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  1. OncologyPRO
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  3. ESMO 2018 Congress

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

927 - Predictive models for CEUS of the breast: Is it feasible in improved performance of BI-RADS evaluation of critical breast lesions?

Date

20 Oct 2018

Session

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

Topics

Staging and Imaging

Tumour Site

Breast Cancer

Presenters

Luo Jun

Citation

Annals of Oncology (2018) 29 (suppl_8): viii479-viii482. 10.1093/annonc/mdy294

Authors

L. Jun1, Q. Chen2, L. Tang3, L. Yang4, E. Han5, Y. Chen6, L. Yuan7

Author affiliations

  • 1 Ultrasound Department, Sichuan Provincial People's Hospital, 610000 - Chengdu/CN
  • 2 Ultrasound Department, Sichuan Provincial People's Hospital, 86 - Chengdu/CN
  • 3 Ultrasound Department, Fujian Provincial Cancer Hospital, 8686 - Fuzhou/CN
  • 4 Ultrasound Department, Yunnan Tumor Hospital Third Affiliated Hospital of Kunming Medical University, 86 - Kunming/CN
  • 5 Ultrasound Department, Huangshi Central Hospital, 86 - Huangshi/CN
  • 6 Ultrasound Department, Chengdu First People's Hospital, 86 - Chengdu/CN
  • 7 Ultrasound Department, Tangdu Hospital, 86 - Xian/CN

Resources

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Abstract 927

Background

This prospective study is to determine whether predictive model for contrast-enhanced ultrasound (CEUS) of the breast can improve the precision of BI-RADS.

Methods

A total of 1060 breast lesions classified as BI-RADS 4 or 5 on ultrasound were evaluated. CEUS was performed before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on 6 predictive models for CEUS of malignant and benign breast lesions as follows: malignant predictive models : (1) hyper-enhancement with enlarged size; (2) hyper-enhancement with perfusion defect; (3) hyper- or iso-enhancement, present penetrating vessels or crab claw-like pattern. Benign predictive models: (4) rapid wash-in with hyper-enhancement, clear margin after enhancement without enlarged size; (5) synchronous or slow wash-in with iso-enhancement, and cannot distinguish margin and shape after enhancement; and (6) synchronous or slow wash-in with hypo-enhancement, with equal or smaller size after enhancement. To evaluate the diagnostic performance of CEUS-based BI-RADS assignment with pathological examination as reference criteria.

Results

The CEUS-based BI-RADS evaluation classified 287/1060 (27.08%) lesions into category 3, 195 (18.40%), 124 (11.7%) and 144 (13.58%) lesions into categories 4A, 4B and 4C, respectively, and 310 (29.24%) into category 5, compared with 423/1060(39.91%), 348(32.83%), 150(14.15%) and 139(13.11%) in BI-RADS 4A, 4B, 4C. and 5 based on conventional ultrasound and mammography. Selecting CEUS- based BI-RADS category 3 as an appropriate cut-off gave accuracy, sensitivity, specificity, positive and negative predictive values of 69.25%, 98.06%, 49.47%, 58.99% and 96.86%, respectively for the diagnosis of malignant disease. The cancer-to-biopsy yield was 60.16% with CEUS-based BI-RADS 3 selected as the biopsy threshold compared with 43.86% otherwise, while the biopsy rate was only 72.92% compared with 100% otherwise (Figure 2). Overall, only 1.94% of invasive cancers were misdiagnosed as BI-RADS 3 we use nowadays.

Conclusions

This study suggests that evaluation of BI-RADS 4 or 5 breast lesions with CEUS result in reduced biopsy rates and increased cancer-to-biopsy yields.

Clinical trial identification

2016-14-1 release date: 9/9/2016.

Legal entity responsible for the study

Ultrasound Department of Sichuan Provincial People's Hospital.

Funding

Has not received any funding.

Editorial Acknowledgement

Disclosure

All authors have declared no conflicts of interest.

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