Imaging Manifestations of Acute and Chronic Renal Infection That Mimics Malignancy: How to Make the Diagnosis Using Computed Tomography and Magnetic Resonance Imaging
Bibliographic record
Abstract
PURPOSE: To review the computed tomography and magnetic resonance imaging manifestations of acute and chronic renal infections that may mimic malignancy and to provide useful tips to establish an imaging diagnosis. CONCLUSION: Acute and chronic bacterial pyelonephritis are usually readily diagnosed clinically and on imaging when the diagnosis is suspected based upon clinical presentation. When unsuspected, focal, extensive or mass-like, acute and chronic bacterial pyelonephritis may mimic infiltrative tumours such as urothelial cell carcinoma (UCC), lymphoma, and metastatic disease. Infection may be suspected when patients are young and otherwise healthy when there is marked associated perinephric changes and in the absence of metastatic adenopathy or disease elsewhere in the abdomen and pelvis. Renal abscesses, from bacterial or atypical microbial agents, can appear as complex cystic renal masses mimicking cystic renal cell carcinoma. Associated inflammatory changes in and around the kidney and local invasion favour infection. Emphysematous pyelonephritis can mimic necrotic or fistulizing tumour; however, infection is more likely and should always be considered first. Xanthogranulomatous pyelonephritis can mimic malignancy when focal or multifocal and in cases without associated renal calculi. Malacoplakia is an inflammatory process that may mimic malignancy and should be considered in patients with chronic infection. Bacillus Calmette-Guerin (BCG)-induced pyelonephritis is rare but can mimic renal malignancy and should be considered in patients presenting with a renal mass when being treated with BCG for urinary bladder UCC.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".