Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis

Abstract

BackgroundXanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making.MethodsWe investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model.ResultsBased on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC.ConclusionsThe diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.

Differential diagnoses of gallbladder tumors using CT‐based deep learning

“…In the field of GBC diagnosis, there are few studies applying machine learning. Zhou et al established a machine‐learning‐based diagnostic prediction model showing good diagnostic accuracy for the preoperative discrimination of XGC and GBC (AUC 0.888) 27 . However, compared with the previous machine‐learning‐based model, our DL‐based model achieved higher predictive accuracy (AUC 0.9893).…”

“…The frequency of the coexistence of GBC and XGC was reported to be ~10% 30 . A past study using machine learning did not assess these cases of coexistence 27 . Therefore, although the patient number was not large, the setting of our test dataset was significant.…”

“…Various approaches have been used to distinguish GBC from benign gallbladder diseases. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 However, there is no gold standard due to insufficient accuracy. These results suggested the need for innovation in the differential diagnosis of gallbladder disease.…”

“… 30 A past study using machine learning did not assess these cases of coexistence. 27 Therefore, although the patient number was not large, the setting of our test dataset was significant. Moreover, our model demonstrated insufficient discriminating performance in the GBC patients after NAC.…”

“…The strength of the current study shows extremely high discriminating performance compared to past reports (Table 5). Various approaches have been used to distinguish GBC from benign gallbladder diseases 12–28 . However, there is no gold standard due to insufficient accuracy.…”