Validation of automated computed tomography segmentation software to assess body composition among cancer patients
Bibliographic record
Abstract
BACKGROUND & AIMS: Assessing body composition using computed tomography (CT) can help predict the clinical outcomes of cancer patients, including surgical complications, chemotherapy toxicity, and survival. However, manual segmentation of CT images is labor-intensive and can lead to significant inter-observer variability. In this study, we validate the accuracy and reliability of automatic CT-based segmentation using the Data Analysis Facilitation Suite (DAFS) Express software package, which rapidly segments single CT slices. METHODS: The study analyzed single-slice images at the third lumbar vertebra (L3) level (n = 5973) of patients diagnosed with non-metastatic colorectal (n = 3098) and breast cancer (n = 2875) at Kaiser Permanente Northern California. Manual segmentation used SliceOmatic with Alberta protocol HU ranges; automated segmentation used DAFS Express with identical HU limits. The accuracy of the automated segmentation was evaluated using the DICE index, the reliability was assessed by intra-class correlation coefficients (ICC) with 95 % CI, and the agreement between automatic and manual segmentations was assessed by Bland-Altman analysis. DICE scores below 20 % and 70 % were considered failed and poor segmentations, respectively, and underwent additional review. The mortality risk associated with each tissue's area was generated using Cox proportional hazard ratios (HR) with 95 % CI, adjusted for patient-specific variables including age, sex, race/ethnicity, cancer stage and grade, treatment receipt, and smoking status. A blinded review process categorized images with various characteristics for sensitivity analysis. RESULTS: for SKM, VAT, SAT and IMAT, respectively, indicating good agreement, with slight underestimation in SKM and SAT. Reliability Coefficients ranged from 0.88 to 1.00 for colorectal and 0.95-1.00 for breast cancer, with Simple Kappa values of 0.65-0.99 and 0.67-0.97, respectively. Additionally, mortality associations for automated and manual segmentations were similar, with comparable hazard ratios, confidence intervals, and p-values. Kaplan-Meier survival estimates showed mortality differences below 2.14 %. CONCLUSION: DAFS Express enables rapid, accurate body composition analysis by automating segmentation, reducing expert time and computational burden. This rapid analysis of body composition is a prerequisite to large-scale research that could potentially enable use in the clinical setting. Automated CT segmentations may be utilized to assess markers of sarcopenia, muscle loss, and adiposity and predict clinical outcomes.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".