Abstract

Anti-vascular endothelial growth factor (VEGF) agents are widely regarded as the first line of therapy for diabetic macular edema (DME) but are not universally effective. An automatic method that can predict whether a patient is likely to respond to anti-VEGF therapy can avoid unnecessary trial and error treatment strategies and promote the selection of more effective first-line therapies. The objective of this study is to automatically predict the efficacy of anti-VEGF treatment of DME in individual patients based on optical coherence tomography (OCT) images. We performed a retrospective study of 127 subjects treated for DME with three consecutive injections of anti-VEGF agents. Patients’ retinas were imaged using spectral-domain OCT (SD-OCT) before and after anti-VEGF therapy, and the total retinal thicknesses before and after treatment were extracted from OCT B-scans. A novel deep convolutional neural network was designed and evaluated using pre-treatment OCT scans as input and differential retinal thickness as output, with 5-fold cross-validation. The group of patients responsive to anti-VEGF treatment was defined as those with at least a 10% reduction in retinal thickness following treatment. The predictive performance of the system was evaluated by calculating the precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The algorithm achieved an average AUC of 0.866 in discriminating responsive from non-responsive patients, with an average precision, sensitivity, and specificity of 85.5%, 80.1%, and 85.0%, respectively. Classification precision was significantly higher when differentiating between very responsive and very unresponsive patients. The proposed automatic algorithm accurately predicts the response to anti-VEGF treatment in DME patients based on OCT images. This pilot study is a critical step toward using non-invasive imaging and automated analysis to select the most effective therapy for a patient’s specific disease condition.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (3)

L. Fang, C. Wang, S. Li, H. Rabbani, X. Chen, and Z. Liu, “Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification,” IEEE Trans. Med. Imaging 38(8), 1959–1970 (2019).
[Crossref]

R. Rasti, A. Mehridehnavi, H. Rabbani, and F. Hajizadeh, “Convolutional mixture of experts model: A comparative study on automatic macular diagnosis in retinal optical coherence tomography imaging,” J Med Signals Sens 9(1), 1–14 (2019).
[Crossref]

T.-T. Lai, Y.-T. Hsieh, C.-M. Yang, T.-C. Ho, and C.-H. Yang, “Biomarkers of optical coherence tomography in evaluating the treatment outcomes of neovascular age-related macular degeneration: a real-world study,” Sci. Rep. 9(1), 529–539 (2019).
[Crossref]

2018 (8)

M. Al-Sheikh, N. A. Iafe, N. Phasukkijwatana, S. R. Sadda, and D. Sarraf, “Biomarkers of neovascular activity in age-related macular degeneration using optical coherence tomography angiography,” Retina 38(2), 220–230 (2018).
[Crossref]

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, and D. Visentin, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
[Crossref]

R. Rasti, A. Mehridehnavi, H. Rabbani, and F. Hajizadeh, “Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier,” J. Biomed. Opt. 23(03), 1 (2018).
[Crossref]

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Y. J. Cho, D. H. Lee, and M. Kim, “Optical coherence tomography findings predictive of response to treatment in diabetic macular edema,” J. Int. Med. Res. 46(11), 4455–4464 (2018).
[Crossref]

P. Prahs, V. Radeck, C. Mayer, Y. Cvetkov, N. Cvetkova, H. Helbig, and D. Märker, “OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(1), 91–98 (2018).
[Crossref]

R. K. Maturi, A. R. Glassman, D. Liu, R. W. Beck, A. R. Bhavsar, N. M. Bressler, L. M. Jampol, M. Melia, O. S. Punjabi, and H. Salehi-Had, “Effect of adding dexamethasone to continued ranibizumab treatment in patients with persistent diabetic macular edema: a DRCR network phase 2 randomized clinical trial,” JAMA Ophthalmol. 136(1), 29–38 (2018).
[Crossref]

N. M. Bressler, W. T. Beaulieu, M. G. Maguire, A. R. Glassman, K. J. Blinder, S. B. Bressler, V. H. Gonzalez, L. M. Jampol, M. Melia, and J. K. Sun, “Early Response to Anti–Vascular Endothelial Growth Factor and Two-Year Outcomes Among Eyes With Diabetic Macular Edema in Protocol T,” Am. J. Ophthalmol. 195, 93–100 (2018).
[Crossref]

