Faculty Bibliography

PURPOSE/OBJECTIVE:To investigate the validity of contrast kinetic parameter estimates from Active Contrast Encoding (ACE)-MRI against those from conventional Dynamic Contrast-Enhanced (DCE)-MRI for evaluation of tumor treatment response in mouse tumor models. METHODS:The ACE-MRI method that incorporates measurement of T1 and B1 into the enhancement curve washout region, was implemented on a 7T MRI scanner to measure tracer kinetic model parameters of 4T1 and GL261 tumors with treatment using bevacizumab and 5FU. A portion of the same ACE-MRI data was used for conventional DCE-MRI data analysis with a separately measured pre-contrast T1 map. Tracer kinetic model parameters, such as Ktrans (permeability area surface product) and ve (extracellular space volume fraction), estimated from ACE-MRI were compared with those from DCE-MRI, in terms of correlation and Bland-Altman analyses. RESULTS:A three-fold increase of the median Ktrans by treatment was observed in the flank 4T1 tumors by both ACE-MRI and DCE-MRI. In contrast, the brain tumors did not show a significant change by the treatment in either ACE-MRI or DCE-MRI. Ktrans and ve values of the tumors from ACE-MRI were strongly correlated with those from DCE-MRI methods with correlation coefficients of 0.92 and 0.78, respectively, for the median values of 17 tumors. The Bland-Altman plot analysis showed a mean difference of -0.01 min-1 for Ktrans with the 95% limits of agreement of -0.12 min-1 to 0.09 min-1, and -0.05 with -0.37 to 0.26 for ve. CONCLUSION/CONCLUSIONS:The tracer kinetic model parameters estimated from ACE-MRI and their changes by treatment closely matched those of DCE-MRI, which suggests that ACE-MRI can be used in place of conventional DCE-MRI for tumor progression monitoring and treatment response evaluation with a reduced scan time.

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. (i) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. (ii) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. (iii) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. (iv) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breastcancerclassifier.

RATIONALE AND OBJECTIVES/OBJECTIVE:Pathologic complete response (pCR) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer after HER2-targeted therapy correlates increased disease-free survival and decreased mastectomy rates. The aim of this study was to explore tumor shrinkage patterns and initial tumor enhancement with pCR in HER2-positive breast cancer. MATERIALS AND METHODS/METHODS:This was an institutional review board-approved retrospective analysis of 51 HER2 positive breast cancer patients with breast MRI both pre- and post-HER2-targeted therapy. Initial enhancement ratio (IER, initial enhancement percentage over baseline at first postcontrast imaging), pattern of tumor shrinkage, and Dynamic contrast enhanced (DCE)-MRI imaging features were assessed. Wilcoxon rank, Spearman correlation, Fisher's exact, and Mann-Whitney tests were used to correlate MRI imaging features with pCR. IER reader agreement was evaluated by intraclass correlation. Binary logistic regression was used to evaluate multivariate associations with pCR. RESULTS:56.9% (29/51) of patients had pCR at surgery. Concentric tumor shrinkage pattern was associated with pCR (p = 0.001, Area under the curve (AUC) 0.778): accuracy 80.4%, specificity 96.6%, and sensitivity of 59.1%. There was no association with pCR and imaging response as defined by RECIST criteria (p = 0.169), pretreatment IER (Reader 1 (R1) p = 0.665, Reader 2 (R2) p = 0.766), or lesion size (p = 0.69). IER was associated with axillary metastases (R1 p = 0.016, R2 < 0.001) and ki-67 (R1 r = 0.52, p = 0.008, R2 r = -0.44, p = 0.028). CONCLUSION/CONCLUSIONS:The shrinkage pattern of HER2-positive tumors after targeted therapy may be associated with pCR. There was no association between IER and pCR. Future studies evaluating the correlation of shrinkage patterns to texture radiomics are of interest.

Purpose of review/UNASSIGNED:This article is to review recent technical developments and their clinical applications in cancer imaging quantitative measurement of cellular and vascular properties of the tumors. Recent findings/UNASSIGNED:Rapid development of fast Magnetic Resonance Imaging (MRI) technologies over last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously. Summary/UNASSIGNED:Diffusion MRI (dMRI) and dynamic contrast enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion weighting gradient strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potentially important methods for quantitative cancer imaging, we discuss the basic concepts and recent developments, as well as future directions for further development.

PURPOSE/OBJECTIVE:). METHODS:estimation using the single-flip-angle (SFA) protocol with that using the double-flip-angle (DFA) protocol. Data analysis was conducted using the two-compartment exchange model combined with the three-site-two-exchange model for water exchange. RESULTS:than the other contrast kinetic parameters. CONCLUSION/CONCLUSIONS:estimation.

PURPOSE/OBJECTIVE:To investigate the potential of diffusional kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) in the evaluation of additional suspicious lesions at preoperative breast magnetic resonance imaging (MRI) in patients with breast cancer. MATERIALS AND METHODS/METHODS:. Histogram parameters (mean, standard deviation, minimum, maximum, 10th, 25th, 50th, 75th, 90th percentiles, kurtosis, skewness and entropy) of ADC from DWI and diffusivity (D), kurtosis (K) from DKI were calculated after postprocessing. Parameters were compared between benign vs. ductal carcinoma in situ (DCIS) vs. invasive breast lesions and diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. Correlation between the mean values of D and K was analyzed according to lesion type. RESULTS: = -0.853), but no significant correlation in DCIS. CONCLUSION/CONCLUSIONS:DKI may aid in the differentiation of additional suspicious lesions at preoperative breast MRI. Both ADC and DKI may have lower potential in differentiating DCIS from benign lesions.

