Assessing the effects of immune checkpoint inhibitors on bone utilizing machine learning–assisted opportunistic quantitative computed tomography
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are widely used in cancer treatment, yet their impact on bone health remains unclear. This study aimed to perform a retrospective cohort study utilizing routine CT scans from patients with melanoma to perform opportunistic QCT analysis to investigate the effects of ICI treatment on skeletal health, including volumetric BMD (vBMD) measurements and osteoarthritis (OA) parameters. A previously established machine learning-assisted opportunistic QCT pipeline was used to estimate lumbar spine vBMD from baseline and 12-mo follow-up CT scans in patients with melanoma treated with ICI therapy and those not treated with ICI therapy. Facet joint OA, osteophyte formation, and endplate sclerosis were also graded. Independent and paired t tests were used to determine any differences in vBMD and OA parameters between ICI users and non-ICI users. Multivariable linear regression models were used to control for confounding variables. Non-ICI users had a significant decrease in vBMD of -6.96 mg/cm3 from baseline to follow-up, whereas the ICI users had no significant change. There was a significant difference in change in vBMD from baseline to follow-up between the 2 groups, with the non-ICI users experiencing a 11.22 mg/cm3 larger decrease in vBMD. After adjusting for baseline age, sex, baseline vBMD, and change in OA parameters, this difference remained significant at -13.04 mg/cm3. Among the ICI users, those who had a decline in vBMD had a lower baseline vBMD compared with those who had increased vBMD. Neither group showed a significant change in OA parameters over the follow-up period, nor a difference in change between ICI and non-ICI users, even after adjusting for sex, age, and baseline OA parameters. While the effects of ICI treatment on vBMD may vary based on baseline bone health, ICIs do not significantly impact OA parameters in the short term.
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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 | 0.001 |
| 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 | 0.000 | 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 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".