A panel of seven protein tumour markers for effective and affordable multi-cancer early detection by artificial intelligence: a large-scale and multicentre case-control study

Background Early detection of cancer aims to reduce 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. We aimed to assess the performance and robustness of a protein assay (OncoSeek) for multi-cancer early detection (MCED) that is likely to be more practical in LMICs.Methods This observational study comprises a retrospective analysis on the data generated from the routine clinical testings at SeekIn and Sun Yat-sen Memorial Hospital. 7565 participants (954 with cancer and 6611 without) from the two sites were divided into training and independent validation cohort. The second validation cohort (1005 with cancer and 812 without) was from Johns Hopkins University School of Medicine. Patients with cancer prior to therapy were eligible for inclusion in the study. Individuals with no history of cancer were enrolled from the participating sites as the non-cancer group. 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 patients with cancer from those without cancer 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 in blood.Findings Between November 2012 and May 2022, 7565 participants were enrolled at SeekIn and Sun Yat-sen Memorial Hospital. The conventional clinical method, which relies only on a single threshold for each PTM, would suffer from a high false positive rate that accumulates as the number of markers increased. OncoSeek was empowered by AI technology to significantly reduce the false positive rate, increasing the specificity from 56.9% (95% confidence interval [CI]: 55.8-58.0) to 92.9% (92.3-93.5). In all cancer types, the overall sensitivity of OncoSeek was 51.7% (49.4-53.9), resulting in 84.3% (83.5-85.0) 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, oesophagus, ovary, pancreas, and stomach), which account for & SIM;59.2% of global cancer deaths annually. Furthermore, it has shown excellent sensitivity in several high-mortality cancer types for which routine screening tests are lacking in the clinic, such as the sensitivity of pancreatic cancer which was 77.6% (69.3-84.6). 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 MCED which is non-invasive, easy, efficient, and robust. Moreover, the accuracy of TOO facilitates the follow-up diagnostic workup.Funding The National Key Research and Development Programme of China.Copyright & COPY; 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The study aimed to evaluate the performance and robustness of a new protein assay for cancer early detection in low- and middle-income countries. The assay utilized artificial intelligence to identify cancer patients based on protein markers and clinical information, as well as predict the tissue of origin. The results showed high specificity and sensitivity, and the assay performed well for various cancer types.