NecroGlobalGCN: : Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks
Authors: Boyang Deng, 
Yu Tian, Qi Zhang, Yangyang Wang, Zhenxin Chai, Qiancheng Ye, Shang Yao, Tingbo Liang, Jingsong LiAuthors Info & Claims
Yu Tian, Qi Zhang, Yangyang Wang, Zhenxin Chai, Qiancheng Ye, Shang Yao, Tingbo Liang, Jingsong LiAuthors Info & ClaimsReferences
[1]
J.D. Yang, P. Hainaut, G.J. Gores, A. Amadou, A. Plymoth, L.R. Roberts, A global view of hepatocellular carcinoma: trends, risk, prevention and management, Nat. Reviews Gastroenterol. Hepatol. 16 (10) (Oct 2019) 589–604.
[2]
A. Forner, M. Reig, J. Bruix, Hepatocellular carcinoma, The Lancet 391 (10127) (Mar 2018) 1301–1314.
[3]
A. Villanueva, Hepatocellular Carcinoma, New Engl. J. Med. 380 (15) (Apr 2019) 1450–1462.
[4]
K. Hasegawa, et al., Comparison of resection and ablation for hepatocellular carcinoma: A cohort study based on a Japanese nationwide survey, J. Hepatol. 58 (4) (Apr 2013) 724–729.
[5]
T. Ishizawa, et al., Neither multiple tumors nor portal hypertension are surgical contraindications for hepatocellular carcinoma, Gastroenterology 134 (7) (Jun 2008) 1908–1916.
[6]
Alberto Quaglia, et al., Hepatocellular carcinoma: a review of diagnostic challenges for the pathologist, J. HepatoCell Carcinoma (2018) 99–108.
[7]
B. Bhinder, C. Gilvary, N.S. Madhukar, O. Elemento, Artificial Intelligence in Cancer Research and Precision Medicine, Cancer Discov. 11 (4) (Apr 2021) 900–915.
[8]
A. Hosny, C. Parmar, J. Quackenbush, L.H. Schwartz, H.J.W.L. Aerts, Artificial intelligence in radiology, Nat. Rev. Cancer 18 (8) (Aug 2018) 500–510.
[9]
G. Campanella, et al., Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat. Med. 25 (8) (Aug 2019) 1301–1309.
[10]
J.C. Ahn, A. Connell, D.A. Simonetto, C. Hughes, V.H. Shah, Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases, Hepatology 73 (6) (Jun 2021) 2546–2563.
[11]
K. Bera, K.A. Schalper, D.L. Rimm, V. Velcheti, A. Madabhushi, Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology, Nature Rev. Clin/ Oncol. 16 (11) (Nov 2019) 703–715.
[12]
Y. Wang, et al., Optimizing hepatocellular carcinoma disease staging systems by incorporating tumor micronecrosis: A multi-institutional retrospective study, Cancer Lett. 585 (Mar 2024) Multicenter Studypp. 216654-216654.
[13]
Y.-h. Ling, et al., Tumor necrosis as a poor prognostic predictor on postoperative survival of patients with solitary small hepatocellular carcinoma, BMC. Cancer 20 (1) (Jun 2020).
[14]
T. Wei, et al., Tumor Necrosis Impacts Prognosis of Patients Undergoing Curative-Intent Hepatocellular Carcinoma, Ann. Surg. Oncol. 28 (2) (Nov 2021) 797–805.
[15]
X. Sun, et al., Development and Validation of Novel Models Including Tumor Micronecrosis for Predicting the Postoperative Survival of Patients with Hepatocellular Carcinoma, J. HepatoCell Carcinoma 10 (May 2023) 1181–1194.
[16]
Y. Wang, et al., Histological tumor micronecrosis in resected specimens after R0 hepatectomy for hepatocellular carcinomas is a factor in determining adjuvant TACE: A retrospective propensity score-matched study, Int. J. Surg. 105 (106852) (Sep 2022).
[17]
Y. Wang, et al., Tumor micronecrosis predicts poor prognosis of patients with hepatocellular carcinoma after liver transplantation, BMC. Cancer 23 (1) (Jan 2023) Art. no. 86.
[18]
Q. Ye, et al., Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions, IEEe J. Biomed. Health Inform. 25 (9) (Sep 2021) 3288–3299.
[19]
M. Roy, et al., Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images, Sci. Rep. 11 (1) (Jan 2021) Art. no. 139.
[20]
U. Cinar, R.C. Atalay, Y.Y. Cetin, Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function, J. ImAging 9 (2) (Feb 2023) Art. no. 25.
[21]
R.A. Khan, M. Fu, B. Burbridge, Y. Luo, F.-X. Wu, A multi-modal deep neural network for multi-class liver cancer diagnosis, Neural Net. 165 (Aug 2023) 553–561.
[22]
X. Wang, et al., A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images, Med. Image Anal. 68 (Feb 2021) Art. no. 101914.
