Automated grading of glioma based on density and atypia analysis in whole slide images_Journal of Biomedical Engineering

Authors：

HAN Jineng ^1,3 , XIE Jiawei ^1,3 , GU Song ^1,3 , YAN Chaoyang ^1,3 , LI Jianrui ² , ZHANG Zhiqiang ² ,  XU Jun ^1,3

1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China;
2. Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, P.R.China;
3. Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China;

Corresponding author：

XU Jun, Email: xujung@gamil.com

Keywords：

glioma grading; histopathological image; cell density; nuclear atypia

DOI：

10.7507/1001-5515.202103050

Video：

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Glioma is the most common malignant brain tumor and classification of low grade glioma (LGG) and high grade glioma (HGG) is an important reference of making decisions on patient treatment options and prognosis. This work is largely done manually by pathologist based on an examination of whole slide image (WSI), which is arduous and heavily dependent on doctors’ experience. In the World Health Organization (WHO) criteria, grade of glioma is closely related to hypercellularity, nuclear atypia and necrosis. Inspired by this, this paper designed and extracted cell density and atypia features to classify LGG and HGG. First, regions of interest (ROI) were located by analyzing cell density and global density features were extracted as well. Second, local density and atypia features were extracted in ROI. Third, balanced support vector machine (SVM) classifier was trained and tested using 10 selected features. The area under the curve (AUC) and accuracy (ACC) of 5-fold cross validation were 0.92 ± 0.01 and 0.82 ± 0.01 respectively. The results demonstrate that the proposed method of locating ROI is effective and the designed features of density and atypia can be used to predict glioma grade accurately, which can provide reliable basis for clinical diagnosis.

Citation： HAN Jineng, XIE Jiawei, GU Song, YAN Chaoyang, LI Jianrui, ZHANG Zhiqiang, XU Jun. Automated grading of glioma based on density and atypia analysis in whole slide images. Journal of Biomedical Engineering, 2021, 38(6): 1062-1071. doi: 10.7507/1001-5515.202103050 Copy

