Comparison of the Total Kidney Volumes Using the Ellipsoid Equation and Manual Segmentation in ADPKD
Bibliographic record
Abstract
Background: Total kidney volume (TKV) is an important prognostic biomarker of disease progression in autosomal dominant polycystic kidney disease (ADPKD). The gold standard of manual segmentation TKV (mTKV) is time and labour-intensive. The ellipsoid equation TKV (eTKV) is commonly used in clinical practice, but assumes uniform growth of the kidneys. Our study examined the correlation between the eTKV and mTKV and we examined cases individually where there was misclassification. Methods: We analyzed coronal T2-weighted MRI slices for 143 patients with ADPKD from a single centre. eTKV was determined using standard orthogonal measurements. The ground truth mTKV was performed by a single trained individual. Pearson’s correlation coefficient was calculated and a Bland-Altman analysis was performed. A confusion matrix was generated to illustrate the misclassification in the Mayo Imaging Classification (MIC) between the two approaches. We explored cyst imaging features where the difference in TKV methods was ≥20%. Results: The mean age of the cohort was 45 (SD 15), 46% were male, hypertension prevalence was 71%, the median eGFR was 76 ml/min/1.73m2 (IQR 46-107), heightadjusted TKV was 865 mL/m (IQR 490-1307), and tolvaptan use was 45%. The correlation coefficient between both TKV measures was 0.96. The Bland-Altman analysis showed wide limits of agreement [-40.43%, 25.15%], and 24 patients were reclassified by one MIC risk category. Of the 25 patients (17%) who exhibited ≥20% difference between the two measures, 23 patients were characterized as having large exophytic cysts. Conclusions: The eTKV is efficient and generally reliable for calculating TKV, but it may lose accuracy in patients with large exophytic cysts. Further study should explore the association of exophytic cysts with kidney disease progression. Understanding whether exophytic cysts should be included in the TKV estimation may aid in risk stratification. Confusion Matrix of Mayo Imaging Class Determined By Total Kidney Volume Using Ellipsoid Equation (eTKV) and Manual Segmentation (mTKV) - eTKV 1A 1B 1C 1D 1E 2 mTKV 1A 11 0 0 0 0 0 1B 3 24 0 0 0 0 1C 0 8 46 2 0 0 1D 0 0 8 23 1 0 1E 0 0 0 2 13 0
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".