Evidence-based pathology_Chinese Journal of Evidence-Based Medicine

Authors：

HE Tingting ¹ , YAN Peijing ² , YANG Kehu ^3,4,5,6 ,  ZHANG Min ⁷

1. School of Basic Medicine, Gansu University of Chinese Medicine, Lanzhou 730000, P.R.China;
2. Department of Epidemiology and Health Statistics, West China School of Public Health and West China Forth Hospital, Sichuan University, Chengdu 610044, P.R.China;
3. Evidence-based Social Sciences Research Center, Lanzhou University, Lanzhou 730000, P.R.China;
4. Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, P.R.China;
5. Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou 730000, P.R.China;
6. School of Public Health, Lanzhou University, Lanzhou 730000, P.R.China;
7. Department of Pathology, Gansu Provincial Hospital, Lanzhou 730000, P.R.China;

Corresponding author：

ZHANG Min, Email: sallyzhangmin@126.com

Keywords：

Evidence-based pathology; Evidence-based medicine; Pathology; Progress; Application

DOI：

10.7507/1672-2531.202006140

Video：

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Abstract Full text Figures/Tables Video References Cited by

Fleming proposed the concept of evidence-based pathology (EBP) in 1996. In recent years, there have been a lot of evidence-based studies on the diagnosis and prognosis of diseases. However, there are still limitations and challenges in the development, and the growth in application of evidence-based medicine in the pathology is still slow. This study introduced the history of evidence-based pathology, summarized the primary application areas and the latest research progress, analyzed current opportunities and challenges of evidence-based pathology, and provided some suggestions.

Citation： HE Tingting, YAN Peijing, YANG Kehu, ZHANG Min. Evidence-based pathology. Chinese Journal of Evidence-Based Medicine, 2020, 20(10): 1214-1220. doi: 10.7507/1672-2531.202006140 Copy

