Scientific Publications

We’re making our research findings available free of charge for readers and are providing open access to published papers and reports. The list will be updated as the project progresses.

Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signaturesS. Sanduleanu et al.Radiotherapy and Oncology30/10/202010.1016/j.radonc.2020.10.016
3D PBV-Net: An automated prostate MRI data segmentation methodY. Jin, G. Yang et al.Computers in Biology and Medicine7/12/202010.1016/j.compbiomed.2020.104160
Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in GlioblastomaM. Verduin et al.Cancers10/2/202110.3390/cancers13040722
ME-Net: Multi-encoder net framework for brain tumor segmentationW. Zhang, G. Yang et al.International Journal of Imaging Systems and Technology7/3/202110.1002/ima.22571
Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patientsR. Casale et al.European Journal of Radiology5/4/202110.1016/j.ejrad.2021.109678
Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imagingC. M. Pirkel et al.Neuroradiology9/4/202110.1007/s00234-021-02703-0
Estimaciones de causalidad con imagen médica en oncología/Estimates of Causality with Medical Image in OncologyL.Martí-BonmatiAnales RANM22/4/202110.32440/ar.2021.138.01.rev02
MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter StudyR. W. Y. Granzier et al.Cancers18/5/202110.3390/cancers13102447
A Deep Multi-Task Learning Framework for Brain Tumor SegmentationH. Huang et al.Frontiers in Oncology4/6/202110.3389/fonc.2021.690244
A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography ImagesS. A. Keek et al. Cancers 29/6/202110.3390/cancers13133271
A deep look into radiomicsC. Scapicchio et al.La radiologia medica2/7/202110.1007/s11547-021-01389-x
Residual learning for 3D motion corrected quantitative MRI:
Robust clinical T1, T2 and proton density mapping
Pirkl, C. M. et al.Proceedings of Machine Learning Research9/7/
An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognosticationO. Morin et al.Nature Cancer22/7/202110.1038/s43018-021-00236-2
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization MethodsS. A. Mali et al.Journal of Personalised Medicine27/8/202110.3390/jpm11090842
A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosisC. Yan et al.European Radiology29/11/202110.1007/s00330-021-08365-z
Bridging gaps between images and data: a systematic update on imaging biobanksGabelloni, M. et al.European Radiology10/1/202210.1007/s00330-021-08431-6
A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast MassesInterlenghi, M, Salvatore C. et al.Diagnostics13/1/202210.3390/diagnostics12010187
Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directionsNan, Y. et al.Information Fusion24/1/202210.1016/j.inffus.2022.01.001
CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management ToolsMartí Bonmatí, L. et al.Frontiers in Oncology24/2/202210.3389/fonc.2022.742701