Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning

Abstract

Objective

The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinoma (HCC) from adjacent normal tissue and predict the prognosis of HCC patients after surgical resection.

Methods

A fully automated pipeline was constructed using computational approaches to analyze the quantitative features of histopathological slides of HCC patients, in which the features were extracted from the hematoxylin and eosin (H&E)-stained whole-slide images of HCC patients from The Cancer Genome Atlas and tissue microarray images from West China Hospital. The extracted features were used to train the statistical models that classify tissue slides and predict patients’ survival outcomes by machine-learning methods.

Results

A total of 1733 quantitative image features were extracted from each histopathological slide. The diagnostic classifier based on 31 features was able to successfully distinguish HCC from adjacent normal tissues in both the test [area under the receiver operating characteristic curve (AUC) 0.988] and external validation sets (AUC 0.886). The random-forest prognostic model using 46 features was able to significantly stratify patients in each set into longer- or shorter-term survival groups according to their assigned risk scores. Moreover, the prognostic model we constructed showed comparable predicting accuracy as TNM staging systems in predicting patients’ survival at different time points after surgery.

Conclusions

Our findings suggest that machine-learning models derived from image features can assist clinicians in HCC diagnosis and its prognosis prediction after hepatectomy.

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References

  1. 1.

    Mak LY, Cruz-Ramon V, Chinchilla-Lopez P, Torres HA, LoConte NK, Rice JP, et al. Global epidemiology, prevention, and management of hepatocellular carcinoma. Am Soc Clin Oncol Educ Book. 2018;38:262–79. https://doi.org/10.1200/edbk_200939.

    Article  PubMed  Google Scholar 

  2. 2.

    Rastogi A. Changing role of histopathology in the diagnosis and management of hepatocellular carcinoma. World J Gastroenterol. 2018;24(35):4000–13. https://doi.org/10.3748/wjg.v24.i35.4000.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Qin LX, Tang ZY. The prognostic molecular markers in hepatocellular carcinoma. World J Gastroenterol. 2002;8(3):385–92. https://doi.org/10.3748/wjg.v8.i3.385.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Lauwers GY, Terris B, Balis UJ, Batts KP, Regimbeau JM, Chang Y, et al. Prognostic histologic indicators of curatively resected hepatocellular carcinomas: a multi-institutional analysis of 425 patients with definition of a histologic prognostic index. Am J Surg Pathol. 2002;26(1):25–34. https://doi.org/10.1097/00000478-200201000-00003.

    Article  PubMed  Google Scholar 

  5. 5.

    Calderaro J, Couchy G, Imbeaud S, Amaddeo G, Letouze E, Blanc JF, et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J Hepatol. 2017;67(4):727–38. https://doi.org/10.1016/j.jhep.2017.05.014.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Ziol M, Pote N, Amaddeo G, Laurent A, Nault JC, Oberti F, et al. Macrotrabecular-massive hepatocellular carcinoma: a distinctive histological subtype with clinical relevance. Hepatology. 2018;68(1):103–12. https://doi.org/10.1002/hep.29762.

    Article  PubMed  Google Scholar 

  7. 7.

    Calderaro J, Meunier L, Nguyen CT, Boubaya M, Caruso S, Luciani A, et al. ESM1 as a Marker of macrotrabecular-massive hepatocellular carcinoma. Clin Cancer Res. 2019;25(19):5859–5865. https://doi.org/10.1158/1078-0432.ccr-19-0859.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Calderaro J, Ziol M, Paradis V, Zucman-Rossi J. Molecular and histological correlations in liver cancer. J Hepatol. 2019;71(3):616–30. https://doi.org/10.1016/j.jhep.2019.06.001.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Cooper LA, Kong J, Gutman DA, Dunn WD, Nalisnik M, Brat DJ. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest. 2015;95(4):366–76. https://doi.org/10.1038/labinvest.2014.153.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    van den Bent MJ. Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician’s perspective. Acta Neuropathol. 2010;120(3):297–304. https://doi.org/10.1007/s00401-010-0725-7.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Friemel J, Rechsteiner M, Frick L, Bohm F, Struckmann K, Egger M, et al. Intratumor heterogeneity in hepatocellular carcinoma. Clin Cancer Res. 2015;21(8):1951–61. https://doi.org/10.1158/1078-0432.ccr-14-0122.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Bishop JW, Marshall CJ, Bentz JS. New technologies in gynecologic cytology. J Reprod Med. 2000;45(9):701–19.

