TY - JOUR
T1 - Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning
AU - Tushar, Fakrul Islam
AU - D’anniballe, Vincent M.
AU - Hou, Rui
AU - Mazurowski, Maciej A.
AU - Fu, Wanyi
AU - Samei, Ehsan
AU - Rubin, Geoffrey D.
AU - Lo, Joseph Y.
N1 - Funding Information:
Supported in part by developmental funds from the Duke Cancer Institute as part of the NIH/NCI P30 CA014236 Cancer Center Support Grant, the Center for Virtual Imaging Trials NIH/NIBIB P41-EB028744, and a GPU equipment grant from NVIDIA.
Publisher Copyright:
© RSNA, 2022.
PY - 2022/1
Y1 - 2022/1
N2 - Purpose: To design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials and Methods: This retrospective study included a total of 12 092 patients (mean age, 57 years 6 18 [standard deviation]; 6172 women) for model development and testing. Rule-based algorithms were used to extract 19 225 disease labels from 13 667 body CT scans performed between 2012 and 2017. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura, liver and gallbladder, and kidneys and ureters. For each organ system, a three-dimensional convolutional neural network clas-sified each as no apparent disease or for the presence of four common diseases, for a total of 15 different labels across all three models. Testing was performed on a subset of 2158 CT volumes relative to 2875 manually derived reference labels from 2133 patients (mean age, 58 years 6 18; 1079 women). Performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. Results: Manual validation of the extracted labels confirmed 91%–99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were as follows: atelectasis, 0.77 (95% CI: 0.74, 0.81); nodule, 0.65 (95% CI: 0.61, 0.69); emphysema, 0.89 (95% CI: 0.86, 0.92); effusion, 0.97 (95% CI: 0.96, 0.98); and no apparent disease, 0.89 (95% CI: 0.87, 0.91). AUCs for liver and gallbladder were as follows: hepatobiliary calcification, 0.62 (95% CI: 0.56, 0.67); lesion, 0.73 (95% CI: 0.69, 0.77); dilation, 0.87 (95% CI: 0.84, 0.90); fatty, 0.89 (95% CI: 0.86, 0.92); and no apparent disease, 0.82 (95% CI: 0.78, 0.85). AUCs for kidneys and ureters were as follows: stone, 0.83 (95% CI: 0.79, 0.87); atrophy, 0.92 (95% CI: 0.89, 0.94); lesion, 0.68 (95% CI: 0.64, 0.72); cyst, 0.70 (95% CI: 0.66, 0.73); and no apparent disease, 0.79 (95% CI: 0.75, 0.83). Conclusion: Weakly supervised deep learning models were able to classify diverse diseases in multiple organ systems from CT scans.
AB - Purpose: To design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials and Methods: This retrospective study included a total of 12 092 patients (mean age, 57 years 6 18 [standard deviation]; 6172 women) for model development and testing. Rule-based algorithms were used to extract 19 225 disease labels from 13 667 body CT scans performed between 2012 and 2017. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura, liver and gallbladder, and kidneys and ureters. For each organ system, a three-dimensional convolutional neural network clas-sified each as no apparent disease or for the presence of four common diseases, for a total of 15 different labels across all three models. Testing was performed on a subset of 2158 CT volumes relative to 2875 manually derived reference labels from 2133 patients (mean age, 58 years 6 18; 1079 women). Performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. Results: Manual validation of the extracted labels confirmed 91%–99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were as follows: atelectasis, 0.77 (95% CI: 0.74, 0.81); nodule, 0.65 (95% CI: 0.61, 0.69); emphysema, 0.89 (95% CI: 0.86, 0.92); effusion, 0.97 (95% CI: 0.96, 0.98); and no apparent disease, 0.89 (95% CI: 0.87, 0.91). AUCs for liver and gallbladder were as follows: hepatobiliary calcification, 0.62 (95% CI: 0.56, 0.67); lesion, 0.73 (95% CI: 0.69, 0.77); dilation, 0.87 (95% CI: 0.84, 0.90); fatty, 0.89 (95% CI: 0.86, 0.92); and no apparent disease, 0.82 (95% CI: 0.78, 0.85). AUCs for kidneys and ureters were as follows: stone, 0.83 (95% CI: 0.79, 0.87); atrophy, 0.92 (95% CI: 0.89, 0.94); lesion, 0.68 (95% CI: 0.64, 0.72); cyst, 0.70 (95% CI: 0.66, 0.73); and no apparent disease, 0.79 (95% CI: 0.75, 0.83). Conclusion: Weakly supervised deep learning models were able to classify diverse diseases in multiple organ systems from CT scans.
KW - CT
KW - Diagnosis/Classification/Application Domain
KW - Semisupervised Learning
KW - Whole-Body Imaging
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U2 - 10.1148/ryai.210026
DO - 10.1148/ryai.210026
M3 - Article
AN - SCOPUS:85126124207
SN - 2638-6100
VL - 4
JO - Radiology: Artificial Intelligence
JF - Radiology: Artificial Intelligence
IS - 1
M1 - e210026
ER -