1.	Louis DN, Gerber GK, Baron JM, et al. Computational pathology: an emerging definition. Arch Pathol Lab Med, 2014, 138(9): 1133-1138.
2.	Fassan M. Molecular diagnostics in pathology: time for a next-generation pathologist? Arch Pathol Lab Med, 2018, 142(3): 313-320.
3.	Bhargava R, Madabhushi A. Emerging themes in image informatics and molecular analysis for digital pathology. Annu Rev Biomed Eng, 2016, 18: 387-412.
4.	Crawford JM. Original research in pathology: judgment, or evidence-based medicine? J Tech Method Path, 2007, 87(2): 104-114.
5.	Evidence-Based Medicine Working Group. Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA, 1992, 268(17): 2420-2425.
6.	Fleming KA. Evidence-based pathology. J Pathol, 1996, 179(2): 127-128.
7.	Malats N, Bustos A, Nascimento CM, et al. P53 as a prognostic marker for bladder cancer: a meta-analysis and review. Lancet Oncol, 2005, 6(9): 678-686.
8.	Wells WA, Carney PA, Eliassen MS, et al. Pathologists' agreement with experts and reproducibility of breast ductal carcinoma-in-situ classification schemes. Am J Surg Pathol, 2000, 24(5): 651-659.
9.	Schmitt AR, Brewer JD, Bordeaux JS, et al. Staging for cutaneous squamous cell carcinoma as a predictor of sentinel lymph node biopsy results: meta-analysis of American Joint Committee on Cancer criteria and a proposed alternative system. JAMA Dermatol, 2014, 150(1): 19-24.
10.	de Haas V, Ismaila N, Advani A, et al. Initial diagnostic work-up of acute leukemia: ASCO clinical practice guideline endorsement of the college of american pathologists and american society of hematology guideline. J Clin Oncol, 2019, 37(3): 239-253.
11.	Fakhry C, Lacchetti C, Rooper LM, et al. Human papillomavirus testing in head and neck carcinomas: ASCO clinical practice guideline endorsement of the college of american pathologists guideline. J Clin Oncol, 2018, 36(31): 3152-3161.
12.	Maghami E, Ismaila N, Alvarez A, et al. Diagnosis and management of squamous cell carcinoma of unknown primary in the head and neck: ASCO Guideline. J Clin Oncol, 2020, 38(22): 2570-2596.
13.	Lindeman NI, Cagle PT, Beasley MB, et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. Arch Pathol Lab Med, 2013, 137(6): 828-860.
14.	Wolff AC, Hammond MEH, Allison KH, et al. Human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical oncology/College of American pathologists clinical practice guideline focused update. J Clin Oncol, 2018, 36(20): 2105-2122.
15.	Pantanowitz L, Sinard JH, Henricks WH, et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch Pathol Lab Med, 2013, 137(12): 1710-1722.
16.	Sekhar H, Zwahlen M, Trelle S, et al. Nodal stage migration and prognosis in anal cancer: a systematic review, meta-regression, and simulation study. Lancet Oncol, 2017, 18(10): 1348-1359.
17.	Kumarasamy C, Madhav MR, Sabarimurugan S, et al. Prognostic value of mirnas in head and neck cancers: a comprehensive systematic and meta-analysis. Cells, 2019, 8(8): 772.
18.	Islam MM, Poly TN, Walther BA, et al. Artificial intelligence in ophthalmology: A meta-analysis of deep learning models for retinal vessels segmentation. J Clin Med, 2020, 9(4): 1018.
19.	何以丰, 徐丛剑, 冯令达. Her-2/neu 与上皮性卵巢癌术后生存率相关性的 Meta 分析. 中国循证医学杂志, 2007, 7(6): 420-432.
20.	田跃军, 刘伟, 景锁世, 等. 缺氧诱导因子-1α 蛋白表达与前列腺癌风险相关性的 Meta 分析. 循证医学, 2016, 16(5): 294-300.
21.	王青松, 尹旭, 徐文硕, 等. 基质金属蛋白酶 2 高表达与骨肉瘤预后关系的 Meta 分析. 中国全科医学, 2016, 19(25): 3112-3119.
22.	郭丹, 宋锦宁, 黄廷钦, 等. Hmgb1 基因在人脑胶质瘤中的表达及临床意义 Meta 分析. 中华神经外科疾病研究杂志, 2018, 17(1): 23-26.
