PACS-integrated machine learning breast density classifier: clinical validation

OBJECTIVE

To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training.

MATERIALS AND METHODS

This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading.

RESULTS

For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category.

CONCLUSIONS

The automated breast density tool showed high agreement with radiologists' assessments of breast density.

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

PURPOSE

The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy.

EXPERIMENTAL DESIGN

We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

RESULTS

Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

CONCLUSIONS

Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study

Background: Although the advent of targeted therapy has substantially improved outcomes for HER2+ breast cancer patients, many will still fail to achieve pathological complete response (pCR) following neoadjuvant chemotherapy (NAC). In order to reduce overtreatment among patients resistant to standard HER2-targeted NAC and identify candidates for alternative therapeutic interventions, there is a need for validated markers of anti-HER2 agent benefit. The computational analysis of pretreatment imaging has shown recent promise in identifying responsive breast cancers. However, previous applications have explored response prediction among cohorts of mixed subtype and therapeutic approach, thus limiting its relevance in informing specific therapeutic strategy.

Methods: This study comprised retrospective contrast-enhanced MRI data from a total of 159 patients who received anti-HER2 therapy at 5 institutions. A deep learning (DL) model was trained and tuned using 100 HER2+ breast cancer patients who received neoadjuvant taxane (T), carboplatin (C), trastuzumab (H), and pertuzumab (P) at Institution A, of which 49 achieved pCR (ypT0/is). A convolutional neural network was designed to analyze pre- and post-contrast MRI images acquired before NAC and compute a patient's probability of achieving pCR. Institution A data was split randomly into a 85 patient training cohort, used to directly train the model, and a 15 patient internal validation cohort, used to monitor and improve training progress. Two external, held-out testing datasets were used to evaluate capability to predict response in HER2+: Testing Cohort 1, consisting of 28 patients (16 pCR, 12 non-pCR) treated with TCHP at Institution B, and Testing Cohort 2, consisting of 29 patients (10 pCR, 19 non-pCR) who received TCH at one of 3 other institutions as part of the BrUOG 211B trial. A multivariable clinical model (MCM) incorporating age, ER/PR status, stage, and size was evaluated separately and in combination with the neural network. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results: The neural network was able to strongly predict response to HER2-targeted NAC in the internal validation (AUC=.93) and testing cohorts (AUC=.84 and AUC=.77). This model offered superior performance compared to a MCM, which performed poorly across institutions. Strikingly, the higher accuracy of DL included correctly identifying responders within the ER+/PR+ subgroup of patients and non-responders within the ER-/PR- subgroup. Combining DL predictions with the clinical model improved performance to AUC of 0.89 in testing cohort 1, but did not improve AUC in cohort 2.

Conclusions: DL analysis of breast DCE-MRI could be used to better identify benefit of HER2-targeted therapeutic approaches prior to administration. As the first exploration of automated response prediction from imaging with respect to a targeted NAC approach, this work uniquely has the potential to help guide therapeutic decision-making. Our approach was effective in predicting response to multiple HER2-targeted NAC regimens, with better performance in the cohort who received TCHP (as in the training cohort). The strong performance of this model across 5 institutions is a promising indicator of its robustness and ability to tailor therapy even within clinically-distinct HER2+ patient subpopulations.

Deep learning (DL) and multivariable clinical model (MCM) pCR prediction by cohortCohortModelAUC (%)Sensitivity (%)Specificity (%)Accuracy (%)Validation (n=15, 53% pCR)DL93888687MCM66637173Testing 1 (n=28, 57% pCR)DL85889289MCM54388350DL+MCM89819286Testing 2 (n=29, 34% pCR)DL77708479MCM53208966DL+MCM76808483

Citation Format: Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Stylianos Drisis, Vinay Varadan, Donna Plecha, Mohammed Benjelloun, Jame Abraham, Anant Madabhushi. Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-13.

