J Shanghai Jiaotong Univ Sci ›› 2021, Vol. 26 ›› Issue (1): 10-16.doi: 10.1007/s12204-021-2255-y
ZENG Judan (曾巨丹), CAO Wenjiao (曹文娇), WANG Lihua (王丽华)
出版日期:
2021-02-28
发布日期:
2021-01-19
通讯作者:
WANG Lihua (王丽华)
E-mail:drwanglh0420@163.com
ZENG Judan (曾巨丹), CAO Wenjiao (曹文娇), WANG Lihua (王丽华)
Online:
2021-02-28
Published:
2021-01-19
Contact:
WANG Lihua (王丽华)
E-mail:drwanglh0420@163.com
摘要: Ovarian cancer has one of the highest mortality rates among gynecological malignancies. This disease has a low early detection rate, a high postoperative recurrence rate, and a 5-year survival rate of only 40%. Hence, there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer. Prediction models can effectively estimate the risk of disease occurrence, as well as its prognosis. Recently, many studies have established multiple ovarian cancer prediction models based on different regions and populations. These models can improve the detection rate and optimize the prognosis management to a certain extent. Herein, the construction principle of the ovarian cancer risk prediction model and its validation are summarized; furthermore, comprehensive reviews and comparisons of the different types of these models are made. Therefore, our review may be of great significance for the whole course of ovarian cancer management.
中图分类号:
ZENG Judan (曾巨丹), CAO Wenjiao (曹文娇), WANG Lihua (王丽华) . Recent Advances and Future Directions of Diagnostic and Prognostic Prediction Models in Ovarian Cancer[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 10-16.
ZENG Judan (曾巨丹), CAO Wenjiao (曹文娇), WANG Lihua (王丽华) . Recent Advances and Future Directions of Diagnostic and Prognostic Prediction Models in Ovarian Cancer[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 10-16.
[1] | BRAY F, FERLAY J, SOERJOMATARAM I, et al.Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA: A Cancer Journal for Clinicians,2018, 68(6): 394-424. |
[2] | ALLEMANI C, MATSUDA T, DI CARLO V, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries[J]. The Lancet, 2018, 391(10125): 1023-1075. |
[3] | LHEUREUX S, GOURLEY C, VERGOTE I, et al. Epithelial ovarian cancer [J]. The Lancet, 2019,393(10177): 1240-1253. |
[4] | SLOMSKI A. Screening women for ovarian cancer still does more harm than good [J]. JAMA, 2012, 307(23):2474-2475. |
[5] | OZA A M. Advances in prediction for ovarian cancer treatment stratification [J]. Nature Reviews Clinical Oncology, 2019, 16: 75-76. |
[6] | THUN M, LINET M S, CERHAN J R, et al. Cancer epidemiology and prevention [M]. 4th ed. New York,USA: Oxford University Press, 2017. |
[7] | RANSTAM J, COOK J A, COLLINS G S. Clinical prediction models [J]. British Journal of Surgery, 2016,103(13): 1880-1886. |
[8] | MOONS K G M, ALTMAN D G, REITSMA J B, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD):Explanation and elaboration [J]. Annals of Internal Medicine, 2015, 162(1): W1-W73. |
[9] | VICKERS A J. Prediction models in cancer care [J].CA: A Cancer Journal for Clinicians, 2011, 61(5):315-326. |
[10] | VAN DE LAAR R, INTHOUT J, VAN GORP T, et al. External validation of three prognostic models for overall survival in patients with advanced-stage epithelial ovarian cancer [J]. British Journal of Cancer, 2014,110: 42-48. |
[11] | PREVIS R, BEVIS K. HUH W, et al. A prognostic nomogram to predict overall survival in women with recurrent ovarian cancer treated with bevacizumab and chemotherapy [J]. Gynecologic Oncology, 2014, 132(3):531-536. |
