Connie Kim

Abbreviated Screening Protocol for Breast MRI: A Feasibility Study

RATIONALE AND OBJECTIVES To compare the performance of two shortened breast magnetic resonance imaging (MRI) protocols to a standard MRI protocol for breast cancer screening. MATERIALS AND METHODS In this Health Insurance Portability and Accountability Act compliant, institutional review board-approved pilot study, three fellowship-trained breast imagers evaluated 48 breast MRIs (24 normal, 12 benign, and 12 malignant) selected from a high-risk screening population. MRIs were presented in three viewing protocols, and a final Breast Imaging-Reporting and Data System assessment was recorded for each case. The first shortened protocol (abbreviated 1) included only fat-saturated precontrast T2-weighted, precontrast T1-weighted, and first pass T1-weighted postcontrast sequences. The second shortened protocol (abbreviated 2) included the abbreviated 1 protocol plus the second pass T1-weighted postcontrast sequence. The third protocol (full), reviewed after a 1-month waiting period, included a nonfat-saturated T1-weighted sequence, fat-saturated T2-weighted, precontrast T1-weighted, and three or four dynamic postcontrast sequences. Interpretation times were recorded for the abbreviated 1 and full protocols. Sensitivity and specificity were compared via a chi-squared analysis. This pilot study was designed to detect a 10% difference in sensitivity with a power of 0.8. RESULTS There was no significant difference in sensitivity between the abbreviated 1 (86%; P = .22) or abbreviated 2 (89%; P = .38) protocols and the full protocol (95%). There was no significant difference in specificity between the abbreviated 1 (52%; P = 1) or abbreviated 2 (45%; P = .34) protocols and the full protocol (52%). The abbreviated 1 and full protocol interpretation times were similar (2.98 vs. 3.56 minutes). CONCLUSIONS In this pilot study, reader performance comparing two shortened breast MRI protocols to a standard protocol in a screening cohort were similar, suggesting that a shortened breast MRI protocol may be clinically useful, warranting further investigation.

Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection

PURPOSE Mammography is known to be one of the most difficult radiographic exams to interpret. Mammography has important limitations, including the superposition of normal tissue that can obscure a mass, chance alignment of normal tissue to mimic a true lesion and the inability to derive volumetric information. It has been shown that stereomammography can overcome these deficiencies by showing that layers of normal tissue lay at different depths. If standard stereomammography (i.e., a single stereoscopic pair consisting of two projection images) can significantly improve lesion detection, how will multiview stereoscopy (MVS), where many projection images are used, compare to mammography? The aim of this study was to assess the relative performance of MVS compared to mammography for breast mass detection. METHODS The MVS image sets consisted of the 25 raw projection images acquired over an arc of approximately 45 degrees using a Siemens prototype breast tomosynthesis system. The mammograms were acquired using a commercial Siemens FFDM system. The raw data were taken from both of these systems for 27 cases and realistic simulated mass lesions were added to duplicates of the 27 images at the same local contrast. The images with lesions (27 mammography and 27 MVS) and the images without lesions (27 mammography and 27 MVS) were then postprocessed to provide comparable and representative image appearance across the two modalities. All 108 image sets were shown to five full-time breast imaging radiologists in random order on a state-of-the-art stereoscopic display. The observers were asked to give a confidence rating for each image (0 for lesion definitely not present, 100 for lesion definitely present). The ratings were then compiled and processed using ROC and variance analysis. RESULTS The mean AUC for the five observers was 0.614 +/- 0.055 for mammography and 0.778 +/- 0.052 for multiview stereoscopy. The difference of 0.164 +/- 0.065 was statistically significant with a p-value of 0.0148. CONCLUSIONS The differences in the AUCs and the p-value suggest that multiview stereoscopy has a statistically significant advantage over mammography in the detection of simulated breast masses. This highlights the dominance of anatomical noise compared to quantum noise for breast mass detection. It also shows that significant lesion detection can be achieved with MVS without any of the artifacts associated with tomosynthesis.

Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms

PURPOSE The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms. METHODS In this retrospective IRB-approved study, we analyzed data from 275 breast cancer patients at a single institution. Recurrence-free survival data were obtained from the medical record. Routine clinical pre-operative breast MRIs were performed in all patients. The tumors were marked on the MRIs by fellowship-trained breast radiologists. A previously developed computer algorithm was applied to the marked tumors to quantify the enhancement dynamics relative to the automatically assessed background parenchymal enhancement. To establish whether the contrast enhancement feature quantified by the algorithm was associated with recurrence-free survival, we constructed a Cox proportional hazards regression model with the computer-extracted feature as a covariate. We controlled for tumor grade and size (major axis length), patient age, patient race/ethnicity, and menopausal status. RESULTS The analysis showed that the semi-automatically obtained feature quantifying MRI tumor enhancement dynamics was independently predictive of recurrence-free survival (p=0.024). CONCLUSION Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients.

