Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study.
Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study.
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DOI：
10.18383/j.tom.2016.00184
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Weizer AZ
中科院分区：
文献类型：
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作者：
Cha KH;Hadjiiski LM;Samala RK;Chan HP;Cohan RH;Caoili EM;Paramagul C;Alva A;Weizer AZ
评估膀胱癌对新辅助化疗的反应对于降低发病率和提高患者的生活质量至关重要。治疗过程中肿瘤体积的变化通常用于预测治疗结果。我们正在开发一种在CT中分割膀胱癌的方法，该方法使用了62例的试点数据集。从处理前的CT图像中提取65cn000个感兴趣区域，用留一例交叉验证的方法训练深度学习卷积神经网络，用于肿瘤边界检测。将结果与我们以前的AI-CALS方法进行了比较。对于数据集中的所有病变，由两名放射科医生测量最长直径及其垂直度，并由一名放射科医生进行3D手动分割。计算世界卫生组织(WHO)标准和实体瘤疗效评价标准(RECIST)，并用受试者工作特征曲线下面积(AUC)评估化疗完全缓解的预测准确性。使用DL-CNN分割、AI-CALS和手动等值线计算的容积变化的AUC分别为0.73±0.06、0.70±0.07和0.70±0.06。这些差异没有达到统计学意义。两位放射科医生按WHO标准计算的AUC分别为0.63±0.07和0.61±0.06，RECIST分别为0.65±007和0.63±0.06。我们的结果表明，DL-CNN可以产生准确的膀胱癌分割，用于计算肿瘤大小随治疗的变化。在预测完全缓解方面，容量变化的表现好于WHO标准和RECIST的估计。
Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation. The results were compared to our previous AI-CALS method. For all lesions in the data set, the longest diameter and its perpendicular were measured by two radiologists, and 3D manual segmentation was obtained from one radiologist. The World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) were calculated, and the prediction accuracy of complete response to chemotherapy was estimated by the area under the receiver operating characteristic curve (AUC). The AUCs were 0.73 ± 0.06, 0.70 ± 0.07, and 0.70 ± 0.06, respectively, for the volume change calculated using DL-CNN segmentation, the AI-CALS and the manual contours. The differences did not achieve statistical significance. The AUCs using the WHO criteria were 0.63 ± 0.07 and 0.61 ± 0.06, while the AUCs using RECIST were 0.65 ± 007 and 0.63 ± 0.06 for the two radiologists, respectively. Our results indicate that DL-CNN can produce accurate bladder cancer segmentation for calculation of tumor size change in response to treatment. The volume change performed better than the estimations from the WHO criteria and RECIST for the prediction of complete response.