한국간담췌외과학회지 (Annals of Hepato-Biliary-Pancreatic Surgery)

  • 제26권1호
  • /
  • Pages.17-30
  • /
  • 2022
  • /
  • 2508-5778(pISSN)
  • /
  • 2508-5859(eISSN)
한국간담췌외과학회 (The Korean Association of Hepato-Biliary-Pancreatic Surgery)
권호 표지
DOI QR코드

DOI QR Code

Scoring systems for the management of oncological hepato-pancreato-biliary patients

  • Alexander W. Coombs (Department of Surgery and Cancer, Imperial College London) ;
  • Chloe Jordan (Department of Surgery and Cancer, Imperial College London) ;
  • Sabba A. Hussain (Department of Surgery and Cancer, Imperial College London) ;
  • Omar Ghandour (Department of Surgery and Cancer, Imperial College London)
  • 투고 : 2021.07.28
  • 심사 : 2021.09.02
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

키워드

과제정보

We thank Dr. Ashley Clift for supporting this review by providing his guidance and expertise in clinical prediction modelling.

참고문헌

  1. Oxford University Hospitals. HPB surgery [Internet]. Oxford: Oxford University Hospitals 2021 [cited 2020 Jan 11]. Available from: https://www.ouh.nhs.uk/services/departments/general-surgery/hpb-surgery/default.aspx.
  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424.
  3. Cirocchi R, Trastulli S, Boselli C, Montedori A, Cavaliere D, Parisi A, et al. Radiofrequency ablation in the treatment of liver metastases from colorectal cancer. Cochrane Database Syst Rev 2012;(6):CD006317.
  4. Alberts SR, Gores GJ, Kim GP, Roberts LR, Kendrick ML, Rosen CB, et al. Treatment options for hepatobiliary and pancreatic cancer. Mayo Clin Proc 2007;82:628-637.
  5. Papis D, Vagliasindi A, Maida P. Hepatobiliary and pancreatic surgery in the elderly: current status. Ann Hepatobiliary Pancreat Surg 2020;24:1-5.
  6. Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ 2009;338:b604.
  7. Jones HJ, de Cossart L. Risk scoring in surgical patients. Br J Surg 1999;86:149-157.
  8. Barnett S, Moonesinghe SR. Clinical risk scores to guide perioperative management. Postgrad Med J 2011;87:535-541.
  9. Knops AM, Legemate DA, Goossens A, Bossuyt PM, Ubbink DT. Decision aids for patients facing a surgical treatment decision: a systematic review and meta-analysis. Ann Surg 2013;257:860-866.
  10. Chandra A, Mangam S, Marzouk D. A review of risk scoring systems utilised in patients undergoing gastrointestinal surgery. J Gastrointest Surg 2009;13:1529-1538.
  11. Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 2016;353:i3140.
  12. Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009;338:b606.
  13. Liao L, Mark DB. Clinical prediction models: are we building better mousetraps? J Am Coll Cardiol 2003;42:851-853.
  14. Bellou V, Belbasis L, Konstantinidis AK, Tzoulaki I, Evangelou E. Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal. BMJ 2019;367:l5358.
  15. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020;369:m1328.
  16. Caetano SJ, Sonpavde G, Pond GR. C-statistic: a brief explanation of its construction, interpretation and limitations. Eur J Cancer 2018;90:130-132.
  17. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011;306:1688-1698.
  18. Yurkovich M, Avina-Zubieta JA, Thomas J, Gorenchtein M, Lacaille D. A systematic review identifies valid comorbidity indices derived from administrative health data. J Clin Epidemiol 2015;68:3-14.
  19. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med 2019;17:230.
  20. Vogel A, Cervantes A, Chau I, Daniele B, Llovet JM, Meyer T, et al. Hepatocellular carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2018;29(Suppl 4):iv238-iv255.
  21. European Association for the Study of the Liver. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 2018;69:182-236.