Abstract P1-10-06: Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Background: Angiogenesis is crucial to a tumor's growth and an important factor in therapeutic outcome. Although quantitative analysis of tumors on dynamic contrast enhanced (DCE) MRI can provide indirect characterization of a tumor's vascularization, direct computational analysis of the tumor-associated vessel network remains a promising, but under-explored potential marker of therapeutic response. For instance, surrounding vasculature with a convoluted 3-dimensional shape and poor blood flow may indicate a more aggressive tumor and poorly facilitate delivery of therapeutic agents. In this work, we present a computational approach for the prediction of neoadjuvant chemotherapy response using quantitative imaging features describing the morphology and function of tumor associated vasculature on pretreatment MRI.

Methods: 243 patients who received DCE-MRI scans prior to neo-adjuvant chemotherapy (NAC) at institution A [n=83], B [n=76], or one of nine other institutions as part of the ISPY1 Trial [n=84] were divided randomly into training (n=123) and testing (n=120) sets. 148 patients were HER2- and received neoadjuvant AC-T, while the 95 HER2+ patients were treated with TCHP (ISPY predates anti-HER2 therapy and HER2+ ISPY patients were excluded). 79 patients achieved pathological complete response [pCR, ypT0/is] following NAC. MRI exams were collected with a 1.5 or 3 Tesla scanner in the axial or sagittal plane. A baseline scan and 2-5 scans after injection of a gadolinium-based contrast agent with a median temporal resolution of 2.5 minutes were acquired. A portion of the tumor was manually delineated, then semi-automatically expanded to 3D. Vasculature was isolated from subtraction images with a specialized filtering approach to detect vessel-shaped objects. Features describing the 3D shape and architecture of the tumor-associated vessel network (e.g. curvature, torsion, and local orientation) and functional semi-quantitative pharmacokinetic (PK) measurements of temporal contrast enhancement changes (e.g. signal enhancement ratio, time to peak enhancement, and rates of uptake and washout) were calculated. Performance was assessed by area under the receiver operating characteristic curve (AUC), as well as the accuracy, sensitivity, and specificity at the operating point corresponding to the Youden Index. The most discriminating features were determined based on frequency of selection by the random forest classifier.

Results: Within the training set, PK parameters of the vessels (AUC=.66) outperformed relative to the PK of tumor (AUC=.63) and the PK of peritumoral regions (AUC=.64); however, a combination of the three yielded best performance (AUC=.75). Vessel shape features alone achieved AUC=.67 in the training set. When multi-region PK features and tumor shape features were combined and applied to the 120-patient independent testing set, the random forest classifier achieved an AUC of 0.70 and identified 81% of patients who would achieve pCR. Non-pCR was best characterized by increased vessel curvature and PK parameters indicating poor perfusion, such as greater time to peak enhancement, slower uptake rate, and quicker washout.

Conclusions: Our findings suggest that properties of the tumor-associated vessel network, such as its shape and enhancement profile, might provide value in identifying patients who will respond to NAC before administration of treatment.

Accuracy (%)Sensitivity (%)Specificity (%)Full testing set (n=120)678160HER2+ (n=44)709548HER2- (n=76)646365HER2-, HR+ (n=51)678364Triple Negative (n=25)605067

Citation Format: Nathaniel Braman, Prateek Prasanna, Kaustav Bera, Mehdi Alilou, Manasa Vulchi, Maryam Etesami, Paulette Turk, Jame Abraham, Donna Plecha, Anant Madabhushi. Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-06.

Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI

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Background: HER2-targeted neoadjuvant chemotherapy (NAC) possesses heterogeneous outcomes and currently lacks clinically-accepted markers of response. A means of predicting which patients will benefit prior to the treatment could reduce toxicity and the delay to effective intervention. Computational analysis of MRI via a deep neural network has shown promise in identifying NAC responders among mixed receptor subtype and treatment regimen cohorts, but faces challenges due to reproducibility across institutions and has not yet been explored in the context of HER2-targeted therapy. Here we present a deep learning approach for predicting response to HER2-targeted NAC from pre-treatment MRI. Methods: 100 HER2+ breast cancer patients who received NAC with docetaxel, carboplatin, trastuzumab, and pertuzumab at Cleveland Clinic (CCF) and had pre-treatment contrast-enhanced MRI’s were included in this analysis. 49 patients achieved pathological complete response (pCR, ypT0/is), while 51 patients retained presence of residual disease following NAC (non-pCR). 85 patients were used to train a convolutional neural network to predict pCR based on pre- and post-contrast MRI images, and the model design was optimized based on performance within a 15 patient internal validation cohort. An external, held-out testing dataset consisting of 28 patients (16 pCR, 12 non-pCR) imaged and treated at University Hospitals (UH) Cleveland Medical Center was used to validate the performance of the model. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A multivariable model incorporating age, hormone receptor status, stage, and tumor size was developed and similarly evaluated. Results: The neural network was able to predict the response to HER2-targeted NAC in the internal validation cohort (AUC = 0.93) as well as in an independent cohort from a separate institution (AUC = 0.85). This model offered superior performance compared to a multivariate clinical model, which achieved AUC = 0.67 and AUC = 0.52, in internal validation and external held-out testing cohorts, respectively. Conclusions: Deep learning analysis of contrast-enhanced MRI could be used to better target anti-HER2 therapy by pre-treatment prediction of response.[Table: see text]

