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| Name | Class |
|---|---|
| Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University | OTHER |
| Tang-Du Hospital | OTHER |
| The First Affiliated Hospital of Guangzhou Medical University | OTHER |
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The goal of this observational study is to screen and differentiate common cancers in participants with or without suspicious lesions. The main question the investigators aim to answer is: Can the developed model, using peripheral blood cell-free DNA sequencing, work well in screening and classifying common cancers especially in the early stages? Participants will undergo the collection of 15~20ml of blood and 1~2 telephone follow-up calls.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Multicenter prospective cancer-screening cohort | The cohort, including cancer patients and non-cancer patients, will be prospectively enrolled in from different departments and centers. The focused cancers include lung cancer, gastric cancer, colorectal cancer, liver cancer, esophagus cancer, breast cancer and pancreas cancer. |
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| Measure | Description | Time Frame |
|---|---|---|
| diagnostic accuracy | This refers to the ability of the test (peripheral blood cell-free DNA sequencing) to correctly classify individuals into the categories of having or not having the disease. It is a measure of the test's overall effectiveness. The reference test is histological test for cancers or one-year follow-up for non-cancers. | Immediately after test completion |
| sensitivity | This is the ability of the test (peripheral blood cell-free DNA sequencing) to correctly identify those with the disease. It is the proportion of true positive results (those with the disease who test positive) to the total number of individuals who actually have the disease. The reference test is histological test for cancers or one-year follow-up for non-cancers. | Immediately after test completion |
| specificity | This is the ability of the test (peripheral blood cell-free DNA sequencing) to correctly identify those without disease. It is the proportion of true negative results (those without the disease who test negative) to the total number of individuals who actually do not have the disease. The reference test is histological test for cancers or one-year follow-up for non-cancers. | Immediately after test completion |
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Inclusion Criteria:
Non-cancer group: Individuals who have not been diagnosed with cancer during routine physical examinations and one-year follow-up.
Exclusion Criteria:
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PASS 21.0 is used to calculate the sample size for diagnostic tests. Based on the specificity and sensitivity of different cancer diagnoses in current and reported studies, the specificity is preset at 98%, and the sensitivity is preset at 75%. A two-sided test is chosen, with a confidence level of 1-α=0.9, and a confidence interval width of 10%. The minimum sample size for the diagnostic test is calculated as follows: each type of cancer should have no less than 202 cases in the diagnostic test, and the total number of cancer subjects should not be less than 1414. The ratio of cancer subjects to non-cancer subjects is set at 1:1. Considering the possibility of loss to follow-up, unqualified sample quality, and dropout during the study, the sample size for this study is preset at 3200.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yanglin Pan, MD | Contact | 86-13991811225 | yanglinpan@hotmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University | Not yet recruiting | Guangzhou | Guangdong | 510000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36096847 | Background | Nikanjam M, Kato S, Kurzrock R. Liquid biopsy: current technology and clinical applications. J Hematol Oncol. 2022 Sep 12;15(1):131. doi: 10.1186/s13045-022-01351-y. | |
| 36849097 | Background | Gao Q, Lin YP, Li BS, Wang GQ, Dong LQ, Shen BY, Lou WH, Wu WC, Ge D, Zhu QL, Xu Y, Xu JM, Chang WJ, Lan P, Zhou PH, He MJ, Qiao GB, Chuai SK, Zang RY, Shi TY, Tan LJ, Yin J, Zeng Q, Su XF, Wang ZD, Zhao XQ, Nian WQ, Zhang S, Zhou J, Cai SL, Zhang ZH, Fan J. Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies. Ann Oncol. 2023 May;34(5):486-495. doi: 10.1016/j.annonc.2023.02.010. Epub 2023 Feb 26. |
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| First Hospital of China Medical University |
| OTHER |
| People's Hospital of Ningxia Hui Autonomous Region | OTHER |
| Xinjiang Medical University | OTHER |
| Guanxian County People's Hospital | OTHER |
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15~20ml peripheral venous blood sample from each participant.
