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

ABSTRACT: 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.

SUBMITTER: Jamshidii N

PROVIDER: S-EPMC6953781 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Jamshidii Neema N Chang Jason J Mock Kyle K Nguyen Brian B Dauphine Christine C Kuo Michael D MD

PloS one 20200110 1

<h4>Purpose</h4>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.<h4>Materials and methods</h4>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 b ...[more]

PMID: 31923222

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Project description:To evaluate the positive predictive values (PPVs) of Breast Imaging and Reporting Data Systems (BI-RADS) assessment categories for breast magnetic resonance (MR) imaging and to identify the BI-RADS MR imaging lesion features most predictive of cancer.This institutional review board-approved HIPAA-compliant prospective multicenter study was performed with written informed consent. Breast MR imaging studies of the contralateral breast in women with a recent diagnosis of breast cancer were prospectively evaluated. Contralateral breast MR imaging BI-RADS assessment categories, morphologic descriptors for foci, masses, non-masslike enhancement (NMLE), and kinetic features were assessed for predictive values for malignancy. PPV of each imaging characteristic of interest was estimated, and logistic regression analysis was used to examine the predictive ability of combinations of characteristics.Of 969 participants, 71.3% had a BI-RADS category 1 or 2 assessment; 10.9%, a BI-RADS category 3 assessment; 10.0%, a BI-RADS category 4 or 5 assessment; and 7.7%, a BI-RADS category 0 assessment on the basis of initial MR images. Thirty-one cancers were detected with MR imaging. Overall PPV for BI-RADS category 4 and 5 lesions was 0.278, with 17 cancers in patients with a BI-RADS category 4 lesion (PPV, 0.205) and 10 cancers in patients with a BI-RADS category 5 lesion (PPV, 0.714). Of the cancers, one was a focus, 17 were masses, and 13 were NMLEs. For masses, irregular shape, irregular margins, spiculated margins, and marked internal enhancement were most predictive of malignancy. For NMLEs, ductal, clumped, and reticular or dendritic enhancement were the features most frequently seen with malignancy. Kinetic enhancement features were less predictive of malignancy than were morphologic features.Standardized terminology of the BI-RADS lexicon enables quantification of the likelihood of malignancy for MR imaging-detected lesions through careful evaluation of lesion features. In particular, BI-RADS assessment categories and morphologic descriptors for masses and NMLE were useful in estimating the probability of cancer.

Project description:Qualification tasks in mammography and breast ultrasound were developed for the American College of Radiology Imaging Network (ACRIN) 6666 Investigators. We sought to assess the effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS feature analysis among a subset of these experienced observers.After a 1-hour didactic session on BI-RADS: Ultrasound, an interpretive skills quiz set of 70 orthogonal sets of breast ultrasound images including 25 (36%) malignancies was presented to 100 experienced breast imaging observers. Thirty-five observers reviewed the quiz set twice: first without and then with immediate feedback of consensus feature analysis, management recommendations, and pathologic truth. Observer performance (sensitivity, specificity, area under the curve [AUC]) was calculated without feedback and with feedback. Kappas were determined for agreement on feature analysis and assessments.For 35 observers without feedback, the mean sensitivity was 89% (range, 68-100%); specificity, 62% (range, 42-82%); and AUC, 82% (range, 73-89%). With feedback, the mean sensitivity was 93% (range, 80-100%; mean increase, 4%; range of increase, 0-12%; p < 0.0001), the mean specificity was 61% (range, 45-73%; mean decrease, 1%; range of change, -18% to 11%; p = 0.19), and the mean AUC was 84% (range, 78-90%; mean increase, 2%; range of change, -3% to 9%; p < 0.0001). Three breast imagers in the lowest quartile of initial performance showed the greatest improvement in sensitivity with no change or improvement in AUC. The kappa values for feature analysis did not change, but there was improved agreement about final assessments, with the kappa value increasing from 0.53 (SE, 0.02) without feedback to 0.59 (SE, 0.02) with feedback (p < 0.0001).Most experienced breast imagers showed excellent breast ultrasound interpretive skills. Immediate feedback of consensus BI-RADS: Ultrasound features and histopathologic results improved performance in ultrasound interpretation across all experience variables.