    CAS  PubMed  Google Scholar 

  13. 13.

    Hipp J, Flotte T, Monaco J, Cheng J, Madabhushi A, Yagi Y, et al. Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess. J Pathol Inform. 2011;2:25. https://doi.org/10.4103/2153-3539.82050.

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Cooper LA, Demicco EG, Saltz JH, Powell RT, Rao A, Lazar AJ. PanCancer insights from The Cancer Genome Atlas: the pathologist’s perspective. J Pathol. 2018;244(5):512–24. https://doi.org/10.1002/path.5028.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra13. https://doi.org/10.1126/scitranslmed.3002564.

  16. 16.

    Ji MY, Yuan L, Jiang XD, Zeng Z, Zhan N, Huang PX, et al. Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma. J Transl Med. 2019;17(1):92. https://doi.org/10.1186/s12967-019-1839-x.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Luo X, Zang X, Yang L, Huang J, Liang F, Rodriguez-Canales J, et al. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. J Thorac Oncol. 2017;12(3):501–9. https://doi.org/10.1016/j.jtho.2016.10.017.

    Article  PubMed  Google Scholar 

  18. 18.

    Sertel O, Kong J, Catalyurek UV, Lozanski G, Saltz JH, Gurcan MN. Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading. J. Signal Process. Syst. 2008;55(1):169. https://doi.org/10.1007/s11265-008-0201-y.

    Article  Google Scholar 

  19. 19.

    Sertel O, Kong J, Shimada H, Catalyurek UV, Saltz JH, Gurcan MN. Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognit. 2009;42(6):1093–103. https://doi.org/10.1016/j.patcog.2008.08.027.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Yu KH, Zhang C, Berry GJ, Altman RB, Re C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7:12474. https://doi.org/10.1038/ncomms12474.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. https://doi.org/10.1158/2159-8290.cd-12-0095.

    Article  PubMed  Google Scholar 

  22. 22.

    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. https://doi.org/10.1126/scisignal.2004088.

  23. 23.

    Linkert M, Rueden CT, Allan C, Burel JM, Moore W, Patterson A, et al. Metadata matters: access to image data in the real world. J Cell Biol. 2010;189(5):777–82. https://doi.org/10.1083/jcb.201004104.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006;7(10):R100. https://doi.org/10.1186/gb-2006-7-10-r100.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL, et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 2011;27(8):1179–80. https://doi.org/10.1093/bioinformatics/btr095.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Breiman LJML. Random forests. Mach. Learn. 2001;45(1):5–32. https://doi.org/10.1023/a:1010933404324.

    Article  Google Scholar 

  27. 27.

    Lê S, Josse J, Husson F. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 2008;25(1):1–18. https://doi.org/10.18637/jss.v025.i01.

    Article  Google Scholar 

  28. 28.

    Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2(3):841–60. https://doi.org/10.1214/08-aoas169.

    Article  Google Scholar 

  29. 29.

    Ishwaran H, Kogalur UB, Gorodeski EZ, Minn AJ, Lauer MS. High-dimensional variable selection for survival data. J. Am. Stat. Assoc. 2010;105(489):205–17. https://doi.org/10.1198/jasa.2009.tm08622.

    CAS  Article  Google Scholar 

  30. 30.

    Martins-Filho SN, Paiva C, Azevedo RS, Alves VAF. Histological grading of hepatocellular carcinoma-a systematic review of literature. Front Med (Lausanne). 2017;4:193. https://doi.org/10.3389/fmed.2017.00193.

