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HEДокторантска мрежа2022–2026

BosomShield · A comprehensive CAD system based on radiologic- and pathologic-image biomarkers for diagnosis and prognosis of breast cancer relapse

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период
2022-09-01 → 2026-08-31
Финансиране от ЕС
2 595 355 €
Участници
25
Схема
HORIZON-TMA-MSCA-DN

Линиите свързват координатора с партньорите.

Накратко на български

Система с изкуствен интелект анализира едновременно рентгенови снимки и тъканни проби, за да открие признаци на рак на гърдата. Това помага на лекарите по-точно да предвидят възможността за повторно развитие на болестта и да персонализират лечението на пациентките.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

A comprehensive CAD system based on radiologic- and pathologic-image biomarkers for diagnosis and prognosis of breast cancer relapse

Breast cancer, as one of the most prevalent cancers affecting women globally, poses significant challenges to public health systems worldwide. Early detection breast cancer relapse and precise prognostic assessment are critical for enhancing patient survival rates and quality of life. Funded by the European Union’s Horizon Europe Programme, the BosomShield project is at the forefront of combating breast cancer by harnessing innovative computational technologies and fostering international collaboration. Main Objective: The core objective of the BosomShield project is to create a sophisticated, cloud-based Computer-Aided Diagnosis (CAD) system that uniquely integrates both histopathological and radiological imaging analyses through the use of advanced artificial intelligence (AI) techniques. This innovative integration aims to enhance the precision of breast cancer detection and the assessment of prognosis, providing clinicians with robust tools to predict breast cancer relapse and personalize treatment strategies for better patient outcomes. Specific Objectives: 1) BosomShield is a pioneer in combining histopathological images obtained from Whole Slide Imaging (WSI) with traditional radiological imaging techniques such as mammography, MRI, and ultrasound. This multimodal approach allows for a more comprehensive analysis of cancer tissues, offering a deeper insight into the disease's characteristics. 2) In response to growing data privacy concerns and stringent regulations, BosomShield employs federated learning. This approach allows the AI models to learn from diverse, decentralized data sources without actual data transfer, ensuring patient confidentiality and data integrity. 3) The CAD system's development is guided by continuous feedback from oncologists and radiologists to ensure its alignment with real-world clinical workflows. The focus is on creating an intuitive user interface that is easy to adopt and facilitates efficient usage in clinical settings. 4) The project not only focuses on technological advancement but also on nurturing talent. Doctoral Candidates involved in BosomShield receive interdisciplinary training, equipping them with the necessary skills to spearhead future breakthroughs in cancer diagnosis, prognosis and treatment. Impact and Vision: BosomShield aims to transform the management of breast cancer care by significantly improving diagnostic accuracy and prognostic evaluations. This project is set to transform treatment decisions, leading to enhanced therapeutic outcomes and improved quality of life for patients. Through its innovative integration of WSI with radiological imaging and its collaborative approach, BosomShield is poised to set new standards in the fight against breast cancer.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Breast cancer (BC) incidence in women produces more than 600,000 deaths each year. The primary cause of death in BC patients is metastasis, whereby cancer cells spread from their primary site of origin and grow in adjacent or distant sites. Distant metastasis produced due to the relapse of the illness is incurable, underscoring the inadequacy of our understanding of its mechanisms. The first step for fighting against disease progression is screening programs for BC focused on image analysis of mammography, MRI and tomosynthesis. Once the tumour has been diagnosed and given the high variability of clinical progressions, another problem arises: classifying the cancer type and determining the proper treatment for specific cancer. Moreover, in BC, the immune response from the tumour microenvironment has played an essential role in tumour evolution. To evaluate the tumour and its microenvironment, one technique garnered a lot of attention in the last years: Whole Slide Imaging (WSI). This technique replaces the use of the microscope for classical diagnosis. Still, it has also been used for developing biomarkers that allow the analysis of tumours and classification of cancer subtypes and the study of the immune tumour microenvironment. The use of WSI has applications for predicting the probability of relapse for distant metastasis. Now, for the first time, BosomShield proposes to join the two disciplines (pathological and radiological imaging) in a software that will analyze these images to classify the cancer subtypes and predict (together with the complete clinical history of the patient) the probability of relapse for distant metastasis. Besides, BosomShield will provide high-level training in BC research to young researchers by offering the necessary transferable skills for thriving careers underpinned using diverse disciplines, digital radiology and pathology, biomedical, AI, privacy and software development.

Оригинален текст от CORDIS (на английски).

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Връзки

Данни: CORDIS, © Европейски съюз