Development of a deep learning-based model to diagnose mixed-type gastric cancer accurately.

Autor: Ning X; Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China., Liu R; Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China., Wang N; Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China., Xiao X; Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China., Wu S; Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China., Wang Y; Department of Respiratory Diseases, Central Medical Branch of PLA General Hospital, Beijing 100081, China., Yi C; Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China; Shenzhen Key Laboratory of Chinese Medicine Active substance screening and Translational Research, Shenzhen 518107, China; Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangzhou 510080, China. Electronic address: yichj@mail.sysu.edu.cn., He Y; Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China. Electronic address: heyulong@sysush.com., Li D; Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China. Electronic address: lidanyy@126.com., Chen H; School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Jazyk: angličtina
Zdroj: The international journal of biochemistry & cell biology [Int J Biochem Cell Biol] 2023 Sep; Vol. 162, pp. 106452. Date of Electronic Publication: 2023 Jul 21.
DOI: 10.1016/j.biocel.2023.106452
Abstrakt: Objective: The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the potential to overcome this hurdle. This study aimed to leverage deep machine learning techniques to establish a precise and efficient diagnostic approach for this cancer type which can also predict the metastatic risk using two software, U-Net and QuPath, which have not been trialled in gastric cancers.
Methods: A U-Net neural network was trained to recognise, and segment differentiated components from 186 pathology images of mixed-type gastric cancer. Undifferentiated components in the same images were annotated using the open-source pathology imaging software QuPath. The outcomes from U-Net and QuPath were used to calculate the ratios of differentiation/undifferentiated components which were correlated to lymph node metastasis.
Results: The models established by U-Net recognised ∼91% of the regions of interest, with precision, recall, and F1 values of 90.2%, 90.9% and 94.6%, respectively, indicating a high level of accuracy and reliability. Furthermore, the receiver operating characteristic curve analysis showed an area under the cure of 91%, indicating good performance. A bell-curve correlation between the differentiated/undifferentiated ratio and lymphatic metastasis was found (highest risk between 0.683 and 1.03), which is paradigm-shifting.
Conclusion: U-Net and QuPath exhibit promising accuracy in the identification of differentiated and undifferentiated components in mixed-type gastric cancer, as well as paradigm-shifting prediction of metastasis. These findings bring us one step closer to their potential clinical application.
Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: