Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

BACKGROUND: Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. PURPOSE: By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. METHODS: The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. RESULTS: The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. CONCLUSION: This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Dosimetric analysis of six whole-breast irradiation techniques in supine and prone positions.

In breast cancer radiation therapy, minimizing radiation-related risks and toxicity is vital for improving life expectancy. Tailoring radiotherapy techniques and treatment positions can reduce radiation doses to normal organs and mitigate treatment-related toxicity. This study entailed a dosimetric comparison of six different external beam whole-breast irradiation techniques in both supine and prone positions. We selected fourteen breast cancer patients, generating six treatment plans in both positions per patient. We assessed target coverage and organs at risk (OAR) doses to evaluate the impact of treatment techniques and positions. Excess absolute risk was calculated to estimate potential secondary cancer risk in the contralateral breast, ipsilateral lung, and contralateral lung. Additionally, we analyzed the distance between the target volume and OARs (heart and ipsilateral lung) while considering the treatment position. The results indicate that prone positioning lowers lung exposure in X-ray radiotherapy. However, particle beam therapies (PBTs) significantly reduce the dose to the heart and ipsilateral lung regardless of the patient's position. Notably, negligible differences were observed between arc-delivery and static-delivery PBTs in terms of target conformity and OAR sparing. This study provides critical dosimetric evidence to facilitate informed decision-making regarding treatment techniques and positions.

Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis.

PURPOSE: This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS: We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS: Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS: Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION: The protocol of this study was registered on PROSPERO (CRD42023426565).

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape.

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

A 70% Ethanol Neorhodomela munita Extract Attenuates RANKL-Induced Osteoclast Activation and H2O2-Induced Osteoblast Apoptosis In Vitro.

The rapid aging of the population worldwide presents a significant social and economic challenge, particularly due to osteoporotic fractures, primarily resulting from an imbalance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation. While conventional therapies offer benefits, they also present limitations and a range of adverse effects. This study explores the protective impact of Neorhodomela munita ethanol extract (EN) on osteoporosis by modulating critical pathways in osteoclastogenesis and apoptosis. Raw264.7 cells and Saos-2 cells were used for in vitro osteoclast and osteoblast models, respectively. By utilizing various in vitro methods to detect osteoclast differentiation/activation and osteoblast death, it was demonstrated that the EN's potential to inhibit RANKL induced osteoclast formation and activation by targeting the MAPKs-NFATc1/c-Fos pathway and reducing H2O2-induced cell death through the downregulation of apoptotic signals. This study highlights the potential benefits of EN for osteoporosis and suggests that EN is a promising natural alternative to traditional treatments.

Evaluation of the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble and its influence on dose.

We aimed to investigate the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble owing to the intrinsic inter-segment break of the Elekta linear accelerator (LINAC) and its adverse influence on the dose to the patient. The deliverability of DCAT was evaluated according to the plan parameters, which affect the gantry rotation speed and resultant positional inaccuracies; the deliverability according to the number of control points and dose rates was investigated by using treatment machine log files and dosimetry devices, respectively. A non-negligible degradation in DCAT deliverability due to gantry wobble was observed in both the treatment machine log files and dosimetry devices. The resulting dose-delivery error occurred below a certain number of control points or above a certain dose rate. Dose simulations in the patient domain showed a similar impact on deteriorated deliverability. For targets located primarily in the isocenter, the dose differences were negligible, whereas for organs at risk located mainly off-isocenter, the dose differences were significant up to - 8.77%. To ensure safe and accurate radiotherapy, optimal plan parameters should be selected, and gantry angle-specific validations should be conducted before treatment.

Extracellular Matrix-Bioinspired Anisotropic Topographical Cues of Electrospun Nanofibers: A Strategy of Wound Healing through Macrophage Polarization.

