Clinical and imaging findings in cervical cancer and their impact on FIGO and TNM staging – An analysis from the EMBRACE study
Bibliographic record
Abstract
OBJECTIVE: To investigate differences in local tumour staging between clinical examination and MRI and differences between FIGO 2009, FIGO 2018 and TNM in patients with primary cervical cancer undergoing definitive radio-chemotherapy. METHODS: Patients from the prospective observational multi-centre study "EMBRACE" were considered for analysis. All patients had gynaecological examination and pelvic MRI before treatment. Nodal status was assessed by MRI, CT, PET-CT or lymphadenectomy. For this analysis, patients were restaged according to the FIGO 2009, FIGO 2018 and TNM staging system. The local tumour stage was evaluated for MRI and clinical examination separately. Descriptive statistics were used to compare local tumour stages and different staging systems. RESULTS: Data was available from 1338 patients. For local tumour staging, differences between MRI and clinical examination were found in 364 patients (27.2%). Affected lymph nodes were detected in 52%. The two most frequent stages with FIGO 2009 are IIB (54%) and IIIB (16%), with FIGO 2018 IIIC1 (43%) and IIB (27%) and with TNM T2b N0 M0 (27%) and T2b N1 M0 (23%) in this cohort. CONCLUSIONS: MRI and clinical examination resulted in a different local tumour staging in approximately one quarter of patients. Comprehensive knowledge of the differential value of clinical examination and MRI is necessary to define one final local stage, especially when a decision about treatment options is to be taken. The use of FIGO 2009, FIGO 2018 and TNM staging system leads to differences in stage distributions complicating comparability of treatment results. TNM provides the most differentiated stage allocation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".