Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.

The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p = 0.17) and SCV95% (p = 0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p = 0.02). For post-mastectomy plans, AxNV95% (p = 0.35) and SCV95% (p = 0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation <5% when DSC > 0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p = 0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.

4D-CT deformable image registration using unsupervised recursive cascaded full-resolution residual networks.

A novel recursive cascaded full-resolution residual network (RCFRR-Net) for abdominal four-dimensional computed tomography (4D-CT) image registration was proposed. The entire network was end-to-end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full-resolution residual subnetworks with different architectures. The training loss consisted of the image similarity loss and the deformation vector field regularization loss, which were calculated based on the final warped image and the fixed image, allowing all cascades to be trained jointly and perform the progressive registration cooperatively. Extensive network testing was conducted using diverse datasets, including an internal 4D-CT dataset, a public DIRLAB 4D-CT dataset, and a 4D cone-beam CT (4D-CBCT) dataset. Compared with the iteration-based demon method and two deep learning-based methods (VoxelMorph and recursive cascaded network), the RCFRR-Net achieved consistent and significant gains, which demonstrated that the proposed method had superior performance and generalization capability in medical image registration. The proposed RCFRR-Net was a promising tool for various clinical applications.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery.

Cancer accounts for nearly 10 million deaths in 2020 and is a leading cause of death worldwide. Radiation therapy is an effective modality to cure cancer. The ultimate goal of successful radiation therapy is to accurately deliver a safe and therapeutic dose of radiation to cancerous target cells while limiting radiation to the healthy tissue in the beam pathway and the surrounding normal tissue. With advancing radiation treatment technology, better imaging techniques are imperative for safe, high-dose delivery. Artificial intelligence developments on image-guided radiation therapy technologies, which range from Kilovoltage and Megavoltage modalities to two-dimensional and three-dimensional techniques, are discussed in depth.

Reductions of Anxiety Symptoms, State Anxiety, and Anxious Arousal in Youth Playing the Videogame MindLight Compared to Online Cognitive Behavioral Therapy.

Objective: Anxiety disorders are the most prevalent form of psychopathology among youth. Because demand for treatment far exceeds availability, there is a need for alternative approaches that are accessible, engaging, and incorporate practice to reach as many youth as possible. MindLight is a novel videogame intervention that combines evidence-based anxiety reduction techniques with neurofeedback mechanics that has been shown to reduce anxiety symptoms in youth. This study examined the effectiveness of MindLight compared with online cognitive behavioral therapy (CBT) to replicate and extend those findings by testing the reduction of reactivity to anxiety-eliciting laboratory stressors. Materials and Methods: A randomized controlled trial was conducted with laboratory assessments at pre-intervention, post-intervention, and 3-month follow-up. Participants were 117 anxious youth (66.7% female, 33.3% male; age range: 8.05-15.93 years) who were randomized into MindLight (n = 56) and CBT (n = 60) conditions. Both interventions were completed in five 1-hour sessions within a 3-week period. At each time point, anxiety symptoms were assessed through self-report, and state anxiety and anxious arousal were measured during laboratory stress tasks. Results: All measures of anxiety significantly decreased over time in both conditions (P < 0.05). Moreover, youth in the MindLight condition showed greater pre-to-post reductions in anxiety symptoms compared with youth in the CBT condition (P < 0.05). Conclusion: Findings demonstrate that the effects of MindLight and online CBT are not only associated with reductions in anxiety symptoms, but also impact how youth react to laboratory stressors in the moment. ClinicalTrials.gov Identifier: NCT02326545.

Multi-Material Decomposition for Single Energy CT Using Material Sparsity Constraint.

Multi-material decomposition (MMD) decomposes CT images into basis material images, and is a promising technique in clinical diagnostic CT to identify material compositions within the human body. MMD could be implemented on measurements obtained from spectral CT protocol, although spectral CT data acquisition is not readily available in most clinical environments. MMD methods using single energy CT (SECT), broadly applied in radiological departments of most hospitals, have been proposed in the literature while challenged by the inferior decomposition accuracy and the limited number of material bases due to the constrained material information in the SECT measurement. In this paper, we propose an image-domain SECT MMD method using material sparsity as an assistance under the condition that each voxel of the CT image contains at most two different elemental materials. L0 norm represents the material sparsity constraint (MSC) and is integrated into the decomposition objective function with a least-square data fidelity term, total variation term, and a sum-to-one constraint of material volume fractions. An accelerated primal-dual (APD) algorithm with line-search scheme is applied to solve the problem. The pixelwise direct inversion method with the two-material assumption (TMA) is applied to estimate the initials. We validate the proposed method on phantom and patient data. Compared with the TMA method, the proposed MSC method increases the volume fraction accuracy (VFA) from 92.0% to 98.5% in the phantom study. In the patient study, the calcification area can be clearly visualized in the virtual non-contrast image generated by the proposed method, and has a similar shape to that in the ground-truth contrast-free CT image. The high decomposition image quality from the proposed method substantially facilitates the SECT-based MMD clinical applications.

Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT.