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer

IMPORTANCE

There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer.

OBJECTIVE

To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy.

DESIGN, SETTING, AND PARTICIPANTS

In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019.

MAIN OUTCOMES AND MEASURES

Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting.

RESULTS

In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002).

CONCLUSIONS AND RELEVANCE

A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.

Segment-specific associations between local haemodynamic and imaging markers of early atherosclerosis at the carotid artery: an in vivo human study

Low and oscillatory wall shear stress (WSS) has long been hypothesized as a risk factor for atherosclerosis; however, evidence has been inferred primarily from model and post-mortem studies, or clinical studies of patients with already-developed plaques. This study aimed to identify associations between local haemodynamic and imaging markers of early atherosclerosis. Comprehensive magnetic resonance imaging allowed quantification of contrast enhancement (CE) (a marker of endothelial dysfunction) and vessel wall thickness at two distinct segments: the internal carotid artery bulb and the common carotid artery (CCA). Strict criteria were applied to a large dataset to exclude inward remodelling, resulting in 41 cases for which personalized computational fluid dynamic simulations were performed. After controlling for cardiovascular risk factors, bulb wall thickening was found to be weakly, but not significantly, associated with oscillatory WSS. CE at the bulb was significantly associated with low WSS (p < 0.001) and low flow helicity (p < 0.05). No significant associations were found for the CCA segment. Local haemodynamics at the bulb were significantly correlated with blood flow rates and heart rates, but not carotid bifurcation geometry (flare and curvature). Therefore low, but not oscillatory, WSS is an early independent marker of atherosclerotic changes preceding intimal thickening at the carotid bulb.

Abstract P4-02-06: Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers

Background: Applying the PAM50 classifier to targeted RNA-Sequencing data allows HER2+ tumors to be sub-categorized into intrinsic breast cancer subtypes. HER2+ breast cancers belonging to the HER2-enriched [HER2-E] subtype exhibit the highest rate of response to neoadjuvant therapy with combination of HER2-blockade and chemotherapy, as well as dual-HER2 blockade alone. A non-invasive predictor of PAM50 subtype from clinical dynamic contrast-enhanced MRI [DCE-MRI] could provide valuable clinical guidance in the treatment of HER2+ breast cancer. In this work, we identify a set of computer-extracted heterogeneity features computed within the lesion and its surrounding peritumoral region capable of distinguishing HER2-E from other HER2+ breast cancers [Non-HER2-E]. We then demonstrate that this imaging signature of HER2-E is also predictive of pathological complete response [pCR] in an independent HER2+ testing set, consistent with the HER2-E subtype's elevated response to HER2-targeted therapy.