23.	朱磊, 李育平, 张恒柱. Ykl-40 表达与脑胶质瘤诊断和预后相关性的 Meta 分析. 中华神经外科杂志, 2019, 35(8): 844-849.
24.	步宏, 魏兵. 循证医学与病理学实践. 中华病理学杂志, 2003, 32(1): 92-94.
25.	陈俊颖, 曾敏. 循证病理学的基本原则及其应用. 临床与实验病理学杂志, 2012, 28(6): 664-665.
26.	Montori VM, Guyatt GH. Progress in evidence-based medicine. JAMA, 2008, 300(15): 1814-1816.
27.	Evidence-Based Radiology Working Group. Evidence-based radiology: a new approach to the practice of radiology. Radiology, 2001, 220(3): 566-575.
28.	Zietman A. Evidence-based medicine, conscience-based medicine, and the management of low-risk prostate cancer. J Clin Oncol, 2009, 27(30): 4935-4936.
29.	Cook SC, Schwartz AC, Kaslow NJ. Evidence-based psychotherapy: advantages and challenges. Neurotherapeutics, 2017, 14(3): 537-545.
30.	Shank CD, Lepard JR, Walters BC, et al. Towards evidence-based guidelines in neurological surgery. Neurosurgery, 2019, 85(5): 613-621.
31.	杨克虎. 循证社会科学研究方法: 系统评价与 Meta 分析. 兰州: 兰州大学出版社, 2018.
32.	杨克虎. 循证社会科学的产生、发展与未来. 图书与情报, 2018, (3): 1-10.
33.	Wick MR, Marchevsky AM. Evidence-based principles in pathology: existing problem areas and the development of "quality" practice patterns. Arch Pathol Lab Med, 2011, 135(11): 1398-1404.
34.	Gurevitch J, Koricheva J, Nakagawa S, et al. Meta-analysis and the science of research synthesis. Nature, 2018, 555(7695): 175-182.
35.	吴景玲, 葛龙, 张俊华, 等. 多个诊断性试验准确性的比较: 网状 Meta 分析方法介绍. 中国循证医学杂志, 2017, 17(8): 987-992.
36.	Shen M, Wang H, Wei K, et al. Five common tumor biomarkers and cea for diagnosing early gastric cancer: A protocol for a network meta-analysis of diagnostic test accuracy. Medicine, 2018, 97(19): e0577.
37.	Schünemann HJ, Oxman AD, Brozek J, et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ, 2008, 336(7653): 1106-1110.
38.	Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ, 2003, 326(7379): 41-44.
39.	刘海宁, 吴昊, 张宁萍, 等. 诊断准确性试验 Meta 分析四格表数据的提取方法. 中国循证医学杂志, 2018, 18(9): 995-1000.
40.	Di Leo A, Desmedt C, Bartlett JM, et al. HER2 and TOP2A as predictive markers for anthracycline-containing chemotherapy regimens as adjuvant treatment of breast cancer: a meta-analysis of individual patient data. Lancet Oncol, 2011, 12(12): 1134-1142.
41.	Feng W, Zhai C, Shi W, et al. Clinicopathological and prognostic value of LINC01296 in cancers: a meta-analysis. Artif Cells Nanomed Biotechnol, 2019, 47(1): 3315-3321.
42.	Lamping M, Benary M, Leyvraz S, et al. Support of a molecular tumour board by an evidence-based decision management system for precision oncology. Eur J Cancer, 2020, 127: 41-51.
43.	Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol, 2019, 20(5): e253-e261.253-261.
44.	Cheng J, Han Z, Mehra R, et al. Computational analysis of pathological images enables a better diagnosis of tfe3 xp11.2 translocation renal cell carcinoma. Nat Commun, 2020, 11(1): 1778.
45.	Fleming KA. Evidence-based cellular pathology. Lancet, 2002, 359(9312): 1149-1150.
46.	Marchevsky AM, Wick MR. Evidence-based medicine, medical decision analysis, and pathology. Hum Pathol, 2004, 35(10): 1179-1188.
47.	Marchevsky AM, Walts AE, Wick MR. Evidence-based pathology in its second decade: toward probabilistic cognitive computing. Hum Pathol, 2017, 61: 1-8.
48.	Marchevsky AM, Wick MR. Evidence-based pathology: systematic literature reviews as the basis for guidelines and best practices. Arch Pathol Lab Med, 2015, 139(3): 394-399.
49.	Yang X, Huang H, Zeng Z, et al. Diagnostic value of bladder tumor fibronectin in patients with bladder tumor: a systematic review with meta-analysis. Clin Biochem, 2013, 46(15): 1377-1382.
