Three-Dimensional-Printed Template-Guided Radioactive Seed Brachytherapy via a Submental Approach for Recurrent Base of Tongue and Floor of Mouth Cancer
Bibliographic record
Abstract
Background: This study assessed clinical outcomes of three-dimensional-printed template (3DPT)-guided radioactive seed brachytherapy (RSBT) via a submental approach for recurrent base of tongue and floor of mouth cancer. Methods: Thirty-one patients with recurrent lingual and floor of mouth squamous cell carcinoma after surgery and radiotherapy were treated with 3DPT-guided RSBT from 2015 to 2022. Seeds were implanted through a submental approach guided by 3DPTs. Local control (LC), overall survival (OS), disease control (DC) and quality of life (QOL) were evaluated. Results: The median follow-up was 13.7 months. The 1-, 3- and 5-year LC rates were 66.1%, 66.1%, and 55.1% respectively. The 1-, 3- and 5-year OS rates were 63.4%, 33.4%, and 8.3%. The 1-, 3- and 5-year DC rates were 37.8%, 26.5%, and 21.2%. Univariate analysis showed tumor size significantly affected LC (P = 0.031). The presence of extraterritorial lesions affected DC and OS on multivariate analysis (P < 0.01). QOL improved significantly in domains of pain, swallowing, chewing, taste, and emotion after treatment compared to baseline. Four patients (13%) developed necrosis and osteoradionecrosis. Conclusions: 3DPT-guided submental RSBT provided favorable LC and QOL for recurrent tongue/floor of mouth cancer with minimal toxicity; moreover, severe toxicity should be noted.
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 | 0.000 | 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) | 0.001 | 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".