Whole-body bone SPECT in breast cancer patients
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to compare the detectability rate of bone metastases in breast cancer patients using whole-body single-photon emission computed tomography (WB-SPECT) performed with a half-time acquisition algorithm with that of planar ± selected field-of-view SPECT [standard bone scintigraphy (BS)]. MATERIALS AND METHODS: Ninety-two consecutive breast cancer patients (age range 35-74 years) underwent planar BS followed by WB-SPECT (acquisition time 28 min). Clinical and imaging follow-up data from BS, 18F-FDG-PET/CT and CT were used as composite reference standards. Institutional review board approval was obtained. For a review of standard BS results, data from a selected SPECT field-of-view were extracted from the WB-SPECT when requested by the readers. Diagnostic confidence of interpretation criteria were defined using a five-point level-of-confidence grading scale of lesions. RESULTS: Bone metastases were diagnosed in 34 of 92 studies (37%). On patient-based analysis, the detectability rate of standard BS was 97% (33/34 patients) as compared with 100% for WB-SPECT (P=NS). On a lesion-based analysis, 268 foci were detected, including 124 metastases. Standard BS detected 195 lesions (73%; 99 metastases and 96 benign) and missed 73 lesions (25 metastases and 48 benign). WB-SPECT detected 266 lesions (99%; 124 metastases and 142 benign) and missed two benign foci because of SPECT reconstruction artefacts. The lesion-based detectability rate of metastases for standard BS was 80% compared with 100% for WB-SPECT (P<0.001). WB-SPECT was associated with a higher level of confidence compared with standard BS for both benign (P<0.01) and malignant lesions (P<0.05). CONCLUSION: WB-SPECT is a useful tool for skeletal assessment, showing good performance in comparison with standard BS in breast cancer patients, and may eliminate the need for an initial planar scan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".