Adaptive Fluorodeoxyglucose-Positron Emission Tomography Based Chemotherapy Selection for Metastatic Non-small Cell Lung Cancer
Bibliographic record
Abstract
Objectives The change in tumor fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET) scan after one cycle of platinum-based chemotherapy has been shown to predict progression-free and overall survival (PFS and OS) among advanced non-small cell lung cancer (NSCLC) patients. Using early FDG-PET response to determine subsequent chemotherapy, we aim to evaluate the role that adaptive chemotherapy regimens have on later CT response, PFS, and OS in patients with advanced NSCLC. Materials and Methods Chemotherapy-naïve patients with metastatic NSCLC received carboplatin and paclitaxel (CP) on day one and repeated FDG-PET on day 18. PET-responding patients continued CP chemotherapy for a total of four cycles. PET non-responders were switched to alternate docetaxel and gemcitabine (DG) for three additional cycles. The primary outcome was the CT Response Evaluation Criteria in Solid Tumors (RECIST 1.0) response. Secondary endpoints included PFS and OS. Results Forty-six patients initiated treatment with chemotherapy on trial and were evaluable by PET/CT. Of these, 19 (41%) met the FDG-PET criteria for the response after a single cycle of CP. Only one non-responding patient had a CT response. Despite the lack of CT response in the DG arm, no trend for worse PFS or OS was seen between the two arms. Conclusions This work demonstrates that changing chemotherapy in the event of non-response by PET did not lead to improved CT RECIST response. However, non-responding patients who switched chemotherapy had similar PFS and OS to those who responded by PET and continued the same regimen.
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".