Analysis of exhaled breath of lung cancer patients using infrared spectroscopy.
Bibliographic record
Abstract
e21037 Background: Currently low-dose computed tomography is used for lung cancer (LC) screening, but is limited by radiation exposure, cost, and a high false detection rate (1,2). An accurate, accessible and affordable screening technology is needed to improve detection of LC in high risk individuals. Methods: We conducted an unblinded, prospective cohort study on the effectiveness of a novel technology utilizing infrared absorption measurements via cavity ringdown spectroscopy (IR-CRDS) to differentiate the expired breath of treatment-naïve LC patients from controls without known cancer. Breath samples were taken from 100 LC patients and 98 control subjects but, only 62 non-small cell lung cancer (NSCLC) and 96 control samples were analyzed. Patients on treatment were eligible but, the protocol was amended to exclude these due to signal ambiguities. Samples were also excluded due to missing data, unclear histologic subtypes, or if they were classified as small cell LC samples to prevent obscuring the NSCLC signal. A piecewise cubic spline interpolation was used for the spectra with missing values (3). After first- and second-derivative spectra were computed to increase the information density, a one-dimensional local binary pattern extracted features from the spectra (4). Meaningful spectra-based features were selected using a minimum redundancy maximum relevance algorithm (5). Finally, a classification model was built using a support vector machine classifier (3). Results: The table below characterizes each cohort. The discriminant analysis differentiated between NSCLC and control cases with a cross validation accuracy of 86.1% (89.6% sensitivity and 80.7% specificity) using 20 selected spectra-based features. Conclusions: IR absorption measurements can be used to accurately discriminate between NSCLC and control participants. We continue to build our database to support more robust machine learning models. To our knowledge, this is the first time IR-CRDS has been used to differentiate between NSCLC and control cases. [Table: see text]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".