50.	Jiang T, Zhai C, Su C, et al. The diagnostic value of circulating cell free DNA quantification in non-small cell lung cancer: A systematic review with meta-analysis. Lung Cancer, 2016, 100: 63-70.
51.	Tong J, Sun X, Cheng H, et al. Expression of p16 in non-small cell lung cancer and its prognostic significance: a meta-analysis of published literatures. Lung Cancer, 2011, 74(2): 155-163.
52.	Stoler MH, Schiffman M. Interobserver reproducibility of cervical cytologic and histologic interpretations: realistic estimates from the ASCUS-LSIL Triage Study. JAMA, 2001, 285(11): 1500-1505.
53.	Hodgson A, Park KJ, Djordjevic B, et al. International endocervical adenocarcinoma criteria and classification: validation and interobserver reproducibility. Am J Surg Pathol, 2019, 43(1): 75-83.
54.	Christenson RH. Evidence-based laboratory medicine - a guide for critical evaluation of in vitro laboratory testing. Ann Clin Biochem, 2007, 44(Pt 2): 111-130.
55.	Dorizzi RM, Maconi M, Giavarina D, et al. An electronic thesaurus of Evidence Based Laboratory Medicine hematological and biochemical diagnostic tests. Int J Lab Hematol, 2009, 31(5): 544-551.
56.	Johnson KM. Using Bayes' rule in diagnostic testing: a graphical explanation. Diagnosis (Berl), 2017, 4(3): 159-167.
57.	Kallner A. Bayes' theorem, the roc diagram and reference values: Definition and use in clinical diagnosis. Biochemia medica, 2018, 28(1): e010101.
58.	雷军强, 杜亮, 王梦书, 等. 制订高质量循证诊断临床实践指南的方法与策略. 中国循证医学杂志, 2016, 16(1): 7-10.
59.	Katsoula A, Paschos P, Haidich AB, et al. Diagnostic accuracy of fecal immunochemical test in patients at increased risk for colorectal cancer: a meta-analysis. JAMA Intern Med, 2017, 177(8): 1110-1118.
60.	McCarthy M. Diagnostic error remains a pervasive, underappreciated problem, us report says. BMJ, 2015, 351: h5064.
61.	Sepulveda AR, Hamilton SR, Allegra CJ, et al. Molecular biomarkers for the evaluation of colorectal cancer: guideline from the american society for clinical pathology, college of american pathologists, association for molecular pathology, and the American society of clinical oncology. J Clin Oncol, 2017, 35(13): 1453-1486.
62.	Kalemkerian GP, Narula N, Kennedy EB, et al. Molecular testing guideline for the selection of patients with lung cancer for treatment with targeted tyrosine kinase inhibitors: American society of clinical oncology endorsement of the college of American pathologists/international association for the study of lung cancer/association for molecular pathology clinical practice guideline update. J Clin Oncol, 2018, 36(9): 911-919.
63.	陶苗苗. Ki-67 对乳腺癌新辅助化疗 pCR 预测价值的 Meta 分析. 重庆: 重庆医科大学, 2018.
64.	Mol BW, Bayram N, Lijmer JG, et al. The performance of CA-125 measurement in the detection of endometriosis: a meta-analysis. Fertil Steril, 1998, 70(6): 1101-1108.
65.	Berry DA, Zhou S, Higley H, et al. Association of minimal residual disease with clinical outcome in pediatric and adult acute lymphoblastic leukemia: A meta-analysis. JAMA oncology, 2017, 3(7): e170580.
66.	Yamauchi H, Stearns V, Hayes DF. When is a tumor marker ready for prime time? A case study of c-erbB-2 as a predictive factor in breast cancer. J Clin Oncol, 2001, 19(8): 2334-2356.
67.	Marchevsky AM, Gupta R, McKenna RJ, et al. Evidence-based pathology and the pathologic evaluation of thymomas: the World Health Organization classification can be simplified into only 3 categories other than thymic carcinoma. Cancer, 2008, 112(12): 2780-2788.
