Update README.md

Author: rcuocolo

README.md

@@ -36,18 +36,18 @@ MRI radiomics and machine learning model to predict risk group in endometrial ca
 
 ## Select Publications
 
-1. 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](https://doi.org/10.1007/s00330-022-09187-3)
-2. 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](https://doi.org/10.1016/j.ebiom.2021.103757)
-3. 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](https://doi.org/10.1007/s00330-021-08009-2)
-4. 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](https://doi.org/10.1007/s00330-021-07856-3)
-5. 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](https://doi.org/10.1002/jmri.27585)
-6. 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](https://doi.org/10.1007/s00330-020-07027-w)
+1. 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](https://doi.org/10.1186/s13244-023-01415-8)
+2. 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](https://doi.org/10.1007/s00330-022-09187-3)
+3. 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](https://doi.org/10.1016/j.ebiom.2021.103757)
+4. 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](https://doi.org/10.1007/s00330-021-08009-2)
+5. 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](https://doi.org/10.1007/s00330-021-07856-3)
+6. 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](https://doi.org/10.1002/jmri.27585)
+7. 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](https://doi.org/10.1007/s00330-020-07027-w)
 8. Stanzione A, Gambardella M, Cuocolo R, et al (2020). Prostate MRI radiomics: A systematic review and radiomic quality score assessment. Eur J Radiol. [https://doi.org/10.1016/j.ejrad.2020.109095](https://doi.org/10.1016/j.ejrad.2020.109095)
-8. 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](https://doi.org/10.1016/j.canlet.2020.03.032)
 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](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](https://doi.org/10.1148/radiol.2017172454)
 
-## Social media and other profile links
+## Social media and other links
 
 - [Twitter](https://twitter.com/renatocuocolo)
 - [ResearchGate](https://www.researchgate.net/profile/Renato_Cuocolo)