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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging

Author(s): Hong-Li Hua, Yu-Qin Deng, Song Li, Si-Te Li, Fen Li, Bai-Kui Xiao, Jin Huang* and Ze-Zhang Tao*

Volume 26, Issue 7, 2023

Published on: 26 September, 2022

Page: [1351 - 1363] Pages: 13

DOI: 10.2174/1386207325666220919091210

open access plus

Abstract

Importance: Accurate pre-treatment prediction of distant metastasis in patients with Nasopharyngeal Carcinoma (NPC) enables the implementation of appropriate treatment strategies for high-risk individuals.

Purpose: To develop and assess a Convolutional Neural Network (CNN) model using pre-therapy Magnetic Resonance (MR) imaging to predict distant metastasis in NPC patients.

Methods: We retrospectively reviewed data of 441 pathologically diagnosed NPC patients who underwent complete radiotherapy and chemotherapy at Renmin Hospital of Wuhan University (Hubei, China) between February 2012 and March 2018. Using Adobe Photoshop, an experienced radiologist segmented MR images with rectangular regions of interest. To develop an accurate model according to the primary tumour, Cervical Metastatic Lymph Node (CMLN), the largest area of invasion of the primary tumour, and image segmentation methods, we constructed intratumoural and intra-peritumoural datasets that were used for training and test of the transfer learning models. Each model’s precision was assessed according to its receiver operating characteristic curve and accuracy. Generated high-risk-related Grad-Cams demonstrated how the model captured the image features and further verified its reliability.

Results: Among the four models, all intra-peritumoural datasets performed better than the corresponding intratumoural datasets, with the CMLN intra-peritumoural dataset exhibiting the best performance (average area under the curves (AUCs) = 0.88). There was no significant difference between average AUCs of the Max and NPC tumour datasets. AUCs of the eight datasets for the four models were higher than those of the Tumour-Node-Metastasis staging system (AUC=0.67). In most datasets, the xception model had higher AUCs than other models. The efficientnet-b0 and xception models efficiently extracted high-risk features.

Conclusion: The CNN model predicted distant metastasis in NPC patients with high accuracy. Compared to the primary tumour, the CMLN better predicted distant metastasis. In addition to intratumoural data, peritumoural information can facilitate the prediction of distant metastasis. With a larger sample size, datasets of the largest areas of tumour invasion may achieve meaningful accuracy. Among the models, xception had the best overall performance.

Keywords: Deep learning, transfer learning, nasopharyngeal carcinoma, distant metastasis, peritumour, cervical metastatic lymph node (CMLN).

« Previous Next »
[1]
An, X.; Wang, F-H.; Ding, P-R.; Deng, L.; Jiang, W-Q.; Zhang, L.; Shao, J-Y.; Li, Y-H. Plasma Epstein-Barr virus DNA level strongly predicts survival in metastatic/recurrent nasopharyngeal carcinoma treated with palliative chemotherapy. Cancer, 2011, 117(16), 3750-3757.
[http://dx.doi.org/10.1002/cncr.25932] [PMID: 21319149]
[2]
Peng, H.; Tang, L.L.; Chen, B.B.; Chen, L.; Li, W.F.; Mao, Y.P.; Liu, X.; Zhang, Y.; Liu, L.Z.; Tian, L.; Guo, Y.; Sun, Y.; Ma, J. Optimizing the induction chemotherapy regimen for patients with locoregionally advanced nasopharyngeal Carcinoma: A big-data intelligence platform-based analysis. Oral Oncol., 2018, 79, 40-46.
[http://dx.doi.org/10.1016/j.oraloncology.2018.02.011] [PMID: 29598949]
[3]
Chen, X.; Lei, H.; Liang, Z.; Li, L.; Qu, S.; Zhu, X. Intensity-modulated radiotherapy controls nasopharyngeal carcinoma distant metastasis and improves survival of patients. Springerplus, 2016, 5(1), 1459.
[http://dx.doi.org/10.1186/s40064-016-3117-1] [PMID: 27652034]
[4]
Lai, S.Z.; Li, W.F.; Chen, L.; Luo, W.; Chen, Y.Y.; Liu, L.Z.; Sun, Y.; Lin, A.H.; Liu, M.Z.; Ma, J. How does intensity-modulated radiotherapy versus conventional two-dimensional radiotherapy influence the treatment results in nasopharyngeal carcinoma patients? Int. J. Radiat. Oncol. Biol. Phys., 2011, 80(3), 661-668.
[http://dx.doi.org/10.1016/j.ijrobp.2010.03.024] [PMID: 20643517]
[5]
Hui, E.P.; Leung, S.F.; Au, J.S.; Zee, B.; Tung, S.; Chua, D.; Sze, W.M.; Law, C.K.; Leung, T.W.; Chan, A.T. Lung metastasis alone in nasopharyngeal carcinoma: A relatively favorable prognostic group. A study by the Hong Kong Nasopharyngeal Carcinoma Study Group. Cancer, 2004, 101(2), 300-306.
[http://dx.doi.org/10.1002/cncr.20358] [PMID: 15241827]
[6]
Zhang, L.; Dong, D.; Li, H.; Tian, J.; Ouyang, F.; Mo, X.; Zhang, B.; Luo, X.; Lian, Z.; Pei, S.; Dong, Y.; Huang, W.; Liang, C.; Liu, J.; Zhang, S. Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study. EBioMedicine, 2019, 40, 327-335.
[http://dx.doi.org/10.1016/j.ebiom.2019.01.013] [PMID: 30642750]
[7]
Wang, W.Y.; Twu, C.W.; Chen, H.H.; Jan, J.S.; Jiang, R.S.; Chao, J.Y.; Liang, K.L.; Chen, K.W.; Wu, C.T.; Lin, J.C.; Plasma, E.B.V. Plasma EBV DNA clearance rate as a novel prognostic marker for metastatic/recurrent nasopharyngeal carcinoma. Clin. Cancer Res., 2010, 16(3), 1016-1024.
[http://dx.doi.org/10.1158/1078-0432.CCR-09-2796] [PMID: 20103659]
[8]
Chan, A.T.; Grégoire, V.; Lefebvre, J.L.; Licitra, L.; Hui, E.P.; Leung, S.F.; Felip, E. Nasopharyngeal cancer: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol., 2012, 23(Suppl. 7), vii83-vii85.
[http://dx.doi.org/10.1093/annonc/mds266] [PMID: 22997460]
[9]
Zhang, L.; Huang, Y.; Hong, S.; Yang, Y.; Yu, G.; Jia, J.; Peng, P.; Wu, X.; Lin, Q.; Xi, X.; Peng, J.; Xu, M.; Chen, D.; Lu, X.; Wang, R.; Cao, X.; Chen, X.; Lin, Z.; Xiong, J.; Lin, Q.; Xie, C.; Li, Z.; Pan, J.; Li, J.; Wu, S.; Lian, Y.; Yang, Q.; Zhao, C. Gemcitabine plus cisplatin versus fluorouracil plus cisplatin in recurrent or metastatic nasopharyngeal carcinoma: A multicentre, randomised, open-label, phase 3 trial. Lancet, 2016, 388(10054), 1883-1892.
[http://dx.doi.org/10.1016/S0140-6736(16)31388-5] [PMID: 27567279]
