Reducing false-positive biopsies: a pilot study to reduce benign biopsy rates for BI-RADS 4A/B assessments through testing risk stratification and new thresholds for intervention

Factors Associated with False Positive Breast Cancer Results in the Real-Time Sonoelastography Evaluation of Solid Breast Lesions

Background and Objectives: Breast cancer is one of the most widespread cancers among the female population around the world and is curable if diagnosed in an early stage. Consequently, breast cancer screening imaging techniques have greatly evolved and adjusted over the last decades. Alongside mammography, sonoelastography became an important tool for breast cancer detection. However, sonoelastography still has its limitations, namely, there is still a high occurrence of false positive results in the BIRADS 4 category. The aim of our study is to identify potential false positive predictors and to ascertain the factors influencing the quality of strain ultrasound elastography for the evaluation of suspicious solid breast lesions categorized as BIRADS 4B, 4C, and 5. Materials and Methods: We conducted a retrospective study in a single private medical center in Timisoara between January 2017 and January 2022 analyzing 1625 solid breast lesions by the sonoelastography strain using a standardized BIRADS-US lexicon. Results: Our study showed that most sonoelastography factors linked to incorrect and overdiagnosis were due to a nodule dimension (OR = 1.02 per unit increase), posterior acoustic shadowing (OR = 12.26), reactive adenopathy (OR = 6.35), and an increased TES score (TES3 OR = 6.60; TES4 OR = 23.02; TES5 OR = 108.24). Regarding patient characteristics, age (OR = 1.09 per unit increase), BMI, (OR = 1.09 per unit increase), and breastfeeding history (OR = 3.00) were observed to increase the likelihood of false positive results. On the other hand, the nodules less likely to be part of the false positive group exhibited the following characteristics: a regular shape (OR = 0.27), homogenous consistency (OR = 0.42), and avascularity (OR = 0.22). Conclusions: Older age, high BMI, patients with a breastfeeding history, and those who exhibit the following specific nodule characteristics were most often linked to false positive results: large tumors with posterior acoustic shadowing and high elasticity scores, accompanied by reactive adenopathy. On the other hand, homogenous, avascular nodules with regular shapes were less likely to be misdiagnosed.

Can Short-term Follow-up with Ultrasound be Offered as an Acceptable Alternative to Immediate Biopsy or Surgery for Patients with First Ultrasound Diagnosis of BI-RADS 4A Lesions?

OBJECTIVES

To evaluate the relevant factors associated with malignancy in Breast Imaging Reporting and Data System (BI-RADS) 4A and to determine whether it was possible to establish a safe follow-up guideline for lower-risk 4A lesions.

METHODS

In this retrospective study, patients categorized as BI-RADS 4A on ultrasound who underwent ultrasound-guided biopsy or/and surgery between June 2014 and April 2020 was analyzed. Classification-tree method and cox regression analysis were used to explore the possible correlation factors of malignancy.

RESULTS

Among 9965 patients enrolled, 1211 (mean age, 44.3 ± 13.5 years; range, 18-91 years) patients categorized as BI-RADS 4A were eligible. The result of cox regression analysis revealed the malignant rate was only associated with patient age (hazard ratio (HR) = 1.038, p < 0.001, 95% confidence interval (CI): 1.029-1.048) and the mediolateral diameter of the lesion (HR = 1.261, p < 0.001, 95% CI: 1.159-1.372). The malignant rate for patients (≤ 36 y) with BI-RADS 4A lesions (the mediolateral diameter ≤ 0.9 cm) was 0.0% (0/72). This subgroup included fibrocystic disease and adenosis in 39 patients (54.2%), fibroadenoma in 16 (22.2%), intraductal papilloma in 8 (11.1%), inflammatory lesions in 6 (8.3%), cyst in 2 (2.8%), and hamartoma in 1 (1.4%).

CONCLUSIONS

Patient age and lesion size are associated with the rate of malignancy in BI-RADS 4A. For patients with lower-risk BI-RADS 4A lesions (≤ 2% likelihood of malignancy), short-term follow-up with ultrasound may be offered as an acceptable alternative to immediate biopsy or surgery.

Potential of Lesion-to-Fat Elasticity Ratio Measured by Shear Wave Elastography to Reduce Benign Biopsies in BI-RADS 4 Breast Lesions

OBJECTIVES

We evaluated whether lesion-to-fat ratio measured by shear wave elastography in patients with Breast Imaging Reporting and Data System (BI-RADS) 3 or 4 lesions has the potential to further refine the assessment of B-mode ultrasound alone in breast cancer diagnostics.

