Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning

Fakrul Islam Tushar, Vincent M. D’anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, Joseph Y. Lo

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere210026
JournalRadiology: Artificial Intelligence
Volume4
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • CT
  • Diagnosis/Classification/Application Domain
  • Semisupervised Learning
  • Whole-Body Imaging

ASJC Scopus subject areas

  • Artificial Intelligence
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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