Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral...

The Yamamoto-Kohama criteria are clinically useful for determining the mode of tumor invasion, especially in Japan. However, this evaluation method is based on subjective visual findings and has led to significant differences in determinations between evaluators and facilities. In this retrospective study, we aimed to develop an automatic method of determining the mode of invasion based on the processing of digital medical images.

Using 101 digitized photographic images of anonymized stained specimen slides, we created a classifier that allowed clinicians to introduce feature values and subjected the cases to machine learning using a random forest approach. We then compared the Yamamoto-Kohama grades (1, 2, 3, 4C, 4D) determined by a human oral and maxillofacial surgeon with those determined using the machine learning approach.

The input of multiple test images into the newly created classifier yielded an overall F-measure value of 87% (grade 1, 93%; grade 2, 67%; grade 3, 89%; grade 4C, 83%; grade 4D, 94%). These results suggest that the output of the classifier was very similar to the judgments of the clinician.

This system may be valuable for diagnostic support to provide an accurate determination of the mode of invasion.