| The First Affiliated Hospital of Guangzhou Medical University | Recruiting | Guangzhou | Guangdong | 510000 | China |
|
| First Hospital of China Medical University | Not yet recruiting | Shenyang | Liaoning | 110000 | China |
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| People's Hospital of Ningxia Hui Autonomous Region | Not yet recruiting | Yinchuan | Ningxia | 750000 | China |
|
| First Affiliated Hospital of Air Force Military Medical University | Recruiting | Xi'an | Shaanxi | 710032 | China |
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| Tang-du Hospital | Recruiting | Xi'an | Shaanxi | 710032 | China |
|
| Guanxian People's Hospital | Recruiting | Liaocheng | Shandong | 252500 | China |
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| Affiliated Cancer Hospital of Xinjiang Medical University | Recruiting | Ürümqi | China |
|
| 36400018 | Background | Jamshidi A, Liu MC, Klein EA, Venn O, Hubbell E, Beausang JF, Gross S, Melton C, Fields AP, Liu Q, Zhang N, Fung ET, Kurtzman KN, Amini H, Betts C, Civello D, Freese P, Calef R, Davydov K, Fayzullina S, Hou C, Jiang R, Jung B, Tang S, Demas V, Newman J, Sakarya O, Scott E, Shenoy A, Shojaee S, Steffen KK, Nicula V, Chien TC, Bagaria S, Hunkapiller N, Desai M, Dong Z, Richards DA, Yeatman TJ, Cohn AL, Thiel DD, Berry DA, Tummala MK, McIntyre K, Sekeres MA, Bryce A, Aravanis AM, Seiden MV, Swanton C. Evaluation of cell-free DNA approaches for multi-cancer early detection. Cancer Cell. 2022 Dec 12;40(12):1537-1549.e12. doi: 10.1016/j.ccell.2022.10.022. Epub 2022 Nov 17. |
| 37330052 | Background | Helzer KT, Sharifi MN, Sperger JM, Shi Y, Annala M, Bootsma ML, Reese SR, Taylor A, Kaufmann KR, Krause HK, Schehr JL, Sethakorn N, Kosoff D, Kyriakopoulos C, Burkard ME, Rydzewski NR, Yu M, Harari PM, Bassetti M, Blitzer G, Floberg J, Sjostrom M, Quigley DA, Dehm SM, Armstrong AJ, Beltran H, McKay RR, Feng FY, O'Regan R, Wisinski KB, Emamekhoo H, Wyatt AW, Lang JM, Zhao SG. Fragmentomic analysis of circulating tumor DNA-targeted cancer panels. Ann Oncol. 2023 Sep;34(9):813-825. doi: 10.1016/j.annonc.2023.06.001. Epub 2023 Jun 16. |
| 37302583 | Background | Fan R, Chen L, Zhao S, Yang H, Li Z, Qian Y, Ma H, Liu X, Wang C, Liang X, Bai J, Xie J, Fan X, Xie Q, Hao X, Wang C, Yang S, Gao Y, Bai H, Dou X, Liu J, Wu L, Jiang G, Xia Q, Zheng D, Rao H, Xia J, Shang J, Gao P, Xie D, Yu Y, Yang Y, Gao H, Liu Y, Sun A, Jiang Y, Yu Y, Niu J, Sun J, Wang H, Hou J. Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures. J Hepatol. 2023 Oct;79(4):933-944. doi: 10.1016/j.jhep.2023.05.039. Epub 2023 Jun 10. |
| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| D008113 | Liver Neoplasms |
| D015179 | Colorectal Neoplasms |
| D004938 | Esophageal Neoplasms |
| D001943 | Breast Neoplasms |
| D010190 | Pancreatic Neoplasms |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |
| D008107 | Liver Diseases |
| D007414 | Intestinal Neoplasms |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
| D006258 | Head and Neck Neoplasms |
| D004935 | Esophageal Diseases |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
| D004701 | Endocrine Gland Neoplasms |
| D010182 | Pancreatic Diseases |
| D004700 | Endocrine System Diseases |
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