7‐Tesla <scp>MR</scp> imaging of non‐melanoma skin cancer samples: correlation with histopathology
Bibliographic record
Abstract
PURPOSE: The aims of this study were to compare in vitro magnetic resonance imaging (MRI) characteristics of keratinocytic skin cancer assessed by a 7-tesla (T) MRI with histopathology, and to describe MRI features of skin tumors. METHODS: This prospective study included 30 skin tumors treated by surgery. MR images of skin samples were acquired on a 7-T MR scanner using a fast spin-echo T(2)-weighted and an isotropic 3D gradient-echo T(1)-weighted sequence. Length, width, Breslow index and margins of the lesions were measured. The presence or absence of the following was noted: healthy margins, ulceration of the dermis, in situ lesions, superficial and deep dermis involvement, subcutaneous involvement, superficial and intratumoral keratin. MR results were compared to histopathology. RESULTS: Interclass correlation coefficient (ICC) was very good for the evaluation of the width (ICC = 0.86) and Breslow index (ICC = 0.87). The ICC was good for the evaluation of the margins (ICC = 0.70) but for length, ICC was lower (ICC = 0.67). Mean bias between MRI and histopathology was inferior to 1 mm for width, Breslow index and margin. CONCLUSION: In vitro 7-T MRI of keratinocytic skin cancer allows delineation of lesions with good correlation with histopathology. After in vivo confirmation it could have a diagnostic role regarding the delineation of surgical margins but its actual limitations prevent its practical adoption at this time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".