TY - JOUR
T1 - Prediction of early-stage melanoma recurrence using clinical and histopathologic features
AU - Wan, Guihong
AU - Nguyen, Nga
AU - Liu, Feng
AU - DeSimone, Mia S.
AU - Leung, Bonnie W.
AU - Rajeh, Ahmad
AU - Collier, Michael R.
AU - Choi, Min Seok
AU - Amadife, Munachimso
AU - Tang, Kimberly
AU - Zhang, Shijia
AU - Phillipps, Jordan S.
AU - Jairath, Ruple
AU - Alexander, Nora A.
AU - Hua, Yining
AU - Jiao, Meng
AU - Chen, Wenxin
AU - Ho, Diane
AU - Duey, Stacey
AU - Németh, István Balázs
AU - Marko-Varga, Gyorgy
AU - Valdés, Jeovanis Gil
AU - Liu, David
AU - Boland, Genevieve M.
AU - Gusev, Alexander
AU - Sorger, Peter K.
AU - Yu, Kun Hsing
AU - Semenov, Yevgeniy R.
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.
AB - Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.
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UR - http://www.scopus.com/inward/citedby.url?scp=85140963991&partnerID=8YFLogxK
U2 - 10.1038/s41698-022-00321-4
DO - 10.1038/s41698-022-00321-4
M3 - Article
AN - SCOPUS:85140963991
VL - 6
JO - npj Precision Oncology
JF - npj Precision Oncology
IS - 1
M1 - 79
ER -