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Bio

Renato Cuocolo obtained his medical degree from the University of Campania “Luigi Vanvitelli” in 2011, completed his residency in Radiology at the University of Naples “Federico II” in 2017, and his PhD in Biomorphological and Surgical Sciences at the Department of Advanced Biomedical Sciences, University of Naples “Federico II”, in 2020. He is currently an Associate Professor at the Department of Medicine, Surgery, and Dentistry "Scuola Medica Salernitana" of the University of Salerno in Italy. Renato Cuocolo serves as a member of the European Society of Radiology (ESR) eHealth and Informatics Subcommittee, of the European Society of Medical Imaging Informatics (EuSoMII) Board, Young Club (as vice-Chair) and Scientific Committees and European Society of Urogenital Radiology (ESUR) Junior Network Committee. He is a researcher at the Augmented Reality for Health Monitoring Laboratory (ARHeMLab) of the University of Naples "Federico II" Department of Electrical Engineering and Information Technology. Renato Cuocolo has also participated in the European Network for Assessment of Imaging in Medicine (EuroAIM), a joint initiative of the European Institute for Biomedical Imaging Research (EIBIR). His research interests include radiomics and machine learning applications in medical imaging, especially musculoskeletal and genitourinary radiology. Editorial Board member for European Radiology, European Radiology Experimental, and European Journal of Radiology.

Qualifications

Research Interests

  • Medical image analysis using machine learning and radiomics
  • Cancer imaging, with a focus on prostate MRI
  • Musculoskeletal imaging
  • Neuroradiology
  • Systematic reviews and meta-analyses

Coding

  • Python
  • Bash
  • R

Repositories

  • PROSTATEx lesion masks
    Results of a quality check on the PROSTATEx public prostate cancer MRI database. The repository contains high-quality lesion masks annotated by expert radiologists in consensus. Its aim is to improve the quality of the dataset and of research conducted on it and provide a template for similar efforts in other public medical imaging datasets (Project Page)
  • Cartilaginous tumor CT classification model
    Model data developed in WEKA for the classification of cartilaginous tumors on CT images. Includes all necessary files and information to reproduce the entire pipeline. (Project Page)
  • Cartilaginous tumor MRI classification model
    MRI radiomics and machine learning model to classify atypical cartilaginous tumor and G2 chondrosarcoma. Includes all necessary files and information to reproduce the entire pipeline. (Project Page)
  • Endometrial cancer MRI classification model
    MRI radiomics and machine learning model to predict risk group in endometrial cancer patients. Includes all necessary files and information to reproduce the entire pipeline. (Project Page)

Select Publications

  1. Kocak B, Akinci D’Antonoli T, Mercaldo N, et al (2024) METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 15:8. https://doi.org/10.1186/s13244-023-01572-w
  2. Kocak B, Baessler B, Bakas S, et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14:75. https://doi.org/10.1186/s13244-023-01415-8
  3. Spadarella G, Stanzione A, Akinci D’Antonoli T, et al (2023) Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol. https://doi.org/10.1007/s00330-022-09187-3
  4. Gitto S, Cuocolo R, van Langevelde K, et al (2022) MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones. EBioMedicine. https://doi.org/10.1016/j.ebiom.2021.103757
  5. Romeo V, Cuocolo R, Apolito R, et al (2021) Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions. Eur Radiol. https://doi.org/10.1007/s00330-021-08009-2
  6. Cuocolo R, Stanzione A, Faletti R, et al (2021) MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol. https://doi.org/10.1007/s00330-021-07856-3
  7. Cuocolo R, Comelli A, Stefano A, et al (2021) Deep Learning Whole‐Gland and Zonal Prostate Segmentation on a Public MRI Dataset. J Magn Reson Imaging. https://doi.org/10.1002/jmri.27585
  8. Cuocolo R, Cipullo MB, Stanzione A, et al (2020). Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol. https://doi.org/10.1007/s00330-020-07027-w
  9. Cuocolo R, Caruso M, Perillo T, et al (2020) Machine Learning in Oncology: A Clinical Appraisal. Cancer Lett. https://doi.org/10.1016/j.canlet.2020.03.032
  10. Imbriaco M, Cuocolo R (2018). Does Texture Analysis of MR Images of Breast Tumors Help Predict Response to Treatment? Radiology. https://doi.org/10.1148/radiol.2017172454

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