Abstract：
OBJECTIVE: This study aimed to predict the probability of grade ≥2 pneumonitis or dyspnea within 12 months of receiving conventionally fractionated or mildly hypofractionated proton beam therapy for locally advanced lung cancer using machine learning.
METHODS: Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling.
RESULTS: The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations.
CONCLUSIONS: In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.
METHODS: Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling.
RESULTS: The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations.
CONCLUSIONS: In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.
摘要：
目的：使用机器学习（ML）预测局部晚期肺癌接受常规分割或轻度低分割质子束治疗（PBT）12个月内≥2级肺炎或呼吸困难的概率。
方法：分析了12个机构中965例接受常规分割或轻度低分割（2.2-3Gy/fx）PBT治疗的连续肺癌患者的人口统计学和治疗特征。三种ML模型(梯度提升，加法树,和Lasso正则化逻辑回归）使用双10倍交叉验证进行参数超调而不泄漏信息，以预测CTCAEv.4级≥2级肺毒性。计算平衡准确度(BA)和曲线下面积(AUC)。使用自举采样获得95%置信区间。
结果：中位年龄为70岁（范围：20-97），主要患有IIIA或IIIB期疾病。他们以2Gy/分数接受60Gy的中位剂量，46.4％接受同步化疗。总的来说,250例（25.9%）肺毒性≥2级。在使用笔形束扫描（PBS）治疗的患者中，肺毒性的可能性为0.08，在使用其他技术治疗的患者中，肺毒性的可能性为0.34（p=8.97e-13）。使用腹部压迫和屏气也是毒性较低的高度显著预测因子(p=2.88e-08)。较高的总放射剂量（p=0.0182）和较高的同侧肺平均剂量增加了肺毒性的可能性（p=0.0035）。梯度提升在所有测试的模型中表现最好，当人口统计学和剂量学特征相结合时，AUC和BA分别为0.75±0.02和0.67±0.02。在分析了性能与用于训练的数据点数量之后，我们观察到,准确性仍然受到观察次数的限制.
结论：在迄今为止最大的前瞻性肺癌患者评估质子治疗肺毒性的分析中，先进的机器学习方法已经确定PBS，腹部压迫,正常肺剂量较低会导致发生≥2级肺炎或呼吸困难的概率明显降低。
方法：分析了12个机构中965例接受常规分割或轻度低分割（2.2-3Gy/fx）PBT治疗的连续肺癌患者的人口统计学和治疗特征。三种ML模型(梯度提升，加法树,和Lasso正则化逻辑回归）使用双10倍交叉验证进行参数超调而不泄漏信息，以预测CTCAEv.4级≥2级肺毒性。计算平衡准确度(BA)和曲线下面积(AUC)。使用自举采样获得95%置信区间。
结果：中位年龄为70岁（范围：20-97），主要患有IIIA或IIIB期疾病。他们以2Gy/分数接受60Gy的中位剂量，46.4％接受同步化疗。总的来说,250例（25.9%）肺毒性≥2级。在使用笔形束扫描（PBS）治疗的患者中，肺毒性的可能性为0.08，在使用其他技术治疗的患者中，肺毒性的可能性为0.34（p=8.97e-13）。使用腹部压迫和屏气也是毒性较低的高度显著预测因子(p=2.88e-08)。较高的总放射剂量（p=0.0182）和较高的同侧肺平均剂量增加了肺毒性的可能性（p=0.0035）。梯度提升在所有测试的模型中表现最好，当人口统计学和剂量学特征相结合时，AUC和BA分别为0.75±0.02和0.67±0.02。在分析了性能与用于训练的数据点数量之后，我们观察到,准确性仍然受到观察次数的限制.
结论：在迄今为止最大的前瞻性肺癌患者评估质子治疗肺毒性的分析中，先进的机器学习方法已经确定PBS，腹部压迫,正常肺剂量较低会导致发生≥2级肺炎或呼吸困难的概率明显降低。