[12] | KIM S I, SONG M S, HWANGBO S Y, et al. Development ofWeb-based nomograms to predict treatment response and prognosis of epithelial ovarian cancer [J].Cancer Research and Treatment, 2019, 51(3): 1144-1155. |
[13] | TORRE L A, TRABERT B, DESANTIS C E, et al.Ovarian cancer statistics, 2018 [J]. CA: A Cancer Journal for Clinicians, 2018, 68(4): 284-296. |
[14] | KURMAN R J, SHIH I M. Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer— Shifting the paradigm [J]. Human Pathology, 2011,42(7): 918-931. |
[15] | AUSTIN P C, TU J V. Bootstrap methods for developing predictive models [J]. The American Statistician,2004, 58(2): 131-137. |
[16] | ROYSTONP, AMBLER G, SAUERBREIW. The use of fractional polynomials to model continuous risk variables in epidemiology [J]. International Journal of Epidemiology,1999, 28(5): 964-974. |
[17] | HARRELL F E. Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis [M]. New York, USA: Springer, 2001. |
[18] | ALBA A C, AGORITSAS T, WALSH M, et al. Discrimination and calibration of clinical prediction models:Users’ guides to the medical literature [J]. JAMA,2017, 318(14): 1377-1384. |
[19] | RAHMAN M S, AMBLER G, CHOODARIOSKOOEI B, et al. Review and evaluation of performance measures for survival prediction models in external validation settings [J]. BMC Medical Research Methodology, 2017, 17: 60. |
[20] | COOK N R. Use and misuse of the receiver operating characteristic curve in risk prediction [J]. Circulation,2007, 115(7): 928-935. |
[21] | STEYERBERG E W, VERGOUWE Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation [J]. European Heart Journal, 2014, 35(29): 1925-1931. |
[22] | MOONS K G M, KENGNE A P, WOODWARD M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker [J]. Heart (British Cardiac Society),2012, 98(9): 683-690. |
[23] | PRSKALO Z ˇS, BULI′C P, LANGER S, et al. Proofs for implementation of higher HE4 and ROMA index cut-off values in ovarian cancer preoperative stratification[J]. Journal of Obstetrics and Gynaecology, 2019,39(2): 195-201. |
[24] | CUI R L, WANG Y C, LI Y, et al. Clinical value of ROMA index in diagnosis of ovarian cancer: Metaanalysis[J]. Cancer Management and Research, 2019,11: 2545-2551. |
[25] | NUNES N, YAZBEK J, AMBLER G, et al. Prospective evaluation of the IOTA logistic regression model LR2 for the diagnosis of ovarian cancer [J]. Ultrasound in Obstetrics & Gynecology, 2012, 40(3): 355-359. |
[26] | VAN CALSTER B, VAN HOORDE K, VALENTIN L, et al. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: Prospective multicentre diagnostic study [J]. BMJ (Clinical Research Ed.), 2014, 349: g5920. |
[27] | KUCHENBAECKER K B, MCGUFFOG L,BARROWDALE D, et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers[J]. Journal of the National Cancer Institute, 2017,109(7): djw302. |
[28] | RAMUS S J, SONG H L, DICKS E, et al. Germline mutations in the BRIP1, BARD1, PALB2, and NBN genes in women with ovarian cancer [J]. Journal of the National Cancer Institute, 2015, 107(11): djv214. |
[29] | KUCHENBAECKER K B, RAMUS S J, TYRER J,et al. Identification of six new susceptibility loci for invasive epithelial ovarian cancer [J]. Nature Genetics,2015, 47: 164-171. |
[30] | BENTAIEB A, LI-CHANG H, HUNTSMAN D, et al.A structured latent model for ovarian carcinoma subtyping from histopathology slides[J]. Medical Image Analysis, 2017, 39: 194-205. |
