Lung cancer histology classification from CT images based on radiomics and deep learning models.
CNN
LSTM
Lung histology classification
NSCLC
Radiomics
Journal
Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869
Informations de publication
Date de publication:
Jan 2021
Jan 2021
Historique:
received:
27
11
2019
accepted:
22
12
2020
pubmed:
8
1
2021
medline:
30
9
2021
entrez:
7
1
2021
Statut:
ppublish
Résumé
Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor's CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc = 0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM + Inception outperformed experts by 7-25% (p < 0.05). The proposed methodology does not require detailed segmentation of the tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making. Lung cancer histology classification from CT images based on CNN + LSTM.
Identifiants
pubmed: 33411267
doi: 10.1007/s11517-020-02302-w
pii: 10.1007/s11517-020-02302-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
215-226Références
Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, Geisinger K, Hirsch FR, Ishikawa Y, Kerr KM, Noguchi M, Pelosi G, Powell CA, Tsao MS, Wistuba I, WHO Panel (2015) The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol 10:1243–1260. https://doi.org/10.1097/JTO.0000000000000630
doi: 10.1097/JTO.0000000000000630
pubmed: 26291008
Zhang L, Wang L, Du B, Wang T, Tian P, Tian S (2016) Classification of non-small cell lung cancer using significance analysis of microarray-gene set reduction algorithm. Biomed Res Int 2016:2491671. https://doi.org/10.1155/2016/2491671
doi: 10.1155/2016/2491671
pubmed: 27446945
pmcid: 4944087
Kawase A, Yoshida J, Ishii G, Nakao M, Aokage K, Hishida T, Nishimura M, Nagai K (2012) Differences between squamous cell carcinoma and adenocarcinoma of the lung: are adenocarcinoma and squamous cell carcinoma prognostically equal? Jpn J Clin Oncol 42:189–195. https://doi.org/10.1093/jjco/hyr188
doi: 10.1093/jjco/hyr188
pubmed: 22210923
Pankratz VS, Sun Z, Aakre J, Li Y, Johnson C, Garces YI, Aubry MC, Molina JR, Wigle DA, Yang P (2011) Systematic evaluation of genetic variants in three biological pathways on patient survival in low-stage non-small cell lung cancer. J Thorac Oncol 6:1488–1495. https://doi.org/10.1097/JTO.0B013E318223BF05
doi: 10.1097/JTO.0B013E318223BF05
pubmed: 21792076
pmcid: 3158278
Wiener RS, Schwartz LM, Woloshin S, Welch HG (2011) Population-based risk for complications after transthoracic needle lung biopsy of a pulmonary nodule: an analysis of discharge records. Ann Intern Med 155:137. https://doi.org/10.7326/0003-4819-155-3-201108020-00003
doi: 10.7326/0003-4819-155-3-201108020-00003
pubmed: 21810706
pmcid: 3150964
Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006
doi: 10.1038/ncomms5006
pubmed: 24892406
pmcid: 4059926
Yip SSF, Aerts HJWL (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166. https://doi.org/10.1088/0031-9155/61/13/R150
doi: 10.1088/0031-9155/61/13/R150
pubmed: 27269645
pmcid: 4927328
Keek SA, Leijenaar RT, Jochems A, Woodruff HC (2018) A review on radiomics and the future of theranostics for patient selection in precision medicine. Br J Radiol 91:20170926. https://doi.org/10.1259/bjr.20170926
doi: 10.1259/bjr.20170926
pubmed: 29947266
pmcid: 6475933
Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhushi A (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 115:34–41. https://doi.org/10.1016/j.lungcan.2017.10.015
doi: 10.1016/j.lungcan.2017.10.015
pubmed: 29290259
Lee G, Park H, Sohn I, Lee S-H, Song SH, Kim H, Lee KS, Shim YM, Lee HY (2018) Comprehensive computed tomography radiomics analysis of lung adenocarcinoma for prognostication. Oncologist 23:806–813. https://doi.org/10.1634/theoncologist.2017-0538
doi: 10.1634/theoncologist.2017-0538
pubmed: 29622699
pmcid: 6058328
Mattonen SA, Palma DA, Haasbeek CJA, Senan S, Ward AD (2014) Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys 41:033502. https://doi.org/10.1118/1.4866219
doi: 10.1118/1.4866219
pubmed: 24593744
Wei G, Cao H, Ma H, Qi S, Qian W, Ma Z (2018) Content-based image retrieval for lung nodule classification using texture features and learned distance metric. J Med Syst 42. https://doi.org/10.1007/s10916-017-0874-5
Patil R, Mahadevaiah G, Dekker A (2016) An approach toward automatic classification of tumor histopathology of non-small cell lung cancer based on radiomic features. Tomogr (Ann Arbor, Mich) 2:374–377. https://doi.org/10.18383/j.tom.2016.00244
