Kubota Seiko, Imai Tomoaki, Nishimoto Ayano, Amekawa Shigeki, Uzawa Narikazu
Department of Oral and Maxillofacial Surgery II, Osaka University Graduate School of Dentistry, Suita, Osaka, Japan.
Department of Oral and Maxillofacial Surgery, Ikeda City Hospital, Ikeda, Osaka, Japan.
Odontology. 2023 Jan;111(1):178-191. doi: 10.1007/s10266-022-00716-6. Epub 2022 May 23.
We previously developed basic and extended models to predict inferior alveolar nerve injuries (IANI) after lower third molar (LM3) removal based on cone-beam computed tomography (CBCT) images. Although these models comprised predictors, including increased age and inferior alveolar canal-related CBCT factors, external validations were lacking. Therefore, this study externally validated these models and compared them with other related models based on their performance. Original and newly validated samples included patients who underwent LM3 removal following CBCT. Subsequently, 39 and 25 patients with IANI, then 457 and 295 randomly selected patients without IANI were chosen of the observed 1573 and 1052 patients, respectively. CBCT- and panoramic radiograph (PAN)-featured models were validated. Then, models' discrimination and calibration abilities were assessed using C-statistics and calibration plots, respectively. Brier scores were also quantified, after which logistic recalibration was achieved to optimize calibration, and a risk calculator was developed. During the external validation, the extended model exhibited the best C-statistic (0.822) and Brier score (0.064), whereas two CBCT- and two PAN-featured models showed lower performances with C-statistics (0.764, 0.706, 0.584, and 0.627) and Brier scores (0.069, 0.074, 0.075, and 0.072). Besides, all models showed a tendency to overpredict its high-risk range. However, recalibration of the extended model resulted in excellent calibration performance. CBCT-featured models, especially the extended model, conclusively showed a superior predictive performance to PAN models. Therefore, the risk calculator on the extended CBCT model is proposed to be a clinical decision-aid tool that preoperatively predicts IANI risk.
我们之前基于锥形束计算机断层扫描(CBCT)图像开发了基础模型和扩展模型,以预测下颌第三磨牙(LM3)拔除术后的下牙槽神经损伤(IANI)。尽管这些模型包含了一些预测因素,如年龄增长和与下牙槽管相关的CBCT因素,但缺乏外部验证。因此,本研究对这些模型进行了外部验证,并根据其性能将它们与其他相关模型进行比较。原始样本和新验证样本均包括在CBCT检查后接受LM3拔除术的患者。随后,在观察到的1573例和1052例患者中,分别选择了39例和25例发生IANI的患者,以及457例和295例随机选择的未发生IANI的患者。对具有CBCT和全景X线片(PAN)特征的模型进行了验证。然后,分别使用C统计量和校准图评估模型的区分能力和校准能力。还对布里尔评分进行了量化,之后进行逻辑重新校准以优化校准,并开发了一个风险计算器。在外部验证过程中,扩展模型表现出最佳的C统计量(0.822)和布里尔评分(0.064),而两个具有CBCT特征和两个具有PAN特征的模型的C统计量(0.764、0.706、0.584和0.627)和布里尔评分(0.069、0.074、0.075和0.072)较低。此外,所有模型都有高估其高风险范围的趋势。然而,扩展模型的重新校准产生了出色的校准性能。具有CBCT特征(尤其是扩展模型)的模型最终显示出比PAN模型更好的预测性能。因此,建议扩展CBCT模型上的风险计算器作为一种术前预测IANI风险的临床决策辅助工具。