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

MSCA (Marie Skłodowska-Curie)HORIZON-TMA-MSCA-DNID: 101073222
EC Contribution
€25,954
Consortium Size
25 orgs
Start Year
2022
Summary

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.

Consortium (25)

Project Results (27)

Source: CORDIS, the EU research results database.

Publications (21)
Automated Radiomics Analysis from Multi-Modal Image Segmentation for Predicting Triple Negative Breast Cancer
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)· 2025DOI
Tewele W. Tareke, Neree Payan, Alexandre Cochet, Yaqeen Ali, Laurent Arnould, Benoît Presles, Jean-Marc Vrigneaud, Fabrice Meriaudeau, Alain Lalande
Deep Learning-Driven Radiomic Feature Extraction for Predicting Complete Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer from <sup>18</sup> F
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)· 2025DOI
Tewele W. Tareke, Neree Payan, Alexandre Cochet, Laurent Arnould, Benoît Presles, Jean-Marc Vrigneaud, Soumya Ghose, Fabrice Meriaudeau, Alain Lalande
Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models
Lecture Notes in Computer Science, Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care· 2025DOI
Zhikai Yang, Mehdi Astaraki, Örjan Smedby, Rodrigo Moreno
Fine-tuning Specialized NER Model for Symptom Extraction from Slovenian Medical Texts
2024 9th International Conference on Mathematics and Computers in Sciences and Industry (MCSI)· 2025DOI
Rigon Sallauka, Umut Arioz, Izidor Mlakar
Leveraging MRI Radiomics and Machine Learning for Accurate Differentiation of Triple-Negative Breast Cancer Subtype
2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)· 2025DOI
Yaqeen Ali, Johannes Gregori, Tewele W. Tareke, Alain Lalande, Fabrice Meriaudeau
MM + CD Fusion: Deep Learning-based 3D Multi-Modal Fusion for Early Pathological Complete Response Prediction in Breast Cancer
Lecture Notes in Computer Science, Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care· 2025DOI
Tewele W. Tareke, Neree Payan, Alexandre Cochet, Alain Lalande, Fabrice Meriaudeau
Reconstruction of Breast Spectral CT with Multi-Material Decomposition using a Two-Stage Learned Primal-Dual Neural Network
Fully3D conference 2025· 2025
Zhikai Yang, Ruihan Huang, Örjan Smedby, and Rodrigo Moreno
Retrospective Case‐Cohort Study on Risk Factors for Developing Distant Metastases in Women With Breast Cancer
Cancer Medicine· 2025DOI
Serena Bertozzi, Ambrogio Pietro Londero, Giovanni Vendramelli, Maria Orsaria, Laura Mariuzzi, Enrico Pegolo, Carla Di Loreto, Carla Cedolini, Vincenzo Della Mea
Two-stage convolutional neural network for breast CT reconstruction
Medical Imaging 2025: Physics of Medical Imaging· 2025DOI
Zhikai Yang, Yihan Xiao, Ozan Öktem, Örjan Smedby, Rodrigo Moreno
Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
Applied Sciences· 2025DOI
Rigon Sallauka, Umut Arioz, Matej Rojc, Izidor Mlakar
3D breast ultrasound image classification using 2.5D deep learning
17th International Workshop on Breast Imaging (IWBI 2024)· 2024DOI
Zhikai Yang, Tianyu Fan, Örjan Smedby, Rodrigo Moreno
Comparison of lesion segmentation methodsusing simulated DBT images
Proceedings Virtual Imaging Trials in Medicine 2024· 2024DOI
Zhikai Yang, Hanna Tomic, Victor Dahlblom, Sophia Zackrisson, Anders Tingberg, Magnus Dustler, Örjan Smedby, Rodrigo Moreno and Predrag Bakic
Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor‑Infiltrating Lymphocyte Datasets
Journal of Imaging Informatics in Medicine· 2024DOI
Lesion localization in digital breast tomosynthesis with deformable transformers by using 2.5D information
Medical Imaging 2024: Computer-Aided Diagnosis· 2024DOI
Zhikai Yang, Tianyu Fan, Örjan Smedby, Rodrigo Moreno
Memory Efficient Two-Stage Diffusion Model in Synthetic Breast Image Generation
European Conference of Radiology· 2024DOI
Zhikai Yang, Örjan Smedby, Rodrigo Moreno
Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification
Journal of Imaging· 2024DOI
Giulia Lucrezia Baroni, Laura Rasotto, Kevin Roitero, Angelica Tulisso, Carla Di Loreto, Vincenzo Della Mea
Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
Medical Imaging 2024: Physics of Medical Imaging· 2024DOI
Hanna Tomic, Zhikai Yang, Anders Tingberg, Sophia Zackrisson, Rodrigo Moreno, Örjan Smedby, Magnus Dustler, Predrag Bakic
Vision Transformers for Breast Cancer Classification
ECDP2024· 2024
G.L. Baroni, L. Rasotto, K. Roitero, A. Tulisso, M. Orsaria, C. Di Loreto, V. Della Mea
Vision Transformers for Breast Cancer Histology Image Classification
Lecture Notes in Computer Science· 2024DOI
Baroni, G.L., Rasotto, L., Roitero, K., Siraj, A.H., Della Mea, V
Physics-Informed Neural Network for T2 and M0 Map Estimation
ESMRMB Annual Scientific Meeting 2023· 2023
Zhikai Yang, Lorenzo Branca, Rodrigo Moreno
The BosomShield project: an integrative approach to diagnosis and prognosis of breast cancer relapse based on radiologic / pathologic image biomarkers
ECDP2023 19thEuropean Congress on Digital Pathology· 2023
Hatem A. Rashwan, Vincenzo Della Mea, Rodrigo Moreno, Ioannis Sechopoulos, Carlos López, Anna Korzyńska, Alain Lalande, Izidor Mlakar, Zouhair Haddi, Johannes Gregori, Domenec Puig
Deliverables (5)
Other Results (1)
Periodic Reporting for period 1 - BosomShield (A comprehensive CAD system based on radiologic- and pathologic-image biomarkers for diagnosis and prognosis of breast cancer relapse)