Establishment Of Radiological Follow-up Databases And Analysis Of Rough Set Methods Used In Aided Diagnosis Of Glioma Grade

Objective:(1) Considering the radiological research and CAD, the records in follow-up database of radiology with standard radiological diagnosis and reports was established. (2) To study the value of Rough set principle in assistant radiological diagnosis by compared Rough set with Decision tree and two-binary-Logistic-Regression in the diagnosis of glioma grade based on routine MR examination.Methods:(1)The ACR code library and ICD-10 library were used to standardized the diagnosis in radiological follow-up database which be build with SQL sever 2000.The ACR code library included anatomical table and pathological table in which the anatomy and pathology was encoded by number. The anatomical codes had 4 ranks and the pathological codes had 6 ranks at most.ICD-10 code library included class table and diagnosis table in which the classes and diagnosis were encoded with alphabet and number.(2)The diagnosis mould of CNS tumor was used to normalize the reports of CNS tumor which included the radiological features in CT and MRI like location, spread, size, shape, margin, edema, mass effect, hemorrhage, necrosis, calcification, hydrocephalus, CT density, MRI intensity (include T1WI, T2WI,Flair),enhancement style (Routine enhancement, enhancement in arterial phase, enhancement in venous phase, enhancement in delay phase). (3) The data tables was build in SQL server 2000 used to store the clinical data (include name, gender, age, history, operation record) and pathological data (pathological diagnosis and picture) and radiological data (include radiological examination method, primitive report, reporter, superior, diagnose accordance, radiological picture, radiological features) of follow-up records among which the pathological pictures and radiological picture were restored in local hardware and connected to the record by table of index.(4)The client program of man-computer interface was developed by using Visual C++ which used to carry out the functions to the database like follow-up record maintainm, inquiry records, statistics of the diagnose accordance rate and entity in database, records import or export, ACR and ICD-10 codes revision, users authoritative. (5)MR images of 275 patients with gliomas (151 low-grade gliomas,124 high-grade gliomas) examined before surgery were collected. The features of MRI with gliomas included numbers, shape, margin, edema, necrosis, mass effect, calcification, hemorrhage, intensity of T1WI and T2WI, enhancement style after administration of contrast agent. The attributes of glioma was reduced by genetic algorithm with software Rosetta and then the diagnostic rules about the grade diagnosis came from reduced attributes. The CR&T algorithm was used to build grade diagnostic model in decision tree. On the other hand the two-binary-Logistic-Regression constructed an equation to predict the grade of glioma.Result:(1)The follow-up database implement the functions of data entry, data inquiry, statistic analysis, system management and data input/output. And the use of encode made the inquiry of data in the database more accurate. (2) The accuracy, sensitivity, Specificity and the averaged area under the ROC curve for Rough set, Decision tree and two binary Logistic Regression were 84.4%,75%,92.1%,0.92; 83.3%,74.2%,91.3%,0.907 and 83.6%,79.8%,86.8%,0.902 respectively. There were no significant differences among the AUC of ROCs of the three methods. Among all features, mass effect, edema and enhancement style were three important features in differentiate grade of glioma.Conclusion:(1) The follow-up database made the management of information of follow-up more standard and its data can be used for clinical research and data mining more effectively. (2) All of three methods had good diagnostic performance. The importance of every feature in diagnostic model can get from decision tree and two-binary-Logistic-Regression. For the explicit and valuable and understandable rules, Rough set was more suitable for practice in diagnosis by experts. The Rough set should be study further to improve the accurate and cover rate of rules.