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Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer

Year 2023, Volume: 23 Issue: 1, 9 - 24, 02.05.2023

Abstract

Background: There is a need for a personalized risk assessment method (IRPM) for women with abnormal uterine bleeding. Management of abnormal vaginal bleeding is based on diagnostic test results with positive/negative predictive values for a cut-off value. Patient has no contribution on the threshold to act for biopsy or not. Aim of present study was to develop a calibrated model with discriminative ability in which the patient can contribute to decision process.
Methods: A cross-sectional one-gate design cohort study was planned to extract data of patients older than 35 years-old. All had index test (endometrial thickness measurement) and reference test (D&C under general anesthesia). Target was histopathological report according to WHO 2014 as benign or pre/cancer. Primary outcome was to develop a useful clinical IRPM and to compare net benefit of IRPM and models mimicking current guidelines for various thresholds of disease in decision curve analysis. Secondary outcomes were to analyse discriminative properties, the number of unnecessary biopsies and missed cases at various thresholds.
Findings: IRPM consisting of symptom status, endometrial thickness and age was the best risk predicting method for pre-/endometrium cancer. IRPM had a higher net benefit than guidelines and to biopsy all or not at entire range of clinical threshold probabilities. IRPM had a good discrimination slope and can also decrease number of missed cases. Models mimicking current guidelines were only useful above a threshold of 3% and below this threshold, it is found to be harmful.
Interpretation: IRPM doesn't need any additional costs or time-consuming analysis. IRPM may aid patient to contribute to decision of further investigation. Clinical usefulness of IRPM is superior to models mimicking current practice. Value of diagnostic discriminatory cut-offs of guidelines is lower than expected in a heterogenous group of patients.
Funding: No funding

