Ultrasound and Clinical Preoperative Characteristics for Discrimination Between Ovarian Metastatic Colorectal Cancer and Primary Ovarian Cancer: A Case-Control Study - Publication - Bridge of Knowledge

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Ultrasound and Clinical Preoperative Characteristics for Discrimination Between Ovarian Metastatic Colorectal Cancer and Primary Ovarian Cancer: A Case-Control Study

Abstract

The aim of this study was to describe the clinical and sonographic features of ovarian metastases originating from colorectal cancer (mCRC), and to discriminate mCRC from primary ovarian cancer (OC). We conducted a multi-institutional, retrospective study of consecutive patients with ovarian mCRC who had undergone ultrasound examination using the International Ovarian Tumor Analysis (IOTA) terminology, with the addition of evaluating signs of necrosis and abdominal staging. A control group included patients with primary OC. Clinical and ultrasound data, subjective assessment (SA), and an assessment of different neoplasias in the adnexa (ADNEX) model were evaluated. Fisher's exact and Student's t-tests, the area under the receiver-operating characteristic curve (AUC), and classification and regression trees (CART) were used to conduct statistical analyses. In total, 162 patients (81 with OC and 81 with ovarian mCRC) were included. None of the patients with OC had undergone chemotherapy for CRC in the past, compared with 40% of patients with ovarian mCRC (p < 0.001). The ovarian mCRC tumors were significantly larger, a necrosis sign was more frequently present, and tumors had an irregular wall or were fixed less frequently; ascites, omental cake, and carcinomatosis were less common in mCRC than in primary OC. In a subgroup of patients with ovarian mCRC who had not undergone treatment for CRC in anamnesis, tumors were larger, and had fewer papillations and more locules compared with primary OC. The highest AUC for the discrimination of ovarian mCRC from primary OC was for CART (0.768), followed by SA (0.735) and ADNEX calculated with CA-125 (0.680). Ovarian mCRC and primary OC can be distinguished based on patient anamnesis, ultrasound pattern recognition, a proposed decision tree model, and an ADNEX model with CA-125 levels.

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
Diagnostics no. 9, pages 1 - 18,
ISSN: 2075-4418
Language:
English
Publication year:
2019
Bibliographic description:
Stukan M., Alcazar J., Gębicki J., Epstein E., Liro M., Sufliarska A., Szubert S., Guerriero S., Braicu E., Szajewski M., Pietrzak-Stukan M., Fischerova D.: Ultrasound and Clinical Preoperative Characteristics for Discrimination Between Ovarian Metastatic Colorectal Cancer and Primary Ovarian Cancer: A Case-Control Study// Diagnostics -Vol. 9,iss. 4 (2019), s.1-18
DOI:
Digital Object Identifier (open in new tab) 10.3390/diagnostics9040210
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  1. GLOBOCAN. Colorectal cancer. Estimated incidence, mortality and prevalence worldwide in 2012. Available online: http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx?cancer=colorectal (accessed on 29 August 2018). open in new tab
  2. Network, N.C.C. Nccn Clinical Practice Guidelines in Oncology, Colon Cancer; Version 3.2018; National Comprehensive Cancer Network: Jenkintown, PA, USA, 2018. open in new tab