  22. The Cancer of the Liver Italian Program (Clip) Investigators. A new prognostic system for hepatocellular carcinoma: a retrospective study of 435 patients: the Cancer of the Liver Italian Program (CLIP) investigators. Hepatology 1998;28:751-755.
  23. Kudo M, Chung H, Osaki Y. Prognostic staging system for hepatocellular carcinoma (CLIP score): its value and limitations, and a proposal for a new staging system, the Japan Integrated Staging Score (JIS score). J Gastroenterol 2003;38:207-215.
  24. Kinoshita A, Onoda H, Fushiya N, Koike K, Nishino H, Tajiri H. Staging systems for hepatocellular carcinoma: current status and future perspectives. World J Hepatol 2015;7:406-424.
  25. Llovet JM, Bruix J. Prospective validation of the Cancer of the Liver Italian Program (CLIP) score: a new prognostic system for patients with cirrhosis and hepatocellular carcinoma. Hepatology 2000;32:679-680.
  26. Chen ZH, Hong YF, Lin J, Li X, Wu DH, Wen JY, et al. Validation and ranking of seven staging systems of hepatocellular carcinoma. Oncol Lett 2017;14:705-714.
  27. Liu PH, Hsu CY, Hsia CY, Lee YH, Su CW, Huang YH, et al. Prognosis of hepatocellular carcinoma: assessment of eleven staging systems. J Hepatol 2016;64:601-608.
  28. Huitzil-Melendez FD, Capanu M, O'Reilly EM, Duffy A, Gansukh B, Saltz LL, et al. Advanced hepatocellular carcinoma: which staging systems best predict prognosis? J Clin Oncol 2010;28:2889-2895.
  29. Ueno S, Tanabe G, Sako K, Hiwaki T, Hokotate H, Fukukura Y, et al. Discrimination value of the new western prognostic system (CLIP score) for hepatocellular carcinoma in 662 Japanese patients. Cancer of the Liver Italian Program. Hepatology 2001;34:529-534.
  30. Levy I, Sherman M. Staging of hepatocellular carcinoma: assessment of the CLIP, Okuda, and Child-Pugh staging systems in a cohort of 257 patients in Toronto. Gut 2002;50:881-885.
  31. Huo TI, Huang YH, Lin HC, Wu JC, Chiang JH, Lee PC, et al. Proposal of a modified Cancer of the Liver Italian Program staging system based on the model for end-stage liver disease for patients with hepatocellular carcinoma undergoing loco-regional therapy. Am J Gastroenterol 2006;101:975-982.
  32. 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;29:735-742.
  33. Llovet JM, Bru C, Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis 1999;19:329-338.
  34. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet 2018;391:1301-1314.
  35. Bruix J, Sherman M. Management of hepatocellular carcinoma: an update. Hepatology 2011;53:1020-1022.
  36. Vitale A, Saracino E, Boccagni P, Brolese A, D'Amico F, Gringeri E, et al. Validation of the BCLC prognostic system in surgical hepatocellular cancer patients. Transplant Proc 2009;41:1260-1263.
  37. Barman PM, Sharma P, Krishnamurthy V, Willatt J, McCurdy H, Moseley RH, et al. Predictors of mortality in patients with hepatocellular carcinoma undergoing transarterial chemoembolization. Dig Dis Sci 2014;59:2821-2825.
  38. Barman PM, Su GL. Limitations of the barcelona clinic liver cancer staging system with a focus on transarterial chemoembolization as a key modality for treatment of hepatocellular carcinoma. Clin Liver Dis (Hoboken) 2016;7:32-35.
  39. Wang YY, Zhong JH, Xu HF, Xu G, Wang LJ, Xu D, et al. A modified staging of early and intermediate hepatocellular carcinoma based on single tumour >7 cm and multiple tumours beyond up-to-seven criteria. Aliment Pharmacol Ther 2019;49:202-210.
  40. Tsukuma H, Hiyama T, Tanaka S, Nakao M, Yabuuchi T, Kitamura T, et al. Risk factors for hepatocellular carcinoma among patients with chronic liver disease. N Engl J Med 1993;328:1797-1801.
  41. El-Serag HB, Rudolph KL. Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology 2007;132:2557-2576.
