Fecal volatile organic compounds for colorectal cancer detection: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Background: Fecal volatile organic compound (VOC) analysis has emerged as a promising non-invasive tool for detecting colorectal cancer (CRC). Despite its reported high diagnostic accuracy, clinical adoption is hindered due to issues like methodological variability, lack of standardization, and limited validation. This systematic review and meta-analysis aim to address these challenges by evaluating the diagnostic performance of fecal VOC analysis and exploring its future potential. METHODS: A comprehensive search of studies assessing fecal VOC analysis for CRC detection and monitoring was conducted. Data on sensitivity, specificity, analytical platforms, and methodological protocols were extracted and analyzed. Seven studies met the inclusion criteria, and pooled diagnostic accuracy metrics were calculated. Emerging applications and challenges were critically reviewed. RESULTS: Fecal VOC analysis showed a pooled sensitivity of 0.86 and specificity of 0.90 for CRC detection, supported by an area under the summary receiver operating characteristic (SROC) curve of 0.89. Individual studies reported area under the curve (AUC) values ranging from 0.84 to 0.96, with some achieving perfect sensitivity and specificity using machine learning algorithms. Preliminary evidence suggests the potential of VOC profiling for monitoring CRC, but small sample sizes, methodological inconsistencies, and lack of external validation limit generalizability. Notably, while sensitivity was consistent, considerable heterogeneity in specificity was observed across studies. Variations in sample collection, storage, and analytical platforms introduce biases, and data on CRC staging and early detection remain scarce. CONCLUSIONS: To maximize the potential of fecal VOC analysis for CRC detection, staging, and post-treatment monitoring, future research must focus on standardizing procedures, conducting multi-center validation studies, and integrating VOC analysis with other diagnostic techniques. These efforts are particularly important given the limited number of studies currently available, which restricts the strength of evidence and highlights the need for larger, rigorous investigations. Overcoming these limitations could transform fecal VOC analysis from a promising research method into a reliable clinical tool for CRC diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".