Radiological Mimics of Popliteal Cysts: An Algorithmic Approach Using US and MRI to Identify the Potentially Malignant Lesions: Case Series
Bibliographic record
Abstract
Background: Popliteal cysts are common and present as asymptomatic lumps in the medial popliteal fossa. Some have complex internal characteristics such as septa and loose-bodies. However, not all are popliteal cysts and can be aggressive. These lesions need to be differentiated by the absence of the communicating neck with the joint on ultrasound. Presence of Doppler flow of non-communicating cysts requires further evaluation on MRI, prior to performing a biopsy. Using a case series, we propose an algorithmic approach that is simple and will help identify the malignant lesions and institute appropriate management. Case-Presentation: Popliteal Cyst: On ultrasound: characteristic neck communicating with knee joint. Synovial Sarcoma: Gadolinium enhancement, with areas of low-, iso- and hyper-intense signal to fat on T2. Synovial-Osteochondromatosis: Non-mineralized: T1-low/intermediate intensity; T2-high intensity. Mineralized type: low intensity on T1 & T2. Thrombosed Popliteal Aneurysm: Lamellated appearance-high/low signal intensity on T2. Myxoid-Liposarcomas: Inhomogeneous appearance; homogenous with gadolinium. Usually require a biopsy for diagnosis. Conclusion: The cystic lesions in the medial aspect of the popliteal fossa can be misdiagnosed. Our article reiterates the importance of the communicating neck that separates popliteal cysts from other mimics. We have proposed an algorithm to identify these mimics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".