1. University of Science and Technology of China, Hefei 230026, China 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China 3. The Central Hospital of Lishui City, Lishui 323000, China
A deep learning method based on DenseNet was proposed to distinguish between IAC and MIA from mixed ground glass nodules low-resolution CT images with 5 mm slice thickness, in order to classify histological subtypes of lung adenocarcinoma from low-dose CT images with low resolution. Samples were obtained from 105 low-resolution CT images with 5 mm slice thickness of 105 patients in the Central Hospital of Lishui City. The data was divided into training set and testing set. Then the training set was augmented; 2D and 3D DenseNet deep learning models were built to distinguish between IAC and MIA. The accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve of the proposed 2D DenseNet method achieved 76.67%, 63.33%, 90.00% and 0.888 9, respectively, which was better than 3D DenseNet and other deep learning models. The deep learning method, especially the 2D DenseNet, may assist doctors in lung cancer screening to predict and guide histological subtypes of patients, which can quickly provide more accurate diagnosis results even under condition of low image resolution.
Jing YANG,Chen GENG,Hai-lin WANG,Jian-song JI,Ya-kang DAI. Classification on histological subtypes of lung adenocarcinoma from low-resolution CT images based on DenseNet. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1164-1170.
Tab.2Details of 2D and 3D DenseNet model architectures
Fig.5Comparison of performance of 2D and 3D DenseNet models
深度学习网络
a/%
e/%
p/%
AUC
注:括号内是与2D DenseNet相应性能指标相比下降的百分比
2D DenseNet
76.67
63.33
90.00
0.888 9
2D DenseNet-无颈瓶层
54.17(↓22.5)
20.84(↓42.49)
87.50(↓2.50)
0.592 6(↓0.296 3)
2D DenseNet-无弃权
58.33(↓18.34)
33.33(↓30.00)
83.33(↓6.67)
0.683 3(↓0.205 6)
2D DenseNet-无数据扩展
58.33(↓18.34)
63.33(↓0.00)
53.34(↓36.66)
0.694 4(↓0.194 5)
Tab.3Comparison of performance of different parameters in 2D DenseNet model
深度学习网络
a/%
e/%
p/%
AUC
LeNet
60.00
66.67
53.33
0.750 0
AlexNet
53.33
56.67
50.00
0.722 2
AgileCNN
53.33
63.33
43.33
0.638 9
Multi-channel CNN
56.66
63.33
50.00
0.777 8
2D DenseNet
76.67
63.33
90.00
0.888 9
Tab.4Comparison of performance of 2D DenseNet and other deep learning models
[1]
SIEGEL R L, MILLER K D, JEMAL A Cancer statistics, 2018[J]. CA: a Cancer Journal for Clinicians, 2018, 68 (1): 7- 30
doi: 10.3322/caac.21442
[2]
YANG J, WANG H, GENG C, et al Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules[J]. Biomedical Engineering Online, 2018, 17 (1): 20
doi: 10.1186/s12938-018-0435-2
[3]
HENSCHKE C I, YANKELEVITZ D F, MIRTCHEVA R, et al CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules[J]. AJR American Journal of Roentgenology, 2002, 178 (5): 1053- 1057
doi: 10.2214/ajr.178.5.1781053
[4]
COHEN J G, REYMOND E, MEDICI M, et al CT-texture analysis of subsolid nodules for differentiating invasive from in-situ and minimally invasive lung adenocarcinoma subtypes[J]. Diagnostic and Interventional Imaging, 2018, 99 (5): 291- 299
doi: 10.1016/j.diii.2017.12.013
[5]
TRAVIS W D, BRAMBILLA E, NOGUCHI M, et al International association for the study of lung cancer/American thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinoma[J]. Journal of Thoracic Oncology Official Publication of the International Association for the Study of Lung Cancer, 2011, 6 (2): 244- 285
doi: 10.1097/JTO.0b013e318206a221
[6]
TRAVIS W D, BRAMBILLA E, NICHOLSON A G, et al The 2015 world health organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification[J]. Journal of Thoracic Oncology Official Publication of the International Association for the Study of Lung Cancer, 2015, 10 (9): 1243- 1260
