Tumor Invasion Distance Based on MRI Is a Novel Prognostic Indicator for I-IIIB Cervical Cancer Patients Treated with Radiotherapy
Bibliographic record
Abstract
Aims:This study aimed to identify the prognostic value of tumor invasion distance (TID) based on MRI findings in cervical-cancer (CC) patients treated with radiotherapy (RT). Methods: A total of 218 CC patients diagnosed at Fujian Cancer Hospital from December 2018 to December 2019 were included in the study. Cox regression analyses were conducted to identify independent prognostic factors for overall survival (OS), including low 1/3 vaginal involvement, a longer TID, and RT without chemotherapy. These factors were subsequently used to construct a nomogram for individualized risk prediction. Kaplan–Meier survival analysis was employed to evaluate survival outcomes and establish a risk stratification system. The performance of the new stratification was assessed using the linear trend χ2 test, Akaike information criterion, and Harrell’s concordance index. Results: A longer TID was associated with worse 3-year OS (p < 0.001, HR: 3.42, 95% CI: 1.67–7.00). A longer TID, lower 1/3 vaginal involvement, and concurrent chemotherapy were independent prognostic survival factors for CC patients. Compared with the 2018 FIGO staging system, the new risk stratification system provided better monotonicity with a higher linear trend χ2 value (28.03 vs. 9.35), better discriminatory ability with smaller Akaike information criterion (312 vs. 331), and a greater Harrell C statistic (0.74 vs. 0.65) for predicting 3-year OS. Conclusions: This was the first study to demonstrate the prognostic value of TID in CC patients who received RT. The new risk stratification system based on TID could complement the 2018 FIGO staging system in identifying high-risk patients for more intense treatment and care. Further prospective research with larger samples is warranted to confirm the significance of TID for CC patients treated with RT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".