Predicting High-Grade Acute Urinary Toxicity and Lower Gastrointestinal Toxicity After Postoperative Volumetric Modulated Arc Therapy for Cervical and Endometrial Cancer Using a Normal Tissue Complication Probability Model
Bibliographic record
Abstract
(1) Background: Volumetric modulated arc therapy (VMAT) can deliver more accurate dose distribution and reduce radiotherapy-induced toxicities for postoperative cervical and endometrial cancer. This study aims to retrospectively analyze the relationship between dosimetric parameters of organs at risk (OARs) and acute toxicities and provide suggestions for the dose constraints. (2) Methods: A total of 164 postoperative cervical and endometrial cancer patients were retrospectively analyzed, and the endpoints were grade ≥ 2 acute urinary toxicity (AUT) and acute lower gastrointestinal toxicity (ALGIT). The normal tissue complication probability (NTCP) model was established using the logistic regression model. Restricted cubic spline (RCS) curves were used to explore the association between dosimetric parameters and toxicities. The receiver operating characteristic (ROC) curve, calibration curve, Akaike’s corrected information criterion (AICc), decision curve analysis (DCA), and clinical impact curve (CIC) were analyzed to evaluate the performance of NTCP models. (3) Results: Bladder V40Gy was identified to develop the NTCP model of AUT, and the mean AUC was 0.69 (CI: 0.58–0.80). Three candidate predictors, namely the small intestine V30Gy, colon D45%, and rectum D55%, were identified to develop the NTCP model of ALGIT, and the mean AUC was 0.71 (CI: 0.61–0.80). Both models were considered to have relatively good discriminative accuracy and could provide a high net benefit in clinical applications. (4) Conclusions: We developed NTCP models to predict the probability for grade ≥ 2 AUT and ALGIT. We recommend that bladder V40Gy, the small intestine V30Gy, colon D45%, and rectum D55% be controlled below 42%, 20.4%, 16.9 Gy, and 32.0 Gy, respectively.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".