Predicting the Molecular Subtypes of Breast Cancer Using Nomograms Based on Three-dimensional Ultrasonography Characteristics

Overview

Journal Front Oncol

Specialty Oncology

Date 2022 Sep 5

PMID 36059623

Authors

Xiaojing Xu

Liren Lu

Luoxi Zhu

Yanjuan Tan

Lifang Yu

LingYun Bao

Affiliations

Soon will be listed here.

Abstract

Background: Molecular subtyping of breast cancer is commonly doneforindividualzed cancer management because it may determines prognosis and treatment. Therefore, preoperativelyidentifying different molecular subtypes of breast cancery can be significant in clinical practice.Thisretrospective study aimed to investigate characteristic three-dimensional ultrasonographic imaging parameters of breast cancer that are associated with the molecular subtypes and establish nomograms to predict the molecular subtypes of breast cancers.

Methods: A total of 309 patients diagnosed with breast cancer between January 2017and December 2019 were enrolled. Sonographic features were compared between the different molecular subtypes. A multinomial logistic regression model was developed, and nomograms were constructed based on this model.

Results: The performance of the nomograms was evaluated in terms of discrimination and calibration.Variables such as maximum diameter, irregular shape, non-parallel growth, heterogeneous internal echo, enhanced posterior echo, lymph node metastasis, retraction phenomenon, calcification, and elasticity score were entered into the multinomial model.Three nomograms were constructed to visualize the final model. The probabilities of the different molecular subtypes could be calculated based on these nomograms. Based on the receiver operating characteristic curves of the model, the macro-and micro-areaunder the curve (AUC) were0.744, and 0.787. The AUC was 0.759, 0.683, 0.747 and 0.785 for luminal A(LA), luminal B(LB), human epidermal growth factor receptor 2-positive(HER2), and triple-negative(TN), respectively.The nomograms for the LA, HER2, and TN subtypes provided good calibration.

Conclusions: Sonographic features such as calcification and posterior acoustic features were significantly associated with the molecular subtype of breast cancer. The presence of the retraction phenomenon was the most important predictor for the LA subtype. Nomograms to predict the molecular subtype were established, and the calibration curves and receiver operating characteristic curves proved that the models had good performance.

Citing Articles

Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice.

Lopez-Gonzalez L, Cendra A, Cendra C, Cervantes E, Espinosa J, Pekarek T Medicina (Kaunas). 2024; 60(1).

PMID: 38256428 PMC: 10819101. DOI: 10.3390/medicina60010168.

References

Tang G, An X, Xiang H, Liu L, Li A, Lin X . Automated Breast Ultrasound: Interobserver Agreement, Diagnostic Value, and Associated Clinical Factors of Coronal-Plane Image Features. Korean J Radiol. 2020; 21(5):550-560. PMC: 7183827. DOI: 10.3348/kjr.2019.0525. View

Barr R, Nakashima K, Amy D, Cosgrove D, Farrokh A, Schafer F . WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 2: breast. Ultrasound Med Biol. 2015; 41(5):1148-60. DOI: 10.1016/j.ultrasmedbio.2015.03.008. View

Coates A, Winer E, Goldhirsch A, Gelber R, Gnant M, Piccart-Gebhart M . Tailoring therapies--improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann Oncol. 2015; 26(8):1533-46. PMC: 4511219. DOI: 10.1093/annonc/mdv221. View

Rella R, Belli P, Giuliani M, Bufi E, Carlino G, Rinaldi P . Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use. Acad Radiol. 2018; 25(11):1457-1470. DOI: 10.1016/j.acra.2018.02.014. View

. Comprehensive molecular portraits of human breast tumours. Nature. 2012; 490(7418):61-70. PMC: 3465532. DOI: 10.1038/nature11412. View

Huang A, Zhu L, Tan Y, Liu J, Xiang J, Zhu Q . Evaluation of automated breast volume scanner for breast conservation surgery in ductal carcinoma . Oncol Lett. 2016; 12(4):2481-2484. PMC: 5038336. DOI: 10.3892/ol.2016.4924. View

