Raquel Tendero

EDUCATION

Degree in Data Science, Universitat Politècnica de València (UPV), Spain
MSc in Artificial Intelligence, Universidad Alfonso X el Sabio (UAX), Spain
PhD Candidate in Computer Science, Universitat Politècnica de València (UPV), Spain

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence

Scopus Publications

Breast cancer risk assessment for screening: a hybrid artificial intelligence approach
Raquel Tendero, Andrés Larroza, Francisco Javier Pérez-Benito, Juan Carlos Perez-Cortes, Marta Román, Rafael Llobet
European Radiology, 2026
Objectives This study evaluates whether integrating clinical data with mammographic features using artificial intelligence (AI) improves 2-year breast cancer risk prediction compared to using either data type alone. Materials and methods This retrospective nested case-control study included 2193 women (mean age, 59 ± 5 years) screened at Hospital del Mar, Spain (2013–2020), with 418 cases (mammograms taken 2 years before diagnosis) and 1775 controls (cancer-free for ≥ 2 years). Three models were evaluated: (1) ERTpd + im, based on Extremely Randomized Trees (ERT), split into sub-models for personal data (ERTpd) and image features (ERTim); (2) an image-only model (CNN); and (3) a hybrid model (ERTpd + im + CNN). Five-fold cross-validation, area under the receiver operating characteristic curve (AUC), bootstrapping for confidence intervals, and DeLong tests for paired data assessed performance. Robustness was evaluated across breast density quartiles and detection type (screen-detected vs. interval cancers). Results The hybrid model achieved an AUC of 0.75 (95% CI: 0.71–0.76), significantly outperforming the CNN model (AUC, 0.74; 95% CI: 0.70–0.75; p < 0.05) and slightly surpassing ERTpd + im (AUC, 0.74; 95% CI: 0.70–0.76). Sub-models ERTpd and ERTim had AUCs of 0.59 and 0.73, respectively. The hybrid model performed consistently across breast density quartiles (p > 0.05) and better for screen-detected (AUC, 0.79) than interval cancers (AUC, 0.59; p < 0.001). Conclusions This study shows that integrating clinical and mammographic data with AI improves 2-year breast cancer risk prediction, outperforming single-source models. The hybrid model demonstrated higher accuracy and robustness across breast density quartiles, with better performance for screen-detected cancers. Key Points Question Current breast cancer risk models have limitations in accuracy. Can integrating clinical and mammographic data using artificial intelligence (AI) improve short-term risk prediction? Findings A hybrid model combining clinical and imaging data achieved the highest accuracy in predicting 2-year breast cancer risk, outperforming models using either data type alone. Clinical relevance Integrating clinical and mammographic data with AI improves breast cancer risk prediction. This approach enables personalized screening strategies and supports early detection. It helps identify high-risk women and optimizes the use of additional assessments within screening programs. Graphical Abstract
Three-Blind Validation Strategy of Deep Learning Models for Image Segmentation
Andrés Larroza, Francisco Javier Pérez-Benito, Raquel Tendero, Juan Carlos Perez-Cortes, Marta Román, Rafael Llobet
Journal of Imaging, 2025
Image segmentation plays a central role in computer vision applications such as medical imaging, industrial inspection, and environmental monitoring. However, evaluating segmentation performance can be particularly challenging when ground truth is not clearly defined, as is often the case in tasks involving subjective interpretation. These challenges are amplified by inter- and intra-observer variability, which complicates the use of human annotations as a reliable reference. To address this, we propose a novel validation framework—referred to as the three-blind validation strategy—that enables rigorous assessment of segmentation models in contexts where subjectivity and label variability are significant. The core idea is to have a third independent expert, blind to the labeler identities, assess a shuffled set of segmentations produced by multiple human annotators and/or automated models. This allows for the unbiased evaluation of model performance and helps uncover patterns of disagreement that may indicate systematic issues with either human or machine annotations. The primary objective of this study is to introduce and demonstrate this validation strategy as a generalizable framework for robust model evaluation in subjective segmentation tasks. We illustrate its practical implementation in a mammography use case involving dense tissue segmentation while emphasizing its potential applicability to a broad range of segmentation scenarios.
Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model
Andrés Larroza, Francisco Javier Pérez-Benito, Raquel Tendero, Juan Carlos Perez-Cortes, Marta Román, Rafael Llobet
Diagnostics, 2024
Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method’s robustness, flexibility, and generalization capabilities.