Kiarash Khosrotehrani1, Augustinus P T van der Ploeg2, Victor Siskind3, Maria Celia Hughes3, Annaliesa Wright4, Janine Thomas5, Andrew Barbour5, Christopher Allan5, Gerard Bayley5, Alexander Eggermont6, Cornelis Verhoef2, B Mark Smithers5, Adele C Green7. 1. The University of Queensland, UQ Centre for Clinical Research, Experimental Dermatology Group, Brisbane, QLD, Australia; The University of Queensland, UQ Diamantina Institute, Translational Research Institute, Woolloongabba, QLD, Australia. Electronic address: k.khosrotehrani@uq.edu.au. 2. Erasmus MC Cancer Institute, Rotterdam, The Netherlands. 3. QIMR Berghofer Medical Research Institute, Cancer and Population Studies Group, Brisbane, Australia. 4. The University of Queensland, UQ Centre for Clinical Research, Experimental Dermatology Group, Brisbane, QLD, Australia. 5. Queensland Melanoma Project, Discipline of Surgery, The University of Queensland, Princess Alexandra Hospital, Woolloongabba, QLD, Australia. 6. Erasmus MC Cancer Institute, Rotterdam, The Netherlands; Institut de Cancérologie Gustave Roussy, Villejuif-Paris, France. 7. QIMR Berghofer Medical Research Institute, Cancer and Population Studies Group, Brisbane, Australia; Institute of Inflammation and Repair, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.
Abstract
BACKGROUND: Current staging algorithms in melanoma patients undergoing therapeutic lymph node dissection (LND) fail to accurately distinguish long-term survivors from those at risk of rapid relapse. Our goal was to establish and validate nomograms for predicting both recurrence and survival after LND. METHODS: A prospective cohort of stage IIIB and IIIC melanoma patients was ascertained from a tertiary hospital in Brisbane, Australia. Failure-time multivariate analysis identified key factors that, in adjusted combinations, generated nomograms to predict 2-year recurrence and 5-year melanoma-specific survival. The predictive value of these nomograms was further validated in a patient cohort from Rotterdam, The Netherlands. RESULTS: In 494 Australian patients, number of positive lymph nodes, extra-capsular extension and nodular histopathological subtype were the main independent predictors of 2-year recurrence while age, number of positive nodes and extra-capsular extension were the independent predictors of survival. Predictive value was confirmed in The Netherlands cohort of 331 patients. The nomograms were able to classify patients according to their 2-year recurrence and 5-year survival rates even within each stage III sub-class. CONCLUSIONS: Models that include extra-capsular extension predict outcomes in patients with clinically involved lymph nodes. This tool may help tailor treatment and monitoring of this group of patients.
BACKGROUND: Current staging algorithms in melanomapatients undergoing therapeutic lymph node dissection (LND) fail to accurately distinguish long-term survivors from those at risk of rapid relapse. Our goal was to establish and validate nomograms for predicting both recurrence and survival after LND. METHODS: A prospective cohort of stage IIIB and IIIC melanomapatients was ascertained from a tertiary hospital in Brisbane, Australia. Failure-time multivariate analysis identified key factors that, in adjusted combinations, generated nomograms to predict 2-year recurrence and 5-year melanoma-specific survival. The predictive value of these nomograms was further validated in a patient cohort from Rotterdam, The Netherlands. RESULTS: In 494 Australian patients, number of positive lymph nodes, extra-capsular extension and nodular histopathological subtype were the main independent predictors of 2-year recurrence while age, number of positive nodes and extra-capsular extension were the independent predictors of survival. Predictive value was confirmed in The Netherlands cohort of 331 patients. The nomograms were able to classify patients according to their 2-year recurrence and 5-year survival rates even within each stage III sub-class. CONCLUSIONS: Models that include extra-capsular extension predict outcomes in patients with clinically involved lymph nodes. This tool may help tailor treatment and monitoring of this group of patients.
Authors: Michael Lattanzi; Yesung Lee; Danny Simpson; Una Moran; Farbod Darvishian; Randie H Kim; Eva Hernando; David Polsky; Doug Hanniford; Richard Shapiro; Russell Berman; Anna C Pavlick; Melissa A Wilson; Tomas Kirchhoff; Jeffrey S Weber; Judy Zhong; Iman Osman Journal: J Natl Cancer Inst Date: 2019-02-01 Impact factor: 13.506
Authors: Devarati Mitra; Gabriel Ologun; Emily Z Keung; Ryan P Goepfert; Rodabe N Amaria; Merrick I Ross; Jeffrey E Gershenwald; Anthony Lucci; Sarah B Fisher; Michael A Davies; Jeffrey E Lee; Andrew J Bishop; Ahsan S Farooqi; Jennifer Wargo; B Ashleigh Guadagnolo Journal: Ann Surg Oncol Date: 2021-04-15 Impact factor: 5.344
Authors: C M C Oude Ophuis; A C J van Akkooi; H J Hoekstra; J J Bonenkamp; J van Wissen; M G Niebling; J H W de Wilt; B van der Hiel; B van de Wiel; S Koljenović; D J Grünhagen; C Verhoef Journal: Ann Surg Oncol Date: 2015-05-27 Impact factor: 5.344
Authors: Adam R Wolfe; Priyanka Chablani; Michael R Siedow; Eric D Miller; Steve Walston; Kari L Kendra; Evan Wuthrick; Terence M Williams Journal: Radiat Oncol Date: 2021-09-18 Impact factor: 3.481