The skin serves as the body's outermost barrier and is the largest organ, providing protection not only to the body but also to various internal organs. Owing to continuous exposure to various external factors, it is susceptible to damage that can range from simple to severe, including serious types of wounds such as burns or chronic wounds. Macrophages play a crucial role in the entire wound-healing process and contribute significantly to skin regeneration. Initially, M1 macrophages infiltrate to phagocytose bacteria, debris, and dead cells in fresh wounds. As tissue repair is activated, M2 macrophages are promoted, reducing inflammation and facilitating restoration of the dermis and epidermis to regenerate the tissue. This suggests that extracellular matrix (ECM) promotes cell adhesion, proliferation, migrationand macrophage polarization. Among the numerous strategies, electrospinning is a versatile technique for obtaining ECM-mimicking structures with anisotropic and isotropic topologies of micro/nanofibers. Various electrospun biomaterials influence macrophage polarization based on their isotropic or anisotropic topologies. Moreover, these fibers possess a high surface-area-to-volume ratio, promoting the effective exchange of vital nutrients and oxygen, which are crucial for cell viability and tissue regeneration. Micro/nanofibers with diverse physical and chemical properties can be tailored to polarize macrophages toward skin regeneration and wound healing, depending on specific requirements. This review describes the significance of micro/nanostructures for activating macrophages and promoting wound healing.

Stimuli-Responsive 3D Printable Conductive Hydrogel: A Step toward Regulating Macrophage Polarization and Wound Healing.

Conductive hydrogels (CHs) are promising alternatives for electrical stimulation of cells and tissues in biomedical engineering. Wound healing and immunomodulation are complex processes that involve multiple cell types and signaling pathways. 3D printable conductive hydrogels have emerged as an innovative approach to promote wound healing and modulate immune responses. CHs can facilitate electrical and mechanical stimuli, which can be beneficial for altering cellular metabolism and enhancing the efficiency of the delivery of therapeutic molecules. This review summarizes the recent advances in 3D printable conductive hydrogels for wound healing and their effect on macrophage polarization. This report also discusses the properties of various conductive materials that can be used to fabricate hydrogels to stimulate immune responses. Furthermore, this review highlights the challenges and limitations of using 3D printable CHs for future material discovery. Overall, 3D printable conductive hydrogels hold excellent potential for accelerating wound healing and immune responses, which can lead to the development of new therapeutic strategies for skin and immune-related diseases.

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation.

U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net ("no-new-Net"). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers.

Risk of on-treatment lymphopenia is associated with treatment outcome and efficacy of consolidation immunotherapy in patients with non-small cell lung cancer treated with concurrent chemoradiotherapy.

BACKGROUND AND PURPOSE: The ability of the effective dose to immune cells (EDIC) and the pre-radiotherapy (RT) absolute lymphocyte count (ALC) to predict lymphopenia during RT, treatment outcomes, and efficacy of consolidation immunotherapy in patients with locally advanced non-small cell lung cancer was investigated. METHODS AND MATERIALS: Among 517 patients treated with concurrent chemoradiotherapy, EDIC was calculated using the mean doses to the lungs, heart, and total body. The patients were grouped according to high and low EDIC and pre-RT ALC, and the correlations with radiation-induced lymphopenia and survival outcomes were determined. RESULTS: Altogether, 195 patients (37.7%) received consolidation immunotherapy. The cutoff values of EDIC and pre-RT ALC for predicting severe lymphopenia were 2.89 Gy and 2.03 × 109 cells/L, respectively. The high-risk group was defined as EDIC ≥ 2.89 Gy and pre-RT ALC < 2.03 × 109 cells/L, while the low-risk group as EDIC < 2.89 Gy and pre-RT ALC ≥ 2.03 × 109 cells/L, and the rest of the patients as the intermediate-risk group. The incidences of severe lymphopenia during RT in the high-, intermediate-, and low-risk groups were 90.1%, 77.1%, and 52.3%, respectively (P < 0.001). The risk groups could independently predict both progression-free (P < 0.001) and overall survival (P < 0.001). The high-risk group showed a higher incidence of locoregional and distant recurrence (P < 0.001). Consolidation immunotherapy showed significant survival benefit in the low- and intermediate-risk groups but not in the high-risk group. CONCLUSIONS: The combination of EDIC and pre-RT ALC predicted severe lymphopenia, recurrence, and survival. It may potentially serve as a biomarker for consolidation immunotherapy.

