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
C. M. Pirkl 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
Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial DataA. Ibrahim et al.Cancers16/09/202110.3390/cancers13184638
Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based ClassificationY. Liu et al.Diagnostics28/09/202110.3390/diagnostics11101785
Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imagingM. Beuque et al.Computers in Biology and Medicine04/10/202110.1016/j.compbiomed.2021.104918
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
Transparency of deep neural networks for medical image analysis: A review of interpretability methodsZ. Salahuddin et al.Computers in Biology and Medicine04/12/202110.1016/j.compbiomed.2021.105111
A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT ImagesM. Astaraki et al.Frontiers in Oncology17/12/202110.3389/fonc.2021.737368
Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability AnalysisY. Liu et al.Frontiers in Oncology21/12/202110.3389/fonc.2021.801876
Bridging gaps between images and data: a systematic update on imaging biobanksM. Gabelloni 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 MassesM. Interlenghi, C. Salvatore 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 directionsY. Nan et al.Information Fusion24/1/202210.1016/j.inffus.2022.01.001
Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients.C. Yan et al.Annals of Translational Medicine28/01/202210.21037/atm-21-4980
Learning residual motion correction for fast and robust 3D multiparametric MRIC. M. Pirkel et al.Medical Image Analysis07/02/202210.1016/
Automatic fine-grained glomerular lesion recognition in kidney pathologyY. Nan et al.Pattern Recognition12/03/202210.1016/j.patcog.2022.108648
CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management ToolsL. Martí Bonmatí et al.Frontiers in Oncology24/2/202210.3389/fonc.2022.742701
AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalusX. Zhou et al.Neural Computing and Applications24/02/202210.1007/s00521-022-07048-0
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep DenoisersK. Fatania et al.2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)26/04/202210.1109/ISBI52829.2022.9761603
Quantitative Parameter Mapping of Prostate using Stack-of-Stars and QTI EncodingR. Schulte et al.Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 202209/05/2022
Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paperL. Martí Bonmatí et al.Insights into Imaging10/05/202210.1186/s13244-022-01220-9
Discussion Paper: The Integrity of Medical AIY. MirskyWDC ’22: Proceedings of the 1st Workshop on Security Implications of Deepfakes and Cheapfakes30/05/202210.1145/3494109.3527191
Automated detection and segmentation of non-small cell lung cancer computed tomography imagesS. P. Primakov et al.Nature Communications14/06/202210.1038/s41467-022-30841-3