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Background: Lymphopenia is known for its significance on poor survivals in breast cancer patients. Considering full dosimetric data, this study aimed to develop and validate predictive models for lymphopenia after radiotherapy (RT) in breast cancer., Material and Methods: Patients with breast cancer treated with adjuvant RT were eligible in this multicenter study. The study endpoint was lympopenia, defined as the reduction in absolute lymphocytes and graded lymphopenia after RT. The dose-volume histogram (DVH) data of related critical structures and clinical factors were taken into account for the development of dense neural network (DNN) predictive models. The developed DNN models were validated using external patient cohorts., Results: A total of 918 consecutive patients with invasive breast cancer enrolled. The training, testing, and external validating datasets consisted of 589, 203, and 126 patients, respectively. Treatment volumes at nearly all dose levels of the DVH were significant predictors for lymphopenia following RT, including volumes at very low-dose 1 Gy (V1) of organs at risk (OARs) including lung, heart and body, especially ipsilateral-lung V1. A final DNN model, combining full DVH dosimetric parameters of OARs and three key clinical factors, achieved a predictive accuracy of 75 % or higher., Conclusion: This study demonstrated and externally validated the significance of full dosimetric data, particularly the volume of low dose at as low as 1 Gy of critical structures on lymphopenia after radiation in patients with breast cancer. The significance of V1 deserves special attention, as modern VMAT RT technology often has a relatively high value of this parameter. Further study is warranted for RT plan optimization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Purpose: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application., Methods and Materials: A total of 1205 photographs of the head and neck region were collected from patients with nasopharyngeal carcinoma (NPC) undergoing radiation therapy. The severity of RD in these photographs was graded by 5 qualified assessors based on the Radiation Therapy Oncology Group guidance. An end-to-end RD grading framework was developed by combining a DL-based segmentation network and a DL-based RD severity classifier, which are used for segmenting the neck region from the camera-captured photographs and grading, respectively. U-Net was used for segmentation and another convolutional neural network classifier (DenseNet-121) was applied to RD severity classification. Dice similarity coefficient was used to evaluate the performance of segmentation. Severity classification was evaluated by several metrics, including overall accuracy, precision, recall, and F1 score., Results: Results of segmentation showed that the averaged dice similarity coefficients were 91.2% and 90.8% for front and side view, respectively. For RD severity classification, the overall accuracy of test photographs was 83.0%. Our method accurately classified 90.5% of grade 0, 67.2% of grade 1, 93.8% of grade 2, and 100% of above grade 2 cases. The overall prediction performance was comparable with human assessors. There was no significant difference in accuracy when using manually or automatically segmented regions (P = .683)., Conclusions: We have successfully demonstrated a DL-based method for automatic assessment of RD severity in patients with NPC. This method holds great potential for efficient and effective assessing and monitoring of RD in patients with NPC., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images., Methods and Materials: This paper presents a retrospective analysis of the pulmonary technetium-99m-labeled macroaggregated albumin single-photon emission CT (SPECT)/CT scans obtained from 73 patients at Queen Mary Hospital in Hong Kong in 2019. The left and right lung scans were separated to double the size of the data set to 146. A 3-dimensional attention residual neural network was constructed to extract textural features from the CT images and reconstruct corresponding functional images. Eighty-four samples were randomly selected for training and cross-validation, and the remaining 62 were used for model testing in terms of voxel-wise agreement and function-wise concordance. To assess the voxel-wise agreement, the Spearman's correlation coefficient (R) and structural similarity index measure between the images predicted by the DL-CTPM and the corresponding SPECT perfusion images were computed to assess the statistical and perceptual image similarities, respectively. To assess the function-wise concordance, the Dice similarity coefficient (DSC) was computed to determine the similarity of the low/high functional lung volumes., Results: The evaluation of the voxel-wise agreement showed a moderate-to-high voxel value correlation (0.6733 ± 0.1728) and high structural similarity (0.7635 ± 0.0697) between the SPECT and DL-CTPM predicted perfusions. The evaluation of the function-wise concordance obtained an average DSC value of 0.8183 ± 0.0752 for high-functional lungs (range, 0.5819-0.9255) and 0.6501 ± 0.1061 for low-functional lungs (range, 0.2405-0.8212). Ninety-four percent of the test cases demonstrated high concordance (DSC >0.7) between the high-functional volumes contoured from the predicted and ground-truth perfusions., Conclusions: We developed a novel DL-CTPM method for estimating perfusion-based lung functional images from the CT domain using a 3-dimensional attention residual neural network, which yielded moderate-to-high voxel-wise approximations of lung perfusion. To further contextualize these results toward future clinical application, a multi-institutional large-cohort study is warranted., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Simple Summary: Dynamic chest radiographs offer a distinct advantage over traditional chest radiographs by integrating motion and functional data, elevating their significance in clinical diagnostics. This study introduces a pioneering technique employing deep neural networks to simulate respiratory lung motion and extract local functional details from single-phase chest X-rays, thereby enhancing lung cancer clinical diagnostic capabilities. Our research establishes the viability of generating patient-specific respiratory motion profiles from single-phase chest radiographs. The evaluation of results from the network developed here underscores its substantial accuracy and fidelity, affirming its robustness in providing valuable supplementary insights into pulmonary function. In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs. [ABSTRACT FROM AUTHOR]