Study protocol for prospective multi-institutional phase III trial of standard of care therapy with or without stereotactic ablative radiation therapy for recurrent ovarian cancer (SABR-ROC).

BACKGROUND: Efforts have been made to investigate the role of salvage radiotherapy (RT) in treating recurrent ovarian cancer (ROC). Stereotactic ablative radiation therapy (SABR) is a state-of-the-art therapy that uses intensity modulation to increase the fractional dose, decrease the number of fractions, and target tumors with high precision. METHODS: The SABR-ROC trial is a phase 3, multicenter, randomized, prospective study to evaluate whether the addition of SABR to the standard of care significantly improves the 3-year overall survival (OS) of patients with ROC. Patients who have completed the standard treatment for primary epithelial ovarian cancer are eligible. In addition, patients with number of metastases ≤ 10 and maximum diameter of each metastatic site of gross tumor ≤ 5 cm are allowed. Randomization will be stratified by (1) No. of the following clinical factors met, platinum sensitivity, absence of ascites, normal level of CA125, and ECOG performance status of 0-1; 0-3 vs. 4; (2) site of recurrence; with vs. without lymph nodes; and (3) PARP inhibitor; use vs. non-use. The target number of patients to be enrolled in this study is 270. Participants will be randomized in a 1:2 ratio. Participants in Arm 2 will receive SABR for recurrent lesions clearly identified in imaging tests as well as the standard of care (Arm 1) based on treatment guidelines and decisions made in multidisciplinary discussions. The RT fraction number can range from 1 to 10, and the accepted dose range is 16-45 Gy. The RT Quality Assurance (QA) program consists of a three-tiered system: general credentialing, trial-specific credentialing, and individual case reviews. DISCUSSION: SABR appears to be preferable as it does not interfere with the schedule of systemic treatment by minimizing the elapsed days of RT. The synergistic effect between systemic treatment and SABR is expected to reduce the tumor burden by eradicating gross tumors identified through imaging with SABR and controlling microscopic cancer with systemic treatment. It might also be beneficial for quality-of-life preservation in older adults or heavily treated patients. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov (NCT05444270) on June 29th, 2022.

Development and evaluation of an integrated model based on a deep segmentation network and demography-added radiomics algorithm for segmentation and diagnosis of early lung adenocarcinoma.

Non-invasive early detection and differentiation grading of lung adenocarcinoma using computed tomography (CT) images are clinically important for both clinicians and patients, including determining the extent of lung resection. However, these are difficult to accomplish using preoperative images, with CT-based diagnoses often being different from postoperative pathologic diagnoses. In this study, we proposed an integrated detection and classification algorithm (IDCal) for diagnosing ground-glass opacity nodules (GGN) using CT images and other patient informatics, and compared its performance with that of other diagnostic modalities. All labeling was confirmed by a thoracic surgeon by referring to the patient's CT image and biopsy report. The detection phase was implemented via a modified FC-DenseNet to contour the lesions as elaborately as possible and secure the reliability of the classification phase for subsequent applications. Then, by integrating radiomics features and other patients' general information, the lesions were dichotomously reclassified into "non-invasive" (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and "invasive" (invasive adenocarcinoma). Data from 168 GGN cases were used to develop the IDCal, which was then validated in 31 independent CT scans. IDCal showed a high accuracy of GGN detection (sensitivity, 0.970; false discovery rate, 0.697) and classification (accuracy, 0.97; f1-score, 0.98; ROAUC, 0.96). In conclusion, the proposed IDCal detects and classifies GGN with excellent performance. Thus, it can be suggested that our multimodal prediction model has high potential as an auxiliary diagnostic tool of GGN to help clinicians.

Cosmetic Outcome and Toxicity After Stereotactic Accelerated Partial Breast Irradiation in Early Breast Cancer: A Prospective Observational Cohort Study.

