ArticleCancers2020
Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 23 citations in OpenAlex.
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- Revolutionizing thyroid nodule diagnosis in Hashimoto's thyroiditis: AI-driven radiomics and deep learning model.International journal of surgery (London, England) · 2025Article
- XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy.Frontiers in endocrinology · 2025Article
- An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson's disease.NPJ Parkinson's disease · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas.Radiation oncology (London, England) · 2022Article
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer.Frontiers in oncology · 2022Article
- Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features.Quantitative imaging in medicine and surgery · 2021Article
- Explaining multivariate molecular diagnostic tests via Shapley values.BMC medical informatics and decision making · 2021Article
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Authors and funding
16 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundThere 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.
methodsThe 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.
resultsOn 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.
conclusionsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.