Early Invasive Cervical Cancer: CT and MR Imaging in Preoperative Evaluation—ACRIN/GOG Comparative Study of Diagnostic Performance and Interobserver Variability
Bibliographic record
Abstract
PURPOSE: To retrospectively compare diagnostic performance and interobserver variability for computed tomography (CT) and magnetic resonance (MR) imaging in the pretreatment evaluation of early invasive cervical cancer, with surgical pathologic findings as the reference standard. MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval and informed consent for evaluation of preoperative CT (n = 146) and/or MR imaging (n = 152) studies in 156 women (median age, 43 years; range, 22-81 years) from a previous prospective multicenter American College of Radiology Imaging Network and Gynecologic Oncology Group study of 172 women with biopsy-proved cervical cancer (clinical stage > or = IB). Four radiologists (experience, 7-15 years) interpreted the CT scans, and four radiologists (experience, 12-20 years) interpreted the MR studies retrospectively. Tumor visualization and detection of parametrial invasion were assessed with receiver operating characteristic curves (with P < or = .05 considered to indicate a significant difference). Descriptive statistics for staging and kappa statistics for reader agreement were calculated. Surgical pathologic findings were the reference standard. RESULTS: For CT and MR imaging, respectively, multirater kappa values were 0.26 and 0.44 for staging, 0.16 and 0.32 for tumor visualization, and -0.04 and 0.11 for detection of parametrial invasion; for advanced stage cancer (> or =IIB), sensitivities were 0.14-0.38 and 0.40-0.57, positive predictive values (PPVs) were 0.38-1.00 and 0.32-0.39, specificities were 0.84-1.00 and 0.77-0.80, and negative predictive values (NPVs) were 0.81-0.84 and 0.83-0.87. MR imaging was significantly better than CT for tumor visualization (P < .001) and detection of parametrial invasion (P = .047). CONCLUSION: Reader agreement was higher for MR imaging than for CT but was low for both. MR imaging was significantly better than CT for tumor visualization and detection of parametrial invasion. The modalities were similar for staging, sharing low sensitivity and PPV but relatively high NPV and specificity.
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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 | 0.000 |
| 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".