PURPOSE: The aim of this work was to prospectively evaluate the toxicity and cosmetic outcomes of 5-fraction, stereotactic, accelerated partial breast irradiation (APBI). METHODS AND MATERIALS: This prospective observational cohort study enrolled women who underwent APBI for invasive carcinoma or carcinoma in situ of the breast. APBI was delivered using a CyberKnife M6 robotic radiosurgery system at 30 Gy in 5 nonconsecutive, once-daily fractions. Women undergoing whole breast irradiation (WBI) were also enrolled for comparison. Patient-reported and physician-assessed adverse events were recorded. Breast fibrosis was measured using a tissue compliance meter, and breast cosmesis was assessed using BCCT.core (an automatic, computer-based software). Outcomes were collected until 24 months posttreatment according to the study protocol. RESULTS: In total, 204 patients (APBI, n = 103; WBI, n = 101) were enrolled. Regarding patient-reported outcomes, the APBI group reported significantly less skin dryness (6.9% vs 18.3%; P = .015), radiation skin reaction (9.9% vs 23.5%; P = .010), and breast hardness (8.0% vs 20.4%; P = .011) at 6 months than the WBI group. On physician assessment, the APBI group had significantly less dermatitis at 12 months (1.0% vs 7.2%; P = .027) than the WBI group. Any severe toxicities after APBI were rare in patient-reported outcomes (score ≥3, 3.0%) and physician assessments (grade ≥3, 2.0%). In the uninvolved quadrants, measured fibrosis in the APBI group was significantly lower than that in the WBI group at 6 (P = .001) and 12 (P = .029) months but not at 24 months. In the involved quadrant, measured fibrosis in the APBI group was not significantly different from that in the WBI group at any time. Cosmetic outcomes in the APBI group were mostly excellent or good (77.6%) at 24 months, and there was no significant cosmetic detriment from the baseline. CONCLUSIONS: Stereotactic APBI was associated with less fibrosis in the uninvolved breast quadrants than WBI. Patients showed minimal toxicity and no detrimental effects on cosmesis after APBI.

Epigallocatechin-3-Gallate Attenuates Myocardial Dysfunction via Inhibition of Endothelial-to-Mesenchymal Transition.

Cardiac tissue damage following ischemia leads to cardiomyocyte apoptosis and myocardial fibrosis. Epigallocatechin-3-gallate (EGCG), an active polyphenol flavonoid or catechin, exerts bioactivity in tissues with various diseases and protects ischemic myocardium; however, its association with the endothelial-to-mesenchymal transition (EndMT) is unknown. Human umbilical vein endothelial cells (HUVECs) pretreated with transforming growth factor ß2 (TGF-ß2) and interleukin 1ß (IL-1ß) were treated with EGCG to verify cellular function. In addition, EGCG is involved in RhoA GTPase transmission, resulting in reduced cell mobility, oxidative stress, and inflammation-related factors. A mouse myocardial infarction (MI) model was used to confirm the association between EGCG and EndMT in vivo. In the EGCG-treated group, ischemic tissue was regenerated by regulating proteins involved in the EndMT process, and cardioprotection was induced by positively regulating apoptosis and fibrosis of cardiomyocytes. Furthermore, EGCG can reactivate myocardial function due to EndMT inhibition. In summary, our findings confirm that EGCG is an impact activator controlling the cardiac EndMT process derived from ischemic conditions and suggest that supplementation with EGCG may be beneficial in the prevention of cardiovascular disease.

Development of Automated Delivery Quality Assurance Analysis Software for Helical Tomotherapy.

BACKGROUND: To develop a fully automated in-house gamma analysis software for the "Cheese" phantom-based delivery quality assurance (QA) of helical tomotherapy plans. METHODS: The developed in-house software was designed to automate several procedures, which need to be manually performed using commercial software packages. The region of interest for the analysis was automatically selected by cropping out film edges and thresholding dose values (>10% of the maximum dose). The film-measured dose was automatically aligned to the computed dose using an image registration algorithm. An optimal film scaling factor was determined to maximize the percentage of pixels passing gamma (gamma passing rate) between the measured and computed doses (3%/3 mm criteria). This gamma analysis was repeated by introducing setup uncertainties in the anterior-posterior direction. For 73 tomotherapy plans, the gamma analysis results using the developed software were compared to those analyzed by medical physicists using a commercial software package. RESULTS: The developed software successfully automated the gamma analysis for the tomotherapy delivery quality assurance. The gamma passing rate (GPR) calculated by the developed software was higher than that by the clinically used software by 3.0%, on average. While, for 1 of the 73 plans, the GPR by the manual gamma analysis was higher than 90% (pass/fail criteria), the gamma analysis using the developed software resulted in fail (GPR < 90%). CONCLUSIONS: The use of automated and standardized gamma analysis software can improve both the clinical efficiency and veracity of the analysis results. Furthermore, the gamma analyses with various film scaling factors and setup uncertainties will provide clinically useful information for further investigations.

