Autosomal Dominant Polycystic Kidney Disease: Role of Imaging in Diagnosis and Management
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder with progressive renal function decline, and disease severity is determined based on the type of genetic mutation. The diagnosis is usually established at imaging, primarily at US, and is based on age-dependent criteria and the number of visible cysts. ADPKD is classified into class 1 (typical) and class 2 (atypical) according to the Mayo Clinic Imaging Classification (MCIC) system. Height-adjusted total kidney volume (TKV) has emerged as a predictor of future renal function decline and renal failure in ADPKD, and several methods can be used for estimation. MCIC class 1 ADPKD is further subdivided into five types based on height-adjusted TKV (A, B, C, D, and E). Patients with a larger height-adjusted TKV (ie, MCIC 1C–E) are at high risk for progression to end-stage renal disease and will potentially benefit from vasopressin receptor antagonists, which have been shown to reduce the rate of cyst growth and slow renal function decline. Other renal complications primarily relate to hemorrhage within cysts or cyst infections. Subtraction images are key for assessment of complex cysts when malignancy is suspected, as the presence of protein and blood can limit the assessment for an enhancing component. The radiologist has a central role in establishing a diagnosis, excluding mimics, identifying complications, assessing severity, and predicting future renal failure. Interventional radiologists play a therapeutic role in management of complications by cyst drainage, sclerotherapy, or embolization. © RSNA, 2022 Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".