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


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: 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:

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


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