Acute coronary event (ACE) prediction following breast radiotherapy by features extracted from 3D CT, dose, and cardiac structures.

PURPOSE: Heart toxicity, such as major acute coronary events (ACE), following breast radiation therapy (RT) is of utmost concern. Thus, many studies have been investigating the effect of mean heart dose (MHD) and dose received in heart sub-structures on toxicity. Most studies focused on the dose thresholds in the heart and its sub-structures, while few studies adopted such computational methods as deep neural networks (DNN) and radiomics. This work aims to construct a feature-driven predictive model for ACE after breast RT. METHODS: A recently proposed two-step predictive model that extracts a number of features from a deep auto-segmentation network and processes the selected features for prediction was adopted. This work refined the auto-segmenting network and feature processing algorithms to enhance performance in cardiac toxicity prediction. In the predictive model, the deep convolutional neural network (CNN) extracted features from 3D computed tomography (CT) images and dose distributions in three automatically segmented heart sub-structures, including the left anterior descending artery (LAD), right coronary artery (RCA), and left ventricle (LV). The optimal feature processing workflow for the extracted features was explored to enhance the prediction accuracy. The regions associated with toxicity were visualized using a class activation map (CAM)-based technique. Our proposed model was validated against a conventional DNN (convolutional and fully connected layers) and radiomics with a patient cohort of 84 cases, including 29 and 55 patient cases with and without ACE. Of the entire 84 cases, 12 randomly chosen cases (5 toxicity and 7 non-toxicity cases) were set aside for independent test, and the remaining 72 cases were applied to 4-fold stratified cross-validation. RESULTS: Our predictive model outperformed the conventional DNN by 38% and 10% and radiomics-based predictive models by 9% and 10% in AUC for 4-fold cross-validations and independent test, respectively. The degree of enhancement was greater when incorporating dose information and heart sub-structures into feature extraction. The model whose inputs were CT, dose, and three sub-structures (LV, LAD, and RCA) reached 96% prediction accuracy on average and 0.94 area under the curve (AUC) on average in the cross-validation, and also achieved prediction accuracy of 83% and AUC of 0.83 in the independent test. On 10 correctly predicted cases out of 12 for the independent test, the activation maps implied that for cases of ACE toxicity, the higher intensity was more likely to be observed inside the LV. CONCLUSIONS: The proposed model characterized by modifications in model input with dose distributions and cardiac sub-structures, and serial processing of feature extraction and feature selection techniques can improve the predictive performance in ACE following breast RT.

Novel framework for determining TPS-calculated doses corresponding to detector locations using 3D camera inin vivosurface dosimetry.

Purpose. To address the shortcomings of current procedures for evaluating the measured-to-planned dose agreement inin vivodosimetry (IVD), this study aimed to develop an accurate and efficient novel framework to identify the detector location placed on a patient's skin surface using a 3D camera and determine the planned dose at the same anatomical position corresponding to the detector location.Methods. Breast cancer treatment was simulated using an anthropomorphic adult female phantom (ATOM 702D; CIRS, Norfolk, VA, USA). An optically stimulated luminescent dosimeter was used for surface dose measurements (MyOSLchip, RadPro International GmbH, Germany) at six IVD points. Three-dimensional surface imaging (3DSI) of the phantom with the detector was performed in the treatment position using a 3D camera. The developed framework, iSMART, was designed to import 3DSI and treatment planning data for determining the position of the IVD detectors in the 3D treatment planning DICOM image. The clinical usefulness of iSMART was evaluated in terms of accuracy and efficiency, for comparison with the results obtained using cone-beam computed tomography (CBCT) image guidance.Results. The relative dose difference between the planned doses determined using iSMART and CBCT images displayed similar accuracies (within approximately ±2.0%) at all detector locations. The relative dose differences between the planned and measured doses at the six detector locations ranged from -4.8% to 3.1% for the CBCT images and -3.5% to 2.1% for iSMART. The total time required to read the planned doses at six detector locations averaged at 8.1 and 0.8 min for the CBCT images and iSMART, respectively.Conclusions. The proposed framework can improve the robustness of IVD analyses and aid in accurate and efficient evaluations of the measured-to-planned dose agreement.

