Assessing and Predicting Cutaneous Leiomyoma Severity in Hereditary Leiomyomatosis and Renal Cell Cancer Syndrome: An Observational Study

Published: 19 December 2025| Version 1 | DOI: 10.17632/96y27zs4gw.1
Contributors:
Megha Udupa, Lydia Ouchene, Ethan Bendayan, Lorena Mija, Ahmad Alamari, Tania Cruz Marino, Elham Rahme, Mohammed Kaouache, William Foulkes, Elena Netchiporouk

Description

- Supplementary Methods and STROBE Statement - Table S1. Number of CLs in Association with Age, Sex and GPV type. Negative Binomial Regression Model - Table S2. CL-related Pain in Association with Age, Sex and GPV type. Ordinal Logistic Regression Model - Table S3. CL-related Pain (0 vs >0) in Association with Body Location and Lesion Count. Binary Logistic Regression Model - Table S4. CL-related DLQI (0 vs >0) in Association with Age, Sex, GPV type, Pain, and Lesion Count. Binary Logistic Regression Model - Table S5. Post-hoc Power Analyses for Predictors with Significant Unadjusted P-values

Files

Steps to reproduce

For any reproduction requested, please contact corresponding author. Dr. Elena Netchiporouk, MD, MSc, FRCPC Division of Dermatology, Department of Medicine, McGill University Health Centre | 1650 Ave Cedar, Montréal, Québec H3G 1A4, Canada. | E-mail: elena.netchiporouk@mcgill.ca

Institutions

  • McGill University Health Centre

Categories

Medicine, Genetics, Dermatology

Licence