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Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

Nishio, Mizuho Matsuo, Hidetoshi Kurata, Yasuhisa Sugiyama, Osamu Fujimoto, Koji 神戸大学

2023.03

概要

We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution (5160 WSIs) were used as the development set, while those from the other institution (5456 WSIs) were used as the unseen test set. Label distribution learning (LDL) was used to address a difference in label characteristics between the development and test sets. A combination of EfficientNet (a deep learning model) and LDL was utilized to develop an automatic prediction system. Quadratic weighted kappa (QWK) and accuracy in the test set were used as the evaluation metrics. The QWK and accuracy were compared between systems with and without LDL to evaluate the usefulness of LDL in system development. The QWK and accuracy were 0.364 and 0.407 in the systems with LDL and 0.240 and 0.247 in those without LDL, respectively. Thus, LDL improved the diagnostic performance of the automatic prediction system for the grading of histopathological images for cancer. By handling the difference in label characteristics using LDL, the diagnostic performance of the automatic prediction system could be improved for prostate cancer grading.

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参考文献

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 2022, 72, 7–33. [CrossRef] [PubMed]

Gleason, D.F. Histologic grading of prostate cancer: A perspective. Hum. Pathol. 1992, 23, 273–279. [CrossRef] [PubMed]

Epstein, J.I.; Egevad, L.; Amin, M.B.; Delahunt, B.; Srigley, J.R.; Humphrey, P.A. The 2014 international society of urological

pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma definition of grading patterns and proposal for

a new grading system. Am. J. Surg. Pathol. 2016, 40, 244–252. [CrossRef]

Ozkan, T.A.; Eruyar, A.T.; Cebeci, O.O.; Memik, O.; Ozcan, L.; Kuskonmaz, I. Interobserver variability in Gleason histological

grading of prostate cancer. Scand. J. Urol. 2016, 50, 420–424. [CrossRef]

Allsbrook, W.C.; Mangold, K.A.; Johnson, M.H.; Lane, R.B.; Lane, C.G.; Epstein, J.I. Interobserver reproducibility of Gleason

grading of prostatic carcinoma: General pathologist. Hum. Pathol. 2001, 32, 81–88. [CrossRef]

Di Loreto, C.; Fitzpatrick, B.; Underhill, S.; Kim, D.H.; Dytch, H.E.; Galera-Davidson, H.; Bibbo, M. Correlation Between Visual

Clues, Objective Architectural Features, and Interobserver Agreement in Prostate Cancer. Am. J. Clin. Pathol. 1991, 96, 70–75.

[CrossRef]

Yamashita, R.; Nishio, M.; Do, R.K.G.; Togashi, K. Convolutional neural networks: An overview and application in radiology.

Insights Imaging 2018, 9, 611–629. [CrossRef]

Moribata, Y.; Kurata, Y.; Nishio, M.; Kido, A.; Otani, S.; Himoto, Y.; Nishio, N.; Furuta, A.; Onishi, H.; Masui, K.; et al. Automatic

segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: A

two-center study. Sci. Rep. 2023, 13, 628. [CrossRef]

Noguchi, S.; Nishio, M.; Sakamoto, R.; Yakami, M.; Fujimoto, K.; Emoto, Y.; Kubo, T.; Iizuka, Y.; Nakagomi, K.; Miyasa, K.; et al.

Deep learning-based algorithm improved radiologists’ performance in bone metastases detection on CT. Eur. Radiol. 2022, 32,

7976–7987. [CrossRef]

Matsuo, H.; Nishio, M.; Kanda, T.; Kojita, Y.; Kono, A.K.; Hori, M.; Teshima, M.; Otsuki, N.; Nibu, K.-i; Murakami, T. Diagnostic

accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: Discriminating malignant parotid

tumors in MRI. Sci. Rep. 2020, 10, 19388. [CrossRef]

Steiner, D.F.; Macdonald, R.; Liu, Y.; Truszkowski, P.; Hipp, J.D.; Gammage, C.; Thng, F.; Peng, L.; Stumpe, M.C. Impact of Deep

Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am. J. Surg. Pathol. 2018, 42,

1636–1646. [CrossRef] [PubMed]

Woerl, A.C.; Eckstein, M.; Geiger, J.; Wagner, D.C.; Daher, T.; Stenzel, P.; Fernandez, A.; Hartmann, A.; Wand, M.; Roth, W.; et al.

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides. Eur.

Urol. 2020, 78, 256–264. [CrossRef] [PubMed]

Wei, J.W.; Tafe, L.J.; Linnik, Y.A.; Vaickus, L.J.; Tomita, N.; Hassanpour, S. Pathologist-level classification of histologic patterns on

resected lung adenocarcinoma slides with deep neural networks. Sci. Rep. 2019, 9, 3358. [CrossRef]

Bulten, W.; Kartasalo, K.; Chen, P.H.C.; Ström, P.; Pinckaers, H.; Nagpal, K.; Cai, Y.; Steiner, D.F.; van Boven, H.; Vink, R.; et al.

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge. Nat. Med. 2022, 28, 154–163.

