Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster session 09

1192P - Optimizing lung cancer screening: Independent verification of an AI/ML computer-aided detection and characterization software as medical device

Date

14 Sep 2024

Session

Poster session 09

Topics

Cancer Intelligence (eHealth, Telehealth Technology, BIG Data);  Cancer Diagnostics;  Cancer in Special Situations/ Populations;  Cancer Research

Tumour Site

Small Cell Lung Cancer;  Non-Small Cell Lung Cancer

Presenters

Sylvain Bodard

Citation

Annals of Oncology (2024) 35 (suppl_2): S762-S774. 10.1016/annonc/annonc1599

Authors

S. Bodard1, C.M. VOYTON2, P. BAUDOT2, E. Geremia2, P. Siot2, G. De Bie2, V.K. Le2, D. Francis2, B. Renoust2, B. Huet2

Author affiliations

  • 1 Paris, GH Necker - Enfants Malades, 75743 - Paris/FR
  • 2 Eyonis, Median Technologies, 06560 - Valbonne/FR

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 1192P

Background

Detection and risk stratification of pulmonary nodules during a Lung Cancer Screening (LCS) examination is time-consuming, prone to false positives and missed detections. Leveraging Artificial Intelligence (AI) in such tasks has outperformed human readers in detection, and current risk models (Brock, MAYO) in risk prediction. Here, we present the results of an independent verification study to explore the software’s detection and characterization performance on an external dataset.

Methods

The model was trained on 10,872 patients (543 cancers) independently annotated from the NLST (National Lung Screening Trial) and LIDC (Lung Image Database Consortium) cohorts. 264 patients meeting the USPSTF criteria (age 50-80, Smoker) were collected from the EU (26%) and the USA (74%) for independent verification. The dataset comprised 88 cancer patients and 176 benign, with average size for all nodules of 6.2 ± 3.2 mm, and 17.5 ± 5.8 mm for cancerous nodules. To generate reference standards, two radiologists independently annotated each patient, with a third acting as an adjudicator to arrive at a consensus for location and nodule diagnosis (histopathology or ≥12 month stability).

Results

The verification AUC for risk prediction was 0.95, with a sensitivity of 93.2% and specificity of 87.5% at the Youden index. Cancer detection sensitivity was 91.2%, with an average of 0.44 false positive detections per scan. Performances were consistent across multiple technical and clinical parameters, including CT manufacturer, kernel hardness, kernel slice thickness, patient sex, data source, and nodule solidity (see the table). Table: 1192P

Subclass type Subclass AUC
Manufacturer SIEMENS Healthineers 0.96
GE Healthcare 0.94
Canon/Toshiba 0.95
Kernel Sharp 0.98
Average 0.94
Soft 0.93
Slice thickness (mm) 0.5-0.75 0.96
1-1.25 0.94
1.25-1.5 0.96
Sex Female 0.95
Male 0.95
Source EU 0.90
USA 0.97
Nodule solidity Solid 0.96
Part-solid 0.93

Conclusions

This study demonstrates that the AI software is robust to external data with similar performances versus the NLST test set (AUC: 0.97) and among subclasses. More accurate screening driven by AI promises to be beneficial to LCS, reducing unneeded exams, costs, and radiologist time.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Median Technologies.

Funding

Median Technologies.

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.