Methods: The training set consisted of 42 HER2+ patients with both 1.5 or 3 T DCE-MRI and targeted RNA sequencing collected prior to neoadjuvant treatment from a multicenter trial [BrUOG 211B, n=35] and The Cancer Genome Atlas-Breast Cancer project [TCGA-BRCA, n=7]. Intrinsic subtypes were assigned by unsupervised hierarchical clustering of the PAM50 gene set. 19 patients were determined to belong to the HER2-E subtype, while the remaining 23 represented non-HER2-E subtypes [19 HER2-Luminal, 4 HER2-basal]. Lesion boundaries were annotated by an expertly trained radiologist and expanded to 5 annular peritumoral regions in 3 mm increments out to a maximum radius of 15 mm. Computer-extracted heterogeneity features were computed voxelwise within intratumoral and peritumoral regions by first order statistics. A top HER2-E-associated feature from each region was identified by Wilcoxon feature selection and used to train a diagonal linear discriminant analysis [DLDA] classifier to predict HER2-E in a 3-fold cross-validation setting. This classifier was then applied to pCR prediction from DCE-MRI in a testing set of 28 HER2+ patients with available post neoadjuvant chemotherapy surgical specimens at one institution. 16 patients achieved pCR (ypT0/is), while the remainder had partial or no response (non-pCR).

Results: A combination of heterogeneity features within the intratumoral region and annular peritumoral regions out to 12 mm from the tumor yielded optimal results within the training set, with an average HER2-E prediction AUC of .77 +/- .03. When applied to response prediction in an independent testing set, this HER2-E classifier was predictive of pCR (AUC = .72).

Conclusions: Computer-extracted heterogeneity features calculated within the tumor and the surrounding peritumoral environment on DCE-MRI were able to distinguish the HER2-E PAM50 intrinsic subtype from other HER2+ breast cancers. HER2-E was characterized by elevated expression of intratumoral and peritumoral heterogeneity features, indicating a more disordered imaging phenotype within and around the tumor. Additional independent validation of these findings is needed.

Citation Format: Braman N, Prasanna P, Singh S, Beig N, Gilmore H, Etesami M, Bates D, Gallagher K, Bloch BN, Somlo G, Sikov W, Harris L, Plecha D, Varadan V, Madabhushi A. Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-06.

Detector-based spectral CT with a novel dual-layer technology: principles and applications

Detector-based spectral computed tomography is a novel dual-energy CT technology that employs two layers of detectors to simultaneously collect low- and high-energy data in all patients using standard CT protocols. In addition to the conventional polyenergetic images created for each patient, projection-space decomposition is used to generate spectral basis images (photoelectric and Compton scatter) for creating multiple spectral images, including material decomposition (iodine-only, virtual non-contrast, effective atomic number) and virtual monoenergetic images, on-demand according to clinical need. These images are useful in multiple clinical applications, including- improving vascular contrast, improving lesion conspicuity, decreasing artefacts, material characterisation and reducing radiation dose. In this article, we discuss the principles of this novel technology and also illustrate the common clinical applications.

Teaching points

• The top and bottom layers of dual-layer CT absorb low- and high-energy photons, respectively.

• Multiple spectral images are generated by projection-space decomposition.

• Spectral images can be generated in all patients scanned in this scanner.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Background

In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

Methods

A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR⁺, HER2⁻) and triple-negative or HER2⁺ (TN/HER2⁺) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.

Results

Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR⁺, HER2⁻ group using DLDA and 0.93 ± 0.018 within the TN/HER2⁺ group using a naive Bayes classifier. In HR⁺, HER2⁻ breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2⁺ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.

Conclusions

Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

Utility of late gadolinium enhancement in pediatric cardiac MRI

Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging sequence is increasingly used in the evaluation of pediatric cardiovascular disorders, and although LGE might be a normal feature at the sites of previous surgeries, it is pathologically seen as a result of extracellular space expansion, either from acute cell damage or chronic scarring or fibrosis. LGE is broadly divided into ischemic and non-ischemic patterns. LGE caused by myocardial infarction occurs in a vascular distribution and always involves the subendocardial portion, progressively involving the outer regions in a waveform pattern. Non-ischemic cardiomyopathies can have a mid-myocardial (either linear or patchy), subepicardial or diffuse subendocardial distribution. Idiopathic dilated cardiomyopathy can have a linear mid-myocardial pattern, while hypertrophic cardiomyopathy can have fine, patchy enhancement in hypertrophied and non-hypertrophied segments as well as right ventricular insertion points. Myocarditis and sarcoidosis have a mid-myocardial or subepicardial pattern of LGE. Fabry disease typically affects the basal inferolateral segment while Danon disease typically spares the septum. Pericarditis is characterized by diffuse or focal pericardial thickening and enhancement. Thrombus, the most common non-neoplastic cardiac mass, is characterized by absence of enhancement in all sequences, while neoplastic masses show at least some contrast enhancement, depending on the pathology. Regardless of the etiology, presence of LGE is associated with a poor prognosis. In this review, we describe the technical modifications required for performing LGE cardiac MR sequence in children, review and illustrate the patterns of LGE in children, and discuss their clinical significance.

