Quantitative imaging in medicine and surgery · 2023
Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images.
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Alimu Parehe, Fang Chen, Han Yingnan, Dai Jun, Xie Chunmei, Wang Jiyong, Mao Yongxin, Chen Yunmeng, Yao Lu, Lv Chuanfeng, Xu Danfeng, Xie Guotong, Sun Fukang
Аннотация
BACKGROUND: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs.
METHODS: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance.
RESULTS: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05).
CONCLUSIONS: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.
METHODS: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance.
RESULTS: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05).
CONCLUSIONS: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.