[10]
Huang, J.; Fogg, M.; Wirth, L.J.; Daley, H.; Ritz, J.; Posner, M.R.; Wang, F.C.; Lorch, J.H. Epstein-Barr virus-specific adoptive immunotherapy for recurrent, metastatic nasopharyngeal carcinoma. Cancer, 2017, 123(14), 2642-2650.
[http://dx.doi.org/10.1002/cncr.30541] [PMID: 28222215]
[11]
Liu, N.; Chen, N.Y.; Cui, R.X.; Li, W.F.; Li, Y.; Wei, R.R.; Zhang, M.Y.; Sun, Y.; Huang, B.J.; Chen, M.; He, Q.M.; Jiang, N.; Chen, L.; Cho, W.C.; Yun, J.P.; Zeng, J.; Liu, L.Z.; Li, L.; Guo, Y.; Wang, H.Y.; Ma, J. Prognostic value of a microRNA signature in nasopharyngeal carcinoma: A microRNA expression analysis. Lancet Oncol., 2012, 13(6), 633-641.
[http://dx.doi.org/10.1016/S1470-2045(12)70102-X] [PMID: 22560814]
[12]
Takenaka, Y.; Kitamura, T.; Oya, R.; Ashida, N.; Shimizu, K.; Takemura, K.; Yamamoto, Y.; Uno, A. Prognostic role of neutrophil-lymphocyte ratio in nasopharyngeal carcinoma: A meta-analysis. PLoS One, 2017, 12(7), e0181478.
[13]
Yao, J. J.; Lin, L.; Jin, Y. N.; Wang, S. Y.; Zhang, W. J.; Zhang, F.; Zhou, G. Q.; Cheng, Z. B.; Qi, Z. Y.; Sun, Y. Prognostic value of serum Epstein-Barr virus antibodies in patients with nasopharyngeal carcinoma and undetectable pretreatment Epstein-Barr virus DNA. Cancer Sci., 2017, 108(8), 1640-1647. PMID: HYPERLINK "http://www.ncbi.nlm.nih.gov/pubmed/28715474"
[http://dx.doi.org/10.1371/journal.pone.0181478] [PMID: 28715474]
[14]
Bruce, J. P.; Hui, A. B.; Shi, W.; Perez-Ordonez, B.; Weinreb, I.; Xu, W.; Haibe-Kains, B.; Waggott, D. M.; Boutros, P. C.; O'Sullivan, B.; Waldron, J.; Huang, S. H.; Chen, E. X.; Gilbert, R.; Liu, F. F., Identification of a microRNA signature associated with risk of distant metastasis in nasopharyngeal carcinoma. Oncotarget, 2015, 6(6), 4537-50.
[http://dx.doi.org/10.18632/oncotarget.3005]
[15]
Liu, J.; Mao, Y.; Li, Z.; Zhang, D.; Zhang, Z.; Hao, S.; Li, B. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J. Magn. Reson. Imaging, 2016, 44(2), 445-455.
[http://dx.doi.org/10.1002/jmri.25156] [PMID: 26778191]
[16]
Peng, H.; Dong, D.; Fang, M.J.; Li, L.; Tang, L.L.; Chen, L.; Li, W.F.; Mao, Y.P.; Fan, W.; Liu, L.Z.; Tian, L.; Lin, A.H.; Sun, Y.; Tian, J.; Ma, J., Prognostic value of deep learning PET/CT-Based radiomics: Potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin. Cancer Res., 2019, 25(14), 4271-4279.
[http://dx.doi.org/10.1158/1078-0432.CCR-18-3065] [PMID: 30975664]
[17]
Liu, K.; Xia, W.; Qiang, M.; Chen, X.; Liu, J.; Guo, X.; Lv, X. Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy. Cancer Med., 2020, 9(4), 1298-1306.
[http://dx.doi.org/10.1002/cam4.2802] [PMID: 31860791]
[18]
Li, C.; Jing, B.; Ke, L.; Li, B.; Xia, W.; He, C.; Qian, C.; Zhao, C.; Mai, H.; Chen, M.; Cao, K.; Mo, H.; Guo, L.; Chen, Q.; Tang, L.; Qiu, W.; Yu, Y.; Liang, H.; Huang, X.; Liu, G.; Li, W.; Wang, L.; Sun, R.; Zou, X.; Guo, S.; Huang, P.; Luo, D.; Qiu, F.; Wu, Y.; Hua, Y.; Liu, K.; Lv, S.; Miao, J.; Xiang, Y.; Sun, Y.; Guo, X.; Lv, X. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies. Cancer Commun. (Lond.), 2018, 38(1), 59.
[http://dx.doi.org/10.1186/s40880-018-0325-9] [PMID: 30253801]
[19]