METHODS

This was a secondary analysis of an international diagnostic multicenter trial (NCT02638935). Data from 1288 women with breast lesions categorized as BI-RADS 3 and 4a-c by conventional B-mode ultrasound were analyzed, whereby the focus was placed on differentiating lesions categorized as BI-RADS 3 and BI-RADS 4a. All women underwent shear wave elastography and histopathologic evaluation functioning as reference standard. Reduction of benign biopsies as well as the number of missed malignancies after reclassification using lesion-to-fat ratio measured by shear wave elastography were evaluated.

RESULTS

Breast cancer was diagnosed in 368 (28.6%) of 1288 lesions. The assessment with conventional B-mode ultrasound resulted in 53.8% (495 of 1288) pathologically benign lesions categorized as BI-RADS 4 and therefore false positives as well as in 1.39% (6 of 431) undetected malignancies categorized as BI-RADS 3. Additional lesion-to-fat ratio in BI-RADS 4a lesions with a cutoff value of 1.85 resulted in 30.11% biopsies of benign lesions which correspond to a reduction of 44.04% of false positives.

CONCLUSIONS

Adding lesion-to-fat ratio measured by shear wave elastography to conventional B-mode ultrasound in BI-RADS 4a breast lesions could help reduce the number of benign biopsies by 44.04%. At the same time, however, 1.98% of malignancies were missed, which would still be in line with American College of Radiology BI-RADS 3 definition of <2% of undetected malignancies.

Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

Background: To compare the diagnostic efficacy of digital breast tomosynthesis (DBT) and ultrasound across breast densities in women recalled for assessment. Methods: A total of 482 women recalled for assessment from January 2017 to December 2019 were selected for the study. Women met the inclusion criteria if they had undergone DBT, ultrasound and had confirmed biopsy results. We calculated sensitivity, specificity, PPV, and AUC for DBT and ultrasound. Results: In dense breasts, DBT showed significantly higher sensitivity than ultrasound (98.2% vs. 80%; p < 0.001), but lower specificity (15.4% vs. 55%; p < 0.001), PPV (61.3% vs. 71%; p = 0.04) and AUC (0.568 vs. 0.671; p = 0.001). In non-dense breasts, DBT showed significantly higher sensitivity than ultrasound (99.2% vs. 84%; p < 0.001), but no differences in specificity (22% vs. 33%; p = 0.14), PPV (69.2% vs. 68.8%; p = 0.93) or AUC (0.606 vs. 0.583; p = 0.57). Around 73% (74% dense and 71% non-dense) and 77% (81% dense and 72% non-dense) of lesions assigned a RANZCR 3 by DBT and ultrasound, respectively, were benign. Conclusion: DBT has higher sensitivity, but lower specificity and PPV than ultrasound in women with dense breasts recalled for assessment. Most lesions rated RANZCR 3 on DBT and ultrasound are benign and may benefit from short interval follow-up rather than biopsy.

The uncertainty of boundary can improve the classification accuracy of BI-RADS 4A ultrasound image

PURPOSE

The Breast Imaging-Reporting and Data System (BI-RADS) for ultrasound imaging provides a widely used reporting schema for breast imaging. Previous studies have shown that in ultrasound imaging, 90% of BI-RADS 4A tumors are benign lesions after biopsies. Unnecessary biopsy procedures can be avoided by accurate classification of BIRADS 4A tumors. However, the classification task is challenging and has not been fully investigated by existing studies. For benign and malignant tumors of BI-RADS 4A, the appearances of intra-class tumors are highly-variable, the characteristics of inter-class tumors is overall-similar. Discriminative features need to be found to improve classification accuracy of BI-RADS 4A tumors.

METHODS

In this study, we designed the network using the clinical features of BI-RADS 4A tumors to improve the discrimination ability of network. The boundary information is embedded into the input of the network using the uncertainty. A fine-grained data augmentation method is used to find discriminative features in tumor information embedded with boundary information. Two mathematical methods, voting-based and variance-based, are used to define the uncertainty of boundary, and the differences of these two definitions are compared in a classification network.

RESULTS

The dataset we used to evaluate our method had 1155 2D gray-scale images. Each image represented a unique BI-RADS 4A tumor. Among them, 248 tumors were proven to be malignant by biopsy, and the remaining 907 were benign. A weakly supervised data augmentation network (WS-DAN) was used as the backbone classification network, which showed competitive performance in finding discriminative features. Using the auxiliary input of the uncertain boundaries defined by the voting method, the area under the curve (AUC) value of our method was 0.8347 (sensitivity = 0.7774, specificity = 0.7459). The AUC value of the variance-based uncertainty was 0.7789. The voting-based uncertainty was higher than the baseline (AUC = 0.803), which only inputs the original image. Compared with the classic classification network, our method had a significant effect improvement (p < 0.01).

CONCLUSIONS

Using the uncertain boundaries defined by the voting methods as auxiliary information, we obtained a better performance in the classification of BI-RADS 4A ultrasound images, while variance-based uncertain boundaries had no effect on improving classification performance. Additionally, fine-grained network helped find discriminative features comparing with the commonly used classification networks. This article is protected by copyright. All rights reserved.

