The Preliminary Study of Spectral CT and Machine Learning Method in Identifying Serosa Invasion of Gastric Cancer
SHI Cen
ZHANG Huan
PANZi-lai
YAN Fu-hua
LI Chao
ZHANG Su
DU Lian-jun
Abstract：Objective: To evaluate the value of spectral CT and machine learning method in identifying serosa invasion of gastric cancer. Method: Total of 24 cases of gastric cancer who underwent dual-phasic scans (arterial phase (AP) and portal phase (PP)) with GSI mode on high-definition computed tomography were retrospectively enrolled in our study, including 8 patients in pT2, 4 patients in pT3, and 12 patients in pT4. 12 patients (pT4 patients) were classified as serosa positive group, and 12 patients (pT2 and pT3 patients) were classified as serosa negative group. The clinical information (e.g.sex, age) of these two groups were compared by using independent sample t test or chi square test. In addition, GE AW4.4 workstation was used for image post-processing, and the dual phase spectrum information of these two groups was obtained. Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm was used to analyze the spectrum information of these two groups. Results:Among the clinical information, only tumor long axis and short axis had statistically significant difference between twogroups (allP<0.05). The accuracies of SVM-RFE were 87.5%~94.4%. The output featuresof SVM-RFEwere fat(calcium)(PP), uricacid(calcium)(PP), calcium(iodine)(AP), water(calcium)(PP), and iodine(water)(PP). Conclusion: Tumor size, fat(calcium)(PP), uricacid(calcium)(PP), calcium(iodine)(AP), water(calcium)(PP), and iodine(water)(PP)were helpful for the diagnosis of gastric cancer serosa invasion.
Keywords：gastric cancerspectral CTsupport vector machine recursive feature elimination
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 515-522 )