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 18

1441P - Clinical application of a machine learning-based pathomics signature for predicting lymph node metastasis in early gastric cancer

Date

14 Sep 2024

Session

Poster session 18

Topics

Cancer Intelligence (eHealth, Telehealth Technology, BIG Data);  Staging and Imaging

Tumour Site

Gastric Cancer

Presenters

Weiyao Li

Citation

Annals of Oncology (2024) 35 (suppl_2): S878-S912. 10.1016/annonc/annonc1603

Authors

W. Li1, X. JI1, Y. Zhao2, Z. Yang3

Author affiliations

  • 1 Gastrointestinal Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, 510555 - Guangzhou/CN
  • 2 Department Of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, 510655 - Guangzhou/CN
  • 3 Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, 510655 - Guangzhou/CN

Resources

Login to get immediate access to this content.

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

Abstract 1441P

Background

The accurate detection of lymph node metastasis (LNM) in early gastric cancer (EGC) remains a challenge, conventional radical gastrectomy may not be warranted for EGC patients with a low LNM risk and a favorable prognosis. This study aims to construct a risk prediction model of lymph node metastasis based on clinicopathologic features, tumor budding, convolutional neural network, and machine learning in early gastric cancer.

Methods

Clinicopathological features and tumor budding (TB) were assessed using the image recognition software Qupath. There was a total of 1523471 patches, 260998 patches, and 166594 patches from three patient cohorts that were retrospectively analyzed. Convolutional neural networks (CNNs) were used for deep transfer learning to classify patches and extract features. LASSO regression and multiple machine learning algorithms were employed to combine CNN features with clinicopathological features. The risk prediction model was also verified in two independent validation cohorts.

Results

Among 200 patients, LNM incidence was 27.14% in the training cohort, 15.15% in the internal validation cohort, and 14.82% in the external validation cohort. Multivariate analysis identified pathological T stage and ITBCC-TB grade as independent LNM risk factors (p < 0.05). The Resnet152 CNN model demonstrated excellent performance with AUC values of 0.995, 0.988, and 0.965 in the training, internal validation, and external validation cohorts, respectively. The CNN-ML model using Gradient Boosting exhibited superior predictive capacity, with AUC values of 0.901 (95% CI 0.844-0.957), 0.871 (95% CI 0.726-0.999), and 0.804 (95% CI 0.555-0.999) in the training and two independent validation cohorts.

Conclusions

Pathological T stage and ITBCC-TB grade were identified as independent risk factors for LNM in EGC. The integration of clinicopathological features, image recognition, and deep transfer learning using CNNs resulted in a highly accurate predictive model for LNM in EGC. This model will provide valuable insights into the development of a more comprehensive and individualized approach to managing EGC, thereby improving the quality of care for affected patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

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.