Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers

Paul Giraud, Philippe Giraud, Eliot Nicolas, Pierre Boisselier, Marc Alfonsi, Michel Rives, Etienne Bardet, Valentin Calugaru, Georges Noel, Enrique Chajon, Pascal Pommier, Magali Morelle, Lionel Perrier, Xavier Liem, Anita Burgun () and Jean Emmanuel Bibault ()
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Magali Morelle: GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - Université de Lyon - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique

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Abstract: Background: There is no evidence to support surgery or radiotherapy as the best treatment for resectable oropharyngeal cancers with a negative HPV status. Predictive algorithms may help to decide which strategy to choose, but they will only be accepted by caregivers and European authorities if they are interpretable. As a proof of concept, we developed a predictive and interpretable algorithm to predict locoregional relapse at 18 months for oropharyngeal cancers as a first step towards that goal. Methods: The model was based on clinical and Pyradiomics features extracted from the dosimetric CT scan. Intraclass correlation was used to filter out features dependant on delineation. Correlated redundant features were also removed. An XGBoost model was cross-validated and optimised on the HN1 cohort (79 patients), and performances were assessed on the ART ORL cohort (45 patients). The Shapley Values were used to provide an overall and local explanation of the model. Results: On the ART ORL cohort, the model trained on HN1 yielded a precision—or predictive positive value—of 0.92, a recall of 0.42, an area under the curve of the receiver operating characteristic of 0.68 and an accuracy of 0.64. The most contributory features were shape Voxel Volume, grey level size zone matrix Small Area Emphasis (glszmSAE), gldm Dependence Non Uniformity Normalized (gldmDNUN), Sex and Age. Conclusions: We developed an interpretable and generalizable model that could yield a good precision—positive predictive value—for relapse at 18 months on a different test cohort.

Date: 2021-01
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Published in Cancers, 2021, 13 (1), pp.57. ⟨10.3390/cancers13010057⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-03428057

DOI: 10.3390/cancers13010057

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