Intermittent fasting (IF) improves cardiometabolic health; however, it is unknown whether these effects are due solely to weight loss. We conducted the first supervised controlled feeding trial to ...test whether IF has benefits independent of weight loss by feeding participants enough food to maintain their weight. Our proof-of-concept study also constitutes the first trial of early time-restricted feeding (eTRF), a form of IF that involves eating early in the day to be in alignment with circadian rhythms in metabolism. Men with prediabetes were randomized to eTRF (6-hr feeding period, with dinner before 3 p.m.) or a control schedule (12-hr feeding period) for 5 weeks and later crossed over to the other schedule. eTRF improved insulin sensitivity, β cell responsiveness, blood pressure, oxidative stress, and appetite. We demonstrate for the first time in humans that eTRF improves some aspects of cardiometabolic health and that IF’s effects are not solely due to weight loss.
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•Early time-restricted feeding (eTRF) increases insulin sensitivity•eTRF also improves β cell function and lowers blood pressure and oxidative stress•eTRF lowers the desire to eat in the evening, which may facilitate weight loss•Intermittent fasting can improve health even in the absence of weight loss
Sutton et al. conduct the first supervised controlled feeding trial to test whether intermittent fasting has benefits in humans in the absence of weight loss. Prediabetic men following a form of intermittent fasting called early time-restricted feeding improved their insulin sensitivity, blood pressure, and oxidative stress levels without losing weight.
Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of ...radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R
= 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.
RSNA, 2016 Online supplemental material is available for this article.
With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific ...genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high‐throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome‐related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.
Level of Evidence: 5
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2017;47:604–620.
While the etiology of preeclampsia continues to be elucidated, it is clear that preeclampsia is a complex obstetrical syndrome associated with maternal vascular dysfunction within which impairments ...in nitric oxide (NO) signaling likely play a key role in driving disease progression and severity. The goal of this review is to present the available evidence for maladaptations in NO and NO signaling in pregnancies complicated by preeclampsia. After a brief overview of preeclampsia, a review of the available evidence for NO and NO signaling adaptations in normal, uncomplicated pregnancy is given to lay a foundation for changes driven by preeclampsia. Next, current evidence for maladaptations of NO and NO signaling in preeclampsia is reviewed. Finally, a brief summary of NO-focused treatments for preeclampsia prevention is discussed. Considering preeclampsia is a syndrome solely occurring among pregnant women, this review focuses on NO signaling in clinical studies, with supplementary evidence from animal studies added when necessary.
•Impairments in nitric oxide signaling likely play a key role in preeclampsia.•We review evidence for maladaptations in NO signaling in preeclampsia.•We review evidence for adaptations in NO signaling in healthy pregnancy.
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.
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 pre-filtering, 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.
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.
This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.
To evaluate maternal and neonatal outcomes in healthy, nulliparous women classified with stage 1 hypertension under the revised American College of Cardiology and American Heart Association ...Guidelines and to evaluate the effects of low-dose aspirin on maternal and neonatal outcomes in this population.
We conducted a secondary analysis of data from a multicenter randomized, double-blind, placebo-controlled trial of low-dose aspirin for prevention of preeclampsia in nulliparous, low-risk women recruited between 13 and 25 weeks of gestation. Of the 3,134 nulliparous women enrolled in the original study, 2,947 women with singleton pregnancies and without missing data were included in this analysis. Blood pressure was measured at enrollment between 13 and 25 weeks of gestation and outcomes were adjudicated from the medical record.
One hundred sixty-four participants were identified with lower range stage 1 hypertension (5.6%), systolic blood pressure 130-135 mm Hg, diastolic blood pressure 80-85 mm Hg, or both by the new American College of Cardiology-American Heart Association guidelines. Within the placebo group (n=1,482), women with stage 1 hypertension had a significantly increased incidence of preeclampsia compared with normotensive women, 15.3% (15/98) vs 5.4% (75/1,384) (relative risk 2.66, 95% CI 1.56-4.54, P<.001). Moreover, women with stage 1 hypertension had an increased incidence of gestational diabetes mellitus (6.1% vs 2.5%, P=.03) and more indicated preterm deliveries (4.2% vs 1.1%, P=.01). Comparing women with stage 1 hypertension and normotensive women receiving low-dose aspirin during pregnancy (n=1,465), no differences in rates of preeclampsia (7.6% vs 4.4%, respectively, P=.2), gestational diabetes mellitus, or indicated preterm deliveries were observed. Rates of placenta abruption, small for gestational age, and spontaneous preterm birth did not differ significantly between groups.
