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.
| Title | Author | Journal | Date | DOI |
| Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures | S. Sanduleanu et al. | Radiotherapy and Oncology | 30/10/2020 | 10.1016/j.radonc.2020.10.016 |
| 3D PBV-Net: An automated prostate MRI data segmentation method | Y. Jin, G. Yang et al. | Computers in Biology and Medicine | 7/12/2020 | 10.1016/j.compbiomed.2020.104160 |
| Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma | M. Verduin et al. | Cancers | 10/2/2021 | 10.3390/cancers13040722 |
| ME-Net: Multi-encoder net framework for brain tumor segmentation | W. Zhang, G. Yang et al. | International Journal of Imaging Systems and Technology | 7/3/2021 | 10.1002/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 patients | R. Casale et al. | European Journal of Radiology | 5/4/2021 | 10.1016/j.ejrad.2021.109678 |
| Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging | C. M. Pirkel et al. | Neuroradiology | 9/4/2021 | 10.1007/s00234-021-02703-0 |
| Estimaciones de causalidad con imagen médica en oncología/Estimates of Causality with Medical Image in Oncology | L.Martí-Bonmati | Anales RANM | 22/4/2021 | 10.32440/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 Study | R. W. Y. Granzier et al. | Cancers | 18/5/2021 | 10.3390/cancers13102447 |
| A Deep Multi-Task Learning Framework for Brain Tumor Segmentation | H. Huang et al. | Frontiers in Oncology | 4/6/2021 | 10.3389/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 Images | S. A. Keek et al. | Cancers | 29/6/2021 | 10.3390/cancers13133271 |
| A deep look into radiomics | C. Scapicchio et al. | La radiologia medica | 2/7/2021 | 10.1007/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 Research | 9/7/2021 | 2021.midl.io/proceedings/pirkl21 |
| An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication | O. Morin et al. | Nature Cancer | 22/7/2021 | 10.1038/s43018-021-00236-2 |
| Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods | S. A. Mali et al. | Journal of Personalised Medicine | 27/8/2021 | 10.3390/jpm11090842 |
| A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis | C. Yan et al. | European Radiology | 29/11/2021 | 10.1007/s00330-021-08365-z |
| Bridging gaps between images and data: a systematic update on imaging biobanks | Gabelloni, M. et al. | European Radiology | 10/1/2022 | 10.1007/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 Masses | Interlenghi, M, Salvatore C. et al. | Diagnostics | 13/1/2022 | 10.3390/diagnostics12010187 |
| Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions | Nan, Y. et al. | Information Fusion | 24/1/2022 | 10.1016/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 Tools | Martí Bonmatí, L. et al. | Frontiers in Oncology | 24/2/2022 | 10.3389/fonc.2022.742701 |
