Abstract
As a discrimination method for estimating invasive potentiality and prognosis of oral squamous cell carcinoma (OSCC), the mode of invasion (Yamamoto Kohama-criteria) centered on the form of pathological tissue specimen has been known to be clinically useful, especially in Japan where it is used frequently. However, evaluation of this mode of invasion is based on subjective visual observation, which has created a large gap between evaluators and facilities using such the mode of invasion. This problem is such that unification of objective evaluations is a challenge. Therefore, this study aimed to develop a method of automatically determining the mode of invasion by medical image processing using digital images of the invasion front of OSCC. A shaped feature of the invasive fronts was extracted to create a classifier by Random Forest, a machine learning algorithm, in conjunction with the mode of invasion opted for based on the judgment of the clinician. As a result of inputting multiple test images to the created classifier, it was confirmed that the classifier outputs a decision that is very close to the judgment of the clinician. Therefore, automatic determination of the mode of invasion by a medical diagnostic imaging system was shown to be feasible and of high accuracy.
