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.
Purpose
To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma.
Materials and methods
Following IRB ...approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data.
Results
One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 (
p
< 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 (
p
< 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively.
Conclusion
Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.
Purpose:
By performing registration of preoperative multiprotocolin vivo magnetic resonance (MR) images of the prostate with corresponding whole-mount histology (WMH) sections from postoperative ...radical prostatectomy specimens, an accurate estimate of the spatial extent of prostate cancer (CaP) on in vivo MR imaging (MRI) can be retrospectively established. This could allow for definition of quantitative image-based disease signatures and lead to development of classifiers for disease detection on multiprotocol in vivo MRI. Automated registration of MR and WMH images of the prostate is complicated by dissimilar image intensities, acquisition artifacts, and nonlinear shape differences.
Methods:
The authors present a method for automated elastic registration of multiprotocolin vivo MRI and WMH sections of the prostate. The method, multiattribute combined mutual information (MACMI), leverages all available multiprotocol image data to drive image registration using a multivariate formulation of mutual information.
Results:
Elastic registration using the multivariate MI formulation is demonstrated for 150 corresponding sets of prostate images from 25 patient studies with T2-weighted and dynamic-contrast enhanced MRI and 85 image sets from 15 studies with an additional functional apparent diffusion coefficient MRI series. Qualitative results of MACMI evaluation via visual inspection suggest that an accurate delineation of CaP extent on MRI is obtained. Results of quantitative evaluation on 150 clinical and 20 synthetic image sets indicate improved registration accuracy using MACMI compared to conventional pairwise mutual information-based approaches.
Conclusions:
The authors’ approach to the registration ofin vivo multiprotocol MRI and ex vivo WMH of the prostate using MACMI is unique, in that (1) information from all available image protocols is utilized to drive the registration with histology, (2) no additional, intermediate ex vivo radiology or gross histology images need be obtained in addition to the routinely acquired in vivo MRI series, and (3) no corresponding anatomical landmarks are required to be identified manually or automatically on the images.
To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast enhanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk ...categories. We collected 96 ER-positive breast lesions with low (< 18, N = 55) and high (> 30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively characterize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.
For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier ...comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI.
Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve.
The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods.
Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.
Purpose:
Transrectal ultrasound (TRUS)‐guided needle biopsy is the current gold standard for prostate cancer diagnosis. However, up to 40% of prostate cancer lesions appears isoechoic on TRUS. Hence, ...TRUS‐guided biopsy has a high false negative rate for prostate cancer diagnosis. Magnetic resonance imaging (MRI) is better able to distinguish prostate cancer from benign tissue. However, MRI‐guided biopsy requires special equipment and training and a longer procedure time. MRI‐TRUS fusion, where MRI is acquired preoperatively and then aligned to TRUS, allows for advantages of both modalities to be leveraged during biopsy. MRI‐TRUS‐guided biopsy increases the yield of cancer positive biopsies. In this work, the authors present multiattribute probabilistic postate elastic registration (MAPPER) to align prostate MRI and TRUS imagery.
Methods:
MAPPER involves (1) segmenting the prostate on MRI, (2) calculating a multiattribute probabilistic map of prostate location on TRUS, and (3) maximizing overlap between the prostate segmentation on MRI and the multiattribute probabilistic map on TRUS, thereby driving registration of MRI onto TRUS. MAPPER represents a significant advancement over the current state‐of‐the‐art as it requires no user interaction during the biopsy procedure by leveraging texture and spatial information to determine the prostate location on TRUS. Although MAPPER requires manual interaction to segment the prostate on MRI, this step is performed prior to biopsy and will not substantially increase biopsy procedure time.
Results:
MAPPER was evaluated on 13 patient studies from two independent datasets—Dataset 1 has 6 studies acquired with a side‐firing TRUS probe and a 1.5 T pelvic phased‐array coil MRI; Dataset 2 has 7 studies acquired with a volumetric end‐firing TRUS probe and a 3.0 T endorectal coil MRI. MAPPER has a root‐mean‐square error (RMSE) for expert selected fiducials of 3.36 ± 1.10 mm for Dataset 1 and 3.14 ± 0.75 mm for Dataset 2. State‐of‐the‐art MRI‐TRUS fusion methods report RMSE of 3.06–2.07 mm.