[23]
S. Feng, et al., Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma, Front. Oncol. 11 (Dec 2021) Art. no. 762733.
[24]
S. Diao, et al., Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network, Am. J. Pathol. 192 (3) (Mar 2022) 553–563.
[25]
S. Diao, et al., Computer Aided Cancer Regions Detection of Hepatocellular Carcinoma in Whole-slide Pathological Images based on Deep Learning, in: 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE), 2019, pp. 1–6.
[26]
T. Albrecht, et al., Deep Learning-Enabled Diagnosis of Liver Adenocarcinoma, Gastroenterology 165 (5) (Nov 2023) 1262–1275.
[27]
H.-J. Jang, J.-H. Go, Y. Kim, S.H. Lee, Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer, Cancers. (Basel) 15 (22) (Nov 2023) Art. no. 5389.
[28]
H. Liao, et al., Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning, Ann. Surg. Oncol. 27 (7) (Jul 2020) 2359–2369.
[29]
H. Liao, et al., Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma, Clin. Transl. Med. 10 (2) (Jun 2020).
[30]
A.A. Aatresh, K. Alabhya, S. Lal, J. Kini, P.U.P. Saxena, LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images, Int. J. Comput. Assist. Radiol. Surg. 16 (9) (May 2021) 1549–1563.
[31]
C. Sun, A. Xu, D. Liu, Z. Xiong, F. Zhao, W. Ding, Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels, IEEe J. Biomed. Health Inform. 24 (6) (Jun 2020) 1643–1651.
[32]
L. Lu, B.J. Daigle Jr., Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma, PeerJ. 8 (Mar 2020) Art. no. e8668.
[33]
E. Wulczyn, et al., Deep learning-based survival prediction for multiple cancer types using histopathology images, PLoS. One 15 (6) (Jun 2020) Art. no. e0233678.
[34]
W. Jia, et al., Identifying immune infiltration by deep learning to assess the prognosis of patients with hepatocellular carcinoma, J. Cancer Res. Clin. Oncol. 149 (14) (Jul 2023) 12621–12635.
[35]
A. Saito, et al., Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning, Modern Pathology 34 (2) (Feb 2021) 417–425.
[36]
J.-Y. Shi, et al., Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning, Gut 70 (5) (May 2021) 951–961.
[37]
J.M. Llovet, et al., Hepatocellular carcinoma, Nat. Reviews Disease Prim. 2 (1) (Apr 2016) 16018.
[38]
J. Liang, et al., Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer, Nat. Mach. Intell. 5 (4) (Apr 2023) 408–420.
[39]
J. Hou, X. Jia, Y. Xie, W. Qin, Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning, Genes. (Basel) 13 (10) (Oct 2022) Art. no. 1770.
[40]
R. Yamashita, J. Long, A. Saleem, D.L. Rubin, J. Shen, Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images, Sci. Rep. 11 (1) (Jan 21 2021) Art. no. 2047.
[41]
W.-F. Qu, et al., Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning, Front. Oncol. 12 (Aug 2022) Art. no. 968202.
[42]
Z. Liu, et al., Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study, Hepatol. Int. 16 (3) (Mar 2022) 577–589.
[43]
Y. Lee et al., "Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning," pp. 1–15, Aug 2022.
[44]
R.J. Chen, et al., Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, Springer, 2021, pp. 339–349. September 27–October 1, 2021, Proceedings, Part VIII 24.
[45]
N. Otsu, et al., A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern 9 (1) (1979) 62–66.
[46]
Y. Zhou, et al., CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images, in: IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, SOUTH KOREA, 2019, pp. 388–398. 2019.
[47]
A. Goode, B. Gilbert, J. Harkes, D. Jukic, M. Satyanarayanan, OpenSlide: A vendor-neutral software foundation for digital pathology, J. Pathol. Inform. 4 (Dec 2013) 27. -27.
[48]
A. Paszke et al., "Automatic differentiation in pytorch," 2017.
[49]
F. Pedregosa, et al., Scikit-learn: Machine Learning in Python, J. Mach.Learn. Res. 12 (2011) 2825–2830.
[50]
J.D. Hunter, Matplotlib: A 2D graphics environment, Comput. Sci. Eng. 9 (3) (2007) 90–95. Editorial Material.
[51]
K. He, X. Zhang, S. Ren, J. Sun, Ieee, Deep Residual Learning for Image Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, 2016, pp. 770–778. 2016.
[52]
R. Mormont, P. Geurts, R. Maree, Ieee, Comparison of deep transfer learning strategies for digital pathology, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, 2018, pp. 2343–2352. 2018.
[53]
Q. Chen, H. Xiao, Y. Gu, et al., Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images, Hepatol. Int. vol.16 (2022) 590–602.
[54]
Xiuming Zhang, et al., Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images, Cancer Med. vol.13 (no.5) (Mar 2024) e7104.
[55]
J Wei, et al., Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study, Cancers. (Basel) 13 (10) (May 2021) 2368.
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Published: 07 January 2025
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