BACKGROUND:Dynamic contrast enhanced (DCE) breast MRI is highly sensitive for breast cancer and requires gadolinium-based contrast agents (GBCA)s, which have potential safety concerns. PURPOSE/OBJECTIVE:Test whether breast cancers imaged by 3T DCE breast MRI with 0.05 mmol/kg of gadobutrol are detectable. METHODS:Analysis of 3T DCE breast MRIs with half dose of gadobutrol from patients included in an IRB-approved and HIPPA-compliant prospective study of breast PET/MRI. Between 11/7/2014 and 3/2/2018, 41 consecutive women with biopsy-proven breast cancer that was at least 2 cm, multi-focal or multi-centric, had axillary metastasis, or had skin involvement who gave informed consent were included. Two breast radiologists independently recorded lesion conspicuity on a 4-point scale (0 = not seen, 1 = questionably seen, 2 = adequately seen, 3 = certainly seen), and measured the lesion. Size was compared between radiologists and with size on available mammogram, ultrasound, MRI, and surgical pathology. Inter-reader agreement was assessed by kappa coefficient for conspicuity. Lesion size comparisons were assessed using the Spearman rank correlation. RESULTS:In 40 patients (ages 28.4-80.5, 51.9 years), there were 49 cancers. 10.1% of lesions were 1 cm or less and 26.5% of lesions were 2 cm or less. Each reader detected 49/49 cancers. Conspicuity scores ranged from 2 to 3, mean 2.9/3 for both readers (p = 0.47). Size on half-dose 3T DCE-MRI correlated with size on surgical pathology (r = 0.6, p = 0.03) while size on mammogram and ultrasound did not (r = 0.25, p = 0.46; r = 0.25, p = 0.42). CONCLUSION/CONCLUSIONS:All breast cancers in this cohort, as small as 0.4 cm, were seen on 3T DCE breast MRI with 0.05 mmol/kg dose of gadobutrol.

Background and Objectives: We previously proposed a nomogram predicting individual risks of malignancy and invasiveness of intraductal papillary mucinous neoplasms and validated it in an external cohort. However, it is difficult to apply if data on tumor marker are lacking. The aim of the current study was to develop a new nomogram based on radiologic findings using previous nomogram development and an external validation cohort.
Material(s) and Method(s): A total of 3049 patients who underwent surgical resection at 30 tertiary institutes in 7 countries were enrolled and clinicopathologic data were retrospectively analyzed. Based on fitted model, area under the receiver operating characteristics curve (AUC) was calculated using 10-fold cross validation by exhaustive search.
Result(s): The study consisted of 1914 (62.8%) patients for previous nomogram development and 1135 patients (37.2%) in the external validation cohort. Among patients, 1898 (62.3%) had low, 577 (18.9%) had high grade dysplasia, and 574 (18.8%) had invasive carcinoma. Patients were allocated randomly into model development and test sets to construct the nomogram, with fixed ratios according to malignancy and invasiveness. Exhaustive search resulted in three variables (cyst size, duct dilatation, and mural nodule) for malignancy and four variables (cyst size, duct dilatation, mural nodule, and location) for invasiveness being selected to construct the nomogram, and AUC was 0.742 and 0.741, respectively. AUC for test set was 0.727 and 0.704, respectively, and Hosmer-Lemeshow goodness of fit test showed good discrimination power (p = 0.066 and 0.067, respectively).
Conclusion(s): The new nomogram based on radiologic findings is accurate and helpful in identifying patients at risk of malignancy and invasiveness and selecting treatment options in clinical settings.
Copyright

Tissue microstructure modeling of diffusion MRI signal is an active research area striving to bridge the gap between macroscopic MRI resolution and cellular-level tissue architecture. Such modeling in neuronal tissue relies on a number of assumptions about the microstructural features of axonal fiber bundles, such as the axonal shape (e.g., perfect cylinders) and the fiber orientation dispersion. However, these assumptions have not yet been validated by sufficiently high-resolution 3-dimensional histology. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a random-walker (RaW)-based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed by a segmentation based on human annotations initiated with conventional machine-learning-based carving, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated MRI-relevant estimates of size-related parameters (inner axonal diameter, its distribution, along-axon variation, and myelin g-ratio), and orientation-related parameters (fiber orientation distribution and its rotational invariants; dispersion angle). The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, while the reported diameter exceeds those in other mouse brain studies. Furthermore, we calculated how these quantities would evolve in actual diffusion MRI experiments as a function of diffusion time, thereby providing a coarse-graining window on the microstructure, and showed that the orientation-related metrics have negligible diffusion time-dependence over clinical and pre-clinical diffusion time ranges. However, the MRI-measured inner axonal diameters, dominated by the widest cross sections, effectively decrease with diffusion time by ~ 17% due to the coarse-graining over axonal caliber variations. Furthermore, our 3d measurement showed that there is significant variation of the diameter along the axon. Hence, fiber orientation dispersion estimated from MRI should be relatively stable, while the "apparent" inner axonal diameters are sensitive to experimental settings, and cannot be modeled by perfectly cylindrical axons.