Computed tomography in the evaluation of vascular rings and slings

Vascular rings are congenital abnormalities of the aortic arch-derived vascular and ligamentous structures, which encircle the trachea and oesophagus to varying degrees, resulting in respiratory or feeding difficulties in children. A sling is an abnormality of the pulmonary arterial system resulting in airway compression. Although several imaging examinations are available for the evaluation of these anomalies, computed tomography (CT) has become the preferred test because of rapid acquisitions, making it feasible to perform the study without sedation or general anaesthesia. Furthermore, CT provides excellent spatial and temporal resolution, a wide field of view, multiplanar reconstruction capabilities and simultaneous evaluation of the airway. In this review, the current role and technique of CT in the evaluation of vascular rings are discussed. A brief discussion of the embryology of the aorta and branch vessels is followed by discussion and illustration of common and some uncommon vascular rings along with critical information required by surgeons. Teaching Points • Computed tomography is valuable in the evaluation of vascular rings.• Due to variable clinical and imaging presentations, diagnosis of vascular rings is often challenging.• Laterality of the arch is critical in surgical management.

Impact of T2 decay on carotid artery wall thickness measurements

PURPOSE

To investigate the impact of T2 relaxation of the carotid wall on measurements of its thickness.

MATERIALS AND METHODS

The common carotid artery wall was imaged using a spin echo sequence acquired at four echo times (17 ms to 68 ms) in 65 participants as part of VALIDATE study. Images were acquired transverse to the artery 1.5 cm proximal to the flow divider. Mean wall thickness, mean wall signal intensity, lumen area, and outer wall area were measured for each echo. Contours were also traced on the image from the fourth echo and then propagated to the images from the preceding echoes. This was repeated using the image from the first echo. Mean wall signal intensity measurements at the four echo times were fit to a mono-exponential decay curve to derive the mean T2 relaxation time for each set of contours.

RESULTS

Mean wall thickness decreased with increasing echo time, with an average thickness reduction of 8.6% between images acquired at the first and last echo times (TE) (0.93 mm at TE 17 ms versus 0.85 mm at TE 68 ms, P < 0.001). Average T2 relaxation time of the carotid wall decreased by 3% when the smaller contours from the last echo were used, which excluded the outer-most layer (54.3 ± 7.6 ms versus 52.7 ± 6.6 ms, P = 0.03).

CONCLUSION

Carotid wall thickness measurements decrease with echo time as expected by the fast T2 relaxation time of the outer-most layer, namely the adventitia. A short echo time is needed for thickness measurements to include adventitia, which plays an important role in plaque development.

Comparison of carotid plaque ulcer detection using contrast-enhanced and time-of-flight MRA techniques

BACKGROUND AND PURPOSE

Ulceration in carotid plaque is a risk indicator for ischemic stroke. Our aim was to compare plaque ulcer detection by standard TOF and CE-MRA techniques and to identify factors that influence its detection.

MATERIALS AND METHODS

Carotid MR imaging scans were acquired on 2066 participants in the ARIC study. We studied the 600 thickest plaques. TOF-MRA, CE-MRA, and black-blood MR images were analyzed together to define ulcer presence (plaque surface niche ≥2 mm in depth). Sixty ulcerated arteries were detected. These arteries were randomly assigned, along with 40 nonulcerated plaques from the remaining 540, for evaluation of ulcer presence by 2 neuroradiologists. Associations between ulcer detection and ulcer characteristics, including orientation, location, and size, were determined and explored by CFD modeling.

