Craif Announces Joint Research Results on Early Detection of Gynecological Tumors Using Urinary microRNA at the 78th Annual Congress of the Japan Society of Obstetrics and Gynecology
Craif Inc. announced joint research results with Hokkaido University on early detection of gynecological tumors using urinary microRNA at the 78th Annual Congress of the Japan Society of Obstetrics and Gynecology. The study demonstrated the potential of a highly accurate, non-invasive screening method.
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- 📰 Published: June 4, 2026 at 10:00
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Craif Inc. (Headquarters: Shinjuku-ku, Tokyo; CEO: Ryuichi Onose), a bio-AI startup, announced that it jointly presented research results on gynecological tumor screening titled "Study on the Applicability of Urinary Exosomes for Gynecological Examinations" with Professor Hidemichi Watari and colleagues from the Department of Obstetrics and Gynecology, Faculty of Medicine, Hokkaido University, at the 78th Annual Congress of the Japan Society of Obstetrics and Gynecology. Craif will continue to challenge its mission of "realizing a society where people can live out their natural lifespan" by broadly disseminating new initiatives for cancer prevention and early detection.
■ Key Research Findings
Highly Accurate Gynecological Tumor Detection Using Urinary Extracellular Vesicle-Derived microRNA
A comprehensive analysis of microRNAs contained in urinary extracellular vesicles (exosomes) was conducted to construct a screening panel for detecting gynecological tumors. Evaluation using a machine learning model achieved high diagnostic accuracy with an AUC of 0.937, sensitivity of 85.6%, and specificity of 94.4%.
Non-Invasive Test Reducing Psychological and Physical Barriers to Gynecological Examinations
In Japan, the participation rate for gynecological examinations remains around 40%, with issues such as psychological resistance to internal examinations and constraints on medical resources. This urine-based testing method has the potential to remove these barriers and allow more women to access early gynecological care.
Potential for Application in Large-Scale Screening
Due to its non-invasive nature and high detection accuracy, this testing method is expected to be applied in future large-scale gynecological screening programs. It is anticipated to contribute to reducing mortality from gynecological cancers through early detection and early treatment.
■ Joint Research Overview
Gynecological malignancies are the second leading cause of cancer incidence and death among women, following breast cancer. Meanwhile, benign diseases such as uterine fibroids and endometriosis also impose a significant clinical burden, yet no systematic screening method has been established. This study investigated a non-invasive screening strategy based on comprehensive profiling of microRNAs derived from urinary extracellular vesicles (EVs) for these malignant and benign gynecological diseases. The constructed diagnostic model achieved an AUC of 0.937, sensitivity of 85.6%, and specificity of 94.4%, demonstrating the potential of urinary microRNAs as useful biomarkers for early screening of gynecological tumors.
■ About the 78th Annual Congress of the Japan Society of Obstetrics and Gynecology
Event Period: Friday, May 15, 2026 – Sunday, May 17, 2026
Location: Sapporo, Hokkaido, Japan
Official Website: https://www.congre.co.jp/jsog2026/index.html
■ Glossary
Extracellular Vesicles (Exosomes): Small, bag-like particles secreted by cells, used for intercellular communication. They contain substances such as microRNA and are attracting attention as important biomarkers useful for cancer diagnosis.
MicroRNA: Very small RNA molecules found inside cells that regulate gene function. Since the types and amounts of microRNA change in cancer cells, they are considered useful for early disease detection and prognosis prediction.
Machine Learning: A type of AI technology that learns patterns from large amounts of data to make future predictions or classifications. In this study, it was used to accurately determine the presence or absence of disease using microRNA data for cancer diagnosis.
AUC (Area Under the Curve): An indicator of diagnostic accuracy, taking values from 0 to 1. Values closer to 1 indicate higher diagnostic performance.
Sensitivity and Specificity: Sensitivity indicates the proportion of individuals with the disease who test positive, while specificity indicates the proportion of individuals without the disease who test negative.
■ About Craif
Craif is a bio-AI startup founded in 2018 dedicated to early cancer detection. By integrating its proprietary analysis technology platform "NANO IP® (NANO Intelligence Platform)", which detects various biomarkers such as DNA and microRNA from urine and other bodily fluids with high precision, with AI technology, Craif is developing innovative tests enabling ultra-early cancer detection, early treatment, and early recovery. By broadly delivering the power of biotechnology and AI to society, Craif promotes its vision of "realizing a society where people can live out their natural lifespan."
