Applying PET-CT for predicting the efficacy of SBRT to inoperable early-stage lung adenocarcinoma: A Brazilian case-series
Bibliographic record
Abstract
Stereotactic body radiotherapy (SBRT) is a treatment option for early-stage inoperable primary lung cancer. Here we report a thorough description of the prognostic value of pre-SBRT SUVmax for predicting the efficacy of SBRT in early-stage lung adenocarcinoma. This is a retrospective study of consecutive cases of early-stage inoperable lung adenocarcinoma, staged with PET-CT, treated with SBRT between 2007 and 17. Kaplan-Meier (KM) curves were used to assess overall survival and compare time to event between those with PET-CT SUVmax values ≤ 5.0 and those > 5. Fisher's Exact tests and the Mann-Whitney U were used to compare the patient and clinical data of those with SUVmax≤5.0 and >5.0, and those with and without any failure. Amongst 50 lung carcinoma lesions, from 47 patients (34 (68%)-T1a or 5 (p = 0.112). In addition, 5 experienced a regional failure and 4 a distant failure. Higher PET-CT SUVmax values before SBRT were associated with an increased risk of any failure (36% versus 0%, p = 0.0040 on Fisher's Exact test) and faster time to event (p = 0.010, log rank test). Both acute and late toxicities profile were acceptable. Patients with early-stage inoperable lung adenocarcinoma present good clinical outcomes when treated with SBRT. We raised the hypothesis that the value of PET-CT SUVmax before SBRT may be an important predictive factor in disease control. None.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".