Gastric Cancer Early Detection Using Routine Blood Tests and Artificial Intelligence: Validation Study at Queen Elizabeth Hospital

Minji SEO; Ka Man CHEUNG; Winnie Wing-Yan SUNG; James Chung-Hang CHOW; Harry Ho-Yin YIU; Henry Hin-Wai LIU; Peter Yat-Ming WOO; Ada Sze-Man YIP; Daisy May-Yee KAN; Stephen Ka-Kei NG; Martin SC LEE; Sau-San KAO; David LAM; Lik To WU

Gastric Cancer Early Detection Using Routine Blood Tests and Artificial Intelligence: Validation Study at Queen Elizabeth Hospital

Minji SEO
, Ka Man CHEUNG^*
, Winnie Wing-Yan SUNG
, James Chung-Hang CHOW
, Harry Ho-Yin YIU
, Henry Hin-Wai LIU
, Peter Yat-Ming WOO
, Ada Sze-Man YIP
, Daisy May-Yee KAN
, Stephen Ka-Kei NG
, Martin SC LEE
, Sau-San KAO
, David LAM^*
, Lik To WU

^*Corresponding author for this work

Department of Mechanical and Aerospace Engineering

Research output: Contribution to conference › Conference Paper › peer-review

Abstract

The high mortality of gastric cancer (GC) is largely due to late diagnosis, and a reliable non-invasive detection method is lacking. While existing tumour markers have low sensitivity, routine blood tests (RBTs) like CBC and LRFT can indicate hidden cancer through signs of anaemia, inflammation, and cachexia. The HKUST, HA clinical team (oncology, medicine, surgery, neurosurgery), and HADCL co-developed the RBT-GC model, which can detect GC up to 6 months prior to clinical diagnosis. Previously presented at ASCO 2023 and ESMO GI 2024, this study aims to validate RBT-GC's sensitivity in a hospital setting up to 6 months before the diagnosis date.

Original language	English
Publication status	Accepted/In press - Mar 2025
Event	Hospital Authority Convention 2025 - Duration: 1 Mar 2025 → 1 Mar 2025

Conference

Conference	Hospital Authority Convention 2025
Period	1/03/25 → 1/03/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

SEO, M., CHEUNG, K. M., SUNG, W. W.-Y., CHOW, J. C.-H., YIU, H. H.-Y., LIU, H. H.-W., WOO, P. Y.-M., YIP, A. S.-M., KAN, D. M.-Y., NG, S. K.-K., LEE, M. SC., KAO, S.-S., LAM, D., & WU, L. T. (Accepted/In press). Gastric Cancer Early Detection Using Routine Blood Tests and Artificial Intelligence: Validation Study at Queen Elizabeth Hospital. Paper presented at Hospital Authority Convention 2025.

@conference{f27ac635adf9450fb725b23e59f2f86d,

title = "Gastric Cancer Early Detection Using Routine Blood Tests and Artificial Intelligence: Validation Study at Queen Elizabeth Hospital",

abstract = "The high mortality of gastric cancer (GC) is largely due to late diagnosis, and a reliable non-invasive detection method is lacking. While existing tumour markers have low sensitivity, routine blood tests (RBTs) like CBC and LRFT can indicate hidden cancer through signs of anaemia, inflammation, and cachexia. The HKUST, HA clinical team (oncology, medicine, surgery, neurosurgery), and HADCL co-developed the RBT-GC model, which can detect GC up to 6 months prior to clinical diagnosis. Previously presented at ASCO 2023 and ESMO GI 2024, this study aims to validate RBT-GC's sensitivity in a hospital setting up to 6 months before the diagnosis date.",

author = "Minji SEO and CHEUNG, \{Ka Man\} and SUNG, \{Winnie Wing-Yan\} and CHOW, \{James Chung-Hang\} and YIU, \{Harry Ho-Yin\} and LIU, \{Henry Hin-Wai\} and WOO, \{Peter Yat-Ming\} and YIP, \{Ada Sze-Man\} and KAN, \{Daisy May-Yee\} and NG, \{Stephen Ka-Kei\} and LEE, \{Martin SC\} and Sau-San KAO and David LAM and WU, \{Lik To\}",

year = "2025",

month = mar,

language = "English",

note = "Hospital Authority Convention 2025 ; Conference date: 01-03-2025 Through 01-03-2025",

}

TY - CONF

T1 - Gastric Cancer Early Detection Using Routine Blood Tests and Artificial Intelligence: Validation Study at Queen Elizabeth Hospital

AU - SEO, Minji

AU - CHEUNG, Ka Man

AU - SUNG, Winnie Wing-Yan

AU - CHOW, James Chung-Hang

AU - YIU, Harry Ho-Yin

AU - LIU, Henry Hin-Wai

AU - WOO, Peter Yat-Ming

AU - YIP, Ada Sze-Man

AU - KAN, Daisy May-Yee

AU - NG, Stephen Ka-Kei

AU - LEE, Martin SC

AU - KAO, Sau-San

AU - LAM, David

AU - WU, Lik To

PY - 2025/3

Y1 - 2025/3

N2 - The high mortality of gastric cancer (GC) is largely due to late diagnosis, and a reliable non-invasive detection method is lacking. While existing tumour markers have low sensitivity, routine blood tests (RBTs) like CBC and LRFT can indicate hidden cancer through signs of anaemia, inflammation, and cachexia. The HKUST, HA clinical team (oncology, medicine, surgery, neurosurgery), and HADCL co-developed the RBT-GC model, which can detect GC up to 6 months prior to clinical diagnosis. Previously presented at ASCO 2023 and ESMO GI 2024, this study aims to validate RBT-GC's sensitivity in a hospital setting up to 6 months before the diagnosis date.

AB - The high mortality of gastric cancer (GC) is largely due to late diagnosis, and a reliable non-invasive detection method is lacking. While existing tumour markers have low sensitivity, routine blood tests (RBTs) like CBC and LRFT can indicate hidden cancer through signs of anaemia, inflammation, and cachexia. The HKUST, HA clinical team (oncology, medicine, surgery, neurosurgery), and HADCL co-developed the RBT-GC model, which can detect GC up to 6 months prior to clinical diagnosis. Previously presented at ASCO 2023 and ESMO GI 2024, this study aims to validate RBT-GC's sensitivity in a hospital setting up to 6 months before the diagnosis date.

M3 - Conference Paper

T2 - Hospital Authority Convention 2025

Y2 - 1 March 2025 through 1 March 2025

ER -

Gastric Cancer Early Detection Using Routine Blood Tests and Artificial Intelligence: Validation Study at Queen Elizabeth Hospital

Abstract

Conference

UN SDGs

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