Development and internal validation of machine-learning models to predict adverse pathological features in upper tract urothelial carcinoma
Year: 2026
Type: article
Authors: Philipp Korn, Maximilian Pallauf, Soum Lokeshwar, Giacomo Musso, Zhenje Wu, Linhui Wang, Alex Kutikov, Randall Lee, Francesco Ditonno, Alessandro Veccia, Riccardo Bertolo, Alessandro Antonelli, Lin Lin, Vitaly Margulis, Alireza Ghoreifi, Hooman Ebrahimi, Hooman Djaladat, Hidefumi Kinoshita, Takashi Yoshida, Gabriele Tuderti, Flavia Proietti, Giuseppe Simone, Andrea Mari, Luca Lambertini, Andrea Minervini, Marco Tozzi, Matteo Ferro, Roberto Contieri, Sisto Perdona, Daniele Amparore, Gabriele Bignante, Francesco Porpiglia, Omri Falik Nativ, Mark L. Gonzalgo, Alfonso Santangelo, Carlo Silvani, Alex Stephens, Firas Abdollah, Melinda Fu, Saum Ghodoussipour, Alec Zhu, Reza Mehrazin, Daniel A. Sidhom, Chandru P. Sundaram, Takashi Matsumoto, Masaki Shiota, Alireza Dehghanmanshadi, Soroush Rais-Bahrami, Karim Daher, Ithar H. Derweesh, Riccardo Autorino, Nirmish Singla
Institutions: Johns Hopkins University, University of Augsburg, Johns Hopkins Medicine, Johns Hopkins Hospital, Paracelsus Medical University, Rush University, Changhai Hospital, University of Verona, The University of Texas Southwestern Medical Center, University of Southern California, USC Norris Comprehensive Cancer Center, Kansai Medical University, Instituto Nazionale Tumori Regina Elena, Azienda Ospedaliero-Universitaria Careggi, University of Florence, European Institute of Oncology, Ospedale San Luigi Gonzaga, University of Turin, University of Miami, Henry Ford Health, Henry Ford Hospital, Rutgers Cancer Institute, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, Indiana University Indianapolis, Kyushu University, University of Alabama, University of Alabama at Birmingham, UAB Medicine, Cleveland Clinic, Glickman Urological Institute, Medical University of Vienna
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: closed
Awards: G2703437604, G2753251414
OpenAlex ID: W7213293133