Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests

English version

Autoři: Neema Jamshidii ^aff001; Jason Chang ^aff002; Kyle Mock ^aff003; Brian Nguyen ^aff003; Christine Dauphine ^aff003; Michael D. Kuo ^aff004
Působiště autorů: UCLA Department of Radiological Sciences, Los Angeles, CA, United States of America ^aff001; UCLA David Geffen School of Medicine, Los Angeles, CA, United States of America ^aff002; Harbor-UCLA Medical Center, Department of Surgery, Los Angeles, CA, United States of America ^aff003; Department of Radiology, The University of Hong Kong, Hong Kong, China ^aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226634

Souhrn

Purpose

The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer.

Materials and methods

This retrospective, single-institution analysis of 219 patients involved two cohorts using one of two FDA approved transcriptome-based tests that were performed as part of the clinical care of breast cancer patients at Harbor-UCLA Medical Center between April 2008 and January 2013. BI-RADS descriptive terminology was collected from the corresponding ultrasound reports for each patient in conjunction with transcriptomic test results. Recursive partitioning and regression trees were used to test and validate classification of the two cohorts.

Results

The area under the curve (AUC) of the receiver operator curves (ROC) for the regression classifier between the two FDA approved tests and ultrasound features were 0.77 and 0.65, respectively; they employed the ‘margins’, ‘retrotumoral’, and ‘internal echoes’ feature descriptors. Notably, the ‘retrotumoral’ and mass ‘margins’ features were used in both classification trees. The identification of sonographic correlates of gene tests provides added value to the ultrasound exam without incurring additional procedures or testing.

Conclusions

The predictive capability using structured language from diagnostic ultrasound reports (BI-RADS) was moderate for the two tests, and provides added value from ultrasound imaging without incurring any additional costs. Incorporation of additional measures, such as ultrasound contrast enhancement, with validation in larger, prospective studies may further substantiate these results and potentially demonstrate even greater predictive utility.

Klíčová slova:

Cancer detection and diagnosis – Transcriptome analysis – Ultrasound imaging – Surgical oncology – Language – Decision trees – Histology – Breast cancer

Zdroje

1. SEER Program (National Cancer Institute (U.S.)), National Center for Health Statistics (U.S.), National Cancer Institute (U.S.). Surveillance Program., National Cancer Institute (U.S.). Cancer Statistics Branch., National Cancer Institute (U.S.). Cancer Control Research Program. SEER cancer statistics review. NIH publication. Bethesda, Md.: U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Cancer Institute; 1993. p. volumes.

2. Henley SJ, Anderson RN, Thomas CC, Massetti GM, Peaker B, Richardson LC. Invasive Cancer Incidence, 2004–2013, and Deaths, 2006–2015, in Nonmetropolitan and Metropolitan Counties—United States. Morbidity and mortality weekly report Surveillance summaries. 2017;66(14):1–13. doi: 10.15585/mmwr.ss6614a1 28683054.

3. Devolli-Disha E, Manxhuka-Kerliu S, Ymeri H, Kutllovci A. Comparative accuracy of mammography and ultrasound in women with breast symptoms according to age and breast density. Bosnian journal of basic medical sciences. 2009;9(2):131–6. doi: 10.17305/bjbms.2009.2832 19485945.

4. Hooley RJ, Greenberg KL, Stackhouse RM, Geisel JL, Butler RS, Philpotts LE. Screening US in patients with mammographically dense breasts: initial experience with Connecticut Public Act 09–41. Radiology. 2012;265(1):59–69. doi: 10.1148/radiol.12120621 22723501.

5. Kim SY, Kim MJ, Moon HJ, Yoon JH, Kim EK. Application of the downgrade criteria to supplemental screening ultrasound for women with negative mammography but dense breasts. Medicine (Baltimore). 2016;95(44):e5279. doi: 10.1097/MD.0000000000005279 27858896; PubMed Central PMCID: PMC5591144.