    Article  Google Scholar 

  31. 31.

    Sobin LH, Compton CC. TNM seventh edition: what’s new, what’s changed: communication from the International Union Against Cancer and the American Joint Committee on Cancer. Cancer. 2010;116(22):5336–9. https://doi.org/10.1002/cncr.25537.

    Article  PubMed  Google Scholar 

  32. 32.

    Blanche P, Dartigues J-F, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. 2013;32(30):5381–97. https://doi.org/10.1002/sim.5958.

    Article  Google Scholar 

  33. 33.

    Chiang C-T, Hung H. Non‐parametric estimation for time-dependent AUC. J. Stat. Plan. Inference. 2010;140(5):1162–74. https://doi.org/10.1016/j.jspi.2009.10.012.

    Article  Google Scholar 

  34. 34.

    Friemel J, Rechsteiner M, Frick L, Böhm F, Struckmann K, Egger M, et al. Intratumor heterogeneity in hepatocellular carcinoma. Clin. Cancer Res. 2015;21(8):1951. https://doi.org/10.1158/1078-0432.ccr-14-0122.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Kim HD, Song GW, Park S, Jung MK, Kim MH, Kang HJ, et al. Association between expression level of PD1 by tumor-infiltrating CD8(+) T cells and features of hepatocellular carcinoma. Gastroenterology. 2018;155(6):1936-50.e17. https://doi.org/10.1053/j.gastro.2018.08.030.

    CAS  Article  Google Scholar 

  36. 36.

    Calderaro J, Rousseau B, Amaddeo G, Mercey M, Charpy C, Costentin C, et al. Programmed death ligand 1 expression in hepatocellular carcinoma: relationship With clinical and pathological features. Hepatology. 2016;64(6):2038–46. https://doi.org/10.1002/hep.28710.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Patel KR, Liu TC, Vaccharajani N, Chapman WC, Brunt EM. Characterization of inflammatory (lymphoepithelioma-like) hepatocellular carcinoma: a study of 8 cases. Arch Pathol Lab Med. 2014;138(9):1193–202. https://doi.org/10.5858/arpa.2013-0371-oa.

    Article  PubMed  Google Scholar 

  38. 38.

    Costentin CE, Ferrone CR, Arellano RS, Ganguli S, Hong TS, Zhu AX. Hepatocellular carcinoma with macrovascular invasion: defining the optimal treatment strategy. Liver Cancer 2017;6(4):360–74. https://doi.org/10.1159/000481315.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Stahl J, Voyvodic F. Biopsy diagnosis of malignant versus benign liver “nodules”: new helpful markers. An update. Adv Anat Pathol 2000;7(4):230–9. https://doi.org/10.1097/00125480-200007040-00005.

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Wee A. Fine needle aspiration biopsy of the liver: Algorithmic approach and current issues in the diagnosis of hepatocellular carcinoma. Cytojournal. 2005;2:7. https://doi.org/10.1186/1742-6413-2-7.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Bergman S, Graeme-Cook F, Pitman MB. The usefulness of the reticulin stain in the differential diagnosis of liver nodules on fine-needle aspiration biopsy cell block preparations. Mod Pathol. 1997;10(12):1258–64.

    CAS  PubMed  Google Scholar 

  42. 42.

    Filmus J, Selleck SB. Glypicans: proteoglycans with a surprise. J Clin Invest. 2001;108(4):497–501. https://doi.org/10.1172/jci13712.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Capurro M, Wanless IR, Sherman M, Deboer G, Shi W, Miyoshi E, et al. Glypican-3: a novel serum and histochemical marker for hepatocellular carcinoma. Gastroenterology. 2003;125(1):89–97. https://doi.org/10.1016/s0016-5085(03)00689-9.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Coston WM, Loera S, Lau SK, Ishizawa S, Jiang Z, Wu CL, et al. Distinction of hepatocellular carcinoma from benign hepatic mimickers using Glypican-3 and CD34 immunohistochemistry. Am J Surg Pathol. 2008;32(3):433–44. https://doi.org/10.1097/pas.0b013e318158142f.