References

  • References 1 Henley SJ, Miller JW, Dowling NF, Benard VB, Richardson LC. Uterin Cancer Incidence and mortality- United states, 1999-2016. Morbidity and Mortality Weekly Report 2018; 67: 1333-38.
  • 2 Pennant ME, Mehta R, Moody P, et al. Premenopausal abnormal uterine bleeding and risk of endometrial cancer. BJOG 2017; 124: 404-11.
  • 3 Emery J, Vedsted J. New NICE guidance on diagnosing cancer in general practice. Br J Gen Pract 2015; 65: 446-47.
  • 4 Ioannidis JP, Haidich AB, Pappa M, et al. Comparison of evidence of treatment effects in randomized and nonrandomized studies. JAMA 2001; 286: 821-30.
  • 5 Wong ASW, Lao TTH, Cheung CW, et al. Reappriasal of endometrial thickness for the detection of endometrial cancer in postmenapausal bleeding, a retrospective cohort study. BJOG 2016; 123: 439-46.
  • 6 Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 2015; 351: h5527.
  • 7 Initiative S. STROBE checklist for cohort, case- control, and cross-sectional studies. 2007. https://www.strobe-statement.org/download/strobe-checklist-wide-cohort-case-control-and-cross-sectional-studies-combined-pdf (accessed 17 Dec, 2021).
  • 8 Ellenson LH, Matias-Guiu X, Mutter GL. Endometrial hyperplasia without atypia. In Female Genital Tumours WHO Classification of Tumours Series, 5th Edition, Volume 4. IARC Press, Lyon, 2020; 248-49.
  • 9 Lax SF, Mutter GL. Endometrial atypical hyperplasia/endometrioid intraepithelial neoplasia. In Female Genital Tumours WHO Classification of Tumours Series, 5th Edition, Volume 4. IARC Press, Lyon, 2020; 250-51.
  • 10 Sobczuk K, Sobczuk A. New classification system of endometrial hyperplasia WHO 2014 and its clinical implications. Prz Menopauzalny 2017; 16: 107-11
  • 11 Bindman RS, Weiss E, Feldstein V. How thick is too thick? When endometrial thickness should prompt biopsy in postmenopausal women without vaginal bleeding. Ultrasound Obstet Gynecol 2004; 24: 558-65.
  • 12 Clarke MA, Long BJ, Sherman ME, et al. Risk Assessment of endometrial cancer and endometrial intraepithelial neoplasia in women with abnormal bleeding and implications for clinical management algorithms. Am J Obstet Gynecol 2020; 223: 549.
  • 13 Huang Y, Lİ W, Macheret F, Gabriel RA, Machado LO. A tutorial on calibration measurements and calibration models for clinical prediction models. Journal of the American Medical Informatics Association 2020; 27: 621-33.
  • 14 Calster BV, Vickers AJ. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Making 2015; 35: 162–9.
  • 15 Calster BV, Wynants L, Verbeek JFM, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. Eur Urol 2018; 74: 796-04.
  • 16 Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clinical Chemistry and Laboratory Medicine 2010; 48: 1703-11.
  • 17 Chawdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction modelling. Fam Med Com Health 2020; 8: e000262.
  • 18 Alblas M, Velt KB, Pashayan N, Widschwentter M, Steyerberg EW, Vergouwe Y. Prediction models for endometrial cancer for the general population or symptomatic women: A systematic review. Critical Reviews in Oncology/Hematology 2018; 126: 92-99.
  • 19 Angioli R, Capriglione S, Aloisi A, et al. REM (risk of endometrial malignancy): a proposal for a new scoring system to evaluate risk of endometrial malignancy. Clin Cancer Res 2013; 19: 5733-39.
  • 20 Madkour NM. An ultrasound risk-scoring model for prediction of endometrial cancer in post-menopausal women (using IETA terminology). Middle East Fertility Society Journal 2017; 22: 201-05.
  • 21 Opolskiene G, Sladkevicius P, Valentin L. Prediction of endometrial malignancy in women with postmenopausal bleeding and sonographic endometrial thickness ≥ 4.5 mm. Ultrasound in Obstetrics and Gynecology 2011; 37: 232-40.
  • 22 Giannella L , Mfuta K, Setti T, Cerami LB, Bergamini E, Boselli F. A Risk-Scoring Model for the Prediction of Endometrial Cancer among Symptomatic Postmenopausal Women with Endometrial Thickness > 4 mm. Biomed Res Int 2014; 2014: 130569.
  • 23 Husing A, Dossus L, Ferrari P, et al. An epidemiological model for prediction of endometrial cancer risk in Europe. Eur J Epidemiol 2016; 31: 51-60.
  • 24 Pfeiffer RM, Park Y, Kreimer AR, et al. Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med 2013; 10: e1001492.
  • 25 Patel V, Wilkinson EJ, Chamala S, Lu X, Castagno J, Rush D. Endometrial Thickness as Measured by Transvaginal Ultrasound and the Corresponding Histopathologic Diagnosis in Women With Postmenopausal Bleeding. Int J Gynecol Pathol 2017; 36: 348-55.
  • 26 Gupta JK, Chien PFW, Voit D, Clark TJ, Khan KS. Ultrasonographic endometrial thickness for diagnosing endometrial pathology in women with postmenopausal bleeding: a meta-analysis. Acta Obstet Gynecol Scand 2002; 81: 799-16.
  • 27 Saccardi C, Vitagliano A, Marchetti M, et al. Endometrial Cancer Risk Prediction According to Indication of Diagnostic Hysterescopy in Post – menopausal Women. Diagnostics(Basel) 2020; 10: 257.
  • 28 Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014; 35: 1925-31.
  • 29 Boos DD, Stefanski LA. P-Value Precision and Reproducibility. Am Stat 2011; 65: 213–21.
  • 30 Young S. Everything is Dangerous: a Controversy. National Institute of Statistical Sciences. 2008. https://www.openphilanthropy.org/sites/default/files/Stanley%20Young%20slides%20on%20multiple%20testing%201.pdf (accessed 7 Dec, 2021).
  • 31 Hanegem N, Breijer MC, Slockers SA, et al. Diagnostic workup for postmenopausal bleeding: a randomised controlled trial. BJOG 2017; 124: 231–40.
  • 32 Obermair A, Geramou M, Gucer F, et al. Does hysteroscopy facilitate tumor cell dissemination? Incidence of peritoneal cytology from patients with early stage endometrial carcinoma following dilatation and curettage (D & C) versus hysteroscopy and D & C. Cancer 2000; 88: 139-43.
  • 33 Clarke MA, Long BJ, Morillo ADM, Arbyn M, Bakkum-Gamez JN, Wentzensen N. Association of Endometrial Cancer Risk With Postmepausal Bleeding in Women. A Systematic Rewiev and Meta-analiysis. JAMA intern Med 2018; 178: 1210-22.
  • 34 Shipe ME, Deppen SA, Farjah F, Grogan EL. Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis 2019; 11: 574–84.

