Highlights
- •Molecular assays are not accessible to all uveal melanoma patients.
- •We investigate machine learning models on clinicopathologic variables for risk stratification.
- •Machine learning models included random survival forest and survival gradient boosting.
- •They performed similarly or better than gene expression prognostic signature.
- •Readily accessible clinicopathologic variables can provide adequate prognostic information.
Abstract
Purpose
Since molecular assays are not accessible to all uveal melanoma patients, we aim to
identify cost-effective prognostic tool in risk stratification using machine learning
models based on routine histologic and clinical variables.
Experimental design
We identified important prognostic parameters in a discovery cohort of 164 enucleated
primary uveal melanomas from 164 patients without prior therapies. We then validated
the prognostic prediction of top important parameters identified in the discovery
cohort using 80 uveal melanomas from the Tumor Cancer Genome Atlas database with available
gene expression prognostic signature (GEPS). The performance of three different survival
analysis models (Cox proportional hazards (CPH), random survival forest (RSF), and
survival gradient boosting (SGB)) was compared against GEPS using receiver operating
curves (ROC).
Results
In all three selection methods, BAP1 status, nucleoli size, age, mitotic rate per
1 mm2, and ciliary body infiltration were identified as significant overall survival (OS)
predictors; and BAP1 status, nucleoli size, largest basal tumor diameter, tumor-infiltrating
lymphocyte density, and tumor-associated macrophage density were identified as significant
progression-free survival (PFS) predictors. ROC plots for the median survival time
point showed that significant parameters in SGB studied model can predict OS better
than GEPS. For PFS, SGB model performed similarly to GEPS. The time-dependent area
under the curve (AUC) showed SGB model performing better than GEPS in predicting OS
and metastatic risk.
Conclusions
Our study shows that routine histologic and clinical variables are adequate for patient
risk stratification in comparison with not readily accessible GEPS.
Graphical abstract
Graphical Abstract
Keywords
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Article info
Publication history
Published online: September 03, 2022
Accepted:
July 27,
2022
Received in revised form:
July 12,
2022
Received:
May 5,
2022
Identification
Copyright
© 2022 Elsevier Ltd. All rights reserved.