  3. Van Cutsem, E.; Cervantes, A.; Adam, R.; Sobrero, A.; Van Krieken, J.H.; Aderka, D.; Aranda Aguilar, E.; Bardelli, A.; Benson, A.; Bodoky, G.; et al. Esmo consensus guidelines for the management of patients with metastatic colorectal cancer. Ann. Oncol. 2016, 27, 1386-1422. [CrossRef] [PubMed] open in new tab
  4. Mori, Y.; Nyuya, A.; Yasui, K.; Toshima, T.; Kawai, T.; Taniguchi, F.; Kimura, K.; Inada, R.; Nishizaki, M.; Haraga, J.; et al. Clinical outcomes of women with ovarian metastases of colorectal cancer treated with oophorectomy with respect to their somatic mutation profiles. Oncotarget 2018, 9, 16477-16488. [CrossRef] [PubMed] open in new tab
  5. Sekine, K.; Hamaguchi, T.; Shoji, H.; Takashima, A.; Honma, Y.; Iwasa, S.; Kato, K.; Takahashi, K.; Kato, T.; Kanemitsu, Y.; et al. Retrospective analyses of systemic chemotherapy and cytoreductive surgery for patients with ovarian metastases from colorectal cancer: A single-center experience. Oncology 2018, 95, 220-228. [CrossRef] [PubMed] open in new tab
  6. Kuijpers, A.M.; Mehta, A.M.; Aalbers, A.G.; van Driel, W.J.; Boot, H.; Verwaal, V.J. Treatment of ovarian metastases of colorectal and appendiceal carcinoma in the era of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Eur. J. Surg. Oncol. 2014, 40, 937-942. [CrossRef] [PubMed] open in new tab
  7. Zikan, M.; Fischerova, D.; Pinkavova, I.; Dundr, P.; Cibula, D. Ultrasonographic appearance of metastatic non-gynecological pelvic tumors. Ultrasound Obstet. Gynecol. 2012, 39, 215-225. [CrossRef] [PubMed] open in new tab
  8. Testa, A.C.; Ferrandina, G.; Timmerman, D.; Savelli, L.; Ludovisi, M.; Van Holsbeke, C.; Malaggese, M.; Scambia, G.; Valentin, L. Imaging in gynecological disease (1): Ultrasound features of metastases in the ovaries differ depending on the origin of the primary tumor. Ultrasound Obstet. Gynecol. 2007, 29, 505-511. [CrossRef] [PubMed] open in new tab
  9. Guerriero, S.; Alcazar, J.L.; Pascual, M.A.; Ajossa, S.; Olartecoechea, B.; Hereter, L. Preoperative diagnosis of metastatic ovarian cancer is related to origin of primary tumor. Ultrasound Obstet. Gynecol. 2012, 39, 581-586. [CrossRef] [PubMed] open in new tab
  10. Epstein, E.; Van Calster, B.; Timmerman, D.; Nikman, S. Subjective ultrasound assessment, the adnex model and ultrasound-guided tru-cut biopsy to differentiate disseminated primary ovarian cancer from metastatic non-ovarian cancer. Ultrasound Obstet. Gynecol. 2016, 47, 110-116. [CrossRef] [PubMed] open in new tab
  11. Moro, F.; Pasciuto, T.; Djokovic, D.; Di Legge, A.; Granato, V.; Moruzzi, M.C.; Mancari, R.; Annoni, G.F.; Fischerova, D.; Franchi, D.; et al. Role of ca125/cea ratio and ultrasound parameters in identifying metastases to the ovaries in patients with multilocular and multilocular-solid ovarian masses. Ultrasound Obstet. Gynecol. 2018, 53, 116-123. [CrossRef] [PubMed] open in new tab
  12. Timmerman, D.; Valentin, L.; Bourne, T.H.; Collins, W.P.; Verrelst, H.; Vergote, I. International Ovarian Tumor Analysis, G. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: A consensus opinion from the international ovarian tumor analysis (iota) group. Ultrasound Obstet. Gynecol. 2000, 16, 500-505. [CrossRef] [PubMed] open in new tab
  13. Testa, A.C.; Ludovisi, M.; Mascilini, F.; Di Legge, A.; Malaggese, M.; Fagotti, A.; Fanfani, F.; Salerno, M.G.; Ercoli, A.; Scambia, G.; et al. Ultrasound evaluation of intra-abdominal sites of disease to predict likelihood of suboptimal cytoreduction in advanced ovarian cancer: A prospective study. Ultrasound Obstet. Gynecol. 2012, 39, 99-105. [CrossRef] [PubMed] open in new tab
