The impact of PET-CT on staging, management, and prognostication of small-cell lung cancer.
Bibliographic record
Abstract
6611 Background: Although frequently used, there is no randomized data assessing the role of PET CT in the staging process of SCLC patients, or its impact on patients’ management. We aimed to review the utility of PET-CT in patients diagnosed with SCLC in a single tertiary medical center. Methods: All patients diagnosed with SCLC for 5 consecutive years were included, either staged by PET-CT or not. We retrieved clinical data, staging procedures, PET parameters (SUVmax and TLG) progression free survival (PFS) and Overall Survival (OS). In the group of patients who underwent PET CT during initial evaluation, re-assessment staging was performed by two independent radiologists: one according to PET-CT findings, and the other according to chest, abdomen and pelvic contrast-enhanced CT scan findings (with bone scan results if available) and blinded to the PET results. Results: 108 patients were identified. 2 patients were excluded from the analysis for lack pathology or staging procedure data and 10 patients were excluded since their PET imaging was not accessible. Out of 96 patients, 54 had a PET-CT done as part of their staging procedure. PET-staged patients had significantly less staging procedures (including FDG-PET scan, CT scans, MRIs, Bone Scans, BMBs) done than non-PET-staged patients (24% underwent 3-4 staging procedures versus 62%, p=0.04). PET altered management in 19 patients (35%), with 13 patients with suspected metastatic disease, who benefited from down-staging by PET. Treatment was delayed in PET-staged patients by 4 days; 30 vs 26 days from diagnostic procedure to treatment (p=0.04). High TLG level predicted poorer survival (HR=3.38, p=0.007). Conclusions: PET-CT adds to SCLC patients’ management by reducing the amount of staging procedures and possibly down staging patients who otherwise would have been treated for palliative intent. In the setup of a public health system, waiting for the PET to be done and reported could result in treatment delay. TLG appears to be a new promising prognostic biomarker in small cell lung tumors. Prospective randomized trials are warranted to properly evaluate sensitivity, specificity and influence on management.
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.
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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".