  42. Okuda H. Hepatocellular carcinoma development in cirrhosis. Best Pract Res Clin Gastroenterol 2007;21:161-173.
  43. Johnson P, Berhane S, Satomura S, Tada T, Kumada T, Teng M, et al. O110 An international collaborative study assessing the role of aetiology and stage in survival in HCC-implications for screening. J Hepatol 2014;60:S45-S46.
  44. Johnson PJ, Berhane S, Kagebayashi C, Satomura S, Teng M, Reeves HL, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol 2015;33:550-558.
  45. Pinato DJ, Sharma R, Allara E, Yen C, Arizumi T, Kubota K, et al. The ALBI grade provides objective hepatic reserve estimation across each BCLC stage of hepatocellular carcinoma. J Hepatol 2017;66:338-346.
  46. Toyoda H, Lai PB, O'Beirne J, Chong CC, Berhane S, Reeves H, et al. Long-term impact of liver function on curative therapy for hepatocellular carcinoma: application of the ALBI grade. Br J Cancer 2016;114:744-750.
  47. Cho WR, Hung CH, Chen CH, Lin CC, Wang CC, Liu YW, et al. Ability of the post-operative ALBI grade to predict the outcomes of hepatocellular carcinoma after curative surgery. Sci Rep 2020;10:7290.
  48. Hiraoka A, Kumada T, Tsuji K, Takaguchi K, Itobayashi E, Kariyama K, et al. Validation of modified ALBI grade for more detailed assessment of hepatic function in hepatocellular carcinoma patients: a multicenter analysis. Liver Cancer 2019;8:121-129.
  49. Clavien PA, Lesurtel M, Bossuyt PM, Gores GJ, Langer B, Perrier A. Recommendations for liver transplantation for hepatocellular carcinoma: an international consensus conference report. Lancet Oncol 2012;13:e11-e22.
  50. Lingiah VA, Niazi M, Olivo R, Paterno F, Guarrera JV, Pyrsopoulos NT. Liver transplantation beyond milan criteria. J Clin Transl Hepatol 2020;8:69-75.
  51. Mehta N, Heimbach J, Harnois DM, Sapisochin G, Dodge JL, Lee D, et al. Validation of a risk estimation of tumor recurrence after transplant (RETREAT) score for hepatocellular carcinoma recurrence after liver transplant. JAMA Oncol 2017;3:493-500.
  52. Hoffman D, Mehta N. Recurrence of hepatocellular carcinoma following liver transplantation. Expert Rev Gastroenterol Hepatol 2021;15:91-102.
  53. Lee DD, Sapisochin G, Mehta N, Gorgen A, Musto KR, Hajda H, et al. Surveillance for HCC after liver transplantation: increased monitoring may yield aggressive treatment options and improved postrecurrence survival. Transplantation 2020;104:2105-2112.
  54. Mehta N, Dodge JL, Roberts JP, Yao FY. Validation of the prognostic power of the RETREAT score for hepatocellular carcinoma recurrence using the UNOS database. Am J Transplant 2018;18:1206-1213.
  55. Marrero JA, Kulik LM, Sirlin CB, Zhu AX, Finn RS, Abecassis MM, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology 2018;68:723-750.
  56. Agopian VG, Harlander-Locke M, Zarrinpar A, Kaldas FM, Farmer DG, Yersiz H, et al. A novel prognostic nomogram accurately predicts hepatocellular carcinoma recurrence after liver transplantation: analysis of 865 consecutive liver transplant recipients. J Am Coll Surg 2015;220:416-427.
  57. Halazun KJ, Najjar M, Abdelmessih RM, Samstein B, Griesemer AD, Guarrera JV, et al. Recurrence after liver transplantation for hepatocellular carcinoma: a new MORAL to the story. Ann Surg 2017;265:557-564.
  58. Kim SH, Moon DB, Park GC, Lee SG, Hwang S, Ahn CS, et al. Preoperative prediction score of hepatocellular carcinoma recurrence in living donor liver transplantation: validation of SNAPP score developed at Asan Medical Center. Am J Transplant 2021;21:604-613.
  59. Creasy JM, Sadot E, Koerkamp BG, Chou JF, Gonen M, Kemeny NE, et al. Actual 10-year survival after hepatic resection of colorectal liver metastases: what factors preclude cure? Surgery 2018;163:1238-1244.
