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 | C. M. Pirkl 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 |
| Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data | A. Ibrahim et al. | Cancers | 16/09/2021 | 10.3390/cancers13184638 |
| Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification | Y. Liu et al. | Diagnostics | 28/09/2021 | 10.3390/diagnostics11101785 |
| Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging | M. Beuque et al. | Computers in Biology and Medicine | 04/10/2021 | 10.1016/j.compbiomed.2021.104918 |
| A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis | C. Yan et al. | European Radiology | 29/11/2021 | 10.1007/s00330-021-08365-z |
| Transparency of deep neural networks for medical image analysis: A review of interpretability methods | Z. Salahuddin et al. | Computers in Biology and Medicine | 04/12/2021 | 10.1016/j.compbiomed.2021.105111 |
| A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images | M. Astaraki et al. | Frontiers in Oncology | 17/12/2021 | 10.3389/fonc.2021.737368 |
| Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability Analysis | Y. Liu et al. | Frontiers in Oncology | 21/12/2021 | 10.3389/fonc.2021.801876 |
| Bridging gaps between images and data: a systematic update on imaging biobanks | M. Gabelloni 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 | M. Interlenghi, C. Salvatore 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 | Y. Nan et al. | Information Fusion | 24/1/2022 | 10.1016/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 Medicine | 28/01/2022 | 10.21037/atm-21-4980 |
| Learning residual motion correction for fast and robust 3D multiparametric MRI | C. M. Pirkel et al. | Medical Image Analysis | 07/02/2022 | 10.1016/j.media.2022.102387 |
| Automatic fine-grained glomerular lesion recognition in kidney pathology | Y. Nan et al. | Pattern Recognition | 12/03/2022 | 10.1016/j.patcog.2022.108648 |
| CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools | L. Martí Bonmatí et al. | Frontiers in Oncology | 24/2/2022 | 10.3389/fonc.2022.742701 |
| AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus | X. Zhou et al. | Neural Computing and Applications | 24/02/2022 | 10.1007/s00521-022-07048-0 |
| A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers | K. Fatania et al. | 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) | 26/04/2022 | 10.1109/ISBI52829.2022.9761603 |
| Quantitative Parameter Mapping of Prostate using Stack-of-Stars and QTI Encoding | R. Schulte et al. | Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2022 | 09/05/2022 | https://index.mirasmart.com/ISMRM2022/PDFfiles/0182.html |
| Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper | L. Martí Bonmatí et al. | Insights into Imaging | 10/05/2022 | 10.1186/s13244-022-01220-9 |
| Discussion Paper: The Integrity of Medical AI | Y. Mirsky | WDC ’22: Proceedings of the 1st Workshop on Security Implications of Deepfakes and Cheapfakes | 30/05/2022 | 10.1145/3494109.3527191 |
| Automated detection and segmentation of non-small cell lung cancer computed tomography images | S. P. Primakov et al. | Nature Communications | 14/06/2022 | 10.1038/s41467-022-30841-3 |
