Computed tomography imaging characteristics of clear cell papillary renal cell carcinoma
Bibliographic record
Abstract
ABSTRACT Purpose: Clear cell papillary (CCP) renal cell carcinoma (RCC) is a new subtype of RCC that was formally recognized by the International Society of Urological Pathology Vancouver Classification of Renal Neoplasia in 2013. Subsequently, CCP RCC was added to the 2016 World Health Organization Classification of Tumors of the Urinary System and Male Genital Organs. In this study, we retrospectively investigated the computed tomography (CT) findings of pathologically diagnosed CCP RCC. Materials and Methods: This study included 12 patients pathologically diagnosed with CCP RCC at our institution between 2015 and 2017. We reviewed the patient's CT data and analyzed the characteristics. Results: Nine solid masses and 3 cystic masses with a mean tumor size of 22.7±9.2mm were included. Solid masses exhibited slight hyper-density on unenhanced CT with a mean value of 34±6 Hounsfield units (HU), good enhancement in the corticomedullary phase with a mean of 195±34HU, and washout in the nephrogenic phase with a mean of 133±29HU. The walls of cystic masses enhanced gradually during the corticomedullary and nephrogenic phases. Solid and cystic masses were preoperatively diagnosed as clear cell RCC and cystic RCC, respectively. Conclusions: The CT imaging characteristics of CCP RCCs could be categorized into either the solid or cystic type. These masses were diagnosed radiologically as clear cell RCC and cystic RCC, respectively.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".