Diffusion-weighted Imaging in Cervical Cancer with an Endovaginal Technique: Potential Value for Improving Tumor Detection in Stage Ia and Ib1 Disease
Bibliographic record
Abstract
PURPOSE: To establish apparent diffusion coefficients (ADCs) of invasive cervical carcinoma compared with nontumor cervical epithelium and determine sensitivity and specificity of diffusion-weighted (DW) magnetic resonance (MR) imaging used in conjunction with T2-weighted MR imaging to help detect invasive cervical carcinoma in patients with stage Ia and Ib1 disease. MATERIALS AND METHODS: Local research ethics committee approval was obtained with written consent from each subject. Group 1 comprised patients (mean age, 38.7 years +/- 13.2 [standard deviation]) with histologically confirmed cervical intraepithelial neoplasia (CIN) found on smear (n = 20) or stage Ib1 cervical tumors (n = 18). Patients were imaged with endovaginal T2-weighted fast spin-echo and single-shot DW echo-planar MR imaging of the cervix. ADCs from invasive cervical carcinoma and nontumor regions were compared within (t test) and between (U test) patients. A derived threshold ADC level indicative of invasive cervical carcinoma was used with T2-weighted imaging by two independent observers to identify possible invasive cervical carcinoma in group 2, patients with suspected disease (n = 21; mean age, 42.0 years +/- 16.4). Surgical specimens were the reference standard. Interobserver agreement was assessed. RESULTS: In group 1, ADCs from cervical carcinoma (757 x 10(-6) mm(2)/sec +/- 110) and adjacent epithelium (1331 x 10(-6) mm(2)/sec +/- 159) or CIN (1291 x 10(-6) mm(2)/sec +/- 156) were significantly different (P < .0001). In group 2, respective sensitivity and specificity to help detect invasive cervical carcinoma on T2-weighted images were 55.6% and 75% for observer 1 and 66.7% and 41.7% for observer 2, and 88.9% and 66.7% for observer 1 and 77.8% and 58.3% for observer 2 when ADC maps with a threshold level of 1100 x 10(-6) mm(2)/sec were added. Interobserver agreement was fair (kappa = 0.37) for T2-weighted images alone and good (kappa = 0.80) with ADC included. CONCLUSION: ADCs from invasive cervical carcinoma are significantly lower than those from nontumor epithelium; good interobserver agreement by using T2-weighted and DW MR imaging makes this technique potentially useful to help detect early-stage disease.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".