Descrizione del progetto
Test di screening di precisione per il carcinoma mammario
La mammografia digitale è lo standard di riferimento stabilito per il rilevamento del cancro al seno, ma ha comunque i suoi limiti: sotto-diagnosi e sovra-diagnosi (falsi positivi). La tomosintesi mammaria digitale (DBT, Digital Breast Tomosynthesis) è stata progressivamente adottata nello screening del cancro. Il progetto SIMULTANEOUS DBTMI combina DBT e imaging meccanico (MI, Mechanical Imaging) con l’intelligenza artificiale (IA) per costruire un sistema prototipo con la massima qualità d’immagine. Il prototipo sarà valutato preclinicamente attraverso sperimentazioni cliniche virtuali e phantom fisici, seguiti da una sperimentazione pilota co-clinica (che combina dati clinici e simulati). Il progetto mira inoltre a introdurre metodi di intelligenza artificiale, sotto forma di reti di apprendimento profondo (DLN, Deep Learning Networks), per scoprire correlazioni tra caratteristiche sconosciute e migliorare le prestazioni DBTMI.
Obiettivo
This MSCA is designed to support Dr. Predrag Bakic in his professional development and reintegration into European research community, after he obtained Ph.D. and spent 12 years as a faculty in USA. The host institution, Lund University (LU), is one of the largest in Sweden and among the most prestigious in Europe. Dr. Bakic and his LU supervisors, Dr. Sophia Zackrisson and Dr. Anders Tingberg, share the research focus in breast imaging, with unique complementary expertise: Dr. Bakic in Virtual Clinical Trials (VCTs) based upon the simulation of breast anatomy and imaging systems, and LU team in Mechanical Imaging (MI) and conducting clinical trials of breast imaging.
Our action is motivated by a persistent challenge of underdiagnosis and false positives in breast cancer healthcare. The four most exciting innovations in breast cancer imaging that have recently emerged include: Digital Breast Tomosynthesis (DBT), MI, VCTs, and artificial intelligence (AI). In this application we will utilize extensive experience of LU and Dr. Bakic to interconnect these innovations efficiently and flexibly, enabling significant benefits. Within the two-year timeline, we will design and build a simultaneous DBT and MI (termed DBTMI) prototype system, and develop image processing and DBT reconstruction to maximize image quality. We will evaluate the prototype, first preclinically by VCTs and physical phantoms, followed by a pilot co-clinical trial (which combines clinical and simulated data). We will also explore introducing modern AI methods, in the form of Deep Learning Networks (DLN) to improve DBTMI performance. DLN has demonstrated ability to discover complex correlations in clinical images, leading to superior detection and classification of clinical findings. Combined complementary experience, carefully designed knowledge-exchange activities, and LU excellent institutional resources, guarantee the success of this application, and Dr. Bakic's successful reintegration.
Campo scientifico
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Meccanismo di finanziamento
MSCA-IF-EF-RI - RI – Reintegration panelCoordinatore
22100 Lund
Svezia