[CrossRef]

Singhal, N.; Soni, S.; Bonthu, S.; Chattopadhyay, N.; Samanta, P.; Joshi, U.; Jojera, A.; Chharchhodawala, T.; Agarwal, A.; Desai,

M.; et al. A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies. Sci.

Rep. 2022, 12, 3383. [CrossRef]

Cancers 2023, 15, 1535

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

12 of 12

Kwak, J.T.; Hewitt, S.M. Nuclear Architecture Analysis of Prostate Cancer via Convolutional Neural Networks. IEEE Access 2017,

5, 18526–18533. [CrossRef]

Ren, J.; Sadimin, E.; Foran, D.J.; Qi, X. Computer aided analysis of prostate histopathology images to support a refined Gleason

grading system. In Proceedings of the Medical Imaging 2017, Image Processing, SPIE, Orlando, FL, USA, 24 February 2017;

p. 101331V. [CrossRef]

Egevad, L.; Swanberg, D.; Delahunt, B.; Ström, P.; Kartasalo, K.; Olsson, H.; Berney, D.M.; Bostwick, D.G.; Evans, A.J.; Humphrey,

P.A.; et al. Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted

grading. Virchows Arch. 2020, 477, 777–786. [CrossRef]

Bulten, W.; Pinckaers, H.; van Boven, H.; Vink, R.; de Bel, T.; van Ginneken, B.; van der Laak, J.; Hulsbergen-van de Kaa, C.;

Litjens, G. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study. Lancet

Oncol. 2020, 21, 233–241. [CrossRef]

Lucas, M.; Jansen, I.; Savci-Heijink, C.D.; Meijer, S.L.; de Boer, O.J.; van Leeuwen, T.G.; de Bruin, D.M.; Marquering, H.A. Deep

learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Arch. 2019, 475,

77–83. [CrossRef]

Jiménez del Toro, O.; Atzori, M.; Otálora, S.; Andersson, M.; Eurén, K.; Hedlund, M.; Rönnquist, P.; Müller, H. Convolutional

neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score. In Proceedings of the

Medical Imaging 2017, Digital Pathology, SPIE, Orlando, FL, USA, 1 March 2017; p. 101400O. [CrossRef]

Nagpal, K.; Foote, D.; Liu, Y.; Chen, P.H.C.; Wulczyn, E.; Tan, F.; Olson, N.; Smith, J.L.; Mohtashamian, A.; Wren, J.H.; et al.

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit. Med. 2019,

2, 48. [CrossRef]

Linkon, A.H.M.; Labib, M.M.; Hasan, T.; Hossain, M.; Jannat, M.E. Deep Learning in Prostate Cancer Diagnosis and Gleason Grading in

Histopathology Images: An Extensive Study. Informatics in Medicine Unlocked; Elsevier: Amsterdam, The Netherlands, 2021; p. 100582.

[CrossRef]

Geng, X.; Yin, C.; Zhou, Z.H. Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell.

2013, 35, 2401–2412. [CrossRef] [PubMed]

Luo, J.; He, B.; Ou, Y.; Li, B.; Wang, K. Topic-based label distribution learning to exploit label ambiguity for scene classification.

Neural Comput. Appl. 2021, 33, 16181–16196. [CrossRef]

Wu, X.; Wen, N.; Liang, J.; Lai, Y.K.; She, D.; Cheng, M.M.; Yang, J. Joint acne image grading and counting via label distribution

learning. In Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea, 27 October 2019–2

November 2019; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2019; pp. 10641–10650. [CrossRef]

Arvaniti, E.; Fricker, K.S.; Moret, M.; Rupp, N.; Hermanns, T.; Fankhauser, C.; Wey, N.; Wild, P.J.; Rüschoff, J.H.; Claassen, M.

Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep. 2018, 8, 12054. [CrossRef] [PubMed]

Bulten, W.; Litjens, G.; Pinckaers, H.; Ström, P.; Eklund, M.; Kartasalo, K.; Demkin, M.; Dane, S. The PANDA challenge: Prostate

cANcer graDe Assessment using the Gleason grading system. In Proceedings of the 23rd International Conference on Medical

Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru, 19 March 2020. [CrossRef]

Prostate cANcer graDe Assessment (PANDA) Challenge | Kaggle. Available online: https://www.kaggle.com/c/prostatecancer-grade-assessment (accessed on 6 January 2023).

GitHub—Kentaroy47/Kaggle-PANDA-1st-Place-Solution: 1st Place Solution for the Kaggle PANDA Challenge. Available online:

https://github.com/kentaroy47/Kaggle-PANDA-1st-place-solution (accessed on 6 January 2023).

RistKaggleWorkshop_20200924_PANDA_1st—Google Slide. Available online: https://docs.google.com/presentation/d/1Ies4

vnyVtW5U3XNDr_fom43ZJDIodu1SV6DSK8di6fs/edit#slide=id.p (accessed on 6 January 2023).

Tan, M.; Le, Q.V. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th

International Conference of Machine Learning PMLR 2019, Long Beach, CA, USA, 9–15 June 2019; Volume 97, pp. 10691–10700.

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