The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method
The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method
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DOI：
10.1007/s10278-019-00230-2
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发表时间：
2019-08-01
影响因子：
4.4
通讯作者：
Lee, Hak Jong
中科院分区：
文献类型：
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作者：
Han, Seokmin;Hwang, Sung Il;Lee, Hak Jong
在这项研究中，我们利用基于图像的深度学习框架，使用计算机断层扫描（CT）获取的图像来区分肾细胞癌的三种主要亚型（透明细胞，乳头状和嫌色细胞）。一个活检证实的基准数据集是从169个肾癌病例中建立的。在每种情况下，在三个阶段采集图像（阶段1，注射造影剂前;阶段2，注射后1分钟;阶段3，注射后5分钟）。图像采集后，由放射科医师标记每个相位图像中的矩形ROI（感兴趣区域）。裁剪ROI后，将组合权重乘以三相ROI图像，并将线性组合的图像在拼接后送入深度学习神经网络。使用绘制的ROI作为输入并使用活检结果作为标签，训练深度学习神经网络以分类肾细胞癌的亚型。该网络显示出约0.85的准确性、0.64-0.98的灵敏度、0.83-0.93的特异性和0.9的AUC。该框架基于深度学习方法和放射科医生提供的ROI，在肾细胞亚型分类方面显示出良好的效果。我们希望它能帮助未来的研究在这个问题上，它可以配合放射科医生在分类病变亚型在真实的临床情况。
In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking dataset was built from 169 renal cancer cases. In each case, images were acquired at three phases(phase 1, before injection of the contrast agent; phase 2, 1min after the injection; phase 3, 5min after the injection). After image acquisition, rectangular ROI (region of interest) in each phase image was marked by radiologists. After cropping the ROIs, a combination weight was multiplied to the three-phase ROI images and the linearly combined images were fed into a deep learning neural network after concatenation. A deep learning neural network was trained to classify the subtypes of renal cell carcinoma, using the drawn ROIs as inputs and the biopsy results as labels. The network showed about 0.85 accuracy, 0.64-0.98 sensitivity, 0.83-0.93 specificity, and 0.9 AUC. The proposed framework which is based on deep learning method and ROIs provided by radiologists showed promising results in renal cell subtype classification. We hope it will help future research on this subject and it can cooperate with radiologists in classifying the subtype of lesion in real clinical situation.