The incidence and clinical impact of bone metastases in non-small cell lung cancer.
Bibliographic record
Abstract
e19024 Background: Non-small cell lung cancer (NSCLC) is the leading global cause of cancer death. Bone metastases (BM) in NSCLC are a common cause of morbidity, but use of bone targeted agents (BTA) is variable. We investigated the incidence and impact of BM in an unselected NSCLC population. Methods: With ethics approval, we performed a retrospective chart review of all patients (pts) with NSCLC seen at our institution in 2007. Baseline demographics, stage and initial treatment goals were recorded. In pts with advanced disease (mNSCLC) metastatic sites were identified. In BM pts, skeletal related events (SRE), interventions and outcomes were recorded. Results: In total, 374 pts were identified. Median age was 68 years (IQR 60-76), 54% female. Histological subtypes were adenocarcinoma (36%), squamous (18%), and other (46%). Overall 91% were current or ex-smokers. At initial diagnosis 37% had stage I-IIIa, 18% IIIb and 46% stage IV disease. A total of 160 pts (43%) were treated with curative intent; 211 (56%) were considered palliative. Of the 160 curative therapy pts, 90 (56%) subsequently relapsed, with a median time from diagnosis to relapse of 14.9 months. Of the 301 pts with mNSCLC, common sites of metastasis were lung/pleura (80%), mediastinal lymph nodes (69%), bone (39%), brain (30%), and liver (24%). In total 116 pts had BM; a higher incidence was observed in pts ≤70 years than pts >70 (36% vs 23%). SREs were observed in 69 pts (59%), the median number of SREs per BM pt was 1 (0-16). The incidence of SREs was radiotherapy (63%), fractures (22%), spinal cord compression (6%) and surgery to bone (5%). Factors associated with ≥2 SREs were smoking status (28% never smokers vs 9% ex/current smoker) and younger age (15% vs 5%). In BM pts, 64% required opioid analgesia, only 6% received BTA. Overall survival (OS) in pts with mNSCLC was 7.3 months (IQR 3.1-20.5). Pts with BM had significantly shorter OS compared to those without BM (5.5 vs. 9.9 months, p=0.02). Median OS in pts with or without SRE were 5.5 and 6.4 months (p=0.58). Conclusions: BM in NSCLC pts are common, and most pts will develop an SRE and/or require opioids. In mNSCLC, the presence of BM is associated with significantly shorter survival, which raises challenging questions around the use of BTA.
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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) | 0.000 | 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) | 0.004 | 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".