Background: Most available four‐dimensional (4D)‐magnetic resonance imaging (MRI) techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D‐MRI. Purpose: This study aims to develop a fast ultra‐quality (UQ) 4D‐MRI reconstruction method using a commercially available 4D‐MRI sequence and dual‐supervised deformation estimation model (DDEM). Methods: Thirty‐nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a time‐resolved imaging with interleaved stochastic trajectories (TWIST)–lumetric interpolated breath‐hold examination (VIBE) MRI sequence to acquire 4D‐magnetic resonance (MR) images. They also received 3D T1‐/T2‐weighted MRI scans as prior images, and UQ 4D‐MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D‐MRI data, and the prior images were deformed using this 4D‐DVF to generate UQ 4D‐MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross‐correlation [NCC] supervised), VoxelMorph (end‐to‐end point error [EPE] supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D‐MRI was evaluated quantitatively using region of interest (ROI) tracking errors, while image quality was evaluated using the contrast‐to‐noise ratio (CNR), lung–liver edge sharpness, and perceptual blur metric (PBM). Results: The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised), and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D‐MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior–inferior, anterior–posterior, and mid–lateral directions, respectively. From the original 4D‐MRI to UQ 4D‐MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung–liver edge full‐width‐at‐half‐maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in‐plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross‐plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. Conclusion: This novel DDEM method successfully generated UQ 4D‐MR images based on a commercial 4D‐MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy. [ABSTRACT FROM AUTHOR]

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy. [ABSTRACT FROM AUTHOR]

• We demonstrate that using only a single deep convolutional neural network (DCNN) is sufficient to detect pistons from broadband extended images. To the best of our knowledge, the technique, reported in this paper, has never been reported. • Compared to other piston sensing methods using neural network, the reported technology being able to discern pistons directly from the broadband intensity summations rather than the intensities of separated wavelengths uses only a single DCNN, which extremely relaxes the optics complexity. • Above all, we believe this paper is suitable for optics and lasers in engineering. It is crucial for sparse aperture systems to preserve imaging quality, which can be addressed when fast corrections of pistons within a fraction of a wavelength are available. In this paper, we demonstrate that only a single deep convolutional neural network is sufficient to extract pistons from wide-band extended images once being appropriately trained. To eliminate the object characters, the feature vector is calculated as the input by a pair of focused and defocused images. This method possesses the capability of fine phasing with high sensing accuracy, and a large-scale capture range without the use of combined wavelengths. Simple and fast, the proposed technique might find wide applications in phasing telescope arrays or segmented mirrors. [ABSTRACT FROM AUTHOR]