Poster viewing and lunch

32P - Improvement of Diagnostic Accuracy of Breast Cancer Using Multi-protein Signature Markers through Machine Learning (ID 252)

Lecture Time
12:15 - 12:15
Session Name
Poster viewing and lunch
Exhibition area
Fri, 12.05.2023
12:15 - 13:00
  • Yumi Kim (Seoul, Korea, Republic of)
  • Yumi Kim (Seoul, Korea, Republic of)
  • Sungsoo Kim (Yongin-si, Korea, Republic of)
  • HyeonSeok Shin (Yongin-si, Korea, Republic of)
  • Kyung-Geun Ahn (Yongin-si, Korea, Republic of)
  • Won Shik Han (Seoul, Korea, Republic of)
  • Dong-Young Noh (Seoul, Korea, Republic of)



We have developed a 3-protein signature blood marker (Mastocheck®) for early diagnosis of breast cancer. The purpose of this study is to improve the performance of the previously developed blood markers.


Blood from 196 breast cancer patients and 196 healthy control groups were prospectively collected.Through the development of a biomarker detectable library, PepQuant, peptides that are optimal for MS/MS detection were selected. After chemically synthesizing these selected proteins, these were quantified by multiple reaction monitoring (MRM) methods. Seven final proteins were derived by applying the PepQuant library for breast cancer biomarker discovery and verification. Machine learning algorithms was trained as protein candidates identified between breast cancer patients and healthy controls. As in previous studies, performance evaluation was conducted based on sensitivity, specificity, accuracy, false positive rate, false negative rate, positive predictive value, and negative predictive value for breast cancer diagnosis.


The sensitivity, specificity, and accuracy of Mastocheck®, were 69.4%, 83.7%, and 76.5%, respectively, which is relatively similar to that of previous studies’ results. The false positive rate(FPR) and the false negative rate(FNR) were 16.3% and 30.7%, and the positive predictive value(PPV) and negative predictive value(NPV) were 81.0% and 73.2%, respectively. During the study when 7-protein signature was combined with an artificial intelligence(AI) technique for the analysis, the sensitivity, specificity, and accuracy of diagnosis were 88.3%, 83.2%, and 85.7%, respectively, showing superior performance compared to Mastocheck®. The FPR and FNR were 16.8% and 11.7%, indicating that the FNR was improved by 20% compared to Mastecheck®. In addition, it was recognized that the PPV and NPV were also improved to 84.0% and 87.6%.


Through the collection of new prospective samples, the study confirmed that the diagnostic performance of Mastocheck® was repeatedly maintained. In addition, breast cancer diagnosis using 7-protein signatures with AI model showed that breast cancer diagnosis can be remarkably improved.

Legal entity responsible for the study

CHA Gangnam Medical Center.


Bertis. Inc.


Y. Kim: Financial Interests, Personal, Stocks/Shares: Bertis Inc. S. Kim, H. Shin, K. Ahn: Financial Interests, Personal, Member: Bertis Inc. W.S. Han: Financial Interests, Personal, Stocks/Shares: Bertis Inc; Financial Interests, Personal, Leadership Role: DCgen. D. Noh: Financial Interests, Personal, Leadership Role: Bertis Inc.