Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. (May 2019)

• Record Type: Journal Article
• Authors: Bur, Andrés M.; Holcomb, Andrew; Goodwin, Sara; Woodroof, Janet; Karadaghy, Omar; Shnayder, Yelizaveta; Kakarala, Kiran; Brant, Jason; Shew, Matthew
• Is Part Of: Oral oncology. Volume 92(2019)
• Page Start: 20
• Page End: 25
• Publication Date: 2019-05
• Subjects: Oral cancer -- Squamous cell carcinoma -- Machine learning -- Artificial intelligence; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2019.03.011
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: Prediction of occult nodal metastasis can be used to guide surgical treatment. Improved predictive methods may personalize treatment of early oral cancers. Predictive performance of depth of invasion and machine learning was compared. Machine learning outperformed depth of invasion in predicting nodal metastasis. Abstract: Objectives: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of invasion (DOI). Materials and methods: Patients who underwent primary tumor extirpation and elective neck dissection from 2007 to 2013 for clinical T1-2N0 OCSCC were identified from the National Cancer Database (NCDB). Multiple machine learning algorithms were developed to predict pathologic nodal metastasis using clinicopathologic data from 782 patients. The algorithm was internally validated using test data from 654 patients in NCDB and was then externally validated using data from 71 patients treated at a single academic institution. Performance was measured using area under the receiver operating characteristic (ROC) curve (AUC). Machine learning and DOI model performance were compared using Delong's test for two correlated ROC curves. Results: The best classification performance was achieved with a decision forest algorithm (AUC = 0.840). When applied to the single-institution data, the predictive performance of machine learning exceeded that of the DOI model (AUC = 0.657, p = 0.007). Compared to the DOI model, machine learning reduced the number of neck dissections recommended while simultaneously improving sensitivity and specificity. Conclusion: Machine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-2N0 OCSCC compared to methods based on DOI. Improved predictive algorithms are needed to ensure that patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection in patients without pathologic nodal disease.