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34.	Doyle S, Hwang M, Shah K, et al. Automated grading of prostate cancer using architectural and textural image features// 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Arlington: IEEE, 2007: 1284-1287.
35.	Ali S, Veltri R, Epstein J A, et al. Cell cluster graph for prediction of biochemical recurrence in prostate cancer patients from tissue microarrays// Medical Imaging 2013: Digital Pathology. Lake Buena Vista: SPIE, 2013, 8676: 86760H.
36.	Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE T Pattern Anal, 2005, 27(8): 1226-1238.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. 郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2019, 41(1): 19-28.
3. Ostrom Q T, Gittleman H, Truitt G, et al. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2011-2015. Neuro Oncology, 2018, 20(suppl 4): iv1-iv86.
4. Gladson C L, Prayson R A, Liu W M. The pathobiology of glioma tumors. Annu Rev Pathol Mech, 2010, 5: 33-50.
5. Louis D N, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol, 2016, 131(6): 803-820.
6. Raza S E A, Cheung L, Shaban M, et al. Micro-Net: A unified model for segmentation of various objects in microscopy images. Med Image Anal, 2019, 52: 160-173.
7. Koohbanani N A, Jahanifar M, Gooya A, et al. Nuclear instance segmentation using a proposal-free spatially aware deep learning framework// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Shenzhen: MICCAI Society, 2019: 622-630.
8. Naylor P, Laé M, Reyal F, et al. Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE T Med Imaging, 2018, 38(2): 448-459.
9. He J, Wang C, Jiang D, et al. CycleGAN with an improved loss function for cell detection using partly labeled images. IEEE J Biomed Health, 2020, 24(9): 2473-2480.
10. Koyuncu C F, Gunesli G N, Cetin-Atalay R, et al. DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images. Med Image Anal, 2020, 63: 101720.
11. Sirinukunwattana K, Raza S E A, Tsang Y W, et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE T Med Imaging, 2016, 35(5): 1196-1206.
12. Graham S, Vu Q D, Raza S E A, et al. Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal, 2019, 58: 101563.
13. Meng N, Lam E Y, Tsia K K, et al. Large-scale multi-class image-based cell classification with deep learning. IEEE J Biomed Health, 2018, 23(5): 2091-2098.
14. Martin V, Kim T H, Kwon M, et al. A more comprehensive cervical cell classification using convolutional neural network. J Am Soc Nephrol, 2018, 7(5): S66.
15. Xu J, Cai C, Zhou Y, et al. Multi-tissue partitioning for whole slide images of colorectal cancer histopathology images with deeptissue net// European Congress on Digital Pathology (ECDP). Warwick: ESDIP, 2019: 100-108.
16. Ing N, Ma Z, Li J, et al. Semantic segmentation for prostate cancer grading by convolutional neural networks// Medical Imaging 2018: Digital Pathology. Houston: SPIE, 2018, 10581: 105811B.
17. Ker J, Bai Y, Lee H Y, et al. Automated brain histology classification using machine learning. J Clin Neurosci, 2019, 66: 239-245.
18. Barker J, Hoogi A, Depeursinge A, et al. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med Image Anal, 2016, 30: 60-71.
19. Mousavi H S, Monga V, Rao G, et al. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis. J Pathol Inform, 2015, 6: 15.
20. Yan C, Nakane K, Wang X, et al. Automated Gleason grading on prostate biopsy slides by statistical representations of homology profile. Comput Meth Prog Bio, 2020, 194: 105528.
21. Ertosun M G, Rubin D L. Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks// AMIA Annual Symposium Proceedings. San Francisco: AMIA, 2015, 2015: 1899.
22. Awan R, Sirinukunwattana K, Epstein D, et al. Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Sci Rep UK, 2017, 7(1): 16852.
23. Tolkach Y, Dohmgörgen T, Toma M, et al. High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell, 2020, 2(7): 411-418.
24. Li W, Li J, Sarma K V, et al. Path R-CNN for prostate cancer diagnosis and Gleason grading of histological images. IEEE T Med Imaging, 2018, 38(4): 945-954.
25. Wei J W, Tafe L J, Linnik Y A, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep UK, 2019, 9(1): 3358.
26. Lawson P, Sholl A B, Brown J Q, et al. Persistent homology for the quantitative evaluation of architectural features in prostate cancer histology. Sci Rep UK, 2019, 9(1): 1139.
27. Shi J, Wu J, Li Y, et al. Histopathological image classification with color pattern random binary hashing-based PCANet and matrix-form classifier. IEEE J Biomed Health, 2016, 21(5): 1327-1337.
28. Shi J, Zheng X, Wu J, et al. Quaternion Grassmann average network for learning representation of histopathological image. Pattern Recogn, 2019, 89: 67-76.
29. Wright A I, Magee D, Quirke P, et al. Incorporating local and global context for better automated analysis of colorectal cancer on digital pathology slides. Procedia Comput Sci, 2016, 90: 125-131.
30. Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. P Natl Acad Sci USA, 2018, 115(13): E2970-E2979.
31. Kurc T, Bakas S, Ren X, et al. Segmentation and classification in digital pathology for glioma research: Challenges and deep learning approaches. Front Neurosci Switz, 2020, 14: 27.
32. Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 2818-2826.
33. Huang G, Liu S, Van der Maaten L, et al. Condensenet: An efficient densenet using learned group convolutions// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 2752-2761.
34. Doyle S, Hwang M, Shah K, et al. Automated grading of prostate cancer using architectural and textural image features// 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Arlington: IEEE, 2007: 1284-1287.
35. Ali S, Veltri R, Epstein J A, et al. Cell cluster graph for prediction of biochemical recurrence in prostate cancer patients from tissue microarrays// Medical Imaging 2013: Digital Pathology. Lake Buena Vista: SPIE, 2013, 8676: 86760H.
36. Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE T Pattern Anal, 2005, 27(8): 1226-1238.

Journal of Biomedical Engineering

Automated grading of glioma based on density and atypia analysis in whole slide images

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