6. Beumer I, Witteveen A, Delahaye L, Wehkamp D, Snel M, Dreezen C, et al. Equivalence of MammaPrint array types in clinical trials and diagnostics. Breast cancer research and treatment. 2016;156(2):279–87. doi: 10.1007/s10549-016-3764-5 27002507; PubMed Central PMCID: PMC4819553.

7. Cardoso F, van't Veer LJ, Bogaerts J, Slaets L, Viale G, Delaloge S, et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. The New England journal of medicine. 2016;375(8):717–29. doi: 10.1056/NEJMoa1602253 27557300.

8. Mercado CL. BI-RADS update. Radiologic clinics of North America. 2014;52(3):481–7. doi: 10.1016/j.rcl.2014.02.008 24792650.

9. Orel SG, Kay N, Reynolds C, Sullivan DC. BI-RADS categorization as a predictor of malignancy. Radiology. 1999;211(3):845–50. doi: 10.1148/radiology.211.3.r99jn31845 10352614.

10. Rowe SP, Pienta KJ, Pomper MG, Gorin MA. Proposal of a Structured Reporting System for Prostate-Specific Membrane Antigen (PSMA)-Targeted PET Imaging: PSMA-RADS Version 1.0. Journal of nuclear medicine: official publication, Society of Nuclear Medicine. 2017. doi: 10.2967/jnumed.117.195255 28887401.

11. Mitchell DG, Bruix J, Sherman M, Sirlin CB. LI-RADS (Liver Imaging Reporting and Data System): summary, discussion, and consensus of the LI-RADS Management Working Group and future directions. Hepatology. 2015;61(3):1056–65. doi: 10.1002/hep.27304 25041904.

12. McKee BJ, Regis SM, McKee AB, Flacke S, Wald C. Performance of ACR Lung-RADS in a clinical CT lung screening program. Journal of the American College of Radiology: JACR. 2015;12(3):273–6. doi: 10.1016/j.jacr.2014.08.004 25176499.

13. Granata V, Fusco R, Avallone A, Filice F, Tatangelo F, Piccirillo M, et al. Critical analysis of the major and ancillary imaging features of LI-RADS on 127 proven HCCs evaluated with functional and morphological MRI: Lights and shadows. Oncotarget. 2017;8(31):51224–37. doi: 10.18632/oncotarget.17227 28881643; PubMed Central PMCID: PMC5584244.

14. Sippo DA, Warden GI, Andriole KP, Lacson R, Ikuta I, Birdwell RL, et al. Automated extraction of BI-RADS final assessment categories from radiology reports with natural language processing. J Digit Imaging. 2013;26(5):989–94. doi: 10.1007/s10278-013-9616-5 23868515; PubMed Central PMCID: PMC3782591.

15. Bozkurt S, Gimenez F, Burnside ES, Gulkesen KH, Rubin DL. Using automatically extracted information from mammography reports for decision-support. J Biomed Inform. 2016;62:224–31. doi: 10.1016/j.jbi.2016.07.001 27388877; PubMed Central PMCID: PMC5108519.

16. Kim EK, Ko KH, Oh KK, Kwak JY, You JK, Kim MJ, et al. Clinical application of the BI-RADS final assessment to breast sonography in conjunction with mammography. AJR American journal of roentgenology. 2008;190(5):1209–15. doi: 10.2214/AJR.07.3259 18430833.

17. Yamamoto S, Han W, Kim Y, Du L, Jamshidi N, Huang D, et al. Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis. Radiology. 2015:142698. Epub 2015/03/04. doi: 10.1148/radiol.15142698 25734557.

18. Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007;25(6):675–80. Epub 2007/05/23. nbt1306 [pii] doi: 10.1038/nbt1306 17515910.

19. Jamshidi N, Jonasch E, Zapala M, Korn RL, Aganovic L, Zhao H, et al. The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma. Radiology. 2015;277(1):114–23. doi: 10.1148/radiol.2015150800 26402495.

20. Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results. Radiology. 2012;264(2):387–96. Epub 2012/06/23. doi: 10.1148/radiol.12111607 radiol.12111607 [pii]. 22723499; PubMed Central PMCID: PMC3401348.

21. Flanagan MB, Dabbs DJ, Brufsky AM, Beriwal S, Bhargava R. Histopathologic variables predict Oncotype DX recurrence score. Mod Pathol. 2008;21(10):1255–61. doi: 10.1038/modpathol.2008.54 18360352.

22. Kao KJ, Chang KM, Hsu HC, Huang AT. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization. BMC cancer. 2011;11:143. doi: 10.1186/1471-2407-11-143 21501481; PubMed Central PMCID: PMC3094326.

23. Burnside ES, Sickles EA, Bassett LW, Rubin DL, Lee CH, Ikeda DM, et al. The ACR BI-RADS experience: learning from history. Journal of the American College of Radiology: JACR. 2009;6(12):851–60. doi: 10.1016/j.jacr.2009.07.023 19945040; PubMed Central PMCID: PMC3099247.

24. Woodard GA, Ray KM, Joe BN, Price ER. Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features. Radiology. 2018;286(1):60–70. doi: 10.1148/radiol.2017162333 28885890.

25. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, et al. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology. 2016;281(2):382–91. doi: 10.1148/radiol.2016152110 27144536; PubMed Central PMCID: PMC5069147.

26. Raldow AC, Sher D, Chen AB, Recht A, Punglia RS. Cost Effectiveness of the Oncotype DX DCIS Score for Guiding Treatment of Patients With Ductal Carcinoma In Situ. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2016;34(33):3963–8. doi: 10.1200/JCO.2016.67.8532 27621393.

27. Plans IFoH. International Federation of Health Plans 2015 [cited 2018]. Available from: http://www.ifhp.com/.

28. New Choice Health I. NewChoiceHealth.com [cited 2018]. Available from: https://www.newchoicehealth.com/.

29. Groenewoud JH, Pijnappel RM, van den Akker-Van Marle ME, Birnie E, Buijs-van der Woude T, Mali WP, et al. Cost-effectiveness of stereotactic large-core needle biopsy for nonpalpable breast lesions compared to open-breast biopsy. British journal of cancer. 2004;90(2):383–92. doi: 10.1038/sj.bjc.6601520 14735181; PubMed Central PMCID: PMC2409541.

30. Han Z, Wu X, Roelle S, Chen C, Schiemann WP, Lu ZR. Targeted gadofullerene for sensitive magnetic resonance imaging and risk-stratification of breast cancer. Nature communications. 2017;8(1):692. doi: 10.1038/s41467-017-00741-y 28947734; PubMed Central PMCID: PMC5612990.

31. Sgroi DC, Chapman JA, Badovinac-Crnjevic T, Zarella E, Binns S, Zhang Y, et al. Assessment of the prognostic and predictive utility of the Breast Cancer Index (BCI): an NCIC CTG MA.14 study. Breast cancer research: BCR. 2016;18(1):1. doi: 10.1186/s13058-015-0660-6 26728744; PubMed Central PMCID: PMC4700696.

32. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2009;27(8):1160–7. doi: 10.1200/JCO.2008.18.1370 19204204; PubMed Central PMCID: PMC2667820.

33. Muller BM, Keil E, Lehmann A, Winzer KJ, Richter-Ehrenstein C, Prinzler J, et al. The EndoPredict Gene-Expression Assay in Clinical Practice—Performance and Impact on Clinical Decisions. PloS one. 2013;8(6):e68252. doi: 10.1371/journal.pone.0068252 23826382; PubMed Central PMCID: PMC3694878.

34. Bartlett JM, Thomas J, Ross DT, Seitz RS, Ring BZ, Beck RA, et al. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Breast cancer research: BCR. 2010;12(4):R47. doi: 10.1186/bcr2604 20615243; PubMed Central PMCID: PMC2949634.