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Record W4404005924 · doi:10.1186/s12890-024-03374-2

Inconsistencies in predictive models based on exhaled volatile organic compounds for distinguishing between benign pulmonary nodules and lung cancer: a systematic review

2024· review· en· W4404005924 on OpenAlexaboutno aff
Zhixia Su, Xiaoping Yu, Yuhang He, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Guangyu Lu, Weijuan Gong

Bibliographic record

VenueBMC Pulmonary Medicine · 2024
Typereview
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
FundersJiangsu Provincial Department of FinanceYangzhou UniversityGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMedicineLung cancerExhaled airExhaled breath condensateIntensive care medicinePathologyInternal medicineToxicologyAsthma

Abstract

fetched live from OpenAlex

BACKGROUND: There is a general rise in incidentally found pulmonary nodules (PNs) requiring follow-up due to increased CT use. Biopsy and repeated CT scan are the most useful methods for distinguishing between benign PNs and lung cancer, while they are either invasive or involves radiation exposure. Therefore, there has been increasing interest in the analysis of exhaled volatile organic compounds (VOCs) to distinguish between benign PNs and lung cancer because it's cheap, noninvasive, efficient, and easy-to-use. However, the exact value of breath analysis in this regard remains unclear. METHODS: A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed to include studies that established exhaled VOC-based predictive models to distinguish between benign PNs and lung cancer and reported the exact VOCs used. Data regarding study characteristics, performance of the models, which predictors were incorporated, and methodologies for breath collection and analysis were independently extracted by two researchers. The exhaled VOCs incorporated into the predictive models were narratively synthesized, and those compounds that were reported in > 2 studies and reportedly exhibited consistent associations with lung cancer were considered key breath biomarkers. A quality assessment was independently performed by two researchers using both the Newcastle-Ottawa Scale (NOS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: A total of 11 articles reporting on 46 VOC-based predictive models were included. The majority relied solely on exhaled VOCs (n = 44), while two incorporated VOCs, demographical factors, and radiological signs. The variation in the sensitivity, specificity, and AUC indicators of the models that incorporated multiple factors was lower compared with those of the models that relied solely on exhaled VOCs. A total of 84 VOCs were incorporated. Of these, 2-butanone, 3-hydroxy-2-butanone, and 2-hydroxyacetaldehyde were identified as key predictors that had significantly higher concentrations in the exhaled breath samples of patients with lung cancer. Substantial heterogeneity was observed in terms of the modeling and validation methods used, as well as the approaches to breath collection and analysis. Many of the reports were missing certain key pieces of clinical and methodological information. CONCLUSIONS: Although exhaled VOC-based models for predicting cancer risk might be a conceivable role as monitoring tools for PNs risk, there has been little overall change in the accuracy of these tests over time, and their role in routine clinical practice has not yet been established. CLINICAL TRIAL NUMBER: PROSPERO registration number CRD42023381458.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.317
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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