Abstract 174P
Background
Application of computer and artificial intelligence (AI) has made revolutionary progress in medical fields. Computer-assisted diagnosis (CAD) on pathology is successful in diagnosis, classification, staging and predicting prognosis of cancer.
Methods
In this work, we used the technique of convolutional neural networks (CNN) for training of morphological images of gastric cancer, which is a promising application of artificial intelligence technique for medical image analysis, especially for cancer pathology.
Results
We introduced successful works of AI-aided gastric cancer analysis for over 300 cases. AI-assisted pathology technique could help the pathologist in detecting and locating the abnormal area in images and to diagnose and classify benign or malignant tissues. Therefore, AI exactly improves the diagnostic status of gastric cancer, such as in interpretation of medical images and classification between benign and cancerous tissues. The complete workflow of AI diagnosis for gastric cancer is under construction. AI is more useful in releasing pathologists from repeated work, and improving efficiency.
Conclusions
In conclusions, there are still notable technical obstacles before this approach can be used to improve conventional clinical practice. Higher performance needs corrected marking images by experienced pathologist and require ‘training’ algorithms on tremendous data. We stressed the importance of proper algorithms for improving confidence of analytic results that is close to human experts. Additionally, we indicated the gaps in current research and principal resolutions for advancing the field.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors without further recourse to the authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
221P - Comparative analysis of first-line immune checkpoint inhibitor versus carboplatin-based chemotherapy for cisplatin-ineligible metastatic urothelial carcinoma: A multicenter, retrospective real-world evidence
Presenter: Hsiang‐Lan Lai
Session: Poster display session
Resources:
Abstract
222P - Does tumor grade really improve the prognostic ability of the staging system for men with penile cancer: A SEER database analysis
Presenter: Ravi Kanodia
Session: Poster display session
Resources:
Abstract
223TiP - EV-301: A phase III trial in progress evaluating enfortumab vedotin versus chemotherapy in patients with locally advanced or metastatic urothelial carcinoma
Presenter: Daniel Petrylak
Session: Poster display session
Resources:
Abstract
230P - Olaparib maintenance therapy in patients (pts) with a BRCA1 and/or BRCA2 mutation (BRCAm) and newly diagnosed advanced ovarian cancer (OC): SOLO1 China cohort
Presenter: Lingying Wu
Session: Poster display session
Resources:
Abstract
231P - Cost-effectiveness of olaparib vs routine surveillance in the maintenance setting for patients with BRCA-mutated advanced ovarian cancer after response to first-line platinum-based chemotherapy in Singapore
Presenter: David SP Tan
Session: Poster display session
Resources:
Abstract
233P - Incorporation of correlative studies in ovarian cancers in the era of precision medicine: Assessment of accountability and utility
Presenter: Nadia Hitchen
Session: Poster display session
Resources:
Abstract
234P - Implementation of mainstream BRCA testing in epithelial ovarian cancer in a tertiary centre
Presenter: Edbert Wong
Session: Poster display session
Resources:
Abstract
235P - The efficacy of radiation therapy in ovarian carcinoma: A propensity score analysis of a population-based study
Presenter: Jian-Guo Zhou
Session: Poster display session
Resources:
Abstract
236P - Front-line maintenance therapy for platinum-sensitive ovarian cancer: What’s next PARP inhibitors?
Presenter: Han Gong
Session: Poster display session
Resources:
Abstract
237P - PARP inhibitor in platinum resistant ovarian cancer: Single center real world experience
Presenter: Saphalta Baghmar
Session: Poster display session
Resources:
Abstract