[Company Overview]
Company Name: Craif Inc. (Japanese: クライフ)
Representative: Representative Director Ryuichi Onose
Established: May 2018
Capital: 100 million yen (as of April 1, 2025)
Business: Research and development of next-generation tests for early detection of diseases, primarily in the cancer field, and realization of personalized medicine; provision of the urine cancer risk test "MySignal®"
Headquarters: THE PORTAL iidabashi B1F, 8-30 Shingawa-cho, Shinjuku-ku, Tokyo
URL: https://craif.com/
■ Key Research Findings
Highly Accurate Gynecological Tumor Detection Using Urinary Extracellular Vesicle-Derived microRNA
A comprehensive analysis of microRNAs contained in urinary extracellular vesicles (exosomes) was conducted to construct a screening panel for detecting gynecological tumors. Evaluation using a machine learning model achieved high diagnostic accuracy with an AUC of 0.937, sensitivity of 85.6%, and specificity of 94.4%.
Non-Invasive Test Reducing Psychological and Physical Barriers to Gynecological Examinations
In Japan, the participation rate for gynecological examinations remains around 40%, with issues such as psychological resistance to internal examinations and constraints on medical resources. This urine-based testing method has the potential to remove these barriers and allow more women to access early gynecological care.
Potential for Application in Large-Scale Screening
Due to its non-invasive nature and high detection accuracy, this testing method is expected to be applied in future large-scale gynecological screening programs. It is anticipated to contribute to reducing mortality from gynecological cancers through early detection and early treatment.
■ Joint Research Overview
Gynecological malignancies are the second leading cause of cancer incidence and death among women, following breast cancer. Meanwhile, benign diseases such as uterine fibroids and endometriosis also impose a significant clinical burden, yet no systematic screening method has been established. This study investigated a non-invasive screening strategy based on comprehensive profiling of microRNAs derived from urinary extracellular vesicles (EVs) for these malignant and benign gynecological diseases. The constructed diagnostic model achieved an AUC of 0.937, sensitivity of 85.6%, and specificity of 94.4%, demonstrating the potential of urinary microRNAs as useful biomarkers for early screening of gynecological tumors.
■ About the 78th Annual Congress of the Japan Society of Obstetrics and Gynecology
Event Period: Friday, May 15, 2026 – Sunday, May 17, 2026
Location: Sapporo, Hokkaido, Japan
Official Website: https://www.congre.co.jp/jsog2026/index.html
■ Glossary
Extracellular Vesicles (Exosomes): Small, bag-like particles secreted by cells, used for intercellular communication. They contain substances such as microRNA and are attracting attention as important biomarkers useful for cancer diagnosis.
MicroRNA: Very small RNA molecules found inside cells that regulate gene function. Since the types and amounts of microRNA change in cancer cells, they are considered useful for early disease detection and prognosis prediction.
Machine Learning: A type of AI technology that learns patterns from large amounts of data to make future predictions or classifications. In this study, it was used to accurately determine the presence or absence of disease using microRNA data for cancer diagnosis.
AUC (Area Under the Curve): An indicator of diagnostic accuracy, taking values from 0 to 1. Values closer to 1 indicate higher diagnostic performance.
Sensitivity and Specificity: Sensitivity indicates the proportion of individuals with the disease who test positive, while specificity indicates the proportion of individuals without the disease who test negative.
■ About Craif
Craif is a bio-AI startup founded in 2018 dedicated to early cancer detection. By integrating its proprietary analysis technology platform "NANO IP® (NANO Intelligence Platform)", which detects various biomarkers such as DNA and microRNA from urine and other bodily fluids with high precision, with AI technology, Craif is developing innovative tests enabling ultra-early cancer detection, early treatment, and early recovery. By broadly delivering the power of biotechnology and AI to society, Craif promotes its vision of "realizing a society where people can live out their natural lifespan."
[Company Overview]
Company Name: Craif Inc. (Japanese: クライフ)
Representative: Representative Director Ryuichi Onose
Established: May 2018
Capital: 100 million yen (as of April 1, 2025)
Business: Research and development of next-generation tests for early detection of diseases, primarily in the cancer field, and realization of personalized medicine; provision of the urine cancer risk test "MySignal®"
Headquarters: THE PORTAL iidabashi B1F, 8-30 Shingawa-cho, Shinjuku-ku, Tokyo
URL: https://craif.com/
FAQ
What technology was used in this study?
It uses comprehensive analysis of microRNAs in urinary extracellular vesicles (exosomes) evaluated by a machine learning model.
What is the diagnostic accuracy of the study?
It achieved high diagnostic accuracy with an AUC of 0.937, sensitivity of 85.6%, and specificity of 94.4%.
When will this test be available?
It is currently in the research stage. A timeline for general availability has not been set, pending large-scale clinical trials and regulatory approval.