    Article  PubMed  Google Scholar 

  45. 45.

    Di Tommaso L, Franchi G, Park YN, Fiamengo B, Destro A, Morenghi E, et al. Diagnostic value of HSP70, glypican 3, and glutamine synthetase in hepatocellular nodules in cirrhosis. Hepatology 2007;45(3):725–34. https://doi.org/10.1002/hep.21531.

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Libbrecht L, Severi T, Cassiman D, Vander Borght S, Pirenne J, Nevens F, et al. Glypican-3 expression distinguishes small hepatocellular carcinomas from cirrhosis, dysplastic nodules, and focal nodular hyperplasia-like nodules. Am J Surg Pathol. 2006;30(11):1405–11. https://doi.org/10.1097/01.pas.0000213323.97294.9a.

    Article  PubMed  Google Scholar 

  47. 47.

    Zhu ZW, Friess H, Wang L, Abou-Shady M, Zimmermann A, Lander AD, et al. Enhanced glypican-3 expression differentiates the majority of hepatocellular carcinomas from benign hepatic disorders. Gut. 2001;48(4):558–64. https://doi.org/10.1136/gut.48.4.558.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Garrido C, Gurbuxani S, Ravagnan L, Kroemer G. Heat shock proteins: endogenous modulators of apoptotic cell death. Biochem Biophys Res Commun. 2001;286(3):433–42. https://doi.org/10.1006/bbrc.2001.5427.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Helmbrecht K, Zeise E, Rensing L. Chaperones in cell cycle regulation and mitogenic signal transduction: a review. Cell Prolif 2000;33(6):341–65. https://doi.org/10.1046/j.1365-2184.2000.00189.x.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Chuma M, Sakamoto M, Yamazaki K, Ohta T, Ohki M, Asaka M, et al. Expression profiling in multistage hepatocarcinogenesis: identification of HSP70 as a molecular marker of early hepatocellular carcinoma. Hepatology 2003;37(1):198–207. https://doi.org/10.1053/jhep.2003.50022.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Di Tommaso L, Destro A, Seok JY, Balladore E, Terracciano L, Sangiovanni A, et al. The application of markers (HSP70 GPC3 and GS) in liver biopsies is useful for detection of hepatocellular carcinoma. J Hepatol. 2009;50(4):746–54. https://doi.org/10.1016/j.jhep.2008.11.014.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Haratake J, Horie A. An immunohistochemical study of sarcomatoid liver carcinomas. Cancer. 1991;68(1):93–7. https://doi.org/10.1002/1097-0142(19910701)68:1%3c93::aid-cncr2820680119%3e3.0.co;2-g.

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Kakizoe S, Kojiro M, Nakashima T. Hepatocellular carcinoma with sarcomatous change. Clinicopathologic and immunohistochemical studies of 14 autopsy cases. Cancer. 1987;59(2):310–6. https://doi.org/10.1002/1097-0142(19870115)59:2%3c310::aid-cncr2820590224%3e3.0.co;2-s.

  54. 54.

    Hu L, Lau SH, Tzang CH, Wen JM, Wang W, Xie D, et al. Association of Vimentin overexpression and hepatocellular carcinoma metastasis. Oncogene. 2004;23(1):298–302. https://doi.org/10.1038/sj.onc.1206483.

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Wang Z, Wu Q, Feng S, Zhao Y, Tao C. Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma. PeerJ. 2017;5:e3575. https://doi.org/10.7717/peerj.3575.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Yuan S, Wang J, Yang Y, Zhang J, Liu H, Xiao J, et al. The prediction of clinical outcome in hepatocellular carcinoma based on a six-gene metastasis signature. Clin Cancer Res. 2017;23(1):289–97. https://doi.org/10.1158/1078-0432.ccr-16-0395.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Villanueva A, Portela A, Sayols S, Battiston C, Hoshida Y, Mendez-Gonzalez J, et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology. 2015;61(6):1945–56. https://doi.org/10.1002/hep.27732.

