Multi-modal image confidence: a novel method for tumor and organ boundary representation.

BACKGROUND: The ambiguous boundaries of tumors and organs at risk (OARs) seen in medical images pose challenges in treatment planning and other tasks in radiotherapy. METHODS: This study introduces an innovative analytical algorithm, Multi-Modal Image Confidence (MMC), which exploits the collective strengths of complementary multi-modal medical images to determine a confidence measure for each voxel belonging to the region of interest (ROI). MMC facilitates the creation of modality-specific ROI-enhanced images, enabling a detailed representation of the ROI's boundaries and internal features. By employing an interpretable mathematical model that propagates voxel confidence based on inter-voxel correlations, MMC avoids the need for model training, distinguishing it from deep learning (DL)-based methods. RESULTS: The performance of the proposed algorithm was qualitatively and quantitatively evaluated using 156 nasopharyngeal carcinoma