High-Dose-Rate Three-Dimensional Image-Guided Adaptive Brachytherapy (3D IGABT) for Locally Advanced Cervical Cancer (LACC): A Narrative Review on Imaging Modality and Clinical Evidence
Bibliographic record
Abstract
Background: Brachytherapy (BT) is a critical component of radiotherapy for locally advanced cervical cancer (LACC), and it has rapidly developed in recent decades. Since the advent of three-dimensional image-guided adaptive brachytherapy (3D-IGABT), magnetic resonance imaging (MRI) has emerged as the primary modality for image guidance. Meanwhile, other imaging modalities, such as computed tomography, 18F-fluorodeoxyglucose positron emission tomography, ultrasound, and their combinations have also been widely studied. Materials and methods: We reviewed studies on different imaging modalities utilized for target delineation and planning. Emerging techniques in IGABT like real-time image guidance and 3D printing were also included. We summarized research on their feasibility and concentrated on their clinical outcomes. Results: MRI-guided BT was the gold standard, and CT-guided BT was the most widely applied. Other modalities have shown feasibility and promising efficacy in dosimetry studies and preliminary outcomes. The longer-term clinical outcomes associated with these approaches require further elucidation. Conclusions: As 3D-IGABT was validated by promising clinical outcomes, the future of BT for LACC is expected to progress toward the refinement of more effective image-guided procedures. Moreover, achieving operational consensus and driving technological advancements to mitigate the inherent limitations associated with different imaging modes remain essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".