Downgrade BI-RADS 4A Patients Using Nomogram Based on Breast Magnetic Resonance Imaging, Ultrasound, and Mammography

Objectives

To downgrade BI-RADS 4A patients by constructing a nomogram using R software.

Materials and Methods

A total of 1,717 patients were retrospectively analyzed who underwent preoperative ultrasound, mammography, and magnetic resonance examinations in our hospital from August 2019 to September 2020, and a total of 458 patients of category BI-RADS 4A (mean age, 47 years; range 18–84 years; all women) were included. Multivariable logistic regression was used to screen out the independent influencing parameters that affect the benign and malignant tumors, and the nomogram was constructed by R language to downgrade BI-RADS 4A patients to eligible category.

Results

Of 458 BI-RADS 4A patients, 273 (59.6%) were degraded to category 3. The malignancy rate of these 273 lesions is 1.5% (4/273) (<2%), and the sensitivity reduced to 99.6%, the specificity increased from 4.41% to 45.3%, and the accuracy increased from 63.4% to 78.8%.

Conclusion

By constructing a nomogram, some patients can be downgraded to avoid unnecessary biopsy.

Can Ultrasound Elastography Help Better Manage Mammographic BI-RADS Category 4 Breast Lesions?

BACKGROUND

To assess the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography.

MATERIALS AND METHODS

Women with breast lesions identified as having mammography BI-RADS 4 lesions and underwent US examination were included in China. US features and US BI-RADS assessment were recorded in real-time and prospectively reported. The pathological result was referred to as the gold standard. The performance of US in the mammographic BI-RADS category 4 lesions was evaluated. Diagnostic performances of US BI-RADS, SE and combined both were compared.

RESULTS

A total of 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. For mammographic findings, 530 (70.6%) were true positive and 221 (29.4%) were false positive. Conventional US achieved higher positive predictive value (PPV) than mammography (78.5% vs. 70.6%, P=.001). The specificity increased from 34.4% to 47.1% (P< .001) without any loss in sensitivity and the PPV increased to 81.9% (P = .122) when conventional US was used in combination with SE. For conventional US combined with SE, it led to a correct diagnosis of no breast cancer in 104 of the 221 false-positive findings (47.1%) and achieved higher PPV than mammography regardless of patient age and lesion size.

CONCLUSION

Conventional US combined with SE is a helpful tool for the noninvasive examination of breast lesions classified as BI-RADS category 4 on mammography. It helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography.

Magnetic resonance imaging in diagnosis of indeterminate breast (BIRADS 3 & 4A) in a general population

OBJECTIVE

Currently, mammography and ultrasonography are the most used imaging techniques for breast cancer screening. However, these examinations report many indeterminate studies with a low probability of being malignant, i.e., BIRADS 3 and 4A. This prospective study aims to evaluate the value of breast magnetic resonance imaging (MRI) to clarify the BIRADS categorization of indeterminate mammography or ultrasonography studies.

METHODS

MRI studies acquired prospectively from 105 patients previously classified as BIRADS 3 or 4A were analyzed independently by four radiologists with different experience levels. Interobserver agreement was determined by the first-order agreement coefficient (AC1), and divergent results were re-analyzed for consensus. The possible correlation between the MRI and the mammography/ultrasound findings was evaluated, and each study was independently classified in one of the five BIRADS categories (BIRADS 1 to 5). In lesions categorized as BIRADS 4 or 5 at MRI, histopathological diagnosis was established by image-guided biopsy; while short-term follow-up was performed in lesions rated as BIRADS 3.

RESULTS

Breast MRI was useful in diagnosing three invasive ductal carcinomas, upgraded from BIRADS 4A to BIRADS 5. It also allowed excluding malignancy in 86 patients (81.9%), avoiding 22 unnecessary biopsies and 64 short-term follow-ups. The MRI showed good diagnostic performance with the area under roc curve, sensitivity, specificity, PPV, and NPV of 0.995, 100%, 83.5%, 10.5%, and 100%, respectively.

CONCLUSIONS

MRI showed to be useful as a problem-solving tool to clarify indeterminate findings in breast cancer screening and avoiding unnecessary short-follow-ups and percutaneous biopsies.

Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital

Background:

Older age and dense breast are the important risk factors for breast cancer. The ACR BI-RADS lexicon 5^th edition does not mention how patient age and breast density may affect the category assessment. The aim of this study was to investigate whether patient age and breast density influence the positive predictive value (PPV) of mammographic and ultrasonographic findings categorized as BI-RADS category 4 and subcategories 4a, 4b, and 4c among female patients.

