The Use of Sensitive Imaging Modalities for Cervical Cancer Staging in Japan
Bibliographic record
Abstract
The International Federation of Gynecology and Obstetrics (FIGO) staging system for cervical cancer did not take into account any of the findings determined by imaging modalities as part of the staging work-up. However, in the Japanese clinical settings, computed tomography (CT) and magnetic resonance imaging (MRI) are frequently used. In this study, we aimed to describe the pretreatment use of sensitive imaging modalities in Japan in order to assess the future adaptability of the FIGO staging system.Data from September 2012 to December 2014 were collected from the National Database of the Hospital-Based Cancer Registry and health insurance claims data. A total of 280 hospitals participated. From the database, all patients with cervical cancer who received first-line therapy at the participating hospitals were analyzed. The proportions of patients who had CT, MRI, and positron emission tomography-CT (PET-CT) before receiving the first-line therapy were calculated. For comparison, the proportions of patients who had undergone cystoscopy and/or proctoscopy -- examinations that are incorporated into the FIGO system -- were also calculated. A total of 13 668 patients were included; 77.3% of patients had early stage (stage 0 or I) disease. Among all patients, 88.5% had undergone CT, MRI, or PET-CT before receiving the first-line therapy. Additionally, over 90% of patients with advanced-stage (stage II–IV) disease had undergone CT. Conversely, only 21.0% of patients with stage II–IV disease were reported to have undergone cystoscopy and/or proctoscopy. Promoting a resource-stratified approach in the cervical cancer staging is warranted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".