2017 (7)

H. Bogunović, S. M. Waldstein, T. Schlegl, G. Langs, A. Sadeghipour, X. Liu, B. S. Gerendas, A. Osborne, and U. Schmidt-Erfurth, “Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach,” Invest. Ophthalmol. Visual Sci. 58(7), 3240–3248 (2017).
[Crossref]

W.-D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

A. R. Shah, Y. Yonekawa, B. Todorich, L. V. Laere, R. Hussain, M. A. Woodward, A. M. Abbey, and J. D. Wolfe, “Prediction of anti-VEGF response in diabetic macular edema after 1 injection,” J. Vitreoretin. Dis. 1(3), 169–174 (2017).
[Crossref]

T. Shiraya, S. Kato, F. Araki, T. Ueta, T. Miyaji, and T. Yamaguchi, “Aqueous cytokine levels are associated with reduced macular thickness after intravitreal ranibizumab for diabetic macular edema,” PLoS One 12(3), e0174340 (2017).
[Crossref]

M. J. Allingham, D. Mukherjee, E. B. Lally, H. Rabbani, P. S. Mettu, S. W. Cousins, and S. Farsiu, “A quantitative approach to predict differential effects of anti-VEGF treatment on diffuse and focal leakage in patients with diabetic macular edema: a pilot study,” Trans. Vis. Sci. Tech. 6(2), 7 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref]

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref]

2016 (5)

J. A. Wells, A. R. Glassman, A. R. Ayala, L. M. Jampol, N. M. Bressler, S. B. Bressler, A. J. Brucker, F. L. Ferris, G. R. Hampton, and C. Jhaveri, “Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema: two-year results from a comparative effectiveness randomized clinical trial,” Ophthalmology 123(6), 1351–1359 (2016).
[Crossref]

G. Lemaître, M. Rastgoo, J. Massich, C. Y. Cheung, T. Y. Wong, E. Lamoureux, D. Milea, F. Mériaudeau, and D. Sidibé, “Classification of SD-OCT volumes using local binary patterns: experimental validation for DME detection,” J. Ophthalmol. 2016, 1–14 (2016).
[Crossref]

M. Costa, A. R. Santos, S. Nunes, D. Alves, C. Schwartz, J. Figueira, S. N. Simao, and J. G. Cunha-Vaz, “OCT retinal thickness response after first intravitreal injection is a predictor of visual acuity response to anti-VEGF treatment of DME,” Invest. Ophthalmol. Visual Sci. 57, 2085 (2016).

V. H. Gonzalez, J. Campbell, N. M. Holekamp, S. Kiss, A. Loewenstein, A. J. Augustin, J. Ma, A. C. Ho, V. Patel, and S. M. Whitcup, “Early and long-term responses to anti–vascular endothelial growth factor therapy in diabetic macular edema: analysis of protocol I data,” Am. J. Ophthalmol. 172, 72–79 (2016).
[Crossref]

H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref]

2014 (3)

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Tothfor the Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

R. Lazic, M. Lukic, I. Boras, N. Draca, M. Vlasic, N. Gabric, and Z. Tomic, “Treatment of Anti-Vascular Endothelial Growth Factor–Resistant Diabetic Macular Edema With Dexamethasone Intravitreal Implant,” Retina 34(4), 719–724 (2014).
[Crossref]

P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomed. Opt. Express 5(10), 3568–3577 (2014).
[Crossref]

2013 (1)

A. R. Santos, S. C. Gomes, J. Figueira, S. Nunes, C. L. Lobo, and J. G. Cunha-Vaz, “Degree of decrease in central retinal thickness predicts visual acuity response to intravitreal ranibizumab in diabetic macular edema,” Ophthalmologica 231(1), 16–22 (2013).
[Crossref]

2012 (3)

J. Yau, S. L. Rogers, R. Kawasaki, E. L. Lamoureux, J. W. Kowalski, T. Bek, S. Chen, J. Dekker, A. Fletcher, and J. Grauslund, “Meta-Analysis for Eye Disease (METAEYE) Study Group. Global prevalence and major risk factors of diabetic retinopathy,” Diabetes Care 35(3), 556–564 (2012).
[Crossref]

