عنوان انگلیسی مقاله:
Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma
ترجمه فارسی عنوان مقاله:
یادگیری ماشین برای پیش بینی متاستاز گره غشایی در کارسینوم سلول سنگفرشی اولیه دهان
Sciencedirect - Elsevier - Oral Oncology, 92 (2019) 20-25: doi:10:1016/j:oraloncology:2019:03:011
Andrés M. Bura,⁎, Andrew Holcomba, Sara Goodwina, Janet Woodroofb, Omar Karadaghya, Yelizaveta Shnaydera, Kiran Kakaralaa, Jason Brantc, Matthew Shewa
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.
Keywords: Oral cancer | Squamous cell carcinoma | Machine learning | Artificial intelligence