Endometrium Kanserinde Bireyselleştirilmiş Risk Tahmin Modelinin Karar Eğrisi Analizinde Klinik Kullanılabilirliğinin Değerlendirilmesi

Year 2023, Volume: 23 Issue: 1, 9 - 24, 02.05.2023

Abstract

Endometrium Kanserinde Bireyselleştirilmiş Risk Tahmin Modelinin Karar Eğrisi Analizinde Klinik Kullanılabilirliğinin Değerlendirilmesi
Özet
Giriş: Anormal uterin kanaması olan kadınlar için bireyselleştirilmiş bir risk değerlendirme yöntemine (IRPM) ihtiyaç vardır. Anormal vajinal kanamanın yönetimi, pozitif/negatif prediktif değerlere sahip tanısal test sonuçlarına dayanır. Bunun hastada biyopsi için harekete geçip geçmeme eşiğinde katkısı yoktur. Bu çalışmanın amacı, hastanın karar sürecine katkıda bulunabilecek, ayırt etme yeteneğine sahip kalibre edilmiş bir model geliştirmektir.
Yöntem: Bu çalışma 35 yaşından büyük hastaların verilerini çıkarmak için kesitsel one gate bir kohort çalışması olarak planlanmıştır. Tüm hastalara indeks testi (endometriyal kalınlık ölçümü) ve referans testi (genel anestezi altında D&C) uygulanmış, WHO 2014'e göre bening veya premalign olarak histopatolojik raporlar ayrılmıştır.
Birincil amaç, yararlı bir klinik IRPM geliştirmek ve IRPM'nin net faydasını ve karar eğrisi analizinde çeşitli hastalık eşikleri için mevcut kılavuzları taklit eden modelleri karşılaştırmaktır.
İkincil amaç ise, ayırt edici özellikleri, gereksiz biyopsilerin sayısını ve çeşitli eşiklerde kaçırılan vakaları analiz etmektir.
Bulgular: Semptom durumu, endometriyal kalınlık ve yaştan oluşan IRPM, pre-/endometrium kanseri için en iyi risk öngörme yöntemi olarak bulunmuştur.
IRPM, klinik eşik olasılıklarının tüm aralığından biyopsi alma veya almama konusunda yönergelerden daha yüksek bir net faydaya sahiptir. IRPM gözden kaçan vakaların sayısını da azaltabilir. Mevcut yönergeleri taklit eden modellerin yalnızca %3'lük bir eşiğin üzerinde faydalı olduğu ve bu eşiğin altında zararlı olabileceği bulunmuştur. IRPM herhangi bir ek maliyete veya zaman alıcı analize ihtiyaç duymamaktadır.
Sonuç: IRPM herhangi bir ek maliyete veya zaman alıcı analize ihtiyaç duymaz. IRPM, hastanın daha ileri inceleme kararına katkıda bulunmasına yardımcı olabilir. IRPM'nin klinik kullanışlılığı, mevcut uygulamayı taklit eden modellerden daha üstündür. Heterojen bir hasta grubunda kılavuzların tanısal ayrımcı kesme değerleri beklenenden daha düşüktür.
Finansman: Finansman yok

