The effectiveness of Halifax-produced 18FDG-PET/CT in the evaluation of patients with solitary pulmonary nodule or suspected lung cancer and the impact on patient management
Bibliographic record
Abstract
Integrated Positron Emission Tomography and Computerized Tomography (PET/CT) is a powerful imaging technique that combines functional and anatomical information for early and accurate detection of cancer.Limited availability and cost necessitate appropriate case selection based on its impact on patient management.The Halifax-based Lung Cancer Site Team (LCST) oncologists request PET/CT scans using online requisitions that prospectively capture pre-PET/CT case information including cancer indication, stage, management intent (curative or palliative) and treatment modality (surgery,chemotherapy,radiotherapy,multimodality therapy,or observation) in a database.Using the database we identified 77 scans completed on patients with suspected lung cancer (SLC, including solitary pulmonary nodule, SPN) during July 3 rd ,2010 to November 30 th ,2010.After considering PET/CT results and subsequent follow up medical records, with the aid of oncologists we determined similar post-PET/CT information to assess for changes in these parameters as well as confirmation of the nature of the suspected lung lesion.When used to diagnose a SLC or SPN, the PET/CT showed 88.6% sensitivity, 83.3% specificity,86.5% accuracy,88.6%positive predictive value, and 88.3% negative predictive value.PET/CT established a different diagnosis (usually benign) in 41.6%, and changed stage in 58.4% of cases.PET/CT changed management intent in 7.8% of cases (usually to palliative) and altered treatment modality in 59.7%.If the lung nodule size measured above 30mm, then 61% of the cases were confirmed as malignant.The Halifax PET/CT protocol is effective in correctly diagnosing malignancy in cases of suspected lung cancer at rates similar to that reported in literature.As utilized by the LCST oncologists, the impact of PET/CT on expected management (59.7%) is higher than that reported in literature (26%) suggesting a widening of the case selection criteria.In those cases where nodule size was>30mm, where an unexpectedly low number of malignant cases were confirmed, further analysis is planned to investigate pre-PET imaging criteria of malignancy.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".