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Record W2133221537 · doi:10.1148/radiol.11110639

Accuracy of Unenhanced MR Imaging in the Detection of Axillary Lymph Node Metastasis: Study of Reproducibility and Reliability

2011· article· en· W2133221537 on OpenAlex
Anabel M. Scaranelo, Riham Eiada, Lindsay M. Jacks, Supriya Kulkarni, Pavel Crystal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadiology · 2011
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineReproducibilityConfidence intervalMagnetic resonance imagingRadiologyNuclear medicineLymph nodeEffective diffusion coefficientPathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the accuracy, reproducibility, and reliability of unenhanced magnetic resonance (MR) imaging techniques for detecting metastatic axillary lymph nodes in patients with newly diagnosed breast carcinoma. MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained. Seventy-four consecutive women with invasive breast carcinoma were recruited to undergo preoperative breast MR imaging. Thirteen patients were excluded, two because they were undergoing preoperative chemotherapy and 11 because of the presence of movement or susceptibility artifacts on images. Thus, 61 patients (mean age, 53 years; range, 33-78 years) were included in this study. Axial T1-weighted MR images without fat saturation and diffusion-weighted (DW) MR images were analyzed by two experienced radiologists, who were blinded to the histopathologic findings. Visual and quantitative analyses of unenhanced MR images were performed. Sensitivity, specificity, and accuracy were calculated. To assess the intraobserver agreement, a second reading was performed. Statistical analysis was conducted on a patient-by-affected side basis. RESULTS: The sensitivity, specificity, and accuracy were 88%, 82%, and 85%, respectively, for axial T1-weighted MR imaging and 84%, 77%, and 80% for DW imaging. Apparent diffusion coefficients (ADCs) were significantly lower in the malignant group (P<.05 for all four readings), with the average of the four readings ranging from 0.333×10(-3) mm2/sec to 2.843×10(-3) mm2/sec. The mean Lin coefficient comparing the mean ADC reading for each observer was 0.959 (95% confidence interval: 0.935, 0.975), suggesting very high interobserver agreement between the two observers in terms of reproducibility of ADCs. The Bland-Altman plot showed good inter- and intraobserver agreement. CONCLUSION: Unenhanced MR imaging techniques showed high accuracy in the preoperative evaluation of axillary status in patients with invasive breast cancer. Results indicate reliable and reproducible assessment with DW imaging, but it is unlikely to be useful in clinical practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.313
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it