Dataset Information

High-dose-rate brachytherapy boost for locally advanced cervical cancer: Oncological outcome and toxicity analysis of 4 fractionation schemes

ABSTRACT: Highlights • Brachytherapy boost is a standard of care for locally advanced cervical cancer.• High-dose-rate brachytherapy (HDR-BT) boost procedure is not standardized.• The number of implants, fractions, doses and imaging differ in literature.• Bi-fractionated HDR-BT in 1 implant is feasible with good oncological outcome.• Bi-fractionated HDR-BT dose escalation slightly increases acute toxicity.

Purpose

Brachytherapy (BT) boost after radio-chemotherapy (RCT) is a standard of care in the management of locally advanced cervical cancer (LACC). As there is no consensus on high-dose-rate (HDR) BT fractionation schemes, our aim was to report the oncological outcome and toxicity profile of four different schemes using twice-a-day (BID) HDR-BT.

Patients and methods

This was an observational, retrospective, single institution study for patients with LACC receiving a HDR-BT boost. The latter was performed with a single implant and single imaging done on day 1. The different fractionation schemes were: 7 Gy + 4x3.5 Gy (group 1); 7 Gy + 4x4.5 Gy (group 2); 3x7Gy (group 3) and 3x8Gy (group 4). Local (LFS), nodal (NFS) and metastatic (MFS) recurrence-free survival as well as progression-free survival (PFS) and overall survival (OS) were analyzed. Acute (≤6 months) and late toxicities (>6 months) were reported.

Results

From 2007 to 2018, 191 patients were included. Median follow-up was 57 months [45–132] and median EQD210D90CTVHR was 84, 82 and 90 Gy for groups 2, 3 and 4 respectively (dosimetric data missing for group 1). The 5-year LFS, NFS, MFS, PFS and OS were 85% [81–90], 83% [79–86], 70% [67–73], 61% [57–64] and 75% [69–78] respectively, with no significant difference between the groups. EQD210D90CTVHR < 85 Gy was a prognostic factor for local recurrence in univariate analysis (p = 0.045). The rates of acute/late grade ≥ 2 urinary, digestive and gynecological toxicities were 9%/15%, 3%/15% and 9%/25% respectively.

Conclusion

Bi-fractionated HDR-BT boost seems feasible with good oncological outcome and slightly more toxicity after dose escalation.

SUBMITTER: le Guyader M

PROVIDER: S-EPMC8592834 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy.MethodsFrom a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined.ResultsFeature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods - AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%).ConclusionThe presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.