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Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography

  • Jieun Koh
    Affiliations
    Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Korea

    Department of Radiology, CHA Ilsan Medical Center, CHA University, Goyang, Korea
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  • Youngno Yoon
    Affiliations
    Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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  • Sungwon Kim
    Affiliations
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea

    Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
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  • Kyunghwa Han
    Affiliations
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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  • Eun-Kyung Kim
    Correspondence
    Address for correspondence: Eun-Kyung Kim, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
    Affiliations
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea

    Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
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      Abstract

      Background

      Incidental breast cancers can be detected on chest computed tomography (CT) scans. With the use of deep learning, the sensitivity of incidental breast cancer detection on chest CT would improve. This study aimed to evaluate the performance of a deep learning algorithm to detect breast cancers on chest CT and to validate the results in the internal and external datasets.

      Patients and Methods

      This retrospective study collected 1170 preoperative chest CT scans after the diagnosis of breast cancer for algorithm development (n = 1070), internal test (n = 100), and external test (n = 100). A deep learning algorithm based on RetinaNet was developed and tested to detect breast cancer on chest CT.

      Results

      In the internal test set, the algorithm detected 96.5% of breast cancers with 13.5 false positives per case (FPs/case). In the external test set, the algorithm detected 96.1% of breast cancers with 15.6 FPs/case. When the candidate probability of 0.3 was used as the cutoff value, the sensitivities were 92.0% with 7.36 FPs/case for the internal test set and 93.0% with 8.85 FPs/case for the external test set. When the candidate probability of 0.4 was used as the cutoff value, the sensitivities were 88.5% with 5.24 FPs/case in the internal test set and 90.7% with 6.3 FPs/case in the external test set.

      Conclusion

      The deep learning algorithm could sensitively detect breast cancer on chest CT in both the internal and external test sets.

      Keywords

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