Clinical Impact of MRI-Guided Intracavitary–Interstitial Brachytherapy in the Curative Management of Advanced-Stage Cervical Cancer
Bibliographic record
Abstract
This study investigates the clinical efficacy of MRI-based adaptive brachytherapy (IGABT) using combined intracavitary and interstitial techniques in the curative treatment of patients with advanced cervical cancer (LACC). A retrospective analysis was conducted on 149 LACC patients treated at a single center. The therapeutic protocol included intensity-modulated external beam radiotherapy (IMRT) and IGABT. Dosimetric parameters were evaluated for relevance for local control (LC), progression-free survival (PFS), and overall survival (OS) using Kaplan–Meier estimation, Cox regression, and log-rank test. Patients predominantly presented with stage III/IV tumors (81%, FIGO 2018). The median high-risk clinical target volume (hrCTV) was 34 cm3, with a median D90% dose of 88.9 GyEQD2. At 24 months, OS, PFS, and LC rates were 86%, 57%, and 81%, respectively. FIGO stage, tumor volume, and histology were significant predictors of PFS. Higher total hrCTV doses were strongly correlated with improved LC and PFS, emphasizing the importance of precise dosimetric optimization in IGABT and confirming the critical role of IGABT in achieving very good LC rates for LACC. The reported LC rates are comparable to landmark studies, such as INTERLACE and KEYNOTE-A18. This study validates the effectiveness of MRI-guided IGABT in enhancing local tumor control in advanced-stage cervical cancer while providing insights into the prognostic implications of dosimetric parameters such as hrCTV and point A. Future research should address the persistent challenge of distant metastases by exploring the integration of novel systemic treatment options.
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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 | 0.001 | 0.001 |
| 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 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".