Chuang, W.Y.; Chang, S.H.; Yu, W.H.; Yang, C.K.; Yeh, C.J.; Ueng, S.H.; Liu, Y.J.; Chen, T.D.; Chen, K.H.; Hsieh, Y.Y.; Hsia, Y.; Wang, T.H.; Hsueh, C.; Kuo, C.F.; Yeh, C.Y. Successful identification of nasopharyngeal carcinoma in nasopharyngeal biopsies using deep learning. Cancers (Basel), 2020, 12(2), E507.
[http://dx.doi.org/10.3390/cancers12020507] [PMID: 32098314]
[20]
Du, D.; Feng, H.; Lv, W.; Ashrafinia, S.; Yuan, Q.; Wang, Q.; Yang, W.; Feng, Q.; Chen, W.; Rahmim, A.; Lu, L. Machine learning methods for optimal radiomics-based differentiation between recurrence and inflammation: Application to nasopharyngeal carcinoma post-therapy PET/CT images. Mol. Imaging Biol., 2020, 22(3), 730-738.
[http://dx.doi.org/10.1007/s11307-019-01411-9] [PMID: 31338709]
[21]
Ke, L.; Deng, Y.; Xia, W.; Qiang, M.; Chen, X.; Liu, K.; Jing, B.; He, C.; Xie, C.; Guo, X.; Lv, X.; Li, C. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images. Oral Oncol., 2020, 110, 104862.
[http://dx.doi.org/10.1016/j.oraloncology.2020.104862] [PMID: 32615440]
[22]
Zhong, X.; Li, L.; Jiang, H.; Yin, J.; Lu, B.; Han, W.; Li, J.; Zhang, J. Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization. BMC Med. Imaging, 2020, 20(1), 104.
[http://dx.doi.org/10.1186/s12880-020-00502-2] [PMID: 32873238]
[23]
Peng, L.; Hong, X.; Yuan, Q.; Lu, L.; Wang, Q.; Chen, W. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images. Ann. Nucl. Med., 2021, 35(4), 458-468.
[http://dx.doi.org/10.1007/s12149-021-01585-9] [PMID: 33543393]
[24]
Wu, X.; Dong, D.; Zhang, L.; Fang, M.; Zhu, Y.; He, B.; Ye, Z.; Zhang, M.; Zhang, S.; Tian, J. Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study. Med. Phys., 2021, 48(5), 2374-2385.
[http://dx.doi.org/10.1002/mp.14767] [PMID: 33580497]
[25]
Edge, S.B.; Compton, C.C. The american joint committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann. Surg. Oncol., 2010, 17(6), 1471-4.
[26]
Samani, Z.R.; Parker, D.; Wolf, R.; Hodges, W.; Brem, S.; Verma, R. Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases. Sci. Rep., 2021, 11(1), 14469.
[http://dx.doi.org/10.1038/s41598-021-93804-6] [PMID: 34262079]
[27]
Shen, D.S.; Yan, C.; Chen, K.H.; Li, L.; Qu, S.; Zhu, X.D. A nomogram based on circulating CD4+ T lymphocytes and lactate dehydrogenase to predict distant metastasis in patients with nasopharyngeal carcinoma. J. Inflamm. Res., 2021, 14, 6707-6718.
[http://dx.doi.org/10.2147/JIR.S341897] [PMID: 34916820]
[28]
Zhang, W.; Chen, Y.; Chen, L.; Liu, X.; Sun, Y.; Li, Y.; Ma, J. Importance of maintaining body weight for prevention of distant metastasis of nasopharyngeal carcinoma: An alternative workflow for cancer-risk assessment. J. Cancer, 2017, 8(12), 2269-2276.
[http://dx.doi.org/10.7150/jca.19611] [PMID: 28819430]
[29]
Chen, F.P.; Luo, Y.S.; Chen, K.; Li, J.Y.; Huo, L.Q.; Shi, L.; Ou-Yang, Y.; Cao, X.P. Circulating epstein-barr virus DNA level post induction chemotherapy contributes to prognostication in advanced-stage nasopharyngeal carcinoma. Eur. J. Cancer, 2021, 151, 63-71.
[http://dx.doi.org/10.1016/j.ejca.2021.03.052] [PMID: 33964573]
[30]
Huang, S.; Yang, J.; Fong, S.; Zhao, Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett., 2020, 471, 61-71.
[http://dx.doi.org/10.1016/j.canlet.2019.12.007] [PMID: 31830558]
[31]