68.	Jambusaria-Pahlajani A, Kanetsky PA, Karia PS, et al. Evaluation of AJCC tumor staging for cutaneous squamous cell carcinoma and a proposed alternative tumor staging system. JAMA Dermatol, 2013, 149(4): 402-410.
69.	Heinmöller E, Renke B, Beyser K, et al. Piffalls in diagnostic molecular pathology--significance of sampling error. Virchows Arch, 2001, 439(4): 504-511.
70.	Arbyn M, Verdoodt F, Snijders PJ, et al. Accuracy of human papillomavirus testing on self-collected versus clinician-collected samples: a meta-analysis. Lancet Oncol, 2014, 15(2): 172-183.
71.	Korevaar DA, Crombag LM, Cohen JF, et al. Added value of combined endobronchial and oesophageal endosonography for mediastinal nodal staging in lung cancer: a systematic review and meta-analysis. Lancet Respir Med, 2016, 4(12): 960-968.
72.	Pheby DF, Levine DF, Pitcher RW, et al. Lymph node harvests directly influence the staging of colorectal cancer: evidence from a regional audit. J Clin Pathol, 2004, 57(1): 43-47.
73.	Törnroos A, Garvin S, Olsson H. The number of identified lymph node metastases increases continuously with increased total lymph node recovery in pT3 colon cancer. Acta Oncol, 2009, 48(8): 1152-1156.
74.	Batista TP, Bezerra AL, Martins MR, et al. How important is the number of pelvic lymph node retrieved to locorregional staging of cervix cancer? Einstein (Sao Paulo), 2013, 11(4): 451-455.
75.	Goldstein NS, Sanford W, Coffey M, et al. Lymph node recovery from colorectal resection specimens removed for adenocarcinoma. Trends over time and a recommendation for a minimum number of lymph nodes to be recovered. Am J Clin Pathol, 1996, 106(2): 209-216.
76.	Mainprize KS, Kulacoglu H, Hewavisinthe J, et al. How many lymph nodes to stage colorectal carcinoma? J Clin Pathol, 1998, 51(2): 165-166.
77.	Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med, 2019, 25(1): 24-29.
78.	Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 2017, 318(22): 2199-2210.
79.	Hanna MG, Reuter VE, Hameed MR, et al. Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod Pathol, 2019, 32(7): 916-928.
80.	Delvaux C, Richert B, Lecerf P, et al. Onychopapillomas: a 68-case series to determine best surgical procedure and histologic sectioning. J Eur Acad Dermatol Venereol, 2018, 32(11): 2025-2030.
81.	刘彤华. 国内诊断病理发展的机遇与挑战. 中华病理学杂志, 2005, 34(8): 466-467.
82.	Marchevsky AM, LeStang N, Hiroshima K, et al. The differential diagnosis between pleural sarcomatoid mesothelioma and spindle cell/pleomorphic (sarcomatoid) carcinomas of the lung: evidence-based guidelines from the International Mesothelioma Panel and the MESOPATH National Reference Center. Hum Pathol, 2017, 67: 160-168.
83.	Murali R, Davidson B, Fadare O, et al. High-grade endometrial carcinomas: morphologic and immunohistochemical features, diagnostic challenges and recommendations. Int J Gynecol Pathol, 2019, 38(Iss 1 Suppl 1): S40--S63.40-63.
84.	Sun M, Shen Y, Ren ML, et al. Meta-analysis on the performance of p16/Ki-67 dual immunostaining in detecting high-grade cervical intraepithelial neoplasm. J Cancer Res Ther, 2018, 14(Supplement): S587-S593.587-593.
85.	Qureshi A, Loya A, Azam M, et al. Study of parameters to ensure quality control in histopathology reporting: a meta-analysis at a tertiary care center. Indian J Pathol Microbiol, Apr-Jun 2012, 55(2): 180-182.
86.	Cibas ES, Ali SZ. The 2017 Bethesda system for reporting thyroid cytopathology. Thyroid, 2017, 27(11): 1341-1346.