Materials and Methods:

A retrospective study was conducted in Songklanagarind Hospital between January 1, 2016 and December 31, 2017 in female patients older than 18 years who had breast lesions categorized as BI-RADS category 4 and subcategories 4a, 4b, 4c. A total of 961 breast lesions consisted of 772 (80.33%) benign lesions and 189 (19.67%) malignant lesions. Categorization was done in each lesion based on age ranges of ≤35 years, >35 to 60 years, and >60 years and breast density according to mammographic breast composition. The PPV for each BI-RADS category was calculated based on the pathological diagnoses and were compared using the chi-square test.

Results:

The overall PPV in each subcategory was in the reference range. The PPV increased with increasing age: 4% vs. 22.63% vs. 36.67% for category 4 (p-value=0.01); 0% vs. 5.81% vs. 6.88% for subcategory 4a (p-value=0.002); 6.67% vs. 26.62% vs. 51.35% for subcategory 4b (p-value=0.001); and 33.33% vs. 76.92% vs. 81.82% for subcategory 4c (p-value=0.02). An association was not found between PPV and breast density.

Conclusion:

A significantly positive association was found between PPV and age in patients in BI-RADS subcategories 4a, 4b, and 4c. This study could not determine that mammographic breast composition according to the ACR BI-RADS 5th edition was associated with PPV due to improper sample distribution.

The Positive Predictive Values of the Breast Imaging Reporting and Data System (BI-RADS) 4 Lesions and its Mammographic Morphological Features

The Breast Imaging Reporting and Data System (BI-RADS) is a comprehensive guideline to systematize breast imaging reporting, and as per its recommendations, any lesion with likelihoods of malignancy greater than 2% is deemed as suspicious and tissue diagnosis is recommended. The aim of the study is to determine the positive predictive value (PPV) of BI-RADS categories 4a, 4b, and 4c for malignancy and association of mammographic morphological features of BI-RADS 4 subgroups with malignant outcomes. We retrospectively reviewed all the patients undergoing mammography with BI-RADS score of 4 followed by biopsy from May 2019 to April 2020. The predictive values of BI-RADS 4 subcategories and morphological features with malignancy are performed taking histopathology report as the gold standard. The PPV of BI-RADS subcategories 4a, 4b, and 4c for malignancies were 34, 89, and 97%, respectively. BI-RADS 4c patients tend to be older (50.2 ± 12.2 vs. 44.6 ± 10.3 years) with larger mass (44 ± 16 vs. 32.9 ± 16.8 mm) at presentation than 4a. Postmenopausal state (P = 0.03) and older age (P = 0.019) were significantly associated with malignancy. There is no meaningful difference observed in the predictability of BI-RADS category 4c lesions among different breast density patterns. The overall higher PPV for BI-RADS 4a and 4b reflects subjectivity in subcategory assignments of BI-RADS 4. In patients, less than 40 years with the BI-RADS 4a category on mammograms may undergo supplementary imaging with MRI which may downscale the lesion classification in turn reducing unnecessary biopsy and surgery.

Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis

OBJECTIVE

This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast.

METHODS

The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression.

RESULTS

Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively.

CONCLUSION

Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.

Contrast Enhanced Digital Mammography (CEDM) Helps to Safely Reduce Benign Breast Biopsies for Low to Moderately Suspicious Soft Tissue Lesions

RATIONALE AND OBJECTIVES

To preliminarily asses if Contrast Enhanced Digital Mammography (CEDM) can accurately reduce biopsy rates for soft tissue BI-RADS 4A or 4B lesions.

MATERIALS AND METHODS

Eight radiologists retrospectively and independently reviewed 60 lesions in 54 consenting patients who underwent CEDM under Health Insurance Portability and Accountability Act compliant institutional review board-approved protocols. Readers provided Breast Imaging Reporting & Data System ratings sequentially for digital mammography/digital breast tomosynthesis (DM/DBT), then with ultrasound, then with CEDM for each lesion. Area under the curve (AUC), true positive rates and false positive rates, positive predictive values and negative predictive values were calculated. Statistical analysis accounting for correlation between lesion-examinations and between-reader variability was performed using OR/DBM (for SAS v.3.0), generalized linear mixed model for binary data (proc glimmix, SAS v.9.4, SAS Institute, Cary North Carolina), and bootstrap.

RESULTS

The cohort included 49 benign, two high-risk and nine cancerous lesions in 54 women aged 34-74 (average 50) years. Reader-averaged AUC for CEDM was significantly higher than DM/DBT alone (0.85 versus 0.66, p < 0.001) or with US (0.85 versus 0.75, p = 0.001). CEDM increased true positive rates from 0.74 under DB/DBT, and 0.89 with US, to 0.90 with CEDM, (p = 0.019 DM/DBT versus CEDM, p = 0.78 DM/DBT + US versus CEDM) and decreased false positive rates from 0.47 using DM/DBT and 0.61 with US to 0.39 with CEDM (p = 0.017 DM/DBT versus CEDM, p = 0.001 DM/DBT+ US versus CEDM). For an expected cancer rate of 10%, CEDM positive predictive values was 20.5% (95% CI: 16%-27%) and negative predictive values 98.3% (95% CI: 96%-100%).

