Performance of Ultrasound for Identifying Morphological Characteristics and Thickness of Cutaneous Basal Cell Carcinoma: A Systematic Review
Bibliographic record
Abstract
Advances in ultrasound technology and non-surgical treatments of basal cell carcinomas (BCCs) have raised the need to study the performance of high-frequency ultrasound (HFUS) in BCCs. We aimed to assess the performance of HFUS in the evaluation of BCCs to formulate recommendations for its uses and conducted a systematic review of the literature to do so. A search of Central, Medline, Embase, CINHAL, and Web of Science was performed using key/MESH terms "ultrasonography" and "basal cell carcinoma" (January 2005-December 2020). We included primary studies reporting biopsy-confirmed BCCs for which the target intervention was ultrasound assessment at 15 MHz or higher frequency. Thirty articles were included, studying a total of 1,203 biopsy-confirmed BCCs. HFUS provides accurate depth measurements, especially for BCCs >1 mm. The definition of lateral margins in vivo needs further studies; however, ex vivo margin assessment seems convincing. There is a diagnostic role for HFUS in identifying higher recurrence risk BCC subtypes, which can help in risk stratification. Performance of HFUS is significant in BCC management. Pre-surgical scans may support case selection for Mohs. HFUS can improve safety when used to plan brachytherapy treatments, help with case selection and adjunct treatment choice pre-photodynamic therapy. Finally, HFUS can help follow lesions after intervention, particularly non-surgical management, and support the decision to observe or re-intervene. HFUS can enhance clinical practice by providing useful information that cannot be deducted from the clinical examination. It would be recommended to evaluate the extent, mainly depth, and detect the aggressiveness of the BCCs.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".