RESULTS

One CE-MRA and 3 TOF-MRAs were noninterpretable and excluded. Of 71 ulcers in 56 arteries, readers detected an average of 39 (55%) on both TOF-MRA and CE-MRA, 26.5 (37.5%) only on CE-MRA, and 1 (1.5%) only on TOF-MRA, missing 4.5 (6%) ulcers by both methods. Ulcer detection by TOF-MRA was associated with its orientation (distally pointing versus perpendicular: OR = 5.57 [95% CI, 1.08-28.65]; proximally pointing versus perpendicular: OR = 0.21 [95% CI, 0.14-0.29]); location relative to point of maximum stenosis (distal versus isolevel: OR = 5.17 [95% CI, 2.10-12.70]); and neck-to-depth ratio (OR = 1.96 [95% CI, 1.11-3.45]) after controlling for stenosis and ulcer volume.

CONCLUSIONS

CE-MRA detects more ulcers than TOF-MRA in carotid plaques. Missed ulcers on TOF-MRA are influenced by ulcer orientation, location relative to point of maximum stenosis, and neck-to-depth ratio.

Carotid plaque neovascularization and hemorrhage detected by MR imaging are associated with recent cerebrovascular ischemic events

BACKGROUND AND PURPOSE

Pathologic studies suggest that neovascularization and hemorrhage are important features of plaque vulnerability for disruption. Our aim was to determine the associations of these features in carotid plaques with previous cerebrovascular ischemic events by using high-resolution CE-MRI.

MATERIALS AND METHODS

Forty-seven patients (36 men; mean age 72.5 ± 10 years) underwent CE-MRI and MRA examinations for carotid plaque at 3T. IPH presence was recorded. Neovascularity was categorized by the degree of adventitial enhancement (0, absent; 1, <50%; 2, ≥50%). Reader variability was assessed by using weighted κ. Associations with events were determined by using multivariable logistic regression.

RESULTS

Intra- and inter-reader agreement for grading adventitial enhancement were good to excellent. IPH was present in 49% of patients and was associated with events (P = .03). Patients grouped by categories 0, 1, and 2 adventitial enhancement had increasing frequencies of events (14% category 0, 48% category 1, 65% category 2; P = .02). Events were associated with IPH (OR, 10.18; 95% CI, 1.42-72.21) and adventitial enhancement (compared with category 0: OR, 14.90, 95% CI, 0.98-225.93 for category 1; OR, 51.17, 95% CI, 3.4-469.8 for category 2) after controlling for age, sex, cardiovascular risk factors, wall thickness, and stenosis. Stenosis was not associated with events.

CONCLUSIONS

Adventitial enhancement and IPH are independently associated with previous events and may provide important insight into stroke risk not achievable by stenosis.

Identification of intraplaque hemorrhage on MR angiography images: a comparison of contrast-enhanced mask and time-of-flight techniques

BACKGROUND AND PURPOSE

MRA is widely used to measure carotid narrowing. Standard CE- and TOF-MRA techniques use highly T1-weighted gradient-echo sequences that can detect T1 short blood products, so they have the potential to identify IPH, an indicator of plaque rupture. We sought to determine the accuracy and reliability of these MRA sequences to detect IPH.

MATERIALS AND METHODS

3D TOF and CE carotid MRA scans were obtained at 3T on 15 patients (age range, 58-86 years; 13 men) scheduled for CEA. The source images from the precontrast (mask) CE-MRA and the TOF sequences were reviewed by 2 independent readers for IPH presence (identified as hyperintense signal intensity compared with adjacent muscle). CEA specimens were stained with antibody against glycophorin A and Mallory stain to detect IPH and were correlated with MR images.

RESULTS

Nine of 15 CEA specimens (61 of 144 MR images) contained IPH confirmed by histology. Compared with TOF, CE-MRA mask demonstrated greater sensitivity, specificity, PPV, and NPV for IPH detection. The accuracy for correctly identifying IPH by using CE-MRA mask images and TOF images was 94% and 84%, respectively. Inter- and intraobserver agreement for IPH detection was excellent by mask images (κ = 0.91 and κ = 0.94, respectively) and TOF images (κ = 0.77 and κ = 0.84, respectively).

CONCLUSIONS

CE-MRA mask images are highly accurate and reliable for identifying IPH, more so than the TOF sequence, and can potentially provide valuable information about risk for rupture.