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI‐RADS features

PURPOSE The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. METHODS Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainees likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). RESULTS Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502-0.739, 95% Confidence Interval: 0.543-0.680,p < 0.002). CONCLUSIONS Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

Using the BI-RADS Lexicon in a Restrictive Form of Double Reading as a Strategy for Minimizing Screening Mammography Recall Rates

OBJECTIVE The purpose of this article is to determine the potential reduction in screening recall rates by strictly following standardized BI-RADS lexicon for lesions seen on screening mammography. MATERIALS AND METHODS Of 3084 consecutive mammograms performed at our screening facilities, 345 women with 437 lesions were recalled for additional imaging and constituted our study population. Three radiologists retrospectively classified lesions using the standard BI-RADS lexicon and assigned each to one of four groups: group A, the finding met criteria for recall by the BI-RADS lexicon; group B, the finding did not meet strict BI-RADS criteria for recall but was sufficiently indeterminate to warrant recall by the majority of the study panel; group C, the finding was classifiable by the BI-RADS lexicon but was not recalled because it was benign or stable; and group D, the questioned finding was not considered an abnormality by our study panel. Recall rates and the cancer detection rate were determined. The adjusted recall rate was calculated for lesions considered appropriate for recall (group A), and the reduction in the recall rate was determined. RESULTS Nineteen malignancies were detected in our recalled population, for a cancer detection rate of 0.65%. All 19 malignancies were lesions considered appropriate for recall (group A). If only group A lesions had been recalled, the recall rate would have decreased from 11.4% to 6.2%, representing a 46% reduction in recalls without affecting the cancer detection rate. CONCLUSION Using the BI-RADS lexicon as a decision-making aid may help adjust thresholds for recalling indeterminate or suspicious lesions and reduce recall rates from screening mammography.

Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk

RATIONALE AND OBJECTIVES To determine if background parenchymal enhancement (BPE) on screening breast magnetic resonance imaging (MRI) in high-risk women correlates with future cancer. MATERIALS AND METHODS All screening breast MRIs (n = 1039) in high-risk women at our institution from August 1, 2004, to July 30, 2013, were identified. Sixty-one patients who subsequently developed breast cancer were matched 1:2 by age and high-risk indication with patients who did not develop breast cancer (n = 122). Five fellowship-trained breast radiologists independently recorded the BPE. The median reader BPE for each case was calculated and compared between the cancer and control cohorts. RESULTS Cancer cohort patients were high-risk because of a history of radiation therapy (10%, 6 of 61), high-risk lesion (18%, 11 of 61), or breast cancer (30%, 18 of 61); BRCA mutation (18%, 11 of 61); or family history (25%, 15 of 61). Subsequent malignancies were invasive ductal carcinoma (64%, 39 of 61), ductal carcinoma in situ (30%, 18 of 61) and invasive lobular carcinoma (7%, 4of 61). BPE was significantly higher in the cancer cohort than in the control cohort (P = 0.01). Women with mild, moderate, or marked BPE were 2.5 times more likely to develop breast cancer than women with minimal BPE (odds ratio = 2.5, 95% confidence interval: 1.3-4.8, P = .005). There was fair interreader agreement (κ = 0.39). CONCLUSIONS High-risk women with greater than minimal BPE at screening MRI have increased risk of future breast cancer.

Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation.

One of the methods widely used to measure the proliferative activity of cells in breast cancer patients is the immunohistochemical (IHC) measurement of the percentage of cells stained for nuclear antigen Ki-67. Use of Ki-67 expression as a prognostic marker is still under investigation. However, numerous clinical studies have reported an association between a high Ki-67 and overall survival (OS) and disease free survival (DFS). On the other hand, to offer non-invasive alternative in determining Ki-67 expression, researchers have made recent attempts to study the association of Ki-67 expression with magnetic resonance (MR) imaging features of breast cancer in small cohorts (<30). Here, we present a large scale evaluation of the relationship between imaging features and Ki-67 score as: (a) we used a set of 450 invasive breast cancer patients, (b) we extracted a set of 529 imaging features of shape and enhancement from breast, tumor and fibroglandular tissue of the patients, (c) used a subset of patients as the training set to select features and trained a multivariate logistic regression model to predict high versus low Ki-67 values, and (d) we validated the performance of the trained model in an independent test set using the area-under the receiver operating characteristics (ROC) curve (AUC) of the values predicted. Our model was able to predict high versus low Ki-67 in the test set with an AUC of 0.67 (95% CI: 0.58-0.75, p<1.1e-04). Thus, a moderate strength of association of Ki-67 values and MRextracted imaging features was demonstrated in our experiments.

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

BackgroundRecent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.MethodsWe analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients.ResultsMultivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647–0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589–0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591–0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569–0.674, p < .0001). Associations between individual features and subtypes we also found.ConclusionsThere is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

Positive and negative mood following imaging-guided core needle breast biopsy and receipt of biopsy results

Abstract Positive and negative mood are independent psychological responses to stressful events. Negative mood negatively impacts well-being and co-occurring positive mood leads to improved adjustment. Women undergoing core needle breast biopsies (CNB) experience distress during CNB and awaiting results; however, influences of mood are not well known. This longitudinal study examines psychosocial and biopsy- and spirituality-related factors associated with mood in patients day of CNB and one week after receiving results. Ninety women undergoing CNB completed questionnaires on psychosocial factors (chronic stress, social support), biopsy experiences (pain, radiologist communication), and spirituality (peace, meaning, faith) day of CNB. Measures of positive and negative mood were completed day of CNB and one week after receiving results (benign n = 50; abnormal n = 25). Multiple linear regression analyses were conducted. Greater positive mood correlated with greater peace (β = .25, p = .02) day of CNB. Lower negative mood correlated with greater peace (β = −.29, p = .004) and there was a trend for a relationship with less pain during CNB (β = .19, p = .07). For patients with benign results, day of CNB positive mood predicted positive mood post-results (β = .31, p = .03) and only chronic stress predicted negative mood (β = .33, p = .03). For women with abnormal results, greater meaning day of CNB predicted lower negative mood post-results (β = −.45, p = .03). Meaning and peace may be important for women undergoing CNB and receiving abnormal results.