Multi-cancer Detection Using Pattern Formation in Drying Body Fluids: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies

Abstract

IntroductionThe ability to detect multiple cancer types with high sensitivity has the potential to reduce diagnostic delays and improve treatment outcomes. Diagnostic patterning tests (DPTs), which utilize self-organized patterns in drying body fluids, are a relatively unexplored diagnostic method. This systematic review and meta-analysis assessed their accuracy for multi-cancer detection.MethodsSearches were conducted in PubMed, Web of Science, eLibrary Russia, and other databases for studies evaluating DPT accuracy in cancer detection. Study quality was assessed using the QUADAS-2 tool. Data were analyzed for (i) untreated cancers, (ii) treated cancers, and (iii) precancerous conditions, with controls comprising (iv) healthy individuals and (v) non-cancer patients. Meta-analysis adhered to the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy.ResultsOf the 610 identified records, 41 studies involving 15,969 participants were included, encompassing 5265 cancer cases and 189 precancerous condition cases. Pooled sensitivity and specificity across all DPTs were 0.89 (95% CI, 0.83-0.93) and 0.90 (95% CI, 0.84-0.93), respectively. Copper chloride crystallization applied to blood demonstrated the highest sensitivity (0.93; 95% CI, 0.87-0.96) and specificity (0.93; 95% CI, 0.85-0.97), though differences between tests were not statistically significant.ConclusionDespite high heterogeneity and the potential risk of bias, DPTs showed a satisfactory degree of accuracy in detecting over 50 cancer types. Further research is needed to evaluate their potential for early cancer detection.

Keywords: copper chloride crystallization of blood; diagnostic tests; droplet evaporation; meta-analysis; multi-cancer detection; patterns in drying body fluids.

Figure 1.

Forest Plot Depicting the 2 × 2 Tables and Sensitivity and Specificity Ranges of the Diagnostic Patterning Tests Applied for the Detection of any Type of Cancer (Data Scenario Resembling a Cancer Screening Setting, ie Exclusion of Treated Cancers and Conditions Yielding False Positive Effects). Legend: CCC – Copper Chloride Crystallization with Additives; D – Droplets; TP – True Positive; FP – False Positive; FN – False Negative; TN – True Negative; CI – Confidence Interval. One Study, Kuczkowski (1995), has been Excluded from this Analysis due to its Non-Corresponding Patient Sample.

Figure 2.

Flow Chart of the Literature Search.

Figure 3.

Percentages of Studies that Received Low, Unclear, and High Scores in the Quality Evaluation Following the QUADAS2-tool.

Figure 4.

Summary Receiver Operating Characteristic Curves for the Detection of Cancer for the Copper-chloride Crystallization (CCC) of Blood (a) and the Bolen Test (b) with Displayed Individual Study Estimates (Black Ovals; Width and Height of the Study-markers Represent the Relative Number of Subjects on Which the Estimation of Sensitivity and Specificity, Respectively, is Based), Summary Point (Black Dot) and its 95% CI (Green Oval Area Surrounding the Summary Point) and ROC Curves (Black Lines). Parameter values were Estimated Separately for each Diagnostic Test. Sensitivity – Detection of True Positives; Specificity – Detection of True Negatives.

Figure 5.

Summary Receiver Operating Characteristic Curves for the Detection of Cancer for the Bolen Test and Copper-chloride Crystallization (CCC) of Blood Following a Data Scenario Including a Common Cancer Patient Group Assessed Against Non-cancer Patients (a) and Healthy Subjects (b) and a Common Control Group Assessed Against Treated-only (c) and Untreated-only Cancer Patients (d). Width and Height of the Study-markers Represent the Relative Number of Subjects on which the Estimation of Sensitivity and Specificity, Respectively, is Based. For (c, d) only Accuracy Values for Individual Studies are Given.

Figure 6.

Summary Receiver Operating Characteristic Curves for all Diagnostic Tests with the Four QUADAS 2-tool Domains as Covariates: Patient Selection (a), Index Test (b), Reference Standard (c), and Flow & Timing (d), Named as QUADAS D1-D4, Respectively. Study Points and a Summary Curve are Displayed for n ≥ 10 Studies; Sizes of the Point-markers Indicate the Study-group Sizes.