G. P. Giuliari, “Diabetic retinopathy: current and new treatment options,” Curr. Diabetes Rev. 8(1), 32–41 (2012).
[Crossref]

B. Trawiński, M. Smętek, Z. Telec, and T. Lasota, “Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms,” Int. J. Appl. Math. Comput. Sci. 22(4), 867–881 (2012).
[Crossref]

2010 (1)

M. J. Elman, L. P. Aiello, R. W. Beck, N. M. Bressler, S. B. Bressler, A. R. Edwards, F. L. Ferris, S. M. Friedman, A. R. Glassman, and K. M. Miller, “Randomized trial evaluating ranibizumab plus prompt or deferred laser or triamcinolone plus prompt laser for diabetic macular edema,” Ophthalmology 117(6), 1064–1077.e35 (2010).
[Crossref]

2008 (1)

S. Garcia and F. Herrera, “An extension on ‘statistical comparisons of classifiers over multiple data sets’ for all pairwise comparisons,” Journal of Machine Learning Research 92677–2694 (2008).

2006 (1)

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

2005 (1)

H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” J Royal Statistical Soc B 67(2), 301–320 (2005).
[Crossref]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20, 273–297 (1995).
[Crossref]

1980 (1)

A. M. Treisman and G. Gelade, “A feature-integration theory of attention,” Cogn. Psychol. 12(1), 97–136 (1980).
[Crossref]

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
[Crossref]

1912 (1)

P. Jaccard, “The distribution of the flora in the alpine zone,” New Phytol. 11(2), 37–50 (1912).
[Crossref]

Abadi, M.

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Figures (5)

Fig. 1.
Fig. 1. Example foveal SD-OCT images from pre-treatment (row 1) and post-treatment (row 2) acquisition sets. Only the patients in the second and third columns showed signs of response to treatment.
Fig. 2.
Fig. 2. Histogram of retinal thickness changes in pre-treatment OCT B-scans. The horizontal axis indicates the central thickness difference between post-treatment and baseline screenings. (Left) Differential thickness (µm). (Right) Percentage change in differential thickness.
Fig. 3.
Fig. 3. Overview of the CADNet predictive framework with m = 6 attention blocks. The SE-Unit is demonstrated in detail. Values inside the bracket indicate the kernel size and the number of feature maps according to the block number, respectively. RetiUnet is a developed and pre-trained UNet model used as a non-trainable layer of CADNet for total retina segmentation. The sub-sampling factor and squeeze ratio of the pooling layers and SE-Units were 2 and 8, respectively. The symbols ⊗ and ⊕ indicate element-wise multiplication and summation operations, respectively. (GAP: global average pooling layer; FC: fully connected; ReLU: rectified linear units)
Fig. 4.
Fig. 4. Plot showing cross-validated precision performance against the epoch for the CADNet model. To avoid overfitting, we terminated the training process at the 50th epoch, at which point the validation precision shows lower performance. Due to our limited database and the wide range of DME manifestations on OCT in this prediction problem, our model is prone to overfitting.
Fig. 5.
Fig. 5. Performance of the CADNet + RFE.EN + GNB framework. (Left column) Results at the ROI level. (Right column) Results at the patient level. (Top row) ROC curves. (Bottom row) Confusion matrices.

Tables (4)

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Table 1. Evaluation of different classification algorithms using the 5-fold cross-validation method at T=-10% (mean ± std).

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Table 2. Performance contributions of the RetiUNet and SE layers in the CADNet model using the 5-fold cross-validation method.

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Table 3. Average rankings of the algorithms determined by the Friedman statistical test.

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Table 4. Adjusted p-values for N×N comparisons of diagnostic algorithms over 5 repetitions of 5-fold cross-validation method. The p-values below 0.05 demonstrate that the algorithms differ significantly (marked in italic font) in terms of precision values.

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

L o s s E N = 1 2 n . | | y X w | | 2 2 + α . l 1 r a t i o . | | w | | 1 + α 2 . ( 1 l 1 r a t i o ) . | | w | | 2 2 .
Precision = T P T P + F P ,
S e n s i t i v i t y = T P T P + F N ,
S p e c i f i c i t y = T N T N + F P ,