Bi, W.L.; Hosny, A.; Schabath, M.B.; Giger, M.L.; Birkbak, N.J.; Mehrtash, A.; Allison, T.; Arnaout, O.; Abbosh, C.; Dunn, I.F.; Mak, R.H.; Tamimi, R.M.; Tempany, C.M.; Swanton, C.; Hoffmann, U.; Schwartz, L.H.; Gillies, R.J.; Huang, R.Y.; Aerts, H.J.W.L. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J. Clin., 2019, 69(2), 127-157.
[http://dx.doi.org/10.3322/caac.21552] [PMID: 30720861]
[32]
Wu, L.R.; Liu, Y.T.; Jiang, N.; Fan, Y.X.; Wen, J.; Huang, S.F.; Guo, W.J.; Bian, X.H.; Wang, F.J.; Li, F.; Song, D.; Wu, J.F.; Jiang, X.S.; Liu, J.Y.; He, X. Ten-year survival outcomes for patients with nasopharyngeal carcinoma receiving intensity-modulated radiotherapy: An analysis of 614 patients from a single center. Oral Oncol., 2017, 69, 26-32.
[http://dx.doi.org/10.1016/j.oraloncology.2017.03.015] [PMID: 28559017]
[33]
Ou, X.; Zhou, X.; Shi, Q.; Xing, X.; Yang, Y.; Xu, T.; Shen, C.; Wang, X.; He, X.; Kong, L.; Ying, H.; Hu, C. Treatment outcomes and late toxicities of 869 patients with nasopharyngeal carcinoma treated with definitive intensity modulated radiation therapy: New insight into the value of total dose of cisplatin and radiation boost. Oncotarget, 2015, 6(35), 38381-38397.
[http://dx.doi.org/10.18632/oncotarget.5420] [PMID: 26485757]
[34]
Chen, Y.P.; Tang, L.L.; Yang, Q.; Poh, S.S.; Hui, E.P.; Chan, A.T.C.; Ong, W.S.; Tan, T.; Wee, J.; Li, W.F.; Chen, L.; Ma, B.B.Y.; Tong, M.; Tan, S.H.; Cheah, S.L.; Fong, K.W.; Sommat, K.; Soong, Y.L.; Guo, Y.; Lin, A.H.; Sun, Y.; Hong, M.H.; Cao, S.M.; Chen, M.Y.; Ma, J. Induction chemotherapy plus concurrent chemoradiotherapy in endemic nasopharyngeal carcinoma: Individual patient data pooled analysis of four randomized trials. Clin. Cancer Res., 2018, 24(8), 1824-1833.
[http://dx.doi.org/10.1158/1078-0432.CCR-17-2656] [PMID: 29431618]
[35]
Cao, S.M.; Yang, Q.; Guo, L.; Mai, H.Q.; Mo, H.Y.; Cao, K.J.; Qian, C.N.; Zhao, C.; Xiang, Y.Q.; Zhang, X.P.; Lin, Z.X.; Li, W.X.; Liu, Q.; Qiu, F.; Sun, R.; Chen, Q.Y.; Huang, P.Y.; Luo, D.H.; Hua, Y.J.; Wu, Y.S.; Lv, X.; Wang, L.; Xia, W.X.; Tang, L.Q.; Ye, Y.F.; Chen, M.Y.; Guo, X.; Hong, M.H. Neoadjuvant chemotherapy followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locoregionally advanced nasopharyngeal carcinoma: A phase III multicentre randomised controlled trial. Eur. J. Cancer, 2017, 75, 14-23.
[http://dx.doi.org/10.1016/j.ejca.2016.12.039] [PMID: 28214653]
[36]
Wu, Q.; Wang, S.; Zhang, S.; Wang, M.; Ding, Y.; Fang, J.; Wu, Q.; Qian, W.; Liu, Z.; Sun, K.; Jin, Y.; Ma, H.; Tian, J. Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer. JAMA Netw. Open, 2020, 3(7), e2011625.
[http://dx.doi.org/10.1001/jamanetworkopen.2020.11625] [PMID: 32706384]
[37]
Xiao, B.B.; Lin, D.F.; Sun, X.S.; Zhang, X.; Guo, S.S.; Liu, L.T.; Luo, D.H.; Sun, R.; Wen, Y.F.; Li, J.B.; Lv, X.F.; Han, L.J.; Yuan, L.; Liu, S.L.; Tang, Q.N.; Liang, Y.J.; Li, X.Y.; Guo, L.; Chen, Q.Y.; Fan, W.; Mai, H.Q.; Tang, L.Q. Nomogram for the prediction of primary distant metastasis of nasopharyngeal carcinoma to guide individualized application of FDG PET/CT. Eur. J. Nucl. Med. Mol. Imaging, 2021, 48(8), 2586-2598.
[http://dx.doi.org/10.1007/s00259-020-05128-8] [PMID: 33420610]

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