1. Louis DN, Gerber GK, Baron JM, et al. Computational pathology: an emerging definition. Arch Pathol Lab Med, 2014, 138(9): 1133-1138.
2. Fassan M. Molecular diagnostics in pathology: time for a next-generation pathologist? Arch Pathol Lab Med, 2018, 142(3): 313-320.
3. Bhargava R, Madabhushi A. Emerging themes in image informatics and molecular analysis for digital pathology. Annu Rev Biomed Eng, 2016, 18: 387-412.
4. Crawford JM. Original research in pathology: judgment, or evidence-based medicine? J Tech Method Path, 2007, 87(2): 104-114.
5. Evidence-Based Medicine Working Group. Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA, 1992, 268(17): 2420-2425.
6. Fleming KA. Evidence-based pathology. J Pathol, 1996, 179(2): 127-128.
7. Malats N, Bustos A, Nascimento CM, et al. P53 as a prognostic marker for bladder cancer: a meta-analysis and review. Lancet Oncol, 2005, 6(9): 678-686.
8. Wells WA, Carney PA, Eliassen MS, et al. Pathologists' agreement with experts and reproducibility of breast ductal carcinoma-in-situ classification schemes. Am J Surg Pathol, 2000, 24(5): 651-659.
9. Schmitt AR, Brewer JD, Bordeaux JS, et al. Staging for cutaneous squamous cell carcinoma as a predictor of sentinel lymph node biopsy results: meta-analysis of American Joint Committee on Cancer criteria and a proposed alternative system. JAMA Dermatol, 2014, 150(1): 19-24.
10. de Haas V, Ismaila N, Advani A, et al. Initial diagnostic work-up of acute leukemia: ASCO clinical practice guideline endorsement of the college of american pathologists and american society of hematology guideline. J Clin Oncol, 2019, 37(3): 239-253.
11. Fakhry C, Lacchetti C, Rooper LM, et al. Human papillomavirus testing in head and neck carcinomas: ASCO clinical practice guideline endorsement of the college of american pathologists guideline. J Clin Oncol, 2018, 36(31): 3152-3161.
12. Maghami E, Ismaila N, Alvarez A, et al. Diagnosis and management of squamous cell carcinoma of unknown primary in the head and neck: ASCO Guideline. J Clin Oncol, 2020, 38(22): 2570-2596.
13. Lindeman NI, Cagle PT, Beasley MB, et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. Arch Pathol Lab Med, 2013, 137(6): 828-860.
14. Wolff AC, Hammond MEH, Allison KH, et al. Human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical oncology/College of American pathologists clinical practice guideline focused update. J Clin Oncol, 2018, 36(20): 2105-2122.
15. Pantanowitz L, Sinard JH, Henricks WH, et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch Pathol Lab Med, 2013, 137(12): 1710-1722.
16. Sekhar H, Zwahlen M, Trelle S, et al. Nodal stage migration and prognosis in anal cancer: a systematic review, meta-regression, and simulation study. Lancet Oncol, 2017, 18(10): 1348-1359.
17. Kumarasamy C, Madhav MR, Sabarimurugan S, et al. Prognostic value of mirnas in head and neck cancers: a comprehensive systematic and meta-analysis. Cells, 2019, 8(8): 772.
18. Islam MM, Poly TN, Walther BA, et al. Artificial intelligence in ophthalmology: A meta-analysis of deep learning models for retinal vessels segmentation. J Clin Med, 2020, 9(4): 1018.
19. 何以丰, 徐丛剑, 冯令达. Her-2/neu 与上皮性卵巢癌术后生存率相关性的 Meta 分析. 中国循证医学杂志, 2007, 7(6): 420-432.
20. 田跃军, 刘伟, 景锁世, 等. 缺氧诱导因子-1α 蛋白表达与前列腺癌风险相关性的 Meta 分析. 循证医学, 2016, 16(5): 294-300.
21. 王青松, 尹旭, 徐文硕, 等. 基质金属蛋白酶 2 高表达与骨肉瘤预后关系的 Meta 分析. 中国全科医学, 2016, 19(25): 3112-3119.
22. 郭丹, 宋锦宁, 黄廷钦, 等. Hmgb1 基因在人脑胶质瘤中的表达及临床意义 Meta 分析. 中华神经外科疾病研究杂志, 2018, 17(1): 23-26.
23. 朱磊, 李育平, 张恒柱. Ykl-40 表达与脑胶质瘤诊断和预后相关性的 Meta 分析. 中华神经外科杂志, 2019, 35(8): 844-849.
24. 步宏, 魏兵. 循证医学与病理学实践. 中华病理学杂志, 2003, 32(1): 92-94.
25. 陈俊颖, 曾敏. 循证病理学的基本原则及其应用. 临床与实验病理学杂志, 2012, 28(6): 664-665.
26. Montori VM, Guyatt GH. Progress in evidence-based medicine. JAMA, 2008, 300(15): 1814-1816.
27. Evidence-Based Radiology Working Group. Evidence-based radiology: a new approach to the practice of radiology. Radiology, 2001, 220(3): 566-575.
28. Zietman A. Evidence-based medicine, conscience-based medicine, and the management of low-risk prostate cancer. J Clin Oncol, 2009, 27(30): 4935-4936.
29. Cook SC, Schwartz AC, Kaslow NJ. Evidence-based psychotherapy: advantages and challenges. Neurotherapeutics, 2017, 14(3): 537-545.
30. Shank CD, Lepard JR, Walters BC, et al. Towards evidence-based guidelines in neurological surgery. Neurosurgery, 2019, 85(5): 613-621.
31. 杨克虎. 循证社会科学研究方法: 系统评价与 Meta 分析. 兰州: 兰州大学出版社, 2018.
32. 杨克虎. 循证社会科学的产生、发展与未来. 图书与情报, 2018, (3): 1-10.
33. Wick MR, Marchevsky AM. Evidence-based principles in pathology: existing problem areas and the development of "quality" practice patterns. Arch Pathol Lab Med, 2011, 135(11): 1398-1404.
