(주) 뷰노

(주) 뷰노

문의사항 남기기
KR EN
Publications
BACK TO LIST
RSNA 2020

Multicenter Validation to Evaluate the Diagnostic Performance of Deep Learning-based Lung Nodule Detection System in Chest CT

  • Nov. 2020
  • by Hyunho Park et. al.

PURPOSE

To evaluate the robustness of the diagnostic performance of a deep learning-based lung nodule detection system (DLS) on the reference standard established from multiple institutions

 

METHOD AND MATERIALS

We retrospectively collected 633 cases from 3 different institutions (A: 225, B: 203, C: 205). The slice thickness of CT scans and vendors of CT scanners were variable in each institutions (slice thickness; A: 0.6~1mm, B: 3mm, C: 2.5mm). After nine thoracic radiologists (three from each institution) independently read the CT scans to record the locations and long/short-axis diameters of the nodules, the reference standard was established by consensus of three radiologists of each institution. The diagnostic performance of the DLS was assessed on the reference standard..The DLS consisted of a 2.5D and 3D CNN trained with the LIDC-IDRI dataset.

 

RESULTS

The reference standard included 372 and 261 subjects, without and with nodules, respectively. In the subjects with nodules, the number of nodules was 1.54±0.83 on average. The mean nodule size was 8.66±4.33 mm in long-axis diameter and 6.42±3.06 mm in short-axis diameter.

DLS detected 4.17±3.15 nodule candidates for each cases. For the per lesion analysis, the 0.840 (95% CI: 0.801-0.875) sensitivity with 2.6 false positive per scan (FPPS), 0.902 (0.839-0.947) with 3.6 FPPS, 0.737 (0.653-0.809) with 2.3 FPPS, and 0.881 (0.815-0.931) with 2.4 FPPS was achieved in the institution A, B, and C, respectively. The difference in the per lesion sensitivity per institution was statistically significant (p<0.001). For the per patient analysis, the sensitivity, specificity, PPV, and NPV was 0.912 (0.871-0.943), 0.903 (0.869-0.931), 0.869 (0.829-0.909), and 0.936 (0.911-0.961), respectively.

 

CONCLUSION

The DLS showed reliable but variable performance on the detection of lung nodules of chest CT from three different institutions with various vendors and image acquisition protocols.

 

CLINICAL RELEVANCE/APPLICATION

This study presents an exemplar of a process of thorough validation necessary for the introduction of DLS into clinical practice from the establishment of reference standards to the evaluation of diagnostic performance.

Tag in

#medical_image

#VUNO Med® Solution

#VUNO Med®-LungCT AI™

이전글
Performance Validation of Deep Learning-based Models for the Quantification of COVID-19 Pneumonia and Its Application to Development of Machine Learning Model for Differentiating COVID-19 Cases from ILD and Normal Cases Using Chest CT 2020. 12. 16
다음글
A Deep Learning Framework for Interpretable Diagnoses of Pulmonary Diseases through Odds-Ratio Analysis in Radiographic Findings 2020. 12. 16
List

이 웹사이트는 원활한 서비스 제공을 위하여 당사 개인정보 처리방침에 명시된 쿠키를 사용합니다.

만일 당사의 쿠키 사용 정책에 대해 더 알고 싶으신 경우에는 개인정보 처리방침 을 참고하여 주시기 바랍니다.