doi: 10.18383/j.tom.2016.00244
Haga A, Takahashi W, Aoki S, Nawa K, Yamashita H, Abe O, Nakagawa K (2018) Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis. Radiol Phys Technol 11:27–35. https://doi.org/10.1007/s12194-017-0433-2
doi: 10.1007/s12194-017-0433-2
pubmed: 29209915
Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R, Aerts HJWL (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6:1–11. https://doi.org/10.3389/fonc.2016.00071
doi: 10.3389/fonc.2016.00071
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35:1299–1312. https://doi.org/10.1109/TMI.2016.2535302
doi: 10.1109/TMI.2016.2535302
pubmed: 26978662
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/J.MEDIA.2017.07.005
doi: 10.1016/J.MEDIA.2017.07.005
pubmed: 28778026
Meyer P, Noblet V, Mazzara C, Lallement A (2018) Survey on deep learning for radiotherapy. Comput Biol Med 98:126–146. https://doi.org/10.1016/j.compbiomed.2018.05.018
doi: 10.1016/j.compbiomed.2018.05.018
pubmed: 29787940
Kumar D, Wong A, Clausi DA. Lung nodule classification using deep features in CT images. 2015 12th Conf Comput Robot Vis IEEE; 2015, p. 133–8. https://doi.org/10.1109/CRV.2015.25
Zhang G, Jiang S, Yang Z, Gong L, Ma X, Zhou Z, Bao C, Liu Q (2018) Automatic nodule detection for lung cancer in CT images: A review. Comput Biol Med 103:287–300. https://doi.org/10.1016/J.COMPBIOMED.2018.10.033
doi: 10.1016/J.COMPBIOMED.2018.10.033
pubmed: 30415174
Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35:1160–1169. https://doi.org/10.1109/TMI.2016.2536809
doi: 10.1109/TMI.2016.2536809
pubmed: 26955024
Liu K, Kang G (2017) Multiview convolutional neural networks for lung nodule classification. Int J Imaging Syst Technol 27:12–22. https://doi.org/10.1002/ima.22206
doi: 10.1002/ima.22206
Gao M, Bagci U, Lu L, Wu A, Buty M, Shin H-C, Roth H, Papadakis GZ, Depeursinge A, Summers RM, Xu Z, Mollura DJ (2018) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 6:1–6. https://doi.org/10.1080/21681163.2015.1124249
doi: 10.1080/21681163.2015.1124249
pubmed: 29623248
Chaunzwa TL, Christiani DC, Lanuti M, Shafer A, Diao N, Mak RH, Aerts H (2018) Using deep-learning radiomics to predict lung cancer histology. J Clin Oncol 36:8545–8545. https://doi.org/10.1200/JCO.2018.36.15_suppl.8545
doi: 10.1200/JCO.2018.36.15_suppl.8545
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst:1097–1105
Ravishankar H, Sudhakar P, Venkataramani R, Thiruvenkadam S, Annangi P, Babu N et al (2016) Understanding the mechanisms of deep transfer learning for medical images. Springer, Cham, pp 188–196. https://doi.org/10.1007/978-3-319-46976-8_20
doi: 10.1007/978-3-319-46976-8_20
Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496
doi: 10.1088/0031-9155/60/14/5471
Shafiq-ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 8:10545. https://doi.org/10.1038/s41598-018-28895-9
doi: 10.1038/s41598-018-28895-9
pubmed: 30002441
pmcid: 6043486
Bar Y, Diamant I, Wolf L, Lieberman S, Konen E, Greenspan H (2018) Chest pathology identification using deep feature selection with non-medical training. Comput Methods Biomech Biomed Eng Imaging Vis 6:259–263. https://doi.org/10.1080/21681163.2016.1138324
doi: 10.1080/21681163.2016.1138324
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3:034501. https://doi.org/10.1117/1.JMI.3.3.034501
doi: 10.1117/1.JMI.3.3.034501
Loukas C (2019) Surgical phase recognition of short video shots based on temporal modeling of deep features. Proc. 12th Int. Jt. Conf. Biomed. Eng. Syst. Technol. - Vol. 2 Bioimaging, Prague, Czech Republic, p. 21–9. https://doi.org/10.5220/0007352000210029
Margeta J, Criminisi A, Cabrera Lozoya R, Lee DC, Ayache N (2017) Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Comput Methods Biomech Biomed Eng Imaging Vis 5:339–349. https://doi.org/10.1080/21681163.2015.1061448
doi: 10.1080/21681163.2015.1061448
Li Y, Charalampaki P, Liu Y, Yang G-Z, Giannarou S (2018) Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data. Int J Comput Assist Radiol Surg 13:1187–1199. https://doi.org/10.1007/s11548-018-1806-7
doi: 10.1007/s11548-018-1806-7
pubmed: 29948845
pmcid: 6096753
Ben-Hamo R, Boue S, Martin F, Talikka M, Efroni S (2013) Classification of lung adenocarcinoma and squamous cell carcinoma samples based on their gene expression profile in the sbv IMPROVER Diagnostic Signature Challenge. Syst Biomed 1:268–277. https://doi.org/10.4161/sysb.25983
doi: 10.4161/sysb.25983