Application of PET to the Diagnosis, Staging, and Treatment of Locally Advanced Non-small Cell Lung Cancer
Bibliographic record
Abstract
The strength of positron emission tomography (PET) lies in the application of its underlying technological and molecular biology advancements to clinical practice and pharmacological development. Currently, PET is used to identify malignant pulmonary nodules, evaluate mediastinal disease, detect distant metastasis, assess response to therapy, and identify novel oncologic drug targets. Over the last decade, PET has also increasingly influenced the management of locally advanced nonsmall cell lung cancer (NSCLC) by directing chemoradiation and surgical treatment. Although the majority of clinical trials and practice have used 2-18F- fluoro-2-deoxy-D-glucose (FDG) as the radiopharmaceutical agent for evaluating NSCLC, novel radiolabeled tracers, radiopharmaceutical agents, and targeted immunotherapy drugs are actively being investigated to improve the treatment of NSCLC. This review focuses on the utility of PET for diagnosing malignant pulmonary nodules, staging the extent of disease, and evaluating immuno-oncology therapies in locally advanced NSCLC. Keywords: Drug development, immunotherapy, molecular genetics, NSCLC, PET, staging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".