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Monique D. Dorrius



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    OA06 - Refining Lung Cancer Screening (ID 131)

    • Event: WCLC 2019
    • Type: Oral Session
    • Track: Screening and Early Detection
    • Presentations: 1
    • Now Available
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      OA06.05 - Evaluation of a Deep Learning-Based Automatic Classifier for the Classification of Perifissural Nodules (Now Available) (ID 1928)

      11:00 - 12:30  |  Author(s): Monique D. Dorrius

      • Abstract
      • Presentation
      • Slides

      Background

      Perifissural nodules (PFNs) comprise approximately 20% of screening-detected nodules and are almost certainly benign. Automatic PFN classification could therefore reduce the number of follow-up procedures required for nodule work-up. Prior work has shown some success in AI classification with limited datasets. Here we evaluate the performance of a new deep convolutional neural network (CNN) for PFN classification, trained on a dataset of nodules retrospectively collected from multiple European centers, including validation on an independent reader-study dataset.

      Method

      Data (1103 Patients, 1557 unique nodules and 3320 nodule images) were collected from three centers in the UK and the Netherlands. Each nodule was categorized into subtypes, including “PFN”, by on-site radiologists. Labels were reviewed centrally, overseen by a single clinician to ensure consistency between sites.

      A CNN classifier was trained to produce a score that classifies nodules as (typical) PFN or not, using five-fold cross validation. The PFN classifier was developed by “transfer learning” from an existing benign-vs-malignant AI trained on the US National Lung Screening Trial.

      To compare the CNN with human performance, independent validation was performed on a separate dataset of 158 benign patients (196 nodules/nodule images) from two of the sites. Three readers (two radiologists and a radiology resident) were asked to label each nodule as typical PFN, atypical PFN, or non-PFN. To match the AI training procedure, only the typical-PFN labels were used in the reader study, and compared to atypical/non-PFN classified nodules.

      Model performance was evaluated by area under the ROC curve (AUC). For the independent validation, Cohen’s kappa was used to measure both the model’s agreement with reader consensus (at least 2 in agreement) and inter-reader agreement. For Cohen’s kappa calculations the CNN score was binarized using a threshold determined from the internal validation data.

      Result

      The mean cross-validated AUC on the internal dataset was 92% (95% CI = 90.6–92.9). For the independent dataset, the classifier labelled 61/196 (31%) as typical PFNs, and reader consensus gave 45/196 (23%). Versus reader consensus, the AUC of the CNN on the reader-study dataset was 96% (95% CI 93.3–98.4). Both the classifier–reader agreement [(k=0.74) 90%] and the inter-reader agreement [(k=0.64–0.79) 88%-92%] were substantial.

      Conclusion

      The performance of the PFN classifier is similar to that of radiologists and is within the inter-reader variability of radiologists. This demonstrates the potential utility of CNN-based systems for automatic PFN classification.

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    P1.11 - Screening and Early Detection (ID 177)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Screening and Early Detection
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.11-27 - Computed Tomography Screening for Early Lung Cancer, COPD and Cardiovascular Disease in Shanghai: Rationale and Design of a Population-Based Comparative Study (ID 1863)

      09:45 - 18:00  |  Author(s): Monique D. Dorrius

      • Abstract
      • Slides

      Background

      Volume-based management for lung nodules is associated with a lower rate of unnecessary referral for further work up as compared to diameter-based management in European population. Screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), in addition to lung cancer, may significantly increase the benefits of lung cancer low-dose computed tomography (CT) screening. While this is unclear in Chinese population. The aim of this study is to assess the diagnostic performance of volume-based lung nodule management for lung cancer CT screening as compared to diameter-based management, and to improve the effectiveness of CT screening for COPD and CVD based on quantitative measurement of CT imaging biomarkers in a Chinese screening setting.

      Method

      A comparative population-based study is ongoing, that will include 10,000 asymptomatic participants between 40 and 74 years old from Shanghai urban population in China.

      Participants will be randomized into the intervention and control groups and will undergo a low-dose chest CT scan at baseline and one year after baseline. NELCIN-B3 protocol will be applied in the intervention group. It recommends management of detected solid and part-solid lung nodules based on the volume and volume doubling time (VDT) of a lung nodule. The imaging biomarkers for COPD and CVD, such as emphysema score, bronchial wall thickness from inspiratory and expiratory chest CT scan, and coronary calcium score from ECG-triggered cardiac CT scan will be evaluated. In addition data on laboratory parameters and lung function test will be collected. The participants in the control group will be managed according to the standard hospital protocol based on visual assessment of the CT images. It recommends management of detected lung nodules based on the diameter according to the NCCN Clinical Practice Guideline in Oncology for Lung Cancer Screening. Epidemiological data (eg., risk factors) will be collected through questionnaires for all participants. Four years after the initial assessment the incidence of the three diseases will be evaluated. The design is shown in Figure 1.
      figure1.png

      Result

      The unnecessary referral rate will be compared between the NELCIN-B3 and standard protocol for early detected lung nodules management. The effectiveness of quantitative measurement of CT imaging biomarkers for early detection of lung cancer, COPD and CVD will be evaluated.

      Conclusion

      We expect that the quantitative assessment of the CT imaging biomarkers will reduce the number of unnecessary referrals for early detected lung nodules and improve the early detection of COPD and CVD in Chinese urban populations.

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