PURPOSE: The image quality of cone beam computed tomography (CBCT) is limited by severe shading artifacts, hindering its quantitative applications in radiation therapy. In this work, we propose an image-domain shading correction method using planning CT (pCT) as prior information which is highly adaptive to clinical environment. METHOD: We propose to perform shading correction via sparse sampling on pCT. The method starts with a coarse mapping between the first-pass CBCT images obtained from the Varian TrueBeam system and the pCT. The scatter correction method embedded in the Varian commercial software removes some image errors but the CBCT images still contain severe shading artifacts. The difference images between the mapped pCT and the CBCT are considered as shading errors, but only sparse shading samples are selected for correction using empirical constraints to avoid carrying over false information from pCT. A Fourier-Transform-based technique, referred to as local filtration, is proposed to efficiently process the sparse data for effective shading correction. The performance of the proposed method is evaluated on one anthropomorphic pelvis phantom and 17 patients, who were scheduled for radiation therapy. (The codes of the proposed method and sample data can be downloaded from https://sites.google.com/view/linxicbct) RESULTS: The proposed shading correction substantially improves the CBCT image quality on both the phantom and the patients to a level close to that of the pCT images. On the phantom, the spatial nonuniformity (SNU) difference between CBCT and pCT is reduced from 74 to 1 HU. The root of mean square difference of SNU between CBCT and pCT is reduced from 83 to 10 HU on the pelvis patients, and from 101 to 12 HU on the thorax patients. The robustness of the proposed shading correction is fully investigated with simulated registration errors between CBCT and pCT on the phantom and mis-registration on patients. The sparse sampling scheme of our method successfully avoids false structures in the corrected CBCT even when the maximum registration error is as high as 8 mm. CONCLUSION: We develop an effective shading correction algorithm for CBCT readily implementable on clinical data as a software plug-in without modifications of current imaging hardware and protocol. The algorithm is directly applied on the output images from a commercial CBCT scanner with high computational efficiency and negligible memory burden.

Linking Temperamental Shyness and Social Anxiety in Childhood and Adolescence: Moderating Influences of Sex and Age.

Although childhood shyness has been linked to social anxiety problems, the factors playing a role in this association have gone largely unexplored. Here we examined the potential moderating roles of sex and age on this relation in a sample of 119 (75 girls) children (10-12 years) and adolescents (14-16 years). As predicted, shyness was positively associated with social anxiety symptoms. Sex, but not age, served as a moderating factor in linking shyness and social anxiety. Specifically, shyness was more strongly associated with social anxiety symptoms among girls than boys. These results suggest the importance of considering sex differences when examining the relation between shyness and social anxiety in childhood and adolescence.

Cancer-Associated Mutations in Breast Tumor Kinase/PTK6 Differentially Affect Enzyme Activity and Substrate Recognition.

Brk (breast tumor kinase, also known as PTK6) is a nonreceptor tyrosine kinase that is aberrantly expressed in several cancers and promotes cell proliferation and transformation. Genome sequencing studies have revealed a number of cancer-associated somatic mutations in the Brk gene; however, their effect on Brk activity has not been examined. We analyzed a panel of cancer-associated mutations and determined that several of the mutations activate Brk, while two eliminated enzymatic activity. Three of the mutations (L16F, R131L, and P450L) are located in important regulatory domains of Brk (the SH3, SH2 domains, and C-terminal tail, respectively). Biochemical data suggest that they activate Brk by disrupting intramolecular interactions that normally maintain Brk in an autoinhibited conformation. We also observed differential effects on recognition and phosphorylation of substrates, suggesting that the mutations can influence downstream Brk signaling by multiple mechanisms.

Nerve agent exposure elicits site-specific changes in protein phosphorylation in mouse brain.

Organophosphorus (OP) compounds cause toxic symptoms, including convulsions, coma, and death, as the result of irreversible inhibition of acetylcholinesterase (AChE). The development of effective treatments to block these effects and attenuate long-term cognitive and motor disabilities that result from OP intoxication is hampered by a limited understanding of the CNS pathways responsible for these actions. We employed a candidate method (called CNSProfile) to identify changes in the phosphorylation state of key neuronal phosphoproteins evoked by the OP compound, diisopropyl fluorophosphate (DFP). Focused microwave fixation was used to preserve the phosphorylation state of phosphoproteins in brains of DFP-treated mice; hippocampus and striatum were analyzed by immunoblotting with a panel of phospho-specific antibodies. DFP exposure elicited comparable effects on phosphorylation of brain phosphoproteins in both C57BL/6 and FVB mice. DFP treatment significantly altered phosphorylation at regulatory residues on glutamate receptors, including Serine897 (S897) of the NR1 NMDA receptor. NR1 phosphorylation was bi-directionally regulated after DFP in striatum versus hippocampus. NR1 phosphorylation was reduced in striatum, but elevated in hippocampus, compared with controls. DARPP-32 phosphorylation in striatum was selectively increased at the Cdk5 kinase substrate, Threonine75 (T75). Phencynonate hydrochloride, a muscarinic cholinergic antagonist, prevented seizure-like behaviors and the observed changes in phosphorylation induced by DFP. The data reveal region-specific effects of nerve agent exposure on intracellular signaling pathways that correlate with seizure-like behavior and which are reversed by the muscarinic receptor blockade. This approach identifies specific targets for nerve agents, including substrates for Cdk5 kinase, which may be the basis for new anti-convulsant therapies.