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Xu RH, Wei W, Krawczyk M, Wang W, Luo H, Flagg K, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat. Mater. 2017;16(11):1155–61. https://doi.org/10.1038/nmat4997.

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Miao R, Luo H, Zhou H, Li G, Bu D, Yang X, et al. Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis. J Hepatol. 2014;61(4):840–9. https://doi.org/10.1016/j.jhep.2014.05.025.

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278(2):563–77. https://doi.org/10.1148/radiol.2015151169.

    Article  PubMed  Google Scholar 

  61. 61.

    Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6. https://doi.org/10.1016/j.ejca.2011.11.036.

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuze S, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28(6):1191–206. https://doi.org/10.1093/annonc/mdx034.

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Liao H, Zhang Z, Chen J, Liao M, Xu L, Wu Z, et al. Preoperative radiomic approach to evaluate tumor-infiltrating CD8(+) T cells in hepatocellular carcinoma patients using contrast-enhanced computed tomography. Ann Surg Oncol. 2019;26(13):4537–4547. https://doi.org/10.1245/s10434-019-07815-9.

    Article  PubMed  Google Scholar 

  64. 64.

    Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70(6):1133–44. https://doi.org/10.1016/j.jhep.2019.02.023.

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Kim S, Shin J, Kim DY, Choi GH, Kim MJ, Choi JY. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma. Clin Cancer Res. 2019;25(13):3847–55. https://doi.org/10.1158/1078-0432.ccr-18-2861.

    Article  PubMed  Google Scholar 

  66. 66.

    Chen S, Li J, Wang D, Fung H, Wong LY, Zhao L. The hepatitis B epidemic in China should receive more attention. Lancet. 2018;391(10130):1572. https://doi.org/10.1016/s0140-6736(18)30499-9.

    Article  PubMed  Google Scholar 

  67. 67.

    Ma WJ, Wang HY, Teng LS. Correlation analysis of preoperative serum alpha-fetoprotein (AFP) level and prognosis of hepatocellular carcinoma (HCC) after hepatectomy. World J Surg Oncol. 2013;11:212. https://doi.org/10.1186/1477-7819-11-212.

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Bruix J, Castells A, Bosch J, Feu F, Fuster J, Garcia-Pagan JC, et al. Surgical resection of hepatocellular carcinoma in cirrhotic patients: prognostic value of preoperative portal pressure. Gastroenterology. 1996;111(4):1018–22. https://doi.org/10.1016/s0016-5085(96)70070-7.

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    Poon RT, Fan ST, Lo CM, Liu CL, Ng IO, Wong J. Long-term prognosis after resection of hepatocellular carcinoma associated with hepatitis B-related cirrhosis. J Clin Oncol. 2000;18(5):1094–101. https://doi.org/10.1200/jco.2000.18.5.1094.

    CAS  Article  PubMed  Google Scholar 

  70. 70.

    Sasaki Y, Imaoka S, Masutani S, Ohashi I, Ishikawa O, Koyama H, et al. Influence of coexisting cirrhosis on long-term prognosis after surgery in patients with hepatocellular carcinoma. Surgery. 1992;112(3):515–21.

    CAS  PubMed  Google Scholar 

  71. 71.

    Shimozawa N, Hanazaki K. Longterm prognosis after hepatic resection for small hepatocellular carcinoma. J Am Coll Surg. 2004;198(3):356–65. https://doi.org/10.1016/j.jamcollsurg.2003.10.017.

    Article  PubMed  Google Scholar 

  72. 72.

    Fuster J, Garcia-Valdecasas JC, Grande L, Tabet J, Bruix J, Anglada T, et al. Hepatocellular carcinoma and cirrhosis. Results of surgical treatment in a European series. Ann Surg. 1996;223(3):297–302. https://doi.org/10.1097/00000658-199603000-00011.

  73. 73.

