Descripción del proyecto
Mejorar los resultados del cáncer de pulmón mediante un enfoque de caracterización automatizada del tumor
Un diagnóstico rápido y preciso resulta esencial a fin de ofrecer un tratamiento eficaz para todos los tipos de cáncer. Lamentablemente, la detección avanzada, la segmentación y la caracterización de los tumores dependen de los laboriosos procesos manuales o semimanuales utilizados, lo que limita la precisión del tratamiento y la supervisión de la respuesta al tratamiento. El proyecto AUTO.DISTINCT financiado con fondos europeos, presentará, demostrará y evaluará un revolucionario «software» totalmente automatizado para una segmentación volumétrica y una detección reproducible, independiente del observador, precisa y rápida de los tumores de pulmón y las metástasis en imágenes de tomografía computarizada. La labor del proyecto mejorará drásticamente la caracterización del tumor en el caso de los pacientes con cáncer de pulmón, ya que ajusta la detección de lesiones en imágenes de tomografía computarizada, con un impacto significativo en los resultados del paciente y en la precisión del tratamiento de radioterapia.
Objetivo
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.
Ámbito científico
Programa(s)
Régimen de financiación
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotInstitución de acogida
6200 MD Maastricht
Países Bajos