Published online Feb 15, 2018. doi: 10.4251/wjgo.v10.i2.62
Peer-review started: November 20, 2017
First decision: December 1, 2017
Revised: December 5, 2017
Accepted: December 13, 2017
Article in press: December 13, 2017
Published online: February 15, 2018
Gastric cancer is one of the most common malignancies, and has the highest mortality rates in East Asian countries. Although ABC (D) stratification is effective method for evaluating gastric cancer risk, photofluorography still plays an important role in gastric cancer mass screening since image-based evaluation is mandatory.
If gastric cancer risk information can be provided automatically by analyzing X-ray images, it would be helpful for the future of gastric cancer mass screening.
The aim of this study was investigation of potential of machine learning techniques using photofluorography.
We developed an automatic gastric cancer risk classification system for identification of Helicobacter pylori infection status and atrophic level from photoﬂuorography. All of 2100 patients’ data were acquired at the Medical Examination Center of Yamagata City Medical Association in Japan, from April 2012 to March 2013. From DICOM data, we extracted the image data while securing anonymity.
Experimental results suggested that image-based risk information can be calculated by our system.
Although further investigation and improvement of the system are needed, this retrospective study indicated that machine learning techniques analyzing X-ray images can provide effective gastric cancer risk information. Also, we discussed the potential of machine learning techniques and the future of gastric cancer mass screening.
In the field of breast cancer, computer-aided supporting systems have already become a part of routine clinical work for detection of breast cancer or abnormalities. Gastric cancer as well as breast cancer requires effective and highly accurate mass screening. We believe that this preliminary study will contribute the next step of the future of gastric cancer mass screening.