Project description:We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision-making towards short-interval follow-up versus tissue sampling. From a retrospective 2015-2019 series of ultrasound-guided core needle biopsies performed by four board-certified breast radiologists using six ultrasound systems from three vendors, we collected 821 images of 834 suspicious breast masses from 819 patients, 404 malignant and 430 benign according to histopathology. A balanced image set of biopsy-proven benign (n = 299) and malignant (n = 299) lesions was used for training and cross-validation of ensembles of machine learning algorithms supervised during learning by histopathological diagnosis as a reference standard. Based on a majority vote (over 80% of the votes to have a valid prediction of benign lesion), an ensemble of support vector machines showed an ability to reduce the biopsy rate of benign lesions by 15% to 18%, always keeping a sensitivity over 94%, when externally tested on 236 images from two image sets: (1) 123 lesions (51 malignant and 72 benign) obtained from two ultrasound systems used for training and from a different one, resulting in a positive predictive value (PPV) of 45.9% (95% confidence interval 36.3-55.7%) versus a radiologists' PPV of 41.5% (p < 0.005), combined with a 98.0% sensitivity (89.6-99.9%); (2) 113 lesions (54 malignant and 59 benign) obtained from two ultrasound systems from vendors different from those used for training, resulting into a 50.5% PPV (40.4-60.6%) versus a radiologists' PPV of 47.8% (p < 0.005), combined with a 94.4% sensitivity (84.6-98.8%). Errors in BI-RADS 3 category (i.e., assigned by the model as BI-RADS 4) were 0.8% and 2.7% in the Testing set I and II, respectively. The board-certified breast radiologist accepted the BI-RADS classes assigned by the model in 114 masses (92.7%) and modified the BI-RADS classes of 9 breast masses (7.3%). In six of nine cases, the model performed better than the radiologist did, since it assigned a BI-RADS 3 classification to histopathology-confirmed benign masses that were classified as BI-RADS 4 by the radiologist.

Project description:The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.

Project description:Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P?=?0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

Project description:BackgroundA subset of patients undergoing thyroid surgery for presumed benign thyroid disease presented with papillary thyroid microcarcinoma (PTMC). A non-invasive and precise method for early recognition of PTMC are urgently needed. The aim of this study was to construct and validate a nomogram that combines intratumoral and peritumoral radiomics features as well as clinical features for predicting PTMC in the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) 3 nodules using ultrasonography.MethodsA retrospective review was conducted on a cohort of 221 patients who presented with ACR TI-RADS 3 nodules. These patients were subsequently pathologically diagnosed with either PTMC or benign thyroid nodules. These patients were randomly divided into a training and test cohort with an 8:2 ratio for developing the clinical model, intratumor-region model, peritumor-region model and the combined-region model respectively. The radiomics features were extracted from ultrasound (US) images of each patient. We employed K-nearest neighbor (KNN) model as the base model for building the radiomics signature and clinical signature. Finally, a radiomics-clinical nomogram that combined intratumoral and peritumoral radiomics features as well as clinical features was developed. The prediction performance of each model was assessed by the area under the curve (AUC), sensitivity, specificity and calibration curve.ResultsA total of 23 radiomics features were selected to develop radiomics models. The combined-region radiomics model showed favorable prediction efficiency in both the training dataset (AUC: 0.955) and the test dataset (AUC: 0.923). A radiomics-clinical nomogram was constructed and achieved excellent calibration and discrimination, which yielded an AUC value of 0.950, a sensitivity of 0.950 and a specificity of 0.920.ConclusionsThis study proposed the nomogram that contributes to the accurate and intuitive identification of PTMC in ACR TI-RADS 3 nodules.

Dataset Information

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

Publications

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

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