  60. de Jong MC, Pulitano C, Ribero D, Strub J, Mentha G, Schulick RD, et al. Rates and patterns of recurrence following curative intent surgery for colorectal liver metastasis: an international multi-institutional analysis of 1669 patients. Ann Surg 2009;250:440-448.
  61. Fong Y, Fortner J, Sun RL, Brennan MF, Blumgart LH. Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann Surg 1999;230:309-318; discussion 318-321.
  62. He Y, Ong Y, Li X, Din FV, Brown E, Timofeeva M, et al. Performance of prediction models on survival outcomes of colorectal cancer with surgical resection: a systematic review and meta-analysis. Surg Oncol 2019;29:196-202.
  63. Spelt L, Nilsson J, Andersson R, Andersson B. Artificial neural networks--a method for prediction of survival following liver resection for colorectal cancer metastases. Eur J Surg Oncol 2013;39:648-654.
  64. Bilici A. Prognostic factors related with survival in patients with pancreatic adenocarcinoma. World J Gastroenterol 2014;20:10802-10812.
  65. Dasari A, Shen C, Halperin D, Zhao B, Zhou S, Xu Y, et al. Trends in the incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States. JAMA Oncol 2017;3:1335-1342.
  66. Falconi M, Eriksson B, Kaltsas G, Bartsch DK, Capdevila J, Caplin M, et al. ENETS consensus guidelines update for the management of patients with functional pancreatic neuroendocrine tumors and non-functional pancreatic neuroendocrine tumors. Neuroendocrinology 2016;103:153-171.
  67. Vagefi PA, Razo O, Deshpande V, McGrath DJ, Lauwers GY, Thayer SP, et al. Evolving patterns in the detection and outcomes of pancreatic neuroendocrine neoplasms: the Massachusetts General Hospital experience from 1977 to 2005. Arch Surg 2007;142:347-354.
  68. Bar-Moshe Y, Mazeh H, Grozinsky-Glasberg S. Non-functioning pancreatic neuroendocrine tumors: surgery or observation? World J Gastrointest Endosc 2017;9:153-161.
  69. Genc CG, Jilesen AP, Partelli S, Falconi M, Muffatti F, van Kemenade FJ, et al. A new scoring system to predict recurrent disease in grade 1 and 2 nonfunctional pancreatic neuroendocrine tumors. Ann Surg 2018;267:1148-1154.
  70. Zou S, Jiang Y, Wang W, Zhan Q, Deng X, Shen B. Novel scoring system for recurrence risk classification of surgically resected G1/2 pancreatic neuroendocrine tumors - Retrospective cohort study. Int J Surg 2020;74:86-91.
  71. He L, Li H, Cai J, Chen L, Yao J, Zhang Y, et al. Prognostic value of the Glasgow prognostic score or modified Glasgow prognostic score for patients with colorectal cancer receiving various treatments: a systematic review and meta-analysis. Cell Physiol Biochem 2018;51:1237-1249.
  72. Zhang H, Ren D, Jin X, Wu H. The prognostic value of modified Glasgow Prognostic Score in pancreatic cancer: a meta-analysis. Cancer Cell Int 2020;20:462.
  73. Liu Z, Jin K, Guo M, Long J, Liu L, Liu C, et al. Prognostic value of the CRP/Alb ratio, a novel inflammation-based score in pancreatic cancer. Ann Surg Oncol 2017;24:561-568.
  74. Zhang K, Gao HF, Mo M, Wu CJ, Hua YQ, Chen Z, et al. A novel scoring system based on hemostatic parameters predicts the prognosis of patients with advanced pancreatic cancer. Pancreatology 2019;19:346-351.
  75. Benavides M, Anton A, Gallego J, Gomez MA, Jimenez-Gordo A, La Casta A, et al. Biliary tract cancers: SEOM clinical guidelines. Clin Transl Oncol 2015;17:982-987.
  76. Balachandran P, Agarwal S, Krishnani N, Pandey CM, Kumar A, Sikora SS, et al. Predictors of long-term survival in patients with gallbladder cancer. J Gastrointest Surg 2006;10:848-854.