References

  • References 1 Henley SJ, Miller JW, Dowling NF, Benard VB, Richardson LC. Uterin Cancer Incidence and mortality- United states, 1999-2016. Morbidity and Mortality Weekly Report 2018; 67: 1333-38.
  • 2 Pennant ME, Mehta R, Moody P, et al. Premenopausal abnormal uterine bleeding and risk of endometrial cancer. BJOG 2017; 124: 404-11.
  • 3 Emery J, Vedsted J. New NICE guidance on diagnosing cancer in general practice. Br J Gen Pract 2015; 65: 446-47.
  • 4 Ioannidis JP, Haidich AB, Pappa M, et al. Comparison of evidence of treatment effects in randomized and nonrandomized studies. JAMA 2001; 286: 821-30.
  • 5 Wong ASW, Lao TTH, Cheung CW, et al. Reappriasal of endometrial thickness for the detection of endometrial cancer in postmenapausal bleeding, a retrospective cohort study. BJOG 2016; 123: 439-46.
  • 6 Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 2015; 351: h5527.
  • 7 Initiative S. STROBE checklist for cohort, case- control, and cross-sectional studies. 2007. https://www.strobe-statement.org/download/strobe-checklist-wide-cohort-case-control-and-cross-sectional-studies-combined-pdf (accessed 17 Dec, 2021).
  • 8 Ellenson LH, Matias-Guiu X, Mutter GL. Endometrial hyperplasia without atypia. In Female Genital Tumours WHO Classification of Tumours Series, 5th Edition, Volume 4. IARC Press, Lyon, 2020; 248-49.
  • 9 Lax SF, Mutter GL. Endometrial atypical hyperplasia/endometrioid intraepithelial neoplasia. In Female Genital Tumours WHO Classification of Tumours Series, 5th Edition, Volume 4. IARC Press, Lyon, 2020; 250-51.
  • 10 Sobczuk K, Sobczuk A. New classification system of endometrial hyperplasia WHO 2014 and its clinical implications. Prz Menopauzalny 2017; 16: 107-11
  • 11 Bindman RS, Weiss E, Feldstein V. How thick is too thick? When endometrial thickness should prompt biopsy in postmenopausal women without vaginal bleeding. Ultrasound Obstet Gynecol 2004; 24: 558-65.
  • 12 Clarke MA, Long BJ, Sherman ME, et al. Risk Assessment of endometrial cancer and endometrial intraepithelial neoplasia in women with abnormal bleeding and implications for clinical management algorithms. Am J Obstet Gynecol 2020; 223: 549.
  • 13 Huang Y, Lİ W, Macheret F, Gabriel RA, Machado LO. A tutorial on calibration measurements and calibration models for clinical prediction models. Journal of the American Medical Informatics Association 2020; 27: 621-33.
  • 14 Calster BV, Vickers AJ. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Making 2015; 35: 162–9.
  • 15 Calster BV, Wynants L, Verbeek JFM, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. Eur Urol 2018; 74: 796-04.
  • 16 Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clinical Chemistry and Laboratory Medicine 2010; 48: 1703-11.
  • 17 Chawdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction modelling. Fam Med Com Health 2020; 8: e000262.
  • 18 Alblas M, Velt KB, Pashayan N, Widschwentter M, Steyerberg EW, Vergouwe Y. Prediction models for endometrial cancer for the general population or symptomatic women: A systematic review. Critical Reviews in Oncology/Hematology 2018; 126: 92-99.
  • 19 Angioli R, Capriglione S, Aloisi A, et al. REM (risk of endometrial malignancy): a proposal for a new scoring system to evaluate risk of endometrial malignancy. Clin Cancer Res 2013; 19: 5733-39.
  • 20 Madkour NM. An ultrasound risk-scoring model for prediction of endometrial cancer in post-menopausal women (using IETA terminology). Middle East Fertility Society Journal 2017; 22: 201-05.
  • 21 Opolskiene G, Sladkevicius P, Valentin L. Prediction of endometrial malignancy in women with postmenopausal bleeding and sonographic endometrial thickness ≥ 4.5 mm. Ultrasound in Obstetrics and Gynecology 2011; 37: 232-40.
  • 22 Giannella L , Mfuta K, Setti T, Cerami LB, Bergamini E, Boselli F. A Risk-Scoring Model for the Prediction of Endometrial Cancer among Symptomatic Postmenopausal Women with Endometrial Thickness > 4 mm. Biomed Res Int 2014; 2014: 130569.
  • 23 Husing A, Dossus L, Ferrari P, et al. An epidemiological model for prediction of endometrial cancer risk in Europe. Eur J Epidemiol 2016; 31: 51-60.
  • 24 Pfeiffer RM, Park Y, Kreimer AR, et al. Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med 2013; 10: e1001492.
  • 25 Patel V, Wilkinson EJ, Chamala S, Lu X, Castagno J, Rush D. Endometrial Thickness as Measured by Transvaginal Ultrasound and the Corresponding Histopathologic Diagnosis in Women With Postmenopausal Bleeding. Int J Gynecol Pathol 2017; 36: 348-55.
  • 26 Gupta JK, Chien PFW, Voit D, Clark TJ, Khan KS. Ultrasonographic endometrial thickness for diagnosing endometrial pathology in women with postmenopausal bleeding: a meta-analysis. Acta Obstet Gynecol Scand 2002; 81: 799-16.
  • 27 Saccardi C, Vitagliano A, Marchetti M, et al. Endometrial Cancer Risk Prediction According to Indication of Diagnostic Hysterescopy in Post – menopausal Women. Diagnostics(Basel) 2020; 10: 257.
  • 28 Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014; 35: 1925-31.
  • 29 Boos DD, Stefanski LA. P-Value Precision and Reproducibility. Am Stat 2011; 65: 213–21.
  • 30 Young S. Everything is Dangerous: a Controversy. National Institute of Statistical Sciences. 2008. https://www.openphilanthropy.org/sites/default/files/Stanley%20Young%20slides%20on%20multiple%20testing%201.pdf (accessed 7 Dec, 2021).
  • 31 Hanegem N, Breijer MC, Slockers SA, et al. Diagnostic workup for postmenopausal bleeding: a randomised controlled trial. BJOG 2017; 124: 231–40.
  • 32 Obermair A, Geramou M, Gucer F, et al. Does hysteroscopy facilitate tumor cell dissemination? Incidence of peritoneal cytology from patients with early stage endometrial carcinoma following dilatation and curettage (D & C) versus hysteroscopy and D & C. Cancer 2000; 88: 139-43.
  • 33 Clarke MA, Long BJ, Morillo ADM, Arbyn M, Bakkum-Gamez JN, Wentzensen N. Association of Endometrial Cancer Risk With Postmepausal Bleeding in Women. A Systematic Rewiev and Meta-analiysis. JAMA intern Med 2018; 178: 1210-22.
  • 34 Shipe ME, Deppen SA, Farjah F, Grogan EL. Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis 2019; 11: 574–84.
There are 34 citations in total.

