Stereotactic radiosurgery delivers radiation with great spatial accuracy. To achieve sub-millimeter accuracy for intracranial SRS, a head ring is rigidly fixated to the skull to create a fixed reference. For some patients, the invasiveness of the ring can be highly uncomfortable and not well tolerated. In addition, placing and removing the ring requires special expertise from a neurosurgeon, and patient setup time for SRS can often be long. To reduce the invasiveness, hardware limitations and setup time, we are developing a system for performing accurate head positioning without the use of a head ring. The proposed method uses real-time 6D optical position feedback for turning on and off the treatment beam (gating) and guiding a motorcontrolled 3D head motion compensation stage. The setup consists of a central control computer, an optical patient motion tracking system and a 3D motion compensation stage attached to the front of the LINAC couch. A styrofoam head cast was custom-built for patient support and was mounted on the compensation stage. The motion feedback of the markers was processed by the control computer, and the resulting motion of the target was calculated using a rigid body model. If the target deviated beyond a preset position of 0.2 mm, an automatic position correction was performed with stepper motors to adjust the head position via the couch mount motion platform. In the event the target deviated more than 1 mm, a safety relay switch was activated and the treatment beam was turned off. The feasibility of the concept was tested using five healthy volunteers. Head motion data were acquired with and without the use of motion compensation over treatment times of 15 min. On average, test subjects exceeded the 0.5 mm tolerance 86% of the time and the 1.0 mm tolerance 45% of the time without motion correction.

Background Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail. Purpose To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps. Study Type Retrospective, single center. Population In all, 149 patients, with a total of 165 lesions scored as BI‐RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm3. Field Strength/Sequence Higher spatial resolution T1‐weighted dynamic contrast‐enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T. Assessment Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first‐order statistics, gray level co‐occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (~75% of cases) and a test dataset (~25% of cases). Statistical Tests Comparison of medians was assessed using the nonparametric Mann–Whitney U‐test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models. Results Univariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features (P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75–0.81. High negative (>89%) and positive predictive values (>83%) were found for all models. Data Conclusion Radiomics analysis of small contrast‐enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468–1477.

Background: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery. Methods: This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre-and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre-and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature prefiltering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results: Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions: This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.

Background: Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods: Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant. Results: Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001).Conclusions: Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

We present a segmentation approach that combines GrowCut (GC) with cancer-specific multiparametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2− (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). Breast cancer is one of the most commonly diagnosed cancers in women and the second most common cause of cancer-related deaths 1 . Although the increasing availability of novel treatment options has helped to improve survival among patients, robust tools are critically needed to effectively monitor treatment response 2 . Miranikova et al. 3 have shown that tumour volumes measured on magnetic resonance imaging (MRI) predict treatment response in neoadjuvant settings. However, accurate and reproducible tumour segmentation is crucial for evaluating breast cancer response to treatments 4 and to improve surgical outcomes 5 . Accurate and reasonably fast segmentation is critical for radiomics analysis 6 which consists of extracting image features from large datasets with the purpose of identifying non-invasive image-based surrogates for diagnosis (differentiating disease aggressiveness) and for predicting treatment response. Radiomics analysis of breast cancers have been used for predicting cancer treatment outcomes 7-9 and for differentiating between breast cancers by molecular subytpe 10-13 or for classifying cancers by their aggressiveness 14,15 . The first and crucial step in extracting the various texture measures is segmentation of the cancer. With the exception of 11,15 , the vast majority of works have employed manual tumour segmentation for radiomics analysis due to the difficultly in ensuring accurate computer segmentations. However, manual delineation is time consuming. Therefore, majority of works 12-14 including ours 10,16 have used manual segmentation of one or a few representative slices. Recently, semi-automatic segmentations including GrowCut (GC) 17 have been reported to produce more reproducible texture features compared with features computed from manually delineated lung tumors 18 , thereby, underscoring the importance and utility of computer-generated segmentations for high-throughput radiomics.

Purpose The purpose of the study is to determine short-term reproducibility of apparent diffusion coefficient (ADC) estimated from diffusion-weighted magnetic resonance (DW-MR) imaging of the prostate. Methods Fourteen patients with biopsy-proven prostate cancer were studied under an Institutional Review Board-approved protocol. Each patient underwent two, consecutive and identical DW-MR scans on a 3T system. ADC values were calculated from each scan and a deformable registration was performed to align corresponding images. The prostate and cancerous regions of interest (ROIs) were independently analyzed by two radiologists. The prostate volume was analyzed by sextant. Per-voxel absolute and relative percentage variations in ADC were compared between sextants. Per-voxel and per-ROI variations in ADC were calculated for cancerous ROIs. Results Per-voxel absolute difference in ADC in the prostate ranged from 0 to 1.60 × 10−3 mm2/s (per-voxel relative difference 0% to 200%, mean 10.5%). Variation in ADC was largest in the posterior apex (0% to 200%, mean 11.6%). Difference in ADC variation between sextants was not statistically significant. Cancer ROIs’ per-voxel variation in ADC ranged from 0.001 × 10−3 to 0.841 × 10−3 mm2/s (0% to 67.4%, mean 11.2%) and per-ROI variation ranged from 0 to 0.463 × 10−3 mm2/s (mean 0.122 × 10−3 mm2/s). Conclusions Variation in ADC within the human prostate is reasonably small, and is on the order of 10%.

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OBJECTIVE The objective of our study was to evaluate the role of a hybrid T2-weighted imaging–DWI sequence for prostate cancer diagnosis and differentiation of aggressive prostate cancer from nonaggressive prostate cancer. MATERIALS AND METHODS Twenty-one patients with prostate cancer who underwent preoperative 3-T MRI and prostatectomy were included in this study. Patients underwent a hybrid T2-weighted imaging–DWI examination consisting of DW images acquired with TEs of 47, 75, and 100 ms and b values of 0 and 750 s/mm2. The apparent diffusion coefficient (ADC) and T2 were calculated for cancer and normal prostate ROIs at each TE and b value. Changes in ADC and T2 as a function of increasing the TE and b value, respectively, were analyzed. A new metric termed “PQ4” was defined as the percentage of voxels within an ROI that has increasing T2 with increasing b value and has decreasing ADC with increasing TE. RESULTS ADC values were significantly higher in normal ROIs than in cancer ROIs at all TEs (p < 0.0001). With increasing TE, the mean ADC increased 3% in cancer ROIs and increased 12% in normal ROIs. T2 was significantly higher in normal ROIs than in cancer ROIs at both b values (p ≤ 0.0002). The mean T2 decreased with increasing b value in cancer ROIs (ΔT2 = −17 ms) and normal ROIs (ΔT2 = −52 ms). PQ4 clearly differentiated normal ROIs from prostate cancer ROIs (p = 0.0004) and showed significant correlation with Gleason score (ρ = 0.508, p < 0.0001). CONCLUSION Hybrid MRI measures the response of ADC and T2 to changing TEs and b values, respectively. This approach shows promise for detecting prostate cancer and determining its aggressiveness noninvasively.