34745 US and EU sunscreens: A review of ultraviolet (UV) filters and safety data

A broad range of UV filters are available for use in sunscreen products. Knowledge of UV filters available both domestically and abroad remains important, since these products can be found in the online marketplace and may be included in future FDA monographs as a shift is made to an administrative order process. We reviewed the mechanism and safety data of all US and EU approved UV filters. Currently, there are 17 US FDA approved UV filters while the EU possesses an additional 16 UV filters. Of the US filters, 88.2% (15/17) are organic and 11.8% (2/17) are inorganic filters, with 35.3% (6/17) broad-spectrum, 52.9% (9/17) UVB only, and 11.8% (2/17) UVA only. Notably, 94.1% (16/17) have available human data. Of the EU exclusive filters, all (100%, 16/16) are organic filters. 50% (8/16) have human data while the remaining 50% (8/16) have data primarily related to physiochemical or toxicology profiles. Of these EU exclusive UV filters, 43.75% (7/16) are broad-spectrum, 50% (8/16) cover UVB only, and 6.25% (1/16) cover UVA only. Our review demonstrates that the EU possesses an exciting pool of novel UV filters with expanded options for coverage of all forms of UV radiation. Critically, the majority of sunscreens, both in the US and EU, have limited human data available due to prior limited requirements for such information. This information is likely forthcoming in the US as the FDA updates data requirement guidelines for sunscreens to be generally recognized as safe and effective

Gleason Grade Group Prediction for Prostate Cancer Patients with MR Images Using Convolutional Neural Network

Purpose: Gleason Grading (GG) Grouping system is an important index in determining treatment plan or predicting outcome for prostate cancer patients. Unfortunately, currently GG Grouping results can only be obtained from biopsy-driven pathological tests. We aim to predict GG groups for PCa patients from multiparametric magnetic resonance images (mp-MRI). Methods: The challenges include data heterogeneity, small sample size and highly imbalanced distribution among different groups. A retrospective collection of 201 patients with 320 lesions from the SPIE-AAPM-NCI PROSTATEx Challenge (https://doi.org/10.7937/K9TCIA.2017.MURS5CL) was studied, among which only 98 patients with 110 lesions having GG available. And number of lesions from each group was 36, 39, 20, 8, and 7, respectively, for GG 1-5. We approached the challenging task by bridging though easier one of classifying 320 lesions into benign or malignant, and transferring learned knowledge to GG prediction on 110 lesions. During implementation, a four-convolutional neural network (CNN) was used for malignancy classification. To prevent over-fitting on small sample size, instead of fine-tuning on CNN, learned features were extracted and classified by weighted extreme learning machine (wELM), traditional classifier that assigned larger weight to samples from minority class.Image pre-processing included registration and normalization. Image rotation and scaling were also used to increase sample size and re-balance number of malignant and benign lesions. Results: The best combination of modalities as input to CNN was found to be T2W, apparent diffusion coefficient (ADC) and B-value maps (b=50 s/mm2). During phase 1 of CNN training, average and best results of (Sensitivity, Specificity, G-mean) over 10 folds were (0.53, 0.83, 0.65) and (1, 0.88, 0.91), respectively. Features from best performing model were extracted to represent each lesion, and those from the last convolutional layer were found constantly better than from all other layers (Table 1). This implies that semantic features regarding lesion information is more important than local and detailed features such as contrast change in GG prediction. Conclusion: This work has successfully tackled the challenging task of GG prediction from mp-MRI by bridging through an easier task and has combined feature extraction using deep learning model and small data classification using traditional classifier to benefit from both.https://scholarlycommons.henryford.com/merf2019basicsci/1003/thumbnail.jp

Volumetric and Voxel-Wise Analysis of Dominant Intraprostatic Lesions on Multiparametric MRI

Introduction: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying the dominant intraprostatic lesion (DIL) for radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 90 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted (T2W), apparent diffusion coefficient (ADC), and K(trans) images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. K(trans), and ADC vs. K(trans)). The voxel wise spearman correlation was also obtained between these image pairs. Results: The DILs were located in the anterior fibromuscular stroma, central zone, peripheral zone, and transition zone in 35.2, 5.6, 32.4, and 25.4% of patients, respectively. Gleason grade groups 1-5 represented 29.6, 40.8, 15.5, and 14.1% of the study population, respectively (with group grades 4 and 5 analyzed together). The mean contour volumes for the T2W images, and the ADC and K(trans) maps were 2.14 +/- 2.1, 2.22 +/- 2.2, and 1.84 +/- 1.5 mL, respectively. K(trans) values were indistinguishable between cancerous regions and the rest of prostatic regions for 19 patients. The Dice coefficient and Jaccard index were 0.74 +/- 0.13, 0.60 +/- 0.15 for T2W-ADC and 0.61 +/- 0.16, 0.46 +/- 0.16 for T2W-K(trans). The voxel-based Spearman correlations were 0.20 +/- 0.20 for T2W-ADC and 0.13 +/- 0.25 for T2W-K(trans). Conclusions: The DIL contoured on T2W images had a high level of agreement with those contoured on ADC maps, but there was little to no quantitative correlation of these results with tumor location and Gleason grade group. Technical hurdles are yet to be solved for precision radiotherapy to target the DILs based on physiological imaging. A Boolean sum volume (BSV) incorporating all available MR sequences may be reasonable in delineating the DIL boost volume

