Validating impact of pretreatment tumor growth rate on outcome of early‐stage lung cancer treated with stereotactic body radiation therapy
Bibliographic record
Abstract
Background To assess correlation of pretreatment specific growth rate (SGR) value of 0.43 × 10 ‐2 with overall and failure‐free survival of patients with early‐stage non‐small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). Methods A retrospective chart review of 160 patients with pathologically confirmed stage I NSCLC treated with SBRT between June 2010 and December 2012 in a large, tertiary cancer institute was undertaken. Both diagnostic and archived planning CT were uploaded to the treatment planning system to determine tumor volume at diagnosis (GTV1) and planning time (GTV2). The time (t) between both CTs was recorded. SGR was calculated using GTV1, GTV2, and t. The median SGR (0.43 × 10 ‐2 ) from our previous data was used to group patients into low and high SGR cohorts. Log‐rank test was used to compare overall (OS) and failure‐free survivals (FFS) of SGR groups. Results The median time interval between diagnostic and planning CT scans was 87 days. The median OS was 38 and 66 months for high and low SGR cohorts, respectively ( P = 0.03). The median FFS was 27 and 55 months for high and low SGR cohorts, respectively ( P = 0.005). High SGR ( P < 0.05), male gender ( P = <0.01), and GTV2 ( P = <0.05) were associated with poorer FFS. Conclusions High SGR was associated with poorer outcome in patients with early‐stage NSCLC treated with SBRT. SGR can be used in conjunction with other well‐known predictive factors to formulate a practical predictive model to identify subgroups of the patient at higher risk of recurrence after SBRT.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".