Journal
NPJ DIGITAL MEDICINE
Volume 6, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41746-023-00795-x
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We present NaroNet, a weakly-supervised deep learning framework that can predict recurrence in low-grade, early-stage endometrial cancer by learning the complex tumor-immune interrelations. Our model achieved high accuracy in assessing the risk of recurrence, outperforming current prognostic factors.
Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (kappa = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.
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