CONCLUSION

Addition of CEDM for evaluation of low-moderate suspicion soft tissue breast lesions can substantially reduce biopsy of benign lesions without compromising cancer detection.

Optimal breast cancer diagnostic strategy using combined ultrasound and diffuse optical tomography

Ultrasound (US)-guided near-infrared diffuse optical tomography (DOT) has demonstrated great potential as an adjunct breast cancer diagnosis tool to US imaging alone, especially in reducing unnecessary benign biopsies. However, DOT data processing and image reconstruction speeds remain slow compared to the real-time speed of US. Real-time or near real-time diagnosis with DOT is an important step toward the clinical translation of US-guided DOT. Here, to address this important need, we present a two-stage diagnostic strategy that is both computationally efficient and accurate. In the first stage, benign lesions are identified in near real-time by use of a random forest classifier acting on the DOT measurements and the radiologists' US diagnostic scores. Any lesions that cannot be reliably classified by the random forest classifier will be passed on to the second stage which begins with image reconstruction. Functional information from the reconstructed hemoglobin concentrations is employed by a Support Vector Machine (SVM) classifier for diagnosis at the end of the second stage. This two-step classification approach which combines both perturbation data and functional features, results in improved classification, as denoted by the receiver operating characteristic (ROC) curve. Using this two-step approach, the area under the ROC curve (AUC) is 0.937 ± 0.009, with a sensitivity of 91.4% and specificity of 85.7%. In comparison, using functional features and US score yields an AUC of 0.892 ± 0.027, with a sensitivity of 90.2% and specificity of 74.5%. Most notably, the specificity is increased by more than 10% due to the implementation of the random forest classifier.

Variability in Individual Radiologist BI-RADS 3 Usage at a Large Academic Center: What's the Cause and What Should We Do About It?

RATIONALE AND OBJECTIVES

Although the breast imaging reporting and data system (BI-RADS) lists specific criteria for designating a lesion as BI-RADS category 3 (probably benign), there are no target benchmarks for BI-RADS 3 usage rates. This study investigates the variability of BI-RADS 3 rates among a group of academic breast imagers, with the goal of defining more precise utilization.

MATERIALS AND METHODS

We retrospectively reviewed all diagnostic mammograms performed between July 1, 2013 and August 8, 2017 at our academic institution. The percentage of diagnostic mammograms given a BI-RADS 3 assessment was compared between radiologists using the Chi-square test. We then evaluated for correlation between BI-RADS 3 rate and individual clinical metrics (eg, radiologist experience, cancer detection rate [CDR] and recall rate) using univariate linear regression.

RESULTS

The study included 13 breast imagers and 24,051 diagnostic breast examinations. There was significant variability in BI-RADS 3 rates between radiologists, ranging from 8.0% to 19.3% (p < 0.001). Increased BI-RADS 3 rates negatively correlated with BI-RADS 1 or 2 rate (p < 0.001) and positively correlated with recall rate (p = 0.03). There was no association between BI-RADS 3 rate and the radiologist's level of experience, BI-RADS 4 or 5 rate, or CDR.

CONCLUSION

We found significant variability in BI-RADS 3 usage, which seems to be used in place of BI-RADS 1 or 2 findings rather than to avoid biopsy recommendation. BI-RADS 3 rates also directly correlated with recall rate, suggesting a greater degree of uncertainty among specific radiologists. Importantly, increased usage of BI-RADS 3 did not correlate with provider experience or improved CDR.

Role of Clinical and Imaging Risk Factors in Predicting Breast Cancer Diagnosis Among BI-RADS 4 Cases

PURPOSE

To analyze women with suspicious findings (assessed as Breast Imaging Reporting and Data System [BI-RADS] 4), examining the value of clinical and imaging predictors in predicting cancer diagnosis.

PATIENTS AND METHODS

A set of 2138 examinations (1978 women) given a BI-RADS 4 with matching pathology results were analyzed. Predictors such as patient demographics, clinical risk factors, and imaging-derived features such as BI-RADS assessment and qualitative breast density were considered. Independent predictors of breast cancer were determined by univariate analysis and multivariate logistic regression.

RESULTS

In univariate analysis, age, race, body mass index, age at first live birth, BI-RADS assessment, qualitative breast density, and risk triggers were found to be independent predictors. In multivariate analysis, age, BI-RADS score, breast density, race, presence of a lump, and number of risk triggers were the most predictive. An integrative logistic regression model achieved a performance of 0.84 cross-validated area under the curve. No variable was a constant independent predictor when stratifying the population on the basis of the BI-RADS score.

