Diagnostic Performance of Preoperative Imaging in Endometrial Cancer
Bibliographic record
Abstract
BACKGROUND: Endometrial cancer is one of the most common gynecological malignancies. Because the findings mentioned in radiogram interpretation reports issued by diagnostic radiologists influence treatment strategies, we aimed to evaluate the diagnostic accuracy of preoperative computed tomography (CT) and magnetic resonance imaging (MRI) interpretation results in clinically relevant settings. METHODS: The clinical records of patients diagnosed with endometrial cancer treated at Tohoku University Hospital from January 2012 to December 2021 were reviewed. The preoperative and pathologically estimated cancer stages were compared based on the results mentioned in the radiogram interpretation report. RESULTS: The preoperative and postoperative cancer stages were concordant in 70.0% of the patients. By contrast, the cancer stage was underdiagnosed and overdiagnosed in 21.7% and 8.2% of the patients, respectively. The sensitivities of MRI for deep myometrial invasion, cervical stromal invasion, vaginal invasion, and adnexal metastasis were 65.1%, 58.2%, 33.3%, and 18.4%, respectively. The sensitivity and specificity for pelvic lymph node metastasis using a combination of CT and MRI were 40.9% and 98.4%, respectively. Those for para-aortic lymph node metastases using CT were 37.0% and 99.5%, respectively. CONCLUSIONS: The low sensitivity observed in this study clarified the limitations of preoperative diagnostic performance in current clinical practice.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".