| 描述信息 |
Background: Cancer early detection aims at reducing cancer deaths. Unfortunately, many established cancer screening technologies are not suitable for use in low- and middle-income countries (LMICs) due to cost, complexity, and dependency on extensive medical infrastructure. Method: This observational study from SeekIn Inc. comprises a retrospective analysis on the data generated from the routine clinical testings at SeekIn and Sun Yat-sen Memorial Hospital (Guangzhou, China) from November 2012 to May 2022. 9383 participants (1959 with cancer and 7423 without) who were divided into one training and two independent validation cohorts. Patients with cancer disease prior to therapy and were eligible for inclusion in the study. Individuals with normal general medical tests were enrolled from participating sites to control.One tube of peripheral blood was collected from each participant and quantified a panel of seven selected protein tumour markers (PTMs) by a common clinical electrochemiluminescence immunoassay analyser. An algorithm named OncoSeek was established using artificial intelligence (AI) to distinguish cancer from non-cancer individuals by calculating the probability of cancer (POC) index based on the quantification results of the seven PTMs and clinical information including sex and age of the individuals, and to predict the possible affected tissue of origin (TOO) for those who have been detected with cancer signals. Findings:Between November 2012 and May 2022, 7565 participants were enrolled at SeekIn and Sun Yat-sen Memorial Hospital. The conventional clinical method that relied only on a single threshold for each PTM would make a big problem when combining the results of those markers as the false positive rate would accumulate as the number of markers increased. Nevertheless, OncoSeek was empowered by AI technology to significantly reduce the false positive rate, increasing the specificity from 56.9% [95% confidence interval (CI): 55.8% to 58.0%] to 92.9% (95% CI: 92.3% to 93.5%). In all cancer types, the overall sensitivity of OncoSeek was 51.7% (95% CI: 49.4% to 53.9%), resulting in 84.3% (95% CI: 83.5% to 85.0%) accuracy. The performance was generally consistent in the training and the two validation cohorts. The sensitivities ranged from 37.1% to 77.6% for the detection of the nine common cancer types (breast, colorectum, liver, lung, lymphoma, esophagus, ovary, pancreas, and stomach), which account for ~59.2% of global cancer deaths annually. Furthermore, it has shown excellent sensitivity in several high-mortality cancer types for which there are lacking routine screening tests in the clinic, such as the sensitivity of pancreatic cancer was 77.6% (95% CI: 69.3% to 84.6%). The overall accuracy of TOO prediction in the true positives was 66.8%, which could assist the clinical diagnostic workup. Interpretation: OncoSeek significantly outperforms the conventional clinical method, representing a novel blood-based test for multi-cancer early detection (MCED) which is non-invasive, easy, efficient, and robust. Moreover, the accuracy of TOO facilitates the follow-up diagnostic workup. OncoSeek is affordable (less than $25) and accessible requiring nothing more than a blood draw at the screening sites, which makes it acceptable and sustainable in LMICs. |
