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Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer

 Kiwook Kim  ;  Sungwon Kim  ;  Kyunghwa Han  ;  Heejin Bae  ;  Jaeseung Shin  ;  Joon Seok Lim 
 KOREAN JOURNAL OF RADIOLOGY, Vol.22(6) : 912-921, 2021-06 
Journal Title
Issue Date
Artificial intelligence ; Colorectal neoplasms ; Computer-assisted diagnosis ; Neoplasm metastasis ; X-ray computed tomography
Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists.

Materials and methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured.

Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001).

Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.
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1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Bae, Heejin(배희진) ORCID logo https://orcid.org/0000-0002-1227-8646
Shin, Jaeseung(신재승) ORCID logo https://orcid.org/0000-0002-6755-4732
Lim, Joon Seok(임준석) ORCID logo https://orcid.org/0000-0002-0334-5042
Han, Kyung Hwa(한경화)
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