Use of a novel body mandibular plane (mental foramen-protuberance menti) in analyzing mandibular asymmetry compared with conventional border mandibular plane.

OBJECTIVES: To compare a novel body mandibular horizontal plane (mental foramen-protuberance menti; Body-MHP) with the conventional border mandibular horizontal plane (gonion-menton [Me]; Border-MHP) to assess mandibular body inclination and dental compensation of skeletal Class III patients with and without facial asymmetry. MATERIALS AND METHODS: Retrospective data obtained from diagnostic cone-beam computed tomography of 90 skeletal Class III patients (mean age, 21.67 ± 2.93 years; range, 15.0-30.6 years) were divided into symmetry (n = 30) and asymmetry groups (n = 60). The asymmetry group was subdivided into roll (n = 30) and non-roll types (n = 30). The differences in body inclination and dental measurements (distance and angle) according to two mandibular planes (Body-MHP and Border-MHP) were assessed in the groups and subgroups. RESULTS: Mandibular body inclinations relative to the Body-MHP were not different in the roll-type asymmetric mandible between the sides, while those relative to the Border-MHP were different (P < .001). For the mandibular first molar positions relative to the Border-MHP, the differences in vertical distance between the sides were undermeasured and the inclination differences were overmeasured when compared relative to the Body-MHP. CONCLUSIONS: The Body-MHP demonstrated better bilateral similarity in body inclination compared with the Border-MHP in patients with roll-type facial asymmetry. The novel body mandibular plane ensures an accurate diagnosis for tooth movement and jaw surgery, particularly in the roll-type asymmetric mandible.

A postulated model for photoimmunopathogenesis of chronic actinic dermatitis around adaptive immunity, including Th17 cells, Tregs, TRMs, cytotoxic T cells, and/or common-γ chain receptor+ cells.

BACKGROUND/PURPOSE: The pathogenesis of chronic actinic dermatitis (CAD) is more complicated than other photodermatoses. However, the relationship between the clinical severity of CAD and the offending photocontact or contact allergens or both, and the correlations of CAD immunopathogenesis with the immunoregulatory molecules involved in adaptive immunity are yet to be investigated. METHODS: We performed phototesting with broad-spectrum ultraviolet (UV) B, UVA, and visible light to establish the presence of photosensitivity in 121 patients with CAD, together with photopatch and contact patch testing. Nine patients with CAD were selected according to their clinical severity score for CAD (CSS-CAD), and triple direct immunofluorescence analysis was performed with paraffin-embedded skin biopsy samples. RESULTS: As CSS-CAD was closely correlated with the multiplicity of photo(contact) allergens, particularly photoallergens, three or more photoallergens were detected in the severe CAD group (52.5%); less in the moderate group (32.8%); and only one in the mild group (14.8%; P = .025). In the groups showing greater severity of disease, the absolute numbers of IFN-γ+ , IL-17+ , CD4+, CD8+, common-γ chain receptor (common-γCR)+ , and CD69+ tissue-resident memory cells increased on average; there was also an increase in the CD4+/CD8+ cell ratio, with the more severely affected groups. However, the levels of TNF-α+ and FoxP3+ regulatory T (Treg) cells and the mean IL-17/IFN-γ cell ratio decreased in the more severely affected CSS-CAD subgroups. CONCLUSIONS: Based on the clinical analysis and immunopathogenic results, avoidance of excessive sun exposure, and topical and systemic blocking agents for photo(contact) allergens are recommended. Additionally, conventional immunomodulators and emerging agents including JAK-STAT inhibitors may be administered for CAD treatment in the future.