CONCLUSION

While BI-RADS assessment remains the strongest predictor of breast cancer, the inclusion of clinical risk factors such as age, breast density, presence of a lump, and number of risk triggers derived from guidelines improves the specificity of identifying individuals with imaging descriptors associated with BI-RADS 4A and 4B that are more likely to be diagnosed with breast cancer.

The Value of Outpatient Imaging-Based Cancer Screening Episodes

In order to shift US health care towards greater value, the Centers for Medicare & Medicaid Services (CMS) is exploring outpatient episode-based cost measures under the new Quality Payment Program and planning a bundled payment program that will introduce the first ever outpatient episodes of care. One novel approach to capitalize on this paradigm shift and extend bundled payment policies is to engage primary care physicians and specialists by bundling outpatient imaging studies and associated procedures-central tools in disease screening and diagnosis, but also tools that are expensive and susceptible to increasing health care costs and patient harm. For example, both breast and lung cancer screening represent target areas ripe for bundled payment given high associated costs and variation in management strategies and suboptimal care coordination between responsible clinicians. Benefits to imaging-based screening episodes include stronger alignment between providers (primary care physicians, radiologists, and other clinicians), reduction in unwarranted variation, creation of appropriateness standards, and ability to overcome barriers to cancer screening adherence. Implementation considerations include safeguarding against providers inappropriately withholding care as well as ensuring that accountability and financial risk are distributed appropriately among responsible clinicians.

Can Radiologists Predict the Presence of Ductal Carcinoma In Situ and Invasive Breast Cancer?

OBJECTIVE

We hypothesize that radiologists' estimated percentage likelihood assessments for the presence of ductal carcinoma in situ (DCIS) and invasive cancer may predict histologic outcomes.

MATERIALS AND METHODS

Two hundred fifty cases categorized as BI-RADS category 4 or 5 at four University of California Medical Centers were retrospectively reviewed by 10 academic radiologists with a range of 1-39 years in practice. Readers assigned BI-RADS category (1, 2, 3, 4a, 4b, 4c, or 5), estimated percentage likelihood of DCIS or invasive cancer (0-100%), and confidence rating (1 = low, 5 = high) after reviewing screening and diagnostic mammograms and ultrasound images. ROC curves were generated.

RESULTS

Sixty-two percent (156/250) of lesions were benign and 38% (94/250) were malignant. There were 26 (10%) DCIS, 20 (8%) invasive cancers, and 48 (19%) cases of DCIS and invasive cancer. AUC values were 0.830-0.907 for invasive cancer and 0.731-0.837 for DCIS alone. Sensitivity of 82% (56/68), specificity of 84% (153/182), positive predictive value (PPV) of 66% (56/85), negative predictive value (NPV) of 93% (153/165), and accuracy of 84% ([56 + 153]/250) were calculated using an estimated percentage likelihood of 20% or higher as the prediction threshold for invasive cancer for the radiologist with the highest AUC (0.907; 95% CI, 0.864-0.951). Every 20% increase in the estimated percentage likelihood of invasive cancer increased the odds of invasive cancer by approximately two times (odds ratio, 2.4). For DCIS, using a threshold of 40% or higher, sensitivity of 81% (21/26), specificity of 79% (178/224), PPV of 31% (21/67), NPV of 97% (178/183), and accuracy of 80% ([21 + 178]/250) were calculated. Similarly, these values were calculated at thresholds of 2% or higher (BI-RADS category 4) and 95% or higher (BI-RADS category 5) to predict the presence of malignancy.

CONCLUSION

Using likelihood estimates, radiologists may predict the presence of invasive cancer with fairly high accuracy. Radiologist-assigned estimated percentage likelihood can predict the presence of DCIS, albeit with lower accuracy than that for invasive cancer.

Discovery and Clinical Decision Support for Personalized Healthcare

With the advent of electronic health records, more data are continuously collected for individual patients, and more data are available for review from past patients. Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare. In this paper, we present a novel approach, discovery engine (DE), that discovers which patient characteristics are most relevant for predicting the correct diagnosis and/or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as a personalized recommendation for a specific chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, different patient features are relevant; DE can discover these different relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6% improvement over existing state-of-the-art recommendation algorithms regarding kappa coefficients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18% and 4.20% improvement over existing state-of-the-art prediction algorithms regarding prediction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information and that the relevant features discovered by DE are confirmed by clinical references.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms

Introduction

Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.

Methods

We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.

Results

In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m² versus <25 kg/m²; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m². The SNP panel appeared to have predictive ability among both white and black women.

Conclusions

Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.

