Computer-aided Nodule Detection in the Lung Apices in Head and Neck Computed Tomography Angiography
Bibliographic record
Abstract
PURPOSE: Computed tomography angiography (CTA) of the head and neck includes the pulmonary apices, a common location for pulmonary nodules. Computer-aided detection (CAD) is an adjunctive tool for the detection of lung nodules and is widely used in standard chest CT scans. We evaluated whether the available software can be applied to CTA head and neck examinations, which include the lung apices, resulting in improved accuracy for lung nodule detection. MATERIALS AND METHODS: In this retrospective single-center study, 191 previously reported head and neck CTA scans were re-evaluated for apical pulmonary nodules by 2 radiologists. Subsequently, CAD software ( Syngo .via, Siemens Healthiness AG) was applied to the lung apices and the results were compared between CAD and research radiologists (first reading) or clinical radiologist (null reading). In addition, the CAD performance in limited lung fields was compared with the accepted CAD assessment applied to whole lungs. RESULTS: Of the 191 patients, 110 (57.6%) were men, with a mean age of 68 years. In the 24 CT scans, the research radiologists detected 40 nodules. In the 180 scans evaluated by CAD, the software detected 39 nodules in 22 examinations, with a sensitivity of 60.8% and a PPV of 63.6%. In the remaining 158 examinations in which CAD did not detect nodules, the radiologists concurred in 149 scans, with a specificity of 94.9%, NPV of 94.3%, and accuracy of 90.6%. CONCLUSION: The study results indicate that CAD is an unexpected quick supportive tool for nodule detection, particularly for excluding clinically significant nodules in lung apices on CTA head and neck, showing similar results for partial and full lung fields.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".