Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

CT angiographic measurement of vascular blood flow velocity by using projection data.

PURPOSE: To determine whether flow velocity can be measured by using projection data from computed tomographic (CT) scans obtained during contrast material injection in a phantom model. MATERIALS AND METHODS: The authors constructed a 12.7-mm-diameter single-channel flow phantom with constant water flow velocity settings of 25.3, 43.9, and 70.5 cm/sec. For each flow velocity, serial axial scans were obtained with 16-section multidetector CT while a 10-mL bolus of contrast material was injected upstream of the imaging plane. For each bolus injection, the CT projection data from the scan with the sharpest increase in magnitude of detected contrast material was used for flow velocity measurements. Flow velocity was calculated as the ratio of distance between CT detector rows and the corresponding time lag in the contrast enhancement curves and was correlated with the reference velocities. Five separate contrast material injections and CT measurements were made for each flow velocity setting. RESULTS: The correlation coefficient between the CT measurements of flow velocity and the reference measurements was 0.98 (P < .05). The mean CT measurements of flow velocity were 34.2, 53.9, and 80.8 cm/sec for slow, moderate, and fast velocity settings, respectively, overestimating the corresponding actual flow velocities by 26%, 18%, and 13% and showing precision values (coefficients of variation) of 5.2%, 3.7%, and 6.6%. CONCLUSION: Flow velocity can be measured from row-to-row multidetector CT projectional data obtained during a single gantry revolution as a bolus of contrast material flows through a vascular phantom. With further development, this novel technique could potentially provide physiologic information to complement the anatomic CT angiographic findings of vascular disease.

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival.

11C-L-methyl methionine dynamic PET/CT of skeletal muscle: response to protein supplementation compared to L-[ring 13C6] phenylalanine infusion with serial muscle biopsy.

OBJECTIVE: The objective of this study was to determine if clinical dynamic PET/CT imaging with 11C-L-methyl-methionine (11C-MET) in healthy older women can provide an estimate of tissue-level post-absorptive and post-prandial skeletal muscle protein synthesis that is consistent with the more traditional method of calculating fractional synthesis rate (FSR) of muscle protein synthesis from skeletal muscle biopsies obtained during an infusion of L-[ring 13C6] phenylalanine (13C6-Phe). METHODS: Healthy older women (73 ± 5 years) completed both dynamic PET/CT imaging with 11C-MET and a stable isotope infusion of 13C6-Phe with biopsies to measure the skeletal muscle protein synthetic response to 25 g of a whey protein supplement. Graphical estimation of the Patlak coefficient Ki from analysis of the dynamic PET/CT images was employed as a measure of incorporation of 11 C-MET in the mid-thigh muscle bundle. RESULTS: Post-prandial values [mean ± standard error of the mean (SEM)] were higher than post-absorptive values for both Ki (0.0095 ± 0.001 vs. 0.00785 ± 0.001 min-1, p < 0.05) and FSR (0.083 ± 0.008 vs. 0.049 ± 0.006%/h, p < 0.001) in response to the whey protein supplement. The percent increase in Ki and FSR in response to the whey protein supplement was significantly correlated (r = 0.79, p = 0.015). CONCLUSIONS: Dynamic PET/CT imaging with 11C-MET provides an estimate of the post-prandial anabolic response that is consistent with a traditional, invasive stable isotope, and muscle biopsy approach. These results support the potential future use of 11C-MET imaging as a non-invasive method for assessing conditions affecting skeletal muscle protein synthesis.

Spatial heterogeneity in the response of the proximal femur to two lower-body resistance exercise regimens.

Understanding the skeletal effects of resistance exercise involves delineating the spatially heterogeneous response of bone to load distributions from different muscle contractions. Bone mineral density (BMD) analyses may obscure these patterns by averaging data from tissues with variable mechanoresponse. To assess the proximal femoral response to resistance exercise, we acquired pretraining and posttraining quantitative computed tomography (QCT) images in 22 subjects (25-55 years, 9 males, 13 females) performing two resistance exercises for 16 weeks. One group (SQDL, n = 7) performed 4 sets each of squats and deadlifts, a second group (ABADD, n = 8) performed 4 sets each of standing hip abductions and adductions, and a third group (COMBO, n = 7) performed two sets each of squat/deadlift and abduction/adduction exercise. Subjects exercised three times weekly, and the load was adjusted each session to maximum effort. We used voxel-based morphometry (VBM) to visualize BMD distributions. Hip strength computations used finite element modeling (FEM) with stance and fall loading conditions. We used QCT analysis for cortical and trabecular BMD, and cortical tissue volume. For muscle size and density, we analyzed the cross-sectional area (CSA) and mean Hounsfield unit (HU) in the hip extensor, flexor, abductor, and adductor muscle groups. Whereas SQDL increased vertebral BMD, femoral neck cortical BMD and volume, and stance hip strength, ABADD increased trochanteric cortical volume. The COMBO group showed no changes in any parameter. VBM showed different effects of ABADD and SQDL exercise, with the former causing focal changes of trochanteric cortical bone, and the latter showing diffuse changes in the femoral neck and head. ABADD exercise increased adductor CSA and HU, whereas SQDL exercise increased the hip extensor CSA and HU. In conclusion, we observed different proximal femoral bone and muscle tissue responses to SQDL and ABADD exercise. This study supports VBM and volumetric QCT (vQCT) to quantify the spatially heterogeneous effects of types of muscle contractions on bone.