Publicaciones científicas

Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images

23-mar-2023 | Revista: NPJ Digital Medicine

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


Abstract

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.

CITA DEL ARTÍCULO  NPJ Digit Med. 2023 Mar 23;6(1):48.  doi: 10.1038/s41746-023-00795-x.