Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas
Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas
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DOI：
10.1148/radiol.2020192764
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发表时间：
2020-07-01
期刊：
影响因子：
19.7
通讯作者：
Park, Chang Min
中科院分区：
文献类型：
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作者：
Kim, Hyungjin;Goo, Jin Mo;Park, Chang Min
背景资料：深度学习模型具有预测肺癌的潜力，但模型输出作为一个独立的预后因素必须与临床风险因素进行验证。目的：开发和验证一个术前基于CT的深度学习模型，用于预测肺腺癌患者的无病生存期。材料和方法：在这项回顾性研究中，训练了一个深度学习模型，以从术前CT检查中提取预后信息。用于训练、调整和内部验证的数据集1由2009年至2015年切除的T1- 4 N 0 M0腺癌患者组成。用于外部验证的数据集2包括2014年切除的临床T1- 2aN 0 M0（I期）腺癌患者。通过使用Harrell C指数评估辨别力，并以临床T类别为基准。Greenwood-Nam-D 'Agostino检验用于模型校准。多变量调整后的风险比（HR）与临床预后因素进行了分析，通过使用的考克斯regression.Results：评价了800例患者（中位年龄，64岁;四分位数范围，56-70岁; 450名妇女）在数据集1和108例患者（中位年龄，63岁;四分位数范围，57-71岁; 60名妇女）在数据集2。C指数在内部验证中为0.74-0.80，在外部验证中为0.71-0.78，均与临床T类别相当（内部验证中为0.78，外部验证中为0.74;均P。05）。该模型在所有数据集（P。05）。多变量考克斯回归显示，模型输出是独立的预后因素（内部验证中分类输出的风险比[HR]为2.5[95%置信区间{CI}：1.03，5.9; P =.04]，外部验证中为3.6 [95% CI：1.6，8.5; P =.003]）。除深度学习模型外，只有吸烟状况（HR，3.4; 95% CI：1.4，8.5; P =.007）进一步有助于预测临床I期腺癌切除术后患者的无病生存期。结论：胸部CT深度学习模型可预测临床I期肺腺癌手术患者的无病生存期。(C)RSNA，2020年
Background: Deep learning models have the potential for lung cancer prognostication, but model output as an independent prognostic factor must be validated with clinical risk factors.Purpose: To develop and validate a preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinoma.Materials and Methods: In this retrospective study, a deep learning model was trained to extract prognostic information from preoperative CT examinations. Data set 1 for training, tuning, and internal validation consisted of patients with T1-4N0M0 adenocarcinoma resected between 2009 and 2015. Data set 2 for external validation included patients with clinical T1-2aN0M0 (stage I) adenocarcinomas resected in 2014. Discrimination was assessed by using Harrell C index and benchmarked against the clinical T category. The Greenwood-Nam-D'Agostino test was used for model calibration. The multivariable-adjusted hazard ratios (HRs) were analyzed with clinical prognostic factors by using the Cox regression.Results: Evaluated were 800 patients (median age, 64 years; interquartile range, 56-70 years; 450 women) in data set 1 and 108 patients (median age, 63 years; interquartile range, 57-71 years; 60 women) in data set 2. The C indexes were 0.74-0.80 in the internal validation and 0.71-0.78 in the external validation, both comparable with the clinical T category (0.78 in the internal validation and 0.74 in the external validation; all P..05). The model exhibited good calibration in all data sets (P..05). Multivariable Cox regression revealed that model outputs were independent prognostic factors (hazard ratio [HR] of the categorical output, 2.5[95% confidence interval {CI}: 1.03, 5.9; P =.04] in the internal validation and 3.6 [95% CI: 1.6, 8.5; P =.003] in the external validation). Other than the deep learning model, only smoking status (HR, 3.4; 95% CI: 1.4, 8.5; P =.007) contributed further to prediction of disease-free survival for patients after resection of clinical stage I adenocarcinomas.Conclusion: A deep learning model for chest CT predicted disease-free survival for patients undergoing an operation for clinical stage I lung adenocarcinoma. (C) RSNA, 2020