BackgroundCurrent methods to predict height and growth failure are imprecise. MRI measures of physeal cartilage are promising biomarkers for growth.PurposeIn the physis, to assess how 3D MRI volume measurements, and diffusion tensor imaging (DTI) measurements (tract volume and length) correlate with growth parameters and detect differences in growth. We compared patients exposed to cis‐retinoic acid, which causes physeal damage and growth failure, with normal subjects.Study TypeCase–control.PopulationTwenty pediatric neuroblastoma survivors treated with cis‐retinoic acid and 20 age‐ and sex‐matched controls.Field Strength/Sequence3T; DTI and 3D double‐echo steady‐state (DESS) sequences.AssessmentOn distal femoral MR studies, physeal 3D volume and DTI tract measurements were calculated and compared to height.Statistical TestsWe used partial Spearman correlation, analysis of covariance, logistic regression, Wald test, and the intraclass correlation coefficient (ICC).ResultsThe height percentile correlated most strongly with DTI tract volumes (r = 0.74), followed by mean tract length (r = 0.53) and 3D volume (r = 0.40) (all P < 0.02). Only tract volumes and lengths correlated with annualized growth velocity. Relative to controls, patients showed smaller tract volumes (8.00 cc vs. 13.71 cc, P < 0.01), shorter tract lengths (5.92 mm vs 6.99 mm, P = 0.03), and smaller ratios of 3D cartilage volume to tract length; but no difference (4.51 cc vs 4.85 cc) in 3D MRI volumes. The 10 patients with the lowest height percentiles had smaller tract volumes (5.07 cc vs. 10.93 cc, P < 0.01), but not significantly different 3D MRI volumes. Tract volume is associated with abnormal growth, with an accuracy of 75%.Data ConclusionDTI tract volume of the physis/metaphysis predicts abnormal growth better than physeal cartilage volumetric measurement and correlates best with height percentile and growth velocity.Evidence Level2Technical EfficacyStage 2 J. Magn. Reson. Imaging 2020;52:544–551.
Wastewater from hospitals and facilities that receive patients infected with contagious microorganisms has dense concentrations of these pathogens, which may represent a threat to public health. Thus, proper wastewater treatment to remove these contaminants before discharging to the sewage system has been a great concern to the society. Aside from chemical and physical treatment, biological alternatives have been more widely studied and implemented for wastewater processing to avoid unwanted effects and to lower the costs. This study aimed to use lysing enzymes coupled to magnetic amino nanoparticles functionalized with specific antibodies (NP-NH
PurposeAssess feasibility of ultrashort echo time (UTE) T2* cartilage mapping in sacroiliac (SI) jointsMethodsProspective magnetic resonance imagings with UTE T2* cartilage maps obtained on 20 SI joints in 10 subjects. Each joint was segmented into thirds by 2 radiologists. The UTE T2* maps were analyzed; reliability and differences in UTE T2* values between radiologists were assessed.ResultsMean UTE T2* value was 10.44 ± 0.60 ms. No difference between right/left SI joints (median, 10.52 vs 10.45 ms; P = 0.940), men/women (median, 10.34 vs. 10.57 ms; P = 0.174), or different anatomic regions (median range 10.55–10.69 ms; P = 0.805). Intraclass correlation coefficients were 0.94 to 0.99 (intraobserver) and 0.91 to 0.96 (interobserver). Mean bias ± standard deviation on Bland-Altman was −0.137 ± 0.196 ms (limits of agreement −0.521 and 0.247) without proportional bias (β = 0.148, P = 0.534).ConclusionsThe UTE T2* cartilage mapping in the SI joints is feasible with high reader reliability.
HER2 (Human Epidermal Growth Factor Receptor 2)-positive breast cancer is characterized by amplification of the HER2 gene and is associated with more aggressive tumor growth, increased risk of metastasis, and poorer prognosis when compared to other subtypes of breast cancer. HER2 expression is therefore a critical tumor feature that can be used to diagnose and treat breast cancer. Moving forward, advances in HER2 in vivo imaging, involving the use of techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), may allow for a greater role for HER2 status in guiding the management of breast cancer patients. This will apply both to patients who are HER2-positive and those who have limited-to-minimal immunohistochemical HER2 expression (HER2-low), with imaging ultimately helping clinicians determine the size and location of tumors. Additionally, PET and SPECT could help evaluate effectiveness of HER2-targeted therapies, such as trastuzumab or pertuzumab for HER2-positive cancers, and specially modified antibody drug conjugates (ADC), such as trastuzumab-deruxtecan, for HER2-low variants. This review will explore the current and future role of HER2 imaging in personalizing the care of patients diagnosed with breast cancer.
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.
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