3 Tesla Magnetic Resonance (MR) Microimaging of Primary Cutaneous Melanoma
Bibliographic record
Abstract
Purpose : This study investigates the efficacy of 3 Tesla magnetic resonance (MR) microimaging for characterisation of primary melanoma. Technologies currently in use for non-invasive characterization of melanoma (confocal microscopy, optical coherence tomography, or high frequency ultrasound) are limited by restricted penetration depth. In contrast, MR microimaging can determine the full depth of all tumors. Method s: Five patients with primary melanomas of lateral dimension 1cm or greater were imaged with a 3.7cm surface coil using a 3D gradient echo sequence (1). Nominal spatial resolution was 0.18×0.12mm in-plane, with 1 to 2.0mm slice thickness. Tumour size was measured as the lateral an intradermal extent of hyperintense change in the epidermis. Results : High quality images were obtained from all five tumours. There was close correlation between the tumor surface dimensions, Breslow thickness, and Clark levels estimated by MR microimaging and histopathology. Measurment of tumor cross-sectional area in successive MR microimaging slices enabled the estimation of tumor volume (Table). MRI Pathology n° sex/age site Volume(cm 3) Length(mm) Width (mm) Breslow(mm) Clark Length(mm) Width(mm) Breslow(mm) Clark 1 F/57 shoulder 0.63 11 9 1.2 III - - 0.9 III 2 M/37 leg 0.72 10 9 1.5 III 11 8 1.6 III 3 M/76 nail thumb 0.23 11 8 5.6 IV 10 9 5.5 IV 4 M/36 scapula 0.12 8 7 4.6 IV 9 6 5.2 IV 5 F/47 knee 2.95 26 25 7.6 IV 25 25 7.5 IV Conclusion: MR microimaging of primary melanoma demonstrates close correlation with the microscopic features documented by histopathology. Breslow thickness is currently considered to be the most reliable prognostic indicator. However, primary tumor volume as estimated by MR microimaging may prove to be a more reliable indicator than tumor thickness as tumor volume accounts for both lateral and vertical growth. References: (1) King SB, Ryner LN, Tomanek B, Sharp JC, Smith IC. MR spectroscopy using multi-ring surface coils . Magnetic Resonance in Medicine, 1999. 42: p. 655–664
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".