A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters

Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)

Jerome H. Friedman, Trevor Hastie, Robert Tibshirani · The Annals of Statistics · 2000 · 7K citations · Full text

Computing, Computer Science, Mathematics +16

High cumulative incidence of uterine leiomyoma in black and white women: Ultrasound evidence

Donna D. Baird, David B. Dunson, Michael C. Hill et al. · American Journal of Obstetrics and Gynecology · 2003 · 2.4K citations

Medicine, Gynecology, Obstetrics +11

Surveillance, Epidemiology, and End Results analysis of 2677 cases of uterine sarcoma 1989–1999

Sandra E. Brooks, Min Zhan, Timothy R. Coté et al. · Gynecologic Oncology · 2004 · 522 citations

Medicine, Oncology, Surgery +10

REGRESSION TREES WITH UNBIASED VARIABLE SELECTION AND INTERACTION DETECTION

Wei‐Yin Loh · 2002 · 372 citations

Computing, Biostatistics, Data Science +10

The utility of diffusion-weighted MR imaging for differentiating uterine sarcomas from benign leiomyomas

Ken Tamai, Takashi Koyama, Tsuneo Saga et al. · European Radiology · 2007 · 308 citations · Full text

Medicine, Gynecology, Radiology +16