Li N, Jiang Y, Zhu Q, Zhang J, Dai Q, Liu H . Accuracy of an automated breast volume ultrasound system for assessment of the pre-operative extent of pure ductal carcinoma in situ: comparison with a conventional handheld ultrasound examination. Ultrasound Med Biol. 2013; 39(12):2255-63. DOI: 10.1016/j.ultrasmedbio.2013.07.010. View

Choi W, Cha J, Kim H, Shin H, Chae E . The Accuracy of Breast MR Imaging for Measuring the Size of a Breast Cancer: Analysis of the Histopathologic Factors. Clin Breast Cancer. 2016; 16(6):e145-e152. DOI: 10.1016/j.clbc.2016.07.007. View

Iasonos A, Schrag D, Raj G, Panageas K . How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008; 26(8):1364-70. DOI: 10.1200/JCO.2007.12.9791. View

10.

Cho N . Molecular subtypes and imaging phenotypes of breast cancer. Ultrasonography. 2016; 35(4):281-8. PMC: 5040136. DOI: 10.14366/usg.16030. View

11.

Garcia Fernandez A, Chabrera C, Garcia Font M, Fraile M, Lain J, Gonzalez S . Differential patterns of recurrence and specific survival between luminal A and luminal B breast cancer according to recent changes in the 2013 St Gallen immunohistochemical classification. Clin Transl Oncol. 2014; 17(3):238-46. DOI: 10.1007/s12094-014-1220-8. View

12.

Ardoino I, Lanzoni M, Marano G, Boracchi P, Sagrini E, Gianstefani A . Widen NomoGram for multinomial logistic regression: an application to staging liver fibrosis in chronic hepatitis C patients. Stat Methods Med Res. 2014; 26(2):823-838. DOI: 10.1177/0962280214560045. View

13.

van Zelst J, Balkenhol M, Tan T, Rutten M, Imhof-Tas M, Bult P . Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound. Ultrasound Med Biol. 2017; 43(9):1820-1828. DOI: 10.1016/j.ultrasmedbio.2017.03.019. View

14.

Lo C, Shen Y, Huang C, Chang R . Computer-aided multiview tumor detection for automated whole breast ultrasound. Ultrason Imaging. 2013; 36(1):3-17. DOI: 10.1177/0161734613507240. View

15.

Celebi F, Pilanci K, Ordu C, Agacayak F, Alco G, Ilgun S . The role of ultrasonographic findings to predict molecular subtype, histologic grade, and hormone receptor status of breast cancer. Diagn Interv Radiol. 2015; 21(6):448-53. PMC: 4622390. DOI: 10.5152/dir.2015.14515. View

16.

Zheng F, Yan L, Huang B, Xia H, Wang X, Lu Q . Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner. Eur J Radiol. 2015; 84(11):2123-9. DOI: 10.1016/j.ejrad.2015.07.028. View

17.

Vourtsis A, Kachulis A . The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women. Eur Radiol. 2017; 28(2):592-601. DOI: 10.1007/s00330-017-5011-9. View

18.

Sprague B, Stout N, Schechter C, van Ravesteyn N, Cevik M, Alagoz O . Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann Intern Med. 2014; 162(3):157-66. PMC: 4314343. DOI: 10.7326/M14-0692. View

19.

Kim S, Kim H, Moon W . Automated Breast Ultrasound Screening for Dense Breasts. Korean J Radiol. 2020; 21(1):15-24. PMC: 6960307. DOI: 10.3348/kjr.2019.0176. View

20.

Ng C, Schultheis A, Bidard F, Weigelt B, Reis-Filho J . Breast cancer genomics from microarrays to massively parallel sequencing: paradigms and new insights. J Natl Cancer Inst. 2015; 107(5). DOI: 10.1093/jnci/djv015. View