  77. Hawkins WG, DeMatteo RP, Jarnagin WR, Ben-Porat L, Blumgart LH, Fong Y. Jaundice predicts advanced disease and early mortality in patients with gallbladder cancer. Ann Surg Oncol 2004;11:310-315.
  78. Bartlett DL, Fong Y, Fortner JG, Brennan MF, Blumgart LH. Longterm results after resection for gallbladder cancer. Implications for staging and management. Ann Surg 1996;224:639-646.
  79. Fong Y, Wagman L, Gonen M, Crawford J, Reed W, Swanson R, et al. Evidence-based gallbladder cancer staging: changing cancer staging by analysis of data from the National Cancer Database. Ann Surg 2006;243:767-771; discussion 771-774.
  80. Tran TB, Norton JA, Ethun CG, Pawlik TM, Buettner S, Schmidt C, et al. Gallbladder cancer presenting with jaundice: uniformly fatal or still potentially curable? J Gastrointest Surg 2017;21:1245-1253.
  81. Cubertafond P, Gainant A, Cucchiaro G. Surgical treatment of 724 carcinomas of the gallbladder. Results of the French Surgical Association Survey. Ann Surg 1994;219:275-280.
  82. Shukla PJ, Neve R, Barreto SG, Hawaldar R, Nadkarni MS, Mohandas KM, et al. A new scoring system for gallbladder cancer (aiding treatment algorithm): an analysis of 335 patients. Ann Surg Oncol 2008;15:3132-3137.
  83. Leon AR. A new scoring system for gallbladder cancer: the first step of a long walk. Ann Surg Oncol 2008;15:2991-2992.
  84. Wang K, Zhang H, Xia Y, Liu J, Shen F. Surgical options for intrahepatic cholangiocarcinoma. Hepatobiliary Surg Nutr 2017;6:79-90.
  85. Pan QX, Su ZJ, Zhang JH, Wang CR, Ke SY. Glasgow Prognostic Score predicts prognosis of intrahepatic cholangiocarcinoma. Mol Clin Oncol 2017;6:566-574.
  86. Wang Y, Li J, Xia Y, Gong R, Wang K, Yan Z, et al. Prognostic nomogram for intrahepatic cholangiocarcinoma after partial hepatectomy. J Clin Oncol 2013;31:1188-1195.
  87. Raoof M, Dumitra S, Ituarte PHG, Melstrom L, Warner SG, Fong Y, et al. Development and validation of a prognostic score for intrahepatic cholangiocarcinoma. JAMA Surg 2017;152:e170117.
  88. Hahn F, Muller L, Mahringer-Kunz A, Schotten S, Duber C, Hinrichs JB, et al. Risk prediction in intrahepatic cholangiocarcinoma: direct comparison of the MEGNA score and the 8th edition of the UICC/ AJCC Cancer staging system. PLoS One 2020;15:e0228501.
  89. Schnitzbauer AA, Eberhard J, Bartsch F, Brunner SM, Ceyhan GO, Walter D, et al. The MEGNA score and preoperative anemia are major prognostic factors after resection in the German intrahepatic cholangiocarcinoma cohort. Ann Surg Oncol 2020;27:1147-1155.
  90. Aakre CA, Dziadzko MA, Herasevich V. Towards automated calculation of evidence-based clinical scores. World J Methodol 2017;7:16-24.
  91. Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ 2013;346:e5595.
  92. Riley RD, Hayden JA, Steyerberg EW, Moons KG, Abrams K, Kyzas PA, et al. Prognosis research strategy (PROGRESS) 2: prognostic factor research. PLoS Med 2013;10:e1001380.
  93. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10:e1001381.
  94. Hingorani AD, Windt DA, Riley RD, Abrams K, Moons KG, Steyerberg EW, et al. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ 2013;346:e5793.
  95. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Circulation 2015;131:211-219.
  96. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2014;13:8-17.
  97. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019;110:12-22.
  98. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579.
  99. Dou D, Yang S, Lin Y, Zhang J. An eight-miRNA signature expression-based risk scoring system for prediction of survival in pancreatic adenocarcinoma. Cancer Biomark 2018;23:79-93.
  100. Pencina MJ, D'Agostino RB Sr. Evaluating discrimination of risk prediction models: the C statistic. JAMA 2015;314:1063-1064.