Use of 18F-fluorodeoxyglucose positron emission tomography coupled with computed tomography in early breast cancer management: consensus-based local recommendations by the Hong Kong Breast Cancer Foundation PET/CT Study Group
Bibliographic record
Abstract
INTRODUCTION: F-fluorodeoxyglucose positron emission tomography coupled with computed tomography (PET/CT) has been incorporated into breast cancer management. In Hong Kong, PET/CT use is increasing. This study aimed to establish consensus-based recommendations on the use of PET/CT in the management of early breast cancer. METHODS: A literature search was conducted in September 2023 using the keywords "breast cancer" and "PET/CT" within PubMed to identify research articles related to the use of PET/CT in early breast cancer. Guidelines from major international cancer agencies were also reviewed. Ten recommendation statements were drafted. A two-round modified Delphi consensus process was conducted over a 3-month period (19 December 2023 to 29 February 2024). RESULTS: A total of 76 experts consented to participate in the first round, of whom 71 completed the second round and were included as members of the expert panel, yielding a second-round response rate of 93.4%. The panel comprised oncologists (n=30, 42.3%), surgeons (n=35, 49.3%), and radiologists (including nuclear medicine radiologists) [n=6, 8.5%]. Experts from the Hospital Authority (n=37, 52.1%) and the private sector (n=32, 45.1%) were well represented. Two experts (2.8%) were from one of the two local university medical faculties. Over 75% of expert panel members had at least 15 years of clinical experience. Of the ten statements, consensus was achieved on seven in the first round and one additional statement in the second round. CONCLUSION: Through the consensus process, the proposed recommendations are expected to gain wider acceptance and recognition among local healthcare professionals as guidance for the use of PET/CT in early breast cancer management.
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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 | 0.153 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".