34. Gurevitch J, Koricheva J, Nakagawa S, et al. Meta-analysis and the science of research synthesis. Nature, 2018, 555(7695): 175-182.
35. 吴景玲, 葛龙, 张俊华, 等. 多个诊断性试验准确性的比较: 网状 Meta 分析方法介绍. 中国循证医学杂志, 2017, 17(8): 987-992.
36. Shen M, Wang H, Wei K, et al. Five common tumor biomarkers and cea for diagnosing early gastric cancer: A protocol for a network meta-analysis of diagnostic test accuracy. Medicine, 2018, 97(19): e0577.
37. Schünemann HJ, Oxman AD, Brozek J, et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ, 2008, 336(7653): 1106-1110.
38. Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ, 2003, 326(7379): 41-44.
39. 刘海宁, 吴昊, 张宁萍, 等. 诊断准确性试验 Meta 分析四格表数据的提取方法. 中国循证医学杂志, 2018, 18(9): 995-1000.
40. Di Leo A, Desmedt C, Bartlett JM, et al. HER2 and TOP2A as predictive markers for anthracycline-containing chemotherapy regimens as adjuvant treatment of breast cancer: a meta-analysis of individual patient data. Lancet Oncol, 2011, 12(12): 1134-1142.
41. Feng W, Zhai C, Shi W, et al. Clinicopathological and prognostic value of LINC01296 in cancers: a meta-analysis. Artif Cells Nanomed Biotechnol, 2019, 47(1): 3315-3321.
42. Lamping M, Benary M, Leyvraz S, et al. Support of a molecular tumour board by an evidence-based decision management system for precision oncology. Eur J Cancer, 2020, 127: 41-51.
43. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol, 2019, 20(5): e253-e261.253-261.
44. Cheng J, Han Z, Mehra R, et al. Computational analysis of pathological images enables a better diagnosis of tfe3 xp11.2 translocation renal cell carcinoma. Nat Commun, 2020, 11(1): 1778.
45. Fleming KA. Evidence-based cellular pathology. Lancet, 2002, 359(9312): 1149-1150.
46. Marchevsky AM, Wick MR. Evidence-based medicine, medical decision analysis, and pathology. Hum Pathol, 2004, 35(10): 1179-1188.
47. Marchevsky AM, Walts AE, Wick MR. Evidence-based pathology in its second decade: toward probabilistic cognitive computing. Hum Pathol, 2017, 61: 1-8.
48. Marchevsky AM, Wick MR. Evidence-based pathology: systematic literature reviews as the basis for guidelines and best practices. Arch Pathol Lab Med, 2015, 139(3): 394-399.
49. Yang X, Huang H, Zeng Z, et al. Diagnostic value of bladder tumor fibronectin in patients with bladder tumor: a systematic review with meta-analysis. Clin Biochem, 2013, 46(15): 1377-1382.
50. Jiang T, Zhai C, Su C, et al. The diagnostic value of circulating cell free DNA quantification in non-small cell lung cancer: A systematic review with meta-analysis. Lung Cancer, 2016, 100: 63-70.
51. Tong J, Sun X, Cheng H, et al. Expression of p16 in non-small cell lung cancer and its prognostic significance: a meta-analysis of published literatures. Lung Cancer, 2011, 74(2): 155-163.
52. Stoler MH, Schiffman M. Interobserver reproducibility of cervical cytologic and histologic interpretations: realistic estimates from the ASCUS-LSIL Triage Study. JAMA, 2001, 285(11): 1500-1505.
53. Hodgson A, Park KJ, Djordjevic B, et al. International endocervical adenocarcinoma criteria and classification: validation and interobserver reproducibility. Am J Surg Pathol, 2019, 43(1): 75-83.
54. Christenson RH. Evidence-based laboratory medicine - a guide for critical evaluation of in vitro laboratory testing. Ann Clin Biochem, 2007, 44(Pt 2): 111-130.
55. Dorizzi RM, Maconi M, Giavarina D, et al. An electronic thesaurus of Evidence Based Laboratory Medicine hematological and biochemical diagnostic tests. Int J Lab Hematol, 2009, 31(5): 544-551.
56. Johnson KM. Using Bayes' rule in diagnostic testing: a graphical explanation. Diagnosis (Berl), 2017, 4(3): 159-167.
57. Kallner A. Bayes' theorem, the roc diagram and reference values: Definition and use in clinical diagnosis. Biochemia medica, 2018, 28(1): e010101.
58. 雷军强, 杜亮, 王梦书, 等. 制订高质量循证诊断临床实践指南的方法与策略. 中国循证医学杂志, 2016, 16(1): 7-10.
59. Katsoula A, Paschos P, Haidich AB, et al. Diagnostic accuracy of fecal immunochemical test in patients at increased risk for colorectal cancer: a meta-analysis. JAMA Intern Med, 2017, 177(8): 1110-1118.
60. McCarthy M. Diagnostic error remains a pervasive, underappreciated problem, us report says. BMJ, 2015, 351: h5064.
61. Sepulveda AR, Hamilton SR, Allegra CJ, et al. Molecular biomarkers for the evaluation of colorectal cancer: guideline from the american society for clinical pathology, college of american pathologists, association for molecular pathology, and the American society of clinical oncology. J Clin Oncol, 2017, 35(13): 1453-1486.
