skip to main content
10.1145/3644116.3644229acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisaimsConference Proceedingsconference-collections
research-article

Bi-omics prognostic model for invasive ductal carcinoma using deep learning

Published: 05 April 2024 Publication History

Abstract

Invasive ductal carcinoma is a common subtype of breast cancer, and current prognostic models are mainly single-omics models. We aimed to develop a bi-omics model combining cancer-associated fibroblast(CAF) gene expression and Whole-field digital slice image(WSI) features to improve the accuracy of predicting prognostic effect. A total of 491 patients were included in the experiment and were randomized into training and validation sets. CAF genes associated with patient survival were screened by gene set enrichment analysis (GSEA), differential expression analysis and Cox regression. Whole-field digital slice image features were extracted using deep learning-based U²Net model and artificial feature approaches, and a bi-omics prognostic model was established by multi-factor Cox regression, and finally survival analysis and AUC values were applied to validate the model accuracy. The prognostic prediction performance of the combined bi-omics model was significantly improved compared to the single-omics model. This study highlights the potential of integrating CAF gene expression and WSI features to better predict the prognosis of IDC patients and provide valuable information for clinical decision making and personalized treatment strategies.

References

[1]
Sung H, Ferlay J, Siegel R L, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA: a cancer journal for clinicians, 2021, 71(3): 209-249.
[2]
Papait A, Romoli J, Stefani FR, Chiodelli P, Montresor MC, Agoni L, Silini AR, Parolini O. Fight the Cancer, Hit the CAF! Cancers. 2022; 14(15):3570. https://doi.org/10.3390/cancers14153570
[3]
Benzer S. From the Gene to Behavior. JAMA. 1971; 218(7):1015–1022.
[4]
Liu, Jianfang, "An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics." Cell 173.2. 2018: 400-416.
[5]
Minaee, Shervin, "Image segmentation using deep learning: A survey." IEEE transactions on pattern analysis and machine intelligence 44.7. 2021: 3523-3542.
[6]
Walkowski, Slawomir and Szymas, Janusz. ‘Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI)’. 1 Jan. 2012: 117 – 122.
[7]
Martino, Francesco, "Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images." Applied Sciences 10.22. 2020: 8285.
[8]
Martino, Francesco, "Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images." Applied Sciences 10.22. 2020: 8285.
[9]
Kothari, Charu, "Identification of a gene signature for different stages of breast cancer development that could be used for early diagnosis and specific therapy." Oncotarget 9.100. 2018: 37407.
[10]
Kanavati, Fahdi, Shin Ichihara, and Masayuki Tsuneki. "A deep learning model for breast ductal carcinoma in situ classification in whole slide images." Virchows Archiv 480.5. 2022: 1009-1022.
[11]
Schattenkerk, Laurens D. Eeftinck, "Adhesive small bowel obstruction following abdominal surgery in young children (≤ 3 years): A retrospective analysis of incidence and risk factors using multivariate cox regression." Journal of pediatric surgery 57.9. 2022: 55-60.
[12]
Goode, Adam, "OpenSlide: A vendor-neutral software foundation for digital pathology." Journal of pathology informatics 4.1. 2013: 27.
[13]
Qin, Xuebin, "U2-Net: Going deeper with nested U-structure for salient object detection." Pattern recognition 106. 2020: 107404.
[14]
Li, Yue Feng, Fu Lai Pei, and Ming Zheng Cao. "CircRNA_101951 promotes migration and invasion of colorectal cancer cells by regulating the KIF3A mediated EMT athway." Experimental and Therapeutic Medicine 19.5. 2020: 3355-3361.
[15]
Zhang, Ziyu, "FoxM1 promotes the migration of ovarian cancer cell through KRT5 and KRT7." Gene 757. 2020: 144947.
[16]
Luongo, Francesca, "PTEN tumor-suppressor: the dam of stemness in cancer." Cancers 11.8. 2019: 1076.
[17]
Tsianos EV, Katsanos KH, Tsianos VE. Role of genetics in the diagnosis and prognosis of Crohn's disease. World J Gastroenterol. 2012 Jan 14;18(2):105-18. 22253516; PMCID: PMC3257437.
[18]
Lin, Zhiquan, "A multi-omics signature to predict the prognosis of invasive ductal carcinoma of the breast." Computers in Biology and Medicine 151. 2022: 106291.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
October 2023
1394 pages
ISBN:9798400708138
DOI:10.1145/3644116
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 April 2024

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ISAIMS 2023

Acceptance Rates

Overall Acceptance Rate 53 of 112 submissions, 47%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 5
    Total Downloads
  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media