Apparent diffusion coefficient (ADC) value to evaluate BI-RADS 4 breast lesions: correlation with pathological findings

Diffusion-weighted imaging and apparent diffusion coefficient (ADC) of 36 breast lesions previously categorized as 4 according to the Breast Imaging Reporting and Data System (BI-RADS) were prospectively studied. The pathological results were 21 benign lesions and 15 malignant. The ADC of malignant lesions was significantly lower than that of the benign ones (0.87 ± 0.12 × 10(-3) mm(2)/s vs. 1.41 ± 0.22 × 10(-3) mm(2)/s, respectively) (P<.001). Using a threshold ADC value of 1.08 × 10(-3) mm(2)/s, a sensitivity of 95% and specificity of 100% were obtained.

Magnetic resonance imaging of the breast as a problem-solving method: to be or not to be?

The use of dynamic magnetic resonance imaging (MRI) of the breast as a complementary problem-solving tool was explored in a heterogeneous population sample. A total of 3,076 patients that underwent breast MRI examination between January 2008 and June 2012 in our center were screened retrospectively. Of these, 868 met the following inclusion criteria and were considered eligible for the study: available data on clinical signs, symptoms and on the results of mammography and ultrasound examinations in medical records; at least 1 year of follow-up; and documented pathology findings. Lesions with a stable course over a follow-up period of at least 12 months were considered benign. MRI was suggestive of a suspicious abnormality (BI-RADS 4) or highly suggestive of malignancy (BI-RADS 5) in 129 (15%) of 868 patients, leading to a biopsy examination in these cases. On the other hand, MRI findings were considered normal in 739 (85%) subjects based on normal (BI-RADS 1), benign (BI-RADS 2) or probably benign (BI-RADS 3) findings. Of the 129 patients undergoing a histopathologic examination based on MRI findings, 63 were diagnosed with cancer, and in 66, the biopsy proved to be benign. Forty of the 63 patients (40/63) with a diagnosis of malignancy and 34 of the 66 patients (34/66) with a benign diagnosis had been categorized as BI-RADS 4 with conventional methods. A total of 23 patients with BI-RADS category of 0 to 3 according to conventional methods were diagnosed as having cancer with MRI. In six of these, the family history was positive. The sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of MRI for the detection of cancer were 100%, 92%, 52%, and 100%, respectively. In cases with inconclusive findings on conventional imaging studies or in patients with clinical/radiological suspicion of malignancy, MRI should be more effectively used as a problem-solving approach owing to its high sensitivity and NPV in this condition. Use of MRI as a problem-solving method in such cases may decrease rather than increase unnecessary biopsy procedures and patient anxiety.

Comparison of clinicopathological findings among patients whose mammography results were classified as category 4 subgroups of the BI-RADS

OBJECTIVE:

Our aim is to compare mammographic, demographic and clinicopathological characteristics of patients whose mammographies were classified as subgroups of BI-RADS 4 category (Breast Imaging – Reporting and Data System).

METHODS:

In total, 103 patients with mammography (Senographe 600t Senix HF; General Electric, Moulineaux, France) results classified as BI-RADS 4 were included in the study. Demographic data (age, menopause, and family history) were recorded. All data were compared among BI-RADS 4 subgroups.

RESULTS:

In all, 68.9% (71/103), 7.8% (8/103) and 23.3% (24/103) the patients were in groups BI-RADS 4A, 4B and 4C, respectively. The incidence of malignancy was higher in Groups 4B and 4C than in Group 4A (p<0.05), but similar in Groups 4B and 4C (p>0.05). Mean age was lower in Group 4B than in Groups 4A and 4C (p<0.05). A positive family history was more common in Group 4A than in Group 4B (p=0.025). The frequency of menopausal patients was greater in Groups 4A and 4C than in Group 4B (p=0.021, and 0.003, respectively).

METHODS:

The rate of malignancy was higher in Groups 4B, and 4C than in Group 4A. A positive family history was more common in Group 4A than in Group 4C. Groups 4A, and 4C patients tended to be older and were more likely to be menopausal than Group 4B patients.

Evolving paradigm for imaging, diagnosis, and management of DCIS

Our understanding of the biology of breast cancer has dramatically expanded over the past decade, revealing that breast cancer is a heterogeneous group of diseases. This new knowledge can generate insights to improve screening performance and the management of ductal carcinoma in situ. In this article, the authors review the current state of the science of breast cancer and tools that can be used to improve screening and risk assessment. They describe several opportunities to improve clinical screening: (1) radiologists interpreting mammograms should aim to differentiate between the risk for invasive cancer and ductal carcinoma in situ to better assess the time frame for disease progression and the need for and optimal timing of biopsy; (2) imaging features associated with low risk, slow-growing cancer versus high risk, fast-growing cancer should be better defined and taught; and (3) as we learn more about assessing an individual's risk for developing breast cancer, we should incorporate these factors into a strategy for personalized screening to maximize benefit and minimize harm.