[31] | GAVRIELIDES M A, GALLAS B D, HEWITT S M. Uncertainty in the assessment of immunohistochemical staining with optical and digital microscopy: Lessons from a reader study [C]//Medical Imaging 2015: Digital Pathology. Orlando, Fl, USA: SPIE Medical Imaging,2015: 94200V. |
[32] | CLARK T G, STEWART M E, ALTMAN D G, et al. A prognostic model for ovarian cancer [J]. British Journal of Cancer, 2001, 85(7): 944-952. |
[33] | CHOVANEC M, CIERNA Z, MISKOVSKA V, et al.Systemic immune-inflammation index in germ-cell tumours[J]. British Journal of Cancer, 2018, 118(6):831-838. |
[34] | NIE D, GONG H, MAO X G, et al. Systemic immuneinflammation index predicts prognosis in patients with epithelial ovarian cancer: A retrospective study [J].Gynecologic Oncology, 2019, 152(2): 259-264. |
[35] | SHEN S P, WANG G R, ZHANG R Y, et al. Development and validation of an immune gene-set based prognostic signature in ovarian cancer [J]. EBioMedicine,2019, 40: 318-326. |
[36] | KURTA M L, EDWARDS R P, MOYSICH K B, et al.Prognosis and conditional disease-free survival among patients with ovarian cancer [J]. Journal of Clinical Oncology, 2014, 32(36): 4102-4112. |
[37] | BAGNOLI M, CANEVARI S, CALIFANO D, et al. Development and validation of a microRNA-based signature (MiROvaR) to predict early relapse or progression of epithelial ovarian cancer: A cohort study [J].The Lancet Oncology, 2016, 17(8): 1137-1146. |
[38] | RIESTER M, WEI W, WALDRON L, et al. Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples [J]. Journal of the National Cancer Institute, 2014, 106(5): dju048. |
[39] | YANG J Y, YOSHIHARA K, TANAKA K, et al. Predicting time to ovarian carcinoma recurrence using protein markers [J]. The Journal of Clinical Investigation, 2013, 123(9): 3740-3750. |
[40] | ENGBERSEN M P, VAN’T SANTA I, LOK C, et al.MRI with diffusion-weighted imaging to predict feasibility of complete cytoreduction with the peritoneal cancer index (PCI) in advanced stage ovarian cancer patients [J]. European Journal of Radiology, 2019, 114:146-151. |
[41] | ESPADA M, GARCIA-FLORES J R, JIMENEZ M,et al. Diffusion-weighted magnetic resonance imaging evaluation of intra-abdominal sites of implants to predict likelihood of suboptimal cytoreductive surgery in patients with ovarian carcinoma [J]. European Radiology,2013, 23: 2636-2642. |
[42] | BINDER H, SCHUMACHER M. Allowing for mandatory covariates in boosting estimation of sparse highdimensional survival models [J]. BMC Bioinformatics,2008, 9: 14. |
[43] | FRIEDMAN J, HASTIE T, TIBSHIRANI R. Regularization paths for generalized linear models via coordinate descent [J]. Journal of Statistical Software, 2010,33(1): 1-22. |
[44] | LUIJKEN K, WYNANTS L, VAN SMEDEN M, et al.Changing predictor measurement procedures affected the performance of prediction models in clinical examples[J]. Journal of Clinical Epidemiology, 2020, 119:7-18. |
[45] | STEYERBERG E W, VERGOUWE Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation [J]. European Heart Journal, 2014, 35(29): 1925-1931. |
[46] | COLLINS G S, DE GROOT J A, DUTTON S, et al.External validation of multivariable prediction models:A systematic review of methodological conduct and reporting[J]. BMC Medical Research Methodology, 2014,14: 40. |
[47] | MOONS K G M, ALTMAN D G, VERGOUWE Y,et al. Prognosis and prognostic research: Application and impact of prognostic models in clinical practice [J]. BMJ (Clinical Research Ed.), 2009, 338: b606. |
No related articles found! |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||