62. Kalemkerian GP, Narula N, Kennedy EB, et al. Molecular testing guideline for the selection of patients with lung cancer for treatment with targeted tyrosine kinase inhibitors: American society of clinical oncology endorsement of the college of American pathologists/international association for the study of lung cancer/association for molecular pathology clinical practice guideline update. J Clin Oncol, 2018, 36(9): 911-919.
63. 陶苗苗. Ki-67 对乳腺癌新辅助化疗 pCR 预测价值的 Meta 分析. 重庆: 重庆医科大学, 2018.
64. Mol BW, Bayram N, Lijmer JG, et al. The performance of CA-125 measurement in the detection of endometriosis: a meta-analysis. Fertil Steril, 1998, 70(6): 1101-1108.
65. Berry DA, Zhou S, Higley H, et al. Association of minimal residual disease with clinical outcome in pediatric and adult acute lymphoblastic leukemia: A meta-analysis. JAMA oncology, 2017, 3(7): e170580.
66. Yamauchi H, Stearns V, Hayes DF. When is a tumor marker ready for prime time? A case study of c-erbB-2 as a predictive factor in breast cancer. J Clin Oncol, 2001, 19(8): 2334-2356.
67. Marchevsky AM, Gupta R, McKenna RJ, et al. Evidence-based pathology and the pathologic evaluation of thymomas: the World Health Organization classification can be simplified into only 3 categories other than thymic carcinoma. Cancer, 2008, 112(12): 2780-2788.
68. Jambusaria-Pahlajani A, Kanetsky PA, Karia PS, et al. Evaluation of AJCC tumor staging for cutaneous squamous cell carcinoma and a proposed alternative tumor staging system. JAMA Dermatol, 2013, 149(4): 402-410.
69. Heinmöller E, Renke B, Beyser K, et al. Piffalls in diagnostic molecular pathology--significance of sampling error. Virchows Arch, 2001, 439(4): 504-511.
70. Arbyn M, Verdoodt F, Snijders PJ, et al. Accuracy of human papillomavirus testing on self-collected versus clinician-collected samples: a meta-analysis. Lancet Oncol, 2014, 15(2): 172-183.
71. Korevaar DA, Crombag LM, Cohen JF, et al. Added value of combined endobronchial and oesophageal endosonography for mediastinal nodal staging in lung cancer: a systematic review and meta-analysis. Lancet Respir Med, 2016, 4(12): 960-968.
72. Pheby DF, Levine DF, Pitcher RW, et al. Lymph node harvests directly influence the staging of colorectal cancer: evidence from a regional audit. J Clin Pathol, 2004, 57(1): 43-47.
73. Törnroos A, Garvin S, Olsson H. The number of identified lymph node metastases increases continuously with increased total lymph node recovery in pT3 colon cancer. Acta Oncol, 2009, 48(8): 1152-1156.
74. Batista TP, Bezerra AL, Martins MR, et al. How important is the number of pelvic lymph node retrieved to locorregional staging of cervix cancer? Einstein (Sao Paulo), 2013, 11(4): 451-455.
75. Goldstein NS, Sanford W, Coffey M, et al. Lymph node recovery from colorectal resection specimens removed for adenocarcinoma. Trends over time and a recommendation for a minimum number of lymph nodes to be recovered. Am J Clin Pathol, 1996, 106(2): 209-216.
76. Mainprize KS, Kulacoglu H, Hewavisinthe J, et al. How many lymph nodes to stage colorectal carcinoma? J Clin Pathol, 1998, 51(2): 165-166.
77. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med, 2019, 25(1): 24-29.
78. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 2017, 318(22): 2199-2210.
79. Hanna MG, Reuter VE, Hameed MR, et al. Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod Pathol, 2019, 32(7): 916-928.
80. Delvaux C, Richert B, Lecerf P, et al. Onychopapillomas: a 68-case series to determine best surgical procedure and histologic sectioning. J Eur Acad Dermatol Venereol, 2018, 32(11): 2025-2030.
81. 刘彤华. 国内诊断病理发展的机遇与挑战. 中华病理学杂志, 2005, 34(8): 466-467.
82. Marchevsky AM, LeStang N, Hiroshima K, et al. The differential diagnosis between pleural sarcomatoid mesothelioma and spindle cell/pleomorphic (sarcomatoid) carcinomas of the lung: evidence-based guidelines from the International Mesothelioma Panel and the MESOPATH National Reference Center. Hum Pathol, 2017, 67: 160-168.
83. Murali R, Davidson B, Fadare O, et al. High-grade endometrial carcinomas: morphologic and immunohistochemical features, diagnostic challenges and recommendations. Int J Gynecol Pathol, 2019, 38(Iss 1 Suppl 1): S40--S63.40-63.
84. Sun M, Shen Y, Ren ML, et al. Meta-analysis on the performance of p16/Ki-67 dual immunostaining in detecting high-grade cervical intraepithelial neoplasm. J Cancer Res Ther, 2018, 14(Supplement): S587-S593.587-593.
85. Qureshi A, Loya A, Azam M, et al. Study of parameters to ensure quality control in histopathology reporting: a meta-analysis at a tertiary care center. Indian J Pathol Microbiol, Apr-Jun 2012, 55(2): 180-182.
86. Cibas ES, Ali SZ. The 2017 Bethesda system for reporting thyroid cytopathology. Thyroid, 2017, 27(11): 1341-1346.

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