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification

Quantifying inter-fraction cardiac substructure displacement during radiotherapy via magnetic resonance imaging guidance

Emerging evidence suggests cardiac substructures are highly radiosensitive during radiation therapy for cancer treatment. However, variability in substructure position after tumor localization has not been well characterized. This study quantifies inter-fraction displacement and planning organ at risk volumes (PRVs) of substructures by leveraging the excellent soft tissue contrast of magnetic resonance imaging (MRI). Eighteen retrospectively evaluated patients underwent radiotherapy for intrathoracic tumors with a 0.35 T MRI-guided linear accelerator. Imaging was acquired at a 17–25 s breath-hold (resolution 1.5 × 1.5 × 3 mm3). Three to four daily MRIs per patient (n = 71) were rigidly registered to the planning MRI-simulation based on tumor matching. Deep learning or atlas-based segmentation propagated 13 substructures (e.g., chambers, coronary arteries, great vessels) to daily MRIs and were verified by two radiation oncologists. Daily centroid displacements from MRI-simulation were quantified and PRVs were calculated. Across substructures, inter-fraction displacements for 14% in the left–right, 18% in the anterior-posterior, and 21% of fractions in the superior-inferior were \u3e 5 mm. Due to lack of breath-hold compliance, ~4% of all structures shifted \u3e 10 mm in any axis. For the chambers, median displacements were 1.8, 1.9, and 2.2 mm in the left–right, anterior-posterior, and superior-inferior axis, respectively. Great vessels demonstrated larger displacements (\u3e 3 mm) in the superior-inferior axis (43% of shifts) and were only 25% (left–right) and 29% (anterior-posterior) elsewhere. PRVs from 3 to 5 mm were determined as anisotropic substructure-specific margins. This exploratory work derived substructure-specific safety margins to ensure highly effective cardiac sparing. Findings require validation in a larger cohort for robust margin derivation and for applications in prospective clinical trials

Coronary computed tomography angiography in dialysis patients undergoing pre-renal transplantation cardiac risk stratification

Background: This study addresses the safety, feasibility, and interpretability of coronary computed tomography angiography (CCTA) in excluding significant coronary artery disease in end-stage renal disease patients on dialysis undergoing pre-renal transplant cardiac risk evaluation. Methods: Twenty nine patients (55.5 &#177; 10.2 years) undergoing cardiac risk assessment prior to renal transplantation, underwent research CCTA with calcium scoring and formed the study group. All CCTAs were performed using retrospective acquisition, with beta-blockade provided one hour prior to scanning. Results: No major complications occurred in this group up to 30 days after CCTA. Of the total of 374 segments interpreted by both readers, only 36 (10%) were uninterpretable by both readers. Of these, 31 (86%) were from distal segments or branches. On a segmental level, there was 95% concordance between both readers for < 50% stenosis detection. Only three out of 28 (11%) CCTAs were deemed uninterpretable. Ten patients (36%) had zero calcium score, despite being on dialysis with no evidence of obstructive coronary artery disease by CCTA. Conclusions: CCTA is feasible and safe in end-stage renal disease dialysis patients with the advent of 64-slice CCTA. Despite significant calcium burden, there was excellent inter-observer agreement at segment level for the left main and all three proximal-mid coronary arteries in excluding obstructive coronary artery disease (> 50% stenosis). (Cardiol J 2010; 17, 4: 349-361

Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks

Purpose: Accurate delineation of the prostate gland and intraprostatic lesions (ILs) is essential for prostate cancer dose-escalated radiation therapy. The aim of this study was to develop a sophisticated deep neural network approach to magnetic resonance image analysis that will help IL detection and delineation for clinicians. Methods and Materials: We trained and evaluated mask region-based convolutional neural networks to perform the prostate gland and IL segmentation. There were 2 cohorts in this study: 78 public patients (cohort 1) and 42 private patients from our institution (cohort 2). Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches cropped out. The IL segmentation model was extended to select 5 highly suspicious volumetric lesions within the entire prostate. Results: The mask region-based convolutional neural networks model was able to segment the prostate with dice similarity coefficient (DSC) of 0.88 ± 0.04, 0.86 ± 0.04, and 0.82 ± 0.05; sensitivity (Sens.) of 0.93, 0.95, and 0.95; and specificity (Spec.) of 0.98, 0.85, and 0.90. However, ILs were segmented with DSC of 0.62 ± 0.17, 0.59 ± 0.14, and 0.38 ± 0.19; Sens. of 0.55 ± 0.30, 0.63 ± 0.28, and 0.22 ± 0.24; and Spec. of 0.974 ± 0.010, 0.964 ± 0.015, and 0.972 ± 0.015 in public validation/public testing/private testing patients when trained with patients from cohort 1 only. When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009, 0.969 ± 0.016, and 0.977 ± 0.013. Conclusions: Our research framework is able to perform as an end-to-end system that automatically segmented the prostate gland and identified and delineated highly suspicious ILs within the entire prostate. Therefore, this system demonstrated the potential for assisting the clinicians in tumor delineation

Characterizing Sensitive Cardiac Substructure Excursion Due to Respiration

PURPOSE: Whole-heart dose metrics are not as strongly linked to late cardiac morbidities as radiation doses to individual cardiac substructures. Our aim was to characterize the excursion and dosimetric variation throughout respiration of sensitive cardiac substructures for future robust safety margin design. METHODS AND MATERIALS: Eleven patients with cancer treatments in the thorax underwent 4-phase noncontrast 4-dimensional computed tomography (4DCT) with T2-weighted magnetic resonance imaging in end-exhale. The end-exhale phase of the 4DCT was rigidly registered with the magnetic resonance imaging and refined with an assisted alignment surrounding the heart from which 13 substructures (chambers, great vessels, coronary arteries, etc) were contoured by a radiation oncologist on the 4DCT. Contours were deformed to the other respiratory phases via an intensity-based deformable registration for radiation oncologist verification. Measurements of centroid and volume were evaluated between phases. Mean and maximum dose to substructures were evaluated across respiratory phases for the breast (n = 8) and thoracic cancer (n = 3) cohorts. RESULTS: Paired t tests revealed reasonable maintenance of geometric and anatomic properties (P \u3c .05 for 4/39 volume comparisons). Maximum displacements \u3e5 mm were found for 24.8%, 8.5%, and 64.5% of the cases in the left-right, anterior-posterior, and superior-inferior axes, respectively. Vector displacements were largest for the inferior vena cava and the right coronary artery, with displacements up to 17.9 mm. In breast, the left anterior descending artery D(mean) varied 3.03 ± 1.75 Gy (range, 0.53-5.18 Gy) throughout respiration whereas lung showed patient-specific results. Across all patients, whole heart metrics were insensitive to breathing phase (mean and maximum dose variations \u3c0.5 Gy). CONCLUSIONS: This study characterized the intrafraction displacement of the cardiac substructures through the respiratory cycle and highlighted their increased dosimetric sensitivity to local dose changes not captured by whole heart metrics. Results suggest value of cardiac substructure margin generation to enable more robust cardiac sparing and to reduce the effect of respiration on overall treatment plan quality

Magnetic resonance imaging-only-based radiation treatment planning for simultaneous integrated boost of multiparametric magnetic resonance imaging-defined dominant intraprostatic lesions

Objective: To assess the feasibility of using synthetic computed tomography for treatment planning of the dominant intraprostatic lesion (DIL), a high-risk region of interest that offers potential for increased local tumor control. Methods: A dosimetric study was performed on 15 prostate cancer patients with biopsy-proven prostate cancer who had undergone magnetic resonance imaging. DILs were contoured based on the turbo spin echo T2-weighted and diffusion weighted images. Air, bone, fat, and soft tissue were segmented and assigned bulk-density HU values of –1000, 285, –50, and 40, respectively, to create a synthetic computed tomography. Simultaneous integrated boost (SIB) and standard treatment plans were created for each patient. The total dose was 79.2 Gy to the non-boosted planning target volume for both plans with a boost of 100 Gy for the DIL in the SIB plan. A radiobiological model was created to determine individualized dose–response curves based on the patient\u27s apparent diffusion coefficient maps. Results: Mean doses to the non-boost planning target volume were 81.2 ± 0.3 Gy with the SIB and 81.0 ± 0.4 Gy without. For the DIL, the boosted mean dose was 102.6 ± 0.6 Gy. Total motor unit was 860 ± 100 with the SIB and 730 ±100 without. Femoral heads, rectum, bladder, and penile bulb were within established dose guidelines for either treatment technique. The average tumor control probability was 94% with the SIB compared with 78% without boosting the DIL. Conclusion: This study showed the feasibility of magnetic resonance imaging-only treatment planning for patients with prostate cancer with a SIB to the DIL. DIL dose can be escalated to 100 Gy on synthetic computed tomography, while maintaining the original 79.2 Gy prescription dose and the organ of interest clinical dose limits