โก Quick Summary
This study investigated the average treatment effect (ATE) of tooth extraction prior to radiotherapy on the risk of osteoradionecrosis (ORN) in patients with head and neck cancer (HNC). While the overall ATE was not significant, the findings revealed substantial heterogeneous treatment effects (HTE), indicating that some patients may benefit while others may be harmed by the intervention.
๐ Key Details
- ๐ Dataset: 2,466 patients with head and neck cancer
- ๐ฆท Intervention: Tooth extraction before radiotherapy
- โ๏ธ Technology: Causal machine learning, specifically a causal survival forest
- ๐ Outcome: Osteoradionecrosis (ORN) assessed using ClinRad grade โฅ1
๐ Key Takeaways
- ๐ ATE was not significant at -0.26 months (95% CI: -0.68 to 0.17).
- ๐ CATE distributions showed that patients in the lowest quartile experienced harm, while those in the highest quartile derived benefit.
- ๐ Median RMST differences were -1.47 months in Q1 and +0.94 months in Q4.
- ๐ RATE was positive at 1.10 (95% CI: 0.04 to 2.15).
- ๐งฉ Independent modifiers of benefit included Eastern Cooperative Oncology Group performance status and periodontal grade.
- โก Radiation dose had a nonlinear association with treatment effect.
- ๐๏ธ Study period: 2011 to 2018 with ongoing follow-up.
- ๐ก Importance of individualized treatment effects was emphasized, highlighting the need for targeted interventions.
๐ Background
Osteoradionecrosis (ORN) is a serious complication that can occur in patients undergoing radiotherapy for head and neck cancer. The decision to extract teeth before radiotherapy is often made to mitigate the risk of ORN, but the actual impact of this intervention on patient outcomes has been unclear. This study aimed to clarify the effects of tooth extraction on ORN risk using advanced causal machine learning techniques.
๐๏ธ Study
Conducted as a retrospective cohort study, this research analyzed data from 2,466 adults diagnosed with head and neck cancer who received curative radiotherapy between 2011 and 2018. The primary focus was on the effects of extracting at least one tooth prior to radiotherapy and its association with the development of ORN. The study utilized a causal survival forest model to estimate treatment effects while accounting for various sociodemographic and clinical covariates.
๐ Results
The analysis revealed that the average treatment effect of tooth extraction was not statistically significant. However, the conditional average treatment effect distributions indicated that patients in the lowest quartile experienced a median reduction in survival time of 1.47 months, while those in the highest quartile experienced an increase of 0.94 months. This highlights the variability in treatment response among patients, suggesting that while some may benefit from tooth extraction, others may be adversely affected.
๐ Impact and Implications
The findings from this study underscore the importance of personalized treatment approaches in oncology. By recognizing that the effects of tooth extraction before radiotherapy can vary significantly among patients, healthcare providers can make more informed decisions tailored to individual patient profiles. This could lead to improved outcomes and reduced risks of complications such as ORN, ultimately enhancing the quality of care for patients with head and neck cancer.
๐ฎ Conclusion
This study highlights the potential of causal machine learning in understanding treatment effects in complex clinical scenarios. While tooth extraction before radiotherapy did not show a significant overall benefit, the individualized treatment effects reveal critical insights into patient care. Future research should continue to explore targeted interventions to optimize outcomes for patients undergoing treatment for head and neck cancer.
๐ฌ Your comments
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Estimating the Individualized Effect of Tooth Extraction before Radiotherapy on Osteoradionecrosis Using Causal Machine Learning.
Abstract
The purpose of this study was to determine the average treatment effect (ATE) of tooth extraction before radiotherapy on the risk of osteoradionecrosis (ORN) in patients with head and neck cancer (HNC) and to estimate the conditional average treatment effect (CATE) to characterize heterogeneous treatment effects (HTE). In this retrospective cohort study of adults with HNC treated with curative radiotherapy from 2011 to 2018 with ongoing follow-up, the intervention was the extraction of at least 1 tooth before radiotherapy, and the outcome was ORN (ClinRad grade โฅ1). Twenty sociodemographic and clinical covariates were recorded. A causal survival forest targeting restricted mean survival time (RMST) was trained with 100 repetitions of 5-fold cross-validation. Calibration used out-of-fold augmented inverse probability weighting (AIPW) scores as pseudo-outcomes, and treatment prioritization was assessed using the rank-weighted average treatment effect (RATE). A best linear projection regression identified covariates with direct associations to the predicted benefit. Among 2,466 patients, 810 underwent preradiotherapy extraction, and 183 developed ORN during the follow-up. The ATE was not significant at -0.26โmo (95% confidence interval [CI]: -0.68 to 0.17). However, the CATE distributions revealed substantial HTE, with patients in the lowest quartile (Q1) experiencing harm and those in the highest quartile (Q4) deriving benefit. Calibration against AIPW scores confirmed median RMST differences of -1.47โmo in Q1 and +0.94โmo in Q4. The RATE was positive at 1.10 (95% CI: 0.04 to 2.15). Best linear projection identified Eastern Cooperative Oncology Group performance status 1 to 4 versus 0 (ฮฒโ=โ2.68; 95% CI: 0.19 to 5.17) and periodontal grade III-IV versus 0 (ฮฒโ=โ4.33; 95% CI: 1.05 to 7.60) as independent modifiers of benefit. Radiation dose had a nonlinear association with treatment effect. If preradiotherapy tooth extraction was applied across all eligible patients, it would not alter the overall risk of ORN. However, individualized treatment effects varied, with some patients benefiting and others harmed, underscoring the importance of targeted interventions.
Author: [‘Moharrami M’, ‘Watson E’, ‘Singhal S’, ‘Huang SH’, ‘Yao C’, ‘Hosni A’, ‘Quinonez C’, ‘Glogauer M’]
Journal: J Dent Res
Citation: Moharrami M, et al. Estimating the Individualized Effect of Tooth Extraction before Radiotherapy on Osteoradionecrosis Using Causal Machine Learning. Estimating the Individualized Effect of Tooth Extraction before Radiotherapy on Osteoradionecrosis Using Causal Machine Learning. 2026; (unknown volume):220345261424748. doi: 10.1177/00220345261424748