Application of the new American College of Cardiology-American Heart Association guidelines in a pregnant population identifies a cohort of women who are at increased risk for preeclampsia, gestational diabetes mellitus, and preterm birth.
Recently, the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines revised the recommendations for diagnosis of chronic hypertension. The new ...classification system includes a diagnosis of stage 1 hypertension in adults with blood pressures 130 to 139/80 to 89 mm Hg. We sought to compare outcomes among women at high risk for preeclampsia with stage 1 hypertension and assessed whether women with stage 1 hypertension had benefit from aspirin treatment compared with high-risk normotensive women. We performed a secondary analysis of the high-risk aspirin trial and included women with prior preeclampsia or diabetes mellitus. Among these women, 827 (81%) were classified as normotensive, whereas 193 (19%) were classified as stage 1 hypertensive. Among women receiving placebo, preeclampsia occurred significantly more often in women with stage 1 hypertension compared with normotensive high-risk women after adjustment for maternal age and body mass index (39.1% versus 15.1%; risk ratio, 2.49; 95% confidence interval, 1.74-3.55). Further, women with stage 1 hypertension had a significant risk reduction related to aspirin prophylaxis (risk ratio, 0.61; 95% confidence interval, 0.39-0.94) that was not seen in normotensive high-risk women (risk ratio, 0.97; 95% confidence interval, 0.70-1.34). Application of the American College of Cardiology/American Heart Association guidelines in a high-risk population demonstrates that in the setting of other risk factors, the presence of stage 1 hypertension is associated with a significantly increased risk of preeclampsia when compared with high-risk normotensive women. These findings emphasize the importance of recognition of stage 1 hypertension as an additive risk factor in women at high risk for preeclampsia and the benefit of aspirin.
Purpose
To use features extracted from magnetic resonance (MR) images and a machine‐learning method to assist in differentiating breast cancer molecular subtypes.
Materials and Methods
This ...retrospective Health Insurance Portability and Accountability Act (HIPAA)‐compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006–2011 with: 1) ERPR + (n = 95, 53.4%), ERPR–/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram‐based features were extracted from each tumor contoured on pre‐ and three postcontrast MR images using in‐house software. Clinical and pathologic features were also collected. Machine‐learning‐based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave‐one‐out cross‐validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal–Wallis test.
Results
Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR–/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR–/HER2+), and 81.0% (TN).
Conclusion
We developed a machine‐learning‐based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122–129.
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.
The aims of this study were to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping as a ...stand-alone parameter without any other supportive sequence for breast cancer detection and to assess its combination as multiparametric MRI (mpMRI) of the breast.
In this institutional review board-approved single-center study, prospectively acquired data of 106 patients who underwent breast MRI from 12/2010 to 09/2014 for an imaging abnormality (Breast Imaging Reporting and Data System 0, 4/5) were retrospectively analyzed. Four readers independently assessed DWI and DCE as well as combined as mpMRI. Breast Imaging Reporting and Data System categories, lesion size, and mean apparent diffusion coefficient values were recorded. Histopathology was used as the gold standard. Appropriate statistical tests were used to compare diagnostic values.
There were 69 malignant and 41 benign tumors in 106 patients. Four patients presented with bilateral lesions. Dynamic contrast-enhanced MRI was the most sensitive test for breast cancer detection, with an average sensitivity of 100%. Diffusion-weighted imaging alone was less sensitive (82%; P < 0.001) but more specific than DCE-MRI (86.8% vs 76.6%; P = 0.002). Diagnostic accuracy was 83.7% for DWI and 90.6% for DCE-MRI. Multiparametric MRI achieved a sensitivity of 96.8%, not statistically different from DCE-MRI (P = 0.12) and with a similar specificity as DWI (83.8%; P = 0.195), maximizing diagnostic accuracy to 91.9%. There was almost perfect interreader agreement for DWI (κ = 0.864) and DCE-MRI (κ = 0.875) for differentiation of benign and malignant lesions.
Dynamic contrast-enhanced MRI is most sensitive for breast cancer detection and thus still indispensable. Multiparametric MRI using DCE-MRI and DWI maintains a high sensitivity, increases specificity, and maximizes diagnostic accuracy, often preventing unnecessary breast biopsies. Diffusion-weighted imaging should not be used as a stand-alone parameter because it detects significantly fewer cancers in comparison with DCE-MRI and mpMRI.
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