Addressing overdiagnosis and overtreatment in cancer: a prescription for change

A vast range of disorders--from indolent to fast-growing lesions--are labelled as cancer. Therefore, we believe that several changes should be made to the approach to cancer screening and care, such as use of new terminology for indolent and precancerous disorders. We propose the term indolent lesion of epithelial origin, or IDLE, for those lesions (currently labelled as cancers) and their precursors that are unlikely to cause harm if they are left untreated. Furthermore, precursors of cancer or high-risk disorders should not have the term cancer in them. The rationale for this change in approach is that indolent lesions with low malignant potential are common, and screening brings indolent lesions and their precursors to clinical attention, which leads to overdiagnosis and, if unrecognised, possible overtreatment. To minimise that potential, new strategies should be adopted to better define and manage IDLEs. Screening guidelines should be revised to lower the chance of detection of minimal-risk IDLEs and inconsequential cancers with the same energy traditionally used to increase the sensitivity of screening tests. Changing the terminology for some of the lesions currently referred to as cancer will allow physicians to shift medicolegal notions and perceived risk to reflect the evolving understanding of biology, be more judicious about when a biopsy should be done, and organise studies and registries that offer observation or less invasive approaches for indolent disease. Emphasis on avoidance of harm while assuring benefit will improve screening and treatment of patients and will be equally effective in the prevention of death from cancer.

Physiological COX-2 expression in breast epithelium associates with COX-2 levels in ductal carcinoma in situ and invasive breast cancer in young women

Cyclooxygenase-2 (COX-2) overexpression is implicated in increased risk and poorer outcomes in breast cancer in young women. We investigated COX-2 regulation in normal premenopausal breast tissue and its relationship to malignancy in young women. Quantitative COX-2 immunohistochemistry was performed on adjacent normal and breast cancer tissues from 96 premenopausal women with known clinical reproductive histories, and on rat mammary glands with distinct ovarian hormone exposures. COX-2 expression in the normal breast epithelium varied more than 40-fold between women and was associated with COX-2 expression levels in ductal carcinoma in situ and invasive cancer. Normal breast COX-2 expression was independent of known breast cancer prognostic indicators, including tumor stage and clinical subtype, indicating that factors regulating physiological COX-2 expression may be the primary drivers of COX-2 expression in breast cancer. Ovarian hormones, particularly at pregnancy levels, were identified as modulators of COX-2 in normal mammary epithelium. However, serial breast biopsy analysis in nonpregnant premenopausal women suggested relatively stable baseline levels of COX-2 expression, which persisted independent of menstrual cycling. These data provide impetus to investigate how baseline COX-2 expression is regulated in premenopausal breast tissue because COX-2 levels in normal breast epithelium may prove to be an indicator of breast cancer risk in young women, and predict the chemopreventive and therapeutic efficacy of COX-2 inhibitors in this population.

Aggregate cost of mammography screening in the United States: comparison of current practice and advocated guidelines

BACKGROUND

Controversy exists over how often and at what age mammography screening should be implemented. Given that evidence supports less frequent screening, the cost differences among advocated screening policies should be better understood.

OBJECTIVE

To estimate the aggregate cost of mammography screening in the United States in 2010 and compare the costs of policy recommendations by professional organizations.

DESIGN

A model was developed to estimate the cost of mammography screening in 2010 and 3 screening strategies: annual (ages 40 to 84 years), biennial (ages 50 to 69 years), and U.S. Preventive Services Task Force (USPSTF) guidelines (biennial for those aged 50 to 74 years and personalized based on risk for those younger than 50 years and based on comorbid conditions for those 75 years and older).

SETTING

United States.

PATIENTS

Women aged 40 to 85 years.

INTERVENTION

Mammography annually, biennially, or following USPSTF guidelines.

MEASUREMENTS

Cost of screening per year, using Medicare reimbursements.

RESULTS

The estimated cost of mammography screening in the United States in 2010 was $7.8 billion, with approximately 70% of women screened. The simulated cost of screening 85% of women was $10.1 billion, $2.6 billion, and $3.5 billion for annual, biennial, and USPSTF guidelines, respectively. The largest drivers of cost (in order) were screening frequency, percentage of women screened, cost of mammography, percentage of women screened with digital mammography, and percentage of mammography recalls.

LIMITATION

Cost estimates and assumptions used in the model were conservative.

CONCLUSION

The cost of mammography varies by at least $8 billion per year on the basis of screening strategy. The USPSTF guidelines are based on the scientific evidence to date to maximize patient benefit and minimize harm but also result in far more effective use of resources.

PRIMARY FUNDING SOURCE

University of California and the Safeway Foundation.