Diagnostic accuracy of ≥ 16-slice spiral computed tomography for local staging of colon cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer is one of the most common cancers worldwide with high mortality and is classified as a single entity, although colon cancer and rectal cancer have largely different diagnoses, treatments, surgical methods, and recurrence rates. ≥ 16-slice spiral computed tomography (SCT) is mostly applied to detect the local stage of colon cancer; however, its diagnostic accuracy and whether it is conducive to distinguishing between high-risk and low-risk colon cancer are unclear. AIM: To systematically review the diagnostic accuracy of ≥ 16-slice SCT for local staging of colon cancer. METHODS: Based on the PubMed, EMBASE, Cochrane Library, and Web of Science databases, computers were used to search the literature from the establishment of the database to April 2021, and the results of the diagnostic tests on ≥ 16-slice SCT for local staging of colon cancer were collected according to the inclusion criteria. The data were then extracted and assessed on the basis of the Quality Assessment Checklist of the Institute of Economics of Canada, Reference Citation Analysis (https://www.referencecitationanalysis.com/). Afterward, a meta-analysis was performed using the statistical software Meta-disc 14.0 and Stata 15.0. RESULTS: Eleven studies that provided data on 1613 subjects with computed tomography diagnostic tests were included in this study. Meta-analysis revealed that the pooled sensitivity, pooled specificity, pooled negative likelihood ratio (LR), pooled diagnostic odds ratio, and area under the fitted receiver operating characteristic (ROC) curve of ≥ 16-slice SCT for colon cancer T staging were 0.67 (95%CI: 0.65-0.70), 0.81 (95%CI: 0.80-0.83), 4.13 (95%CI: 2.66-6.41), 0.39 (95%CI: 0.31-0.49), 10.81 (95%CI: 7.33-15.94), and 0.829, respectively, while the specificity, negative LR, diagnostic odds ratio, and area under the fitted ROC curve of ≥ 16-slice SCT for N staging of colon cancer were 0.54 (95%CI: 0.49-0.59), 0.74 (95%CI: 0.70-0.77), 1.92 (95%CI: 1.36-2.70), 0.67 (95%CI: 0.51-0.87), 3.74 (95%CI: 1.76-7.94), and 0.829 respectively. The sensitivity and specificity of ≥ 16-slice SCT for colon cancer T staging were acceptable, while the sensitivity for colon cancer N staging was relatively low, though its specificity was acceptable. CONCLUSION: ≥ 16-slice SCT for local staging of colon cancer has good diagnostic value; however, the accuracy needs to be confirmed by further clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".