Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images
Daniel Jiménez-Sánchez 1 2 , Álvaro López-Janeiro 2 3 , María Villalba-Esparza 2 4 , Mikel Ariz 1 4 , Ece Kadioglu 5 , Ivan Masetto 6 , Virginie Goubert 6 , Maria D Lozano 2 4 7 , Ignacio Melero 4 7 8 9 , David Hardisson 3 7 10 11 , Carlos Ortiz-de-Solórzano 1 4 7 , Carlos E de Andrea 12 13 14
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 (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.