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Timor Kadir



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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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      P1.11-02 - Acceleration of Lung Cancer Diagnosis: Utility Study for AI-Based Stratification of Pulmonary Nodules (ID 2752)

      09:45 - 18:00  |  Presenting Author(s): Timor Kadir

      • Abstract

      Background

      Lung cancer diagnostic pathway guidelines promote the use of risk stratification models. Artificial Intelligence (AI)-based risk models have been shown to achieve better diagnostic accuracy than clinical models like Mayo Clinic (Mayo) for particular clinical populations. The aim of this study is to examine whether this could translate into faster diagnosis for high-risk cancer patients.

      Method

      116 patients (116 nodules) have been collected from a retrospective consecutive cohort acquired at Vanderbilt University Hospital. Time to diagnosis (TTD) was defined as the number of days between the CT scan and diagnosis date. Mean TTD was calculated on the cohort on which TTD could be defined, and on a reduced group comprising of TTD >31 days only.

      Risk scores for each nodule were found using the Mayo model and an AI-based Lung Cancer Prediction model (LCP) based on CT images alone. A 65% risk of cancer was taken to be the threshold at which surgical intervention is indicated (according to ACCP guidelines).

      Result

      Seven patients were dropped due to negative TTD, and six for having no definitive diagnosis date. The eventual cohort contained 61 cancer patients and 42 controls. Mean TTD is 140 days (Interquartile Range – IQR 1-77 days). 25 patients have TTD=0, 60 are within 31 days (28 cancers, 32 controls) and 43 (33 cancers, 10 controls) are above 32 days.

      On the full cohort: Mayo risk score is >=65% for 15 cancers and 4 controls (sensitivity, 24.6%, specificity 90.5%), with a mean cancer TTD of 75 days. The LCP scores >=65% in 43 cancers and 10 controls (sensitivity, 70.5%, specificity 76.2%), mean cancer TTD 81 days.

      On the reduced group: Mayo >=65% for 7 cancers and 2 controls (sensitivity, 21.2%, specificity 80.0%) with mean cancer TTD 150 days. The LCP scores >=65% in 21 cancers and 4 controls (sensitivity, 63.6%, specificity 60.0%), with mean cancer TTD 156 days.

      Conclusion

      The LCP risk model could potentially accelerate the diagnosis in 40% more cancer patients who were not worked up fully in the month following a scan (the jump in sensitivity going from Mayo to LCP risk >=65% is 42.4%). For these patients, time to a cancer diagnosis and treatment could be shortened by up to 156 days compared to recommendations if applying the Mayo risk model.

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    P2.11 - Screening and Early Detection (ID 178)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Screening and Early Detection
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.11-46 - Could Malignancies Diagnosed During Round 2 of Screening Be Worked Up Sooner? (ID 2488)

      10:15 - 18:15  |  Presenting Author(s): Timor Kadir

      • Abstract
      • Slides

      Abstract not provided

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