    Izumi R, Shimizu K, Ii T, Yagi M, Matsui O, Nonomura A, et al. Prognostic factors of hepatocellular carcinoma in patients undergoing hepatic resection. Gastroenterology. 1994;106(3):720–7. https://doi.org/10.1016/0016-5085(94)90707-2.

    CAS  Article  PubMed  Google Scholar 

  74. 74.

    Lim KC, Chow PK, Allen JC, Chia GS, Lim M, Cheow PC, et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg. 2011;254(1):108–13. https://doi.org/10.1097/sla.0b013e31821ad884.

    Article  PubMed  Google Scholar 

  75. 75.

    Pandey D, Lee KH, Wai CT, Wagholikar G, Tan KC. Long term outcome and prognostic factors for large hepatocellular carcinoma (10 cm or more) after surgical resection. Ann Surg Oncol. 2007;14(10):2817–23. https://doi.org/10.1245/s10434-007-9518-1.

    Article  PubMed  Google Scholar 

  76. 76.

    Roayaie S, Blume IN, Thung SN, Guido M, Fiel MI, Hiotis S, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology. 2009;137(3):850–5. https://doi.org/10.1053/j.gastro.2009.06.003.

    Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Nanashima A, Morino S, Yamaguchi H, Tanaka K, Shibasaki S, Tsuji T, et al. Modified CLIP using PIVKA-II for evaluating prognosis after hepatectomy for hepatocellular carcinoma. Eur J Surg Oncol. 2003;29(9):735–42. https://doi.org/10.1016/j.ejso.2003.08.007.

    CAS  Article  PubMed  Google Scholar 

  78. 78.

    Suehiro T, Sugimachi K, Matsumata T, Itasaka H, Taketomi A, Maeda T. Protein induced by vitamin K absence or antagonist II as a prognostic marker in hepatocellular carcinoma. Comparison with alpha-fetoprotein. Cancer 1994;73(10):2464–71. https://doi.org/10.1002/1097-0142(19940515)73:10%3c2464::aid-cncr2820731004%3e3.0.co;2-9.

    CAS  Article  PubMed  Google Scholar 

  79. 79.

    Grieco A, Pompili M, Caminiti G, Miele L, Covino M, Alfei B, et al. Prognostic factors for survival in patients with early-intermediate hepatocellular carcinoma undergoing non-surgical therapy: comparison of Okuda, CLIP, and BCLC staging systems in a single Italian centre. Gut 2005;54(3):411–8. https://doi.org/10.1136/gut.2004.048124.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors would like to thank TCGA working group for offering the slide images and the corresponding cancer information, and are most grateful to the Core Facility of WCH for their technique support on the experiments.

Funding

This work was supported by grants from the National Key Technologies R&D Program (2018YFC1106800), the Natural Science Foundation of China (81972747, 81872004, 81800564, 81770615, 81700555, and 81672882), the Science and Technology Support Program of Sichuan Province (2019YFQ0001, 2018SZ0115, 2017SZ0003), the Science and Technology Program of Tibet Autonomous Region (XZ201801-GB-02), and the 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (ZYJC18008).

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KY and YZ designed the whole project and participated in result evaluation; ML, LX, and ZZ collected the clinical data of the WCH candidates; TX, HL and ZW participated in the IF extraction; HL and JP performed IF selection and construction of the machine-learning based models; HL and TX conducted the data analysis and wrote the manuscript; and KY and YZ modified the structure of the manuscript. All authors reviewed the manuscript and approved the final version.

Corresponding authors

Correspondence to Kefei Yuan PhD or Yong Zeng MD, PhD.

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Haotian Liao, Tianyuan Xiong, Jiajie Peng, Lin Xu, Mingheng Liao, Zhen Zhang, Zhenru Wu, Kefei Yuan and Yong Zeng declare no competing interests in this work.

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Liao, H., Xiong, T., Peng, J. 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, 2359–2369 (2020). https://doi.org/10.1245/s10434-019-08190-1

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