doi: 10.1097/JTO.0000000000000630
[7]
NAIDICH D P, BANKIER A A, MACMAHON H, et al Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the fleischner society[J]. Radiology, 2013, 266 (1): 304- 317
doi: 10.1148/radiol.12120628
[8]
YUE X, LIU S, LIU S, et al HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of </=3 cm[J]. Clinical Radiology, 2018, 73 (4): 411. e7- 411. e15
doi: 10.1016/j.crad.2017.11.014
[9]
VAN SCHIL P E, ASAMURA H, RUSCH V W, et al Surgical implications of the new IASLC/ATS/ERS adenocarcinoma classification[J]. European Respiratory Journal, 2012, 39 (2): 478- 486
doi: 10.1183/09031936.00027511
[10]
LIU S, WANG R, ZHANG Y, et al Precise diagnosis of intraoperative frozen section is an effective method to guide resection strategy for peripheral small-sized lung adenocarcinoma[J]. Journal of Clinical Oncology, 2016, 34 (4): 307- 313
doi: 10.1200/JCO.2015.63.4907
[11]
ZHANG J, WU J, TAN Q, et al Why do pathological stage IA lung adenocarcinomas vary from prognosis?: a clinicopathologic study of 176 patients with pathological stage IA lung adenocarcinoma based on the IASLC/ATS/ERS classification[J]. Journal of Thoracic Oncology Official Publication of the International Association for the Study of Lung Cancer, 2013, 8 (9): 1196- 1202
doi: 10.1097/JTO.0b013e31829f09a7
[12]
涂文婷, 范丽, 顾亚峰, 等 计算机辅助定量分析对磨玻璃密度型肺腺癌浸润性的诊断价值[J]. 临床放射学杂志, 2018, 37 (3): 497- 502 TU Wen-ting, FAN Li, GU Ya-feng, et al The value of computer-aided quantitative analysis in the diagnosis of invasiveness of lung adenocarcinoma manifesting as ground glass nodule[J]. Journal of Clinical Radiology, 2018, 37 (3): 497- 502
[13]
SON J Y, LEE H Y, LEE K S, et al Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma[J]. PLoS One, 2014, 9 (8): e104066
doi: 10.1371/journal.pone.0104066
[14]
左玉强, 冯平勇, 孟庆春, 等 肺纯磨玻璃结节微浸润腺癌与浸润性腺癌的CT鉴别诊断[J]. 临床放射学杂志, 2017, 36 (4): 495- 498 ZUO Yu-qiang, FENG Ping-yong, MENG Qing-chun, et al CT differential diagnoses of pulmonary minimally invasive adenocarcinoma and invasive adenocarcinoma presenting as pure ground glass nodule[J]. Journal of Clinical Radiology, 2017, 36 (4): 495- 498
[15]
WANG H, ZHAO T, LI L C, et al A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation[J]. Journal of X-ray Science and Technology, 2018, 26 (2): 171- 187
doi: 10.3233/XST-17302
[16]
ZHAO X, LIU L, QI S, et al Agile convolutional neural network for pulmonary nodule classification using CT images[J]. International Journal of Computer Assisted Radiology and Surgery, 2018, 13 (4): 585- 595
doi: 10.1007/s11548-017-1696-0
[17]
HUANG G, LIU Z, MAATEN L V D, et al. Densely connected convolutional networks [C] // 2017 IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 2261–2269.
[18]
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15 (1): 1929- 1958
[19]
BAHRAMPOUR S, RAMAKRISHNAN N, SCHOTT L, et al Comparative study of deep learning software frameworks[J]. Computer Science, 2016,
[20]
LING C X, HUANG J, ZHANG H AUC: a better measure than accuracy in comparing learning algorithms[J]. Lecture Notes in Computer Science, 2003, 329- 341
[21]
LECUN Y, BOTTOU L, BENGIO Y, et al Gradient-based learning applied to document recognition[J]. P Ieee, 1998, 86 (11): 2278- 2324
doi: 10.1109/5.726791
[22]
KRIZHEVSKY A, SUTSKEVER I, HINTON G E ImageNet classification with deep convolutional neural networks[J]. Commun ACM, 2017, 60 (6): 84- 90
doi: 10.1145/3098997
[23]
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C] // 2016 IEEE Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2016: 770–778.