In Annals of surgery ; h5-index 104.0
OBJECTIVE : Develop a predictive model to identify patients with 1 pathologic LN (pLN) versus >1pLN using machine learning applied to gene expression profiles and clinical data as input variables.
SUMMARY BACKGROUND DATA : Standard management for clinically detected melanoma lymph node (cLN) metastases is complete therapeutic LN dissection (TLND). However, more than 40% of patients with cLN will only have 1pLN on final review. Recent data suggest that targeted excision of just the one enlarged LN may provide excellent regional control, with less morbidity than TLND. Selection of patients for less morbid surgery requires accurate identification of those with only 1pLN.
METHODS : The Cancer Genome Atlas (TCGA) database was used to identify patients who underwent TLND for melanoma. Pathology reports in TCGA were reviewed to identify the number of pLNs. Patients were included for machine learning analyses if RNA sequencing data were available from a pLN. After feature selection, the top 20 gene expression and clinical input features were used to train a ridge logistic regression (RLR) model to predict patients with 1pLN versus >1pLN using 10-fold cross validation on 80% of samples. The model was then tested on the remaining hold out samples.
RESULTS : A total of 153 patients met inclusion criteria: 64 with 1pLN (42%) and 89 with >1pLNs (58%). Feature selection identified 1 clinical (extranodal extension) and 19 gene expression variables used to predict patients with 1pLN versus >1pLN. The RLR model identified patient groups with an accuracy of 90% and an area under the ROC curve (AUC) of 0.97.
CONCLUSIONS : Gene expression profiles together with clinical variables can distinguish melanoma metastasis patients with 1 pLN versus >1 pLN. Future models trained using PET/CT imaging, gene expression, and relevant clinical variables may further improve accuracy and may predict patients who can be managed with a targeted LN excision rather than a complete TLND.
Meneveau Max O, Vavolizza Rick D, Mohammad Anwaruddin, Kumar Pankaj, Manderfield Joseph T, Callahan Colleen, Lynch Kevin T, Abbas Tarek, Slingluff Craig L, Bekiranov Stefan
2022-Nov-25
