A NOVEL TREE-STRUCTURED ANALYSIS FOR NONINVASIVE DIAGNOSIS OF GASTRICADENOCARCINOMA

Non-invasive diagnosis of gastric adenocarcinoma (GAC) is usually diff icult due to the low sensitivity and specificity of serologic markers, including pepsinogens and gastrin. For the improvement of the diagnos tic values of these markers, a ''recursive partitioning and amalgamati on'' algorithm was employed to construct a decision protocol. A total of 636 subjects including 161 healthy subjects, 163 patients with GAG, 196 with gastric ulcer and 116 with duodenal ulcer were enrolled Seru m levels of gastrin, pepsinogen I, pepsinogen II, and the ratio of pep sinogen I / pepsinogen II were determined for each of the subjects. Th e proposed ''decision tree'' classifies subjects into five subgroups w ith different risks of GAC and peptic ulcer, based on the information of age, serum pepsinogen and gastrin levels. Using this novel analysis system, an expected probability of GAC or ulcers could be obtained Pa tients with an age > 62 years and a serum level of pepsinogen I less t han or equal to 33 ng/ml were strongly indicated for further confirmat ory tests of GAC. This tree-structured analysis is also helpful in cla rifying the interactions between various serologic markers and demogra phic factors.