Use of positron emission tomography-computed tomography (PET-CT) scan in cancer patients: A single Canadian academic oncology center experience.
Bibliographic record
Abstract
e18691 Background: PET-CT scan is key tool in the diagnosis and follow up of cancer patients. In 2017, a governmental study demonstrated that the use of PET-CT scans in the province of Quebec, Canada was two times more elevated than other Canadian provinces, and similar to the United States (507 vs 510 PET-CT scans per 100,000 persons, respectively). In Quebec, the governmental agency Institut national d’excellence en santé et en services sociaux (INESSS) published recommendations for judicious use of PET-CT scans, derived from evidence-based literature and publications from international oncology societies . We sought to evaluate the use of PET-CT scans in our institution with regards to INESSS guidelines. Methods: A total of 1561 PET-CT scans were performed for oncologic purposes at the CHUM between September 1st and November 30th 2019. TEP-CT scans from patients under 18 years of age or who were not followed at our center were excluded. 975 PET-CT scans respected our criteria. We reviewed disease and clinical characteristics of patients, adherence to 2017 INESSS guidelines, imaging indication (staging, response to treatment, or follow up), and type of specialties requesting the exam. Results: Oncologic PET-CT scans were most frequently prescribed by surgeons (39%), followed by oncologists (29%) and radiation-oncologists (12%). 530 (54%) PET-CT scans followed guidelines, 377 (39%) did not, and 68 (7%) were not included in INESSS guidelines. Adherence to guidelines was similar between oncologists and surgeons (54% and 53%, respectively). PET-CT scans ordered by radiation-oncologists followed guidelines in 67% of cases. Lung (29%), breast (19%), and gastro-intestinal cancers (16%) were the sites for which the exam was most requested. Whereas 77% of PET-CT scans requested for lung cancer followed guidelines, only 17% of breast cancer PET-CT scans did. Misuse of this imaging tool was also observed in the investigation of cancer of unknown origin, where only 17% of ordered PET-CT scans followed guidelines. Conclusions: In our institution, 39% of PET-CT scans prescription did not follow guidelines. Variability in adherence to guidelines was significantly more noticeable between primary cancer sites compared to types of specialties requesting the exam. To avoid wasting of resources, we advocate for respecting guidelines and discussing in tumor-boards the off-label use of PET-CT scans. Judicious use of PET-CT scans is particularly important in a public health system for appropriate allocation of resources.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".