Details

Primary Language English
Subjects Surgery
Journal Section Research Article
Authors

Oğuz Kaan Köksal

Evrim Erdemoğlu 0000-0002-5993-6968

Volkan Öztürk 0000-0002-4027-4597

Kemal Bozkurt

İlyas Turan

Early Pub Date April 30, 2023
Publication Date May 2, 2023
Submission Date January 24, 2023
Published in Issue Year 2023 Volume: 23 Issue: 1

Cite

APA Köksal, O. K., Erdemoğlu, E., Öztürk, V., Bozkurt, K., et al. (2023). Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer. Türk Jinekolojik Onkoloji Dergisi, 23(1), 9-24.
AMA Köksal OK, Erdemoğlu E, Öztürk V, Bozkurt K, Turan İ. Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer. TRSGO Dergisi. May 2023;23(1):9-24.
Chicago Köksal, Oğuz Kaan, Evrim Erdemoğlu, Volkan Öztürk, Kemal Bozkurt, and İlyas Turan. “Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer”. Türk Jinekolojik Onkoloji Dergisi 23, no. 1 (May 2023): 9-24.
EndNote Köksal OK, Erdemoğlu E, Öztürk V, Bozkurt K, Turan İ (May 1, 2023) Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer. Türk Jinekolojik Onkoloji Dergisi 23 1 9–24.
IEEE O. K. Köksal, E. Erdemoğlu, V. Öztürk, K. Bozkurt, and İ. Turan, “Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer”, TRSGO Dergisi, vol. 23, no. 1, pp. 9–24, 2023.
ISNAD Köksal, Oğuz Kaan et al. “Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer”. Türk Jinekolojik Onkoloji Dergisi 23/1 (May 2023), 9-24.
JAMA Köksal OK, Erdemoğlu E, Öztürk V, Bozkurt K, Turan İ. Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer. TRSGO Dergisi. 2023;23:9–24.
MLA Köksal, Oğuz Kaan et al. “Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer”. Türk Jinekolojik Onkoloji Dergisi, vol. 23, no. 1, 2023, pp. 9-24.
Vancouver Köksal OK, Erdemoğlu E, Öztürk V, Bozkurt K, Turan İ. Assessment of Clinical Utility in Decision Curve Analysis for an Individualized Risk Prediction Model of Endometrial Cancer. TRSGO Dergisi. 2023;23(1):9-24.