Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma - 11/01/24
Abstract |
Background |
The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality.
Objective |
To develop time-to-event risk prediction models for melanoma metastatic recurrence.
Methods |
Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction.
Results |
This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation.
Limitations |
Retrospective nature and cohort from one geography.
Conclusions |
These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.
Il testo completo di questo articolo è disponibile in PDF.Key words : clinicopathologic factors, locoregional recurrence, metastatic recurrence, stage I/II melanoma, time-to-event prediction
Abbreviations used : AJCC-8, AUC, CCS, Coxnet, CoxPH, DFCI, EHR, FVM, GBS, HOM, HR, MGH, RSF, SLNB, VGT
Mappa
| Drs Wan and Leung are co-first authors. |
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| Drs Sorger and Semenov are co-senior authors. |
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| Funding sources: YRS is supported in part by the Department of Defense under Award Number W81XWH2110819, the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number K23AR080791, and the Melanoma Research Alliance Young Investigator Award. |
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| Patient consent: Not applicable. |
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| IRB approval status: Reviewed and approved by Mass General Brigham Institutional Review Board (Protocol # 2020P002179). |
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| Code availability: The code is available at Time2Event-MelanomaRecurrence. |
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| Data availability: The data generated for this study can only be shared per specific institutional review board (IRB) requirements. Upon request to the corresponding author, a data sharing agreement can be initiated following institution-specific guidelines. The original figures were uploaded separately. |
Vol 90 - N° 2
P. 288-298 - febbraio 2024 Ritorno al numero
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