  14. Van Calster, B.; Van Hoorde, K.; Valentin, L.; Testa, A.C.; Fischerova, D.; Van Holsbeke, C.; Savelli, L.; Franchi, D.; Epstein, E.; Kaijser, J.; 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. BMJ 2014, 349, g5920. [CrossRef] [PubMed] open in new tab
  15. Van Calster, B.; Vergouwe, Y.; Looman, C.W.; Van Belle, V.; Timmerman, D.; Steyerberg, E.W. Assessing the discriminative ability of risk models for more than two outcome categories. Eur. J. Epidemiol. 2012, 27, 761-770. [CrossRef] [PubMed] open in new tab
  16. Van Calster, B.; Van Hoorde, K.; Froyman, W.; Kaijser, J.; Wynants, L.; Landolfo, C.; Anthoulakis, C.; Vergote, I.; Bourne, T.; Timmerman, D. Practical guidance for applying the adnex model from the iota group to discriminate between different subtypes of adnexal tumors. Facts Views Vis. Obgyn. 2015, 7, 32-41. [PubMed]
  17. Breiman, L.; Friedman, J.; Stone, C.; Olshen, R. Classification and Regression Trees, 1st ed.; Chapman and Hall/CRC: New York, NJ, USA, 1984.
  18. Quinlan, J.R. Induction of decision trees. Mach. Learn. 1986, 1, 81-106. [CrossRef] open in new tab
  19. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning, 2 ed.; Springer: New York, NY, USA, 2009. open in new tab
  20. Chang, L.Y.; Wang, H.W. Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accid. Anal. Prev. 2006, 38, 1019-1027. [CrossRef] [PubMed] open in new tab
  21. Stanimirowa, I.; Daszykowski, M.; Walczak, B. Metody uczenia z nadzorem-Kalibracja, dyskryminacja i klasyfikacja [methods of learning with supervision-Calibration, discrimination and classification].
  22. In Chemometria w Analityce; Zuba, D., Parczewski, A., Eds.; Wydawnictwo Instytut Ekspertyz Sądowych: Kraków, Poland, 2008; pp. 34-36. open in new tab
  23. Ranganathan, P.; Pramesh, C.S.; Buyse, M. Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspect. Clin. Res. 2015, 6, 169-170. [CrossRef] [PubMed] open in new tab
  24. Steyerberg, E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, 1st ed.; Springer: New York, NY, USA, 2009. open in new tab
  25. Team, R. Rstudio: Integrated Development for R; 1.1.463; RStudio Inc.: Boston, MA, USA, 2015; Available online: http://www.Rstudio.Com/ (accessed on 19 April 2019). open in new tab
  26. Team, R.C. A language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2018; Available online: https://www.R-project.Org./ (accessed on 19 April 2019).
  27. Lunardon, N.; Menardi, G.; Torelli, N. Rose: A package for binary imbalanced learning. R J. 2014, 6, 79-89. [CrossRef] open in new tab
  28. Therneau, T.; Atkinson, B. Rpart: Recursive Partitioning and Regression Trees. R Package Version 4.1-13. 2018. Available online: https://cran.R-project.Org./package=rpart (accessed on 19 April 2019).
  29. Bossuyt, P.M.; Reitsma, J.B.; Bruns, D.E.; Gatsonis, C.A.; Glasziou, P.P.; Irwig, L.; Lijmer, J.G.; Moher, D.; Rennie, D.; de Vet, H.C.; et al. Stard 2015: An updated list of essential items for reporting diagnostic accuracy studies. Radiology 2015, 277, 826-832. [CrossRef] [PubMed] open in new tab
  30. Ledermann, J.A.; Raja, F.A.; Fotopoulou, C.; Gonzalez-Martin, A.; Colombo, N.; Sessa, C.; Group, E.G.W. Newly diagnosed and relapsed epithelial ovarian carcinoma: Esmo clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2013, 24, vi24-vi32. [CrossRef] [PubMed] open in new tab
  31. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). open in new tab
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