Association of Apparent Diffusion Coefficient with Disease Recurrence in Patients with Locally Advanced Cervical Cancer Treated with Radical Chemotherapy and Radiation Therapy
Bibliographic record
Abstract
PURPOSE: To investigate whether volumetrically derived apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging is associated with disease recurrence in women with locally advanced cervical cancer treated with chemotherapy and radiation therapy. MATERIALS AND METHODS: An ethics board-approved, retrospective study was conducted in 85 women with stage IB-IVA cervical cancer treated with chemo- and radiation therapy in 2009-2013. All patients underwent MR imaging for staging, including T2-weighted and DW MR imaging series, by using a 1.5- or 3.0-T imager. The mean, median, 75th, 90th, and 95th percentile ADCs (ADCmean, ADC50, ADC75, ADC90, and ADC95, respectively) of all voxels that comprised each tumor were extracted and normalized to the mean urine ADC (nADCmean, nADC50, nADC75, nADC90, and nADC95, respectively) to reduce variability. The primary outcome was disease-free survival (DFS). Uni- and multivariable Cox regression analyses were used to evaluate the association of ADC parameters and relevant clinical variables with DFS. RESULTS: Of the 85 women included, 62 were free of disease at last follow-up. Median follow-up was 37 months (range, 5-68 months). Significant variables at univariable analysis included T2-weighted derived tumor diameter, para-aortic nodal involvement, advanced stage, ADC90 and ADC95, nADC75, nADC90, and nADC95. Normalized parameters were more highly associated (hazard ratio per 0.01 increase in normalized ADC, 0.91-0.94; P < .04). Because nADC75, nADC90, and nADC95 were highly correlated, only nADC95 (which had the lowest P value) was included in multivariable analysis. At multivariable analysis, absolute and normalized ADC95 remained associated with DFS (hazard ratio, 0.90-0.98; P < .05). CONCLUSION: The volumetric ADC95 may be a useful imaging metric to predict treatment failure in patients with locally advanced cervical cancer treated with chemo- and radiation therapy.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".