Conclusions:
MAPPER aligns MRI and TRUS imagery without manual intervention ensuring efficient, reproducible registration. MAPPER has a similar RMSE to state‐of‐the‐art methods that require manual intervention.
Abstract Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological ...images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo . However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.
To compare prostate gland volume (PV) estimation of automated computer-generated multifeature active shape models (MFAs) performed with 3-T magnetic resonance (MR) imaging with that of other methods ...of PV assessment, with pathologic specimens as the reference standard.
All subjects provided written informed consent for this HIPAA-compliant and institutional review board-approved study. Freshly weighed prostatectomy specimens from 91 patients (mean age, 59 years; range, 42-84 years) served as the reference standard. PVs were manually calculated by two independent readers from MR images by using the standard ellipsoid formula. Planimetry PV was calculated from gland areas generated by two independent investigators by using manually drawn regions of interest. Computer-automated assessment of PV with an MFA was determined by the aggregate computer-calculated prostate area over the range of axial T2-weighted prostate MR images. Linear regression, linear mixed-effects models, concordance correlation coefficients, and Bland-Altman limits of agreement were used to compare volume estimation methods.
MFA-derived PVs had the best correlation with pathologic specimen PVs (slope, 0.888). Planimetry derived volumes produced slopes of 0.864 and 0.804 for two independent readers when compared with specimen PVs. Ellipsoid formula-derived PVs had slopes closest to one when compared with planimetry PVs. Manual MR imaging and MFA PV estimates had high concordance correlation coefficients with pathologic specimens.
MFAs with axial T2-weighted MR imaging provided an automated and efficient tool with which to assess PV. Both MFAs and MR imaging planimetry require adjustments for optimized PV accuracy when compared with prostatectomy specimens.
The Moon boasts an unexpected variety of landscapes - including volcanic features, sinuous valleys and ghost craters - which are readily visible from Earth. This practical guide explains and ...demonstrates how you can capture impressive images of our nearest neighbour in space using a variety of different techniques. As the first guide to be dedicated to modern lunar imaging, this volume offers an in-depth and illustrated approach to common optics, the essentials of digital images, imaging devices, and image processing software. Even in light-polluted areas, the countless features and finest details of the Moon can be captured by following the instructions in this comprehensive and accessible guide. Covering equipment ranging from smartphones and DSLRs to specialist planetary cameras, whether you are a novice without a telescope, an amateur developing your skills in imaging, or an experienced astrophotographer, you will benefit from the hints, insights and expertise within.
Purpose
In SBRT for prostate cancer, higher fractional dose to the rectum is a major toxicity concern due to using smaller PTV margin and hypofractionation. We investigate the dosimetric impact on ...rectum using endorectal balloon (ERB) in prostate SBRT.
Materials and Methods
Twenty prostate cancer patients were included in a retrospective study, ten with ERB and 10 without ERB. Optimized SBRT plans were generated on CyberKnife MultiPlan for 5 × 7.25 Gy to PTV under RTOG‐0938 protocol for early‐stage prostate cancer. For the rectum and the anterior half rectum, mean dose and percentage of volumes receiving 50%, 80%, 90%, and 100% prescription dose were compared.
Results
Using ERB, mean dose to the rectum was 62 cGy (P = 0.001) lower per fraction, and 50 cGy (P = 0.024) lower per fraction for the anterior half rectum. The average V50%, V80%, V90%, and V100% were lower by 9.9% (P = 0.001), 5.3% (P = 0.0002), 3.4% (P = 0.0002), and 1.2% (P = 0.005) for the rectum, and lower by 10.4% (P = 0.009), 8.3% (P = 0.0004), 5.4% (P = 0.0003), and 2.1% (P = 0.003) for the anterior half rectum.
Conclusions
Significant reductions of dose to the rectum using ERB were observed. This may lead to improvement of the rectal toxicity profiles in prostate SBRT.
PDF
