Perifissural Nodules Seen at CT Screening for Lung Cancer
Bibliographic record
Abstract
PURPOSE: To describe and characterize the potential for malignancy of noncalcified lung nodules adjacent to fissures that are often found in current or former heavy smokers who undergo computed tomography (CT) for lung cancer screening. MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained. Baseline and follow-up thin-section multidetector CT scans obtained in 146 consecutive subjects at high risk for lung cancer (age range, 50-75 years; > 30 pack-year smoking history) were retrospectively reviewed. Noncalcified nodules (NCNs) were categorized according to location (parenchymal, perifissural), shape, septal connection, manually measured diameter, diameter change, and lung cancer outcome at 7(1/2) years. RESULTS: Retrospective review of images from 146 baseline and 311 follow-up CT examinations revealed 837 NCNs in 128 subjects. Of those 837 nodules, 234 (28%), in 98 subjects, were adjacent to a fissure and thus classified as perifissural nodules (PFNs). Multiple (range, 2-14) PFNs were seen in 47 subjects. Most PFNs were triangular (102/234, 44%) or oval (98/234, 42%), were located inferior to the carina (196/234, 84%), and had a septal connection (171/234, 73%). The mean maximal length was 3.2 mm (range, 1-13 mm). During 2-year follow-up in 71 subjects, seven of 159 PFNs increased in size on one scan but were then stable. The authors searched a lung cancer registry 7(1/2) years after study entry and found 10 lung cancers in 139 of 146 study subjects who underwent complete follow-up; none of these cancers had originated from a PFN. CONCLUSION: PFNs are frequently seen on screening CT scans obtained in high-risk subjects. Although PFNs may show increased size at follow-up CT, the authors in this study found none that had developed into lung cancer; this suggests that the malignancy potential of PFNs is low. (c) RSNA, 2010.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".