Lung Cancer Surveillance After Definitive Curative-Intent Therapy: ASCO Guideline
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To provide evidence-based recommendations to practicing clinicians on radiographic imaging and biomarker surveillance strategies after definitive curative-intent therapy in patients with stage I-III non-small-cell lung cancer (NSCLC) and SCLC. METHODS: ASCO convened an Expert Panel of medical oncology, thoracic surgery, radiation oncology, pulmonary, radiology, primary care, and advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2000 through 2019. Outcomes of interest included survival, disease-free or recurrence-free survival, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. RESULTS: The literature search identified 14 relevant studies to inform the evidence base for this guideline. RECOMMENDATIONS: Patients should undergo surveillance imaging for recurrence every 6 months for 2 years and then annually for detection of new primary lung cancers. Chest computed tomography imaging is the optimal imaging modality for surveillance. Fluorodeoxyglucose positron emission tomography/computed tomography imaging should not be used as a surveillance tool. Surveillance imaging may not be offered to patients who are clinically unsuitable for or unwilling to accept further treatment. Age should not preclude surveillance imaging. Circulating biomarkers should not be used as a surveillance strategy for detection of recurrence. Brain magnetic resonance imaging should not be used for routine surveillance in stage I-III NSCLC but may be used every 3 months for the first year and every 6 months for the second year in patients with stage I-III small-cell lung cancer who have undergone curative-intent treatment.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it