dall’osservatore e la segmentazione volumetrica di tumori polmonari e metastasi su TC. Il lavoro del progetto migliorerà radicalmente la caratterizzazione del tumore nel caso di pazienti affetti da cancro del polmone, perfezionando il rilevamento delle lesioni sulle TC, con impatti notevoli sui risultati dei pazienti e sull’accuratezza del trattamento radioterapico.
Obiettivo
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.
Campo scientifico
Programma(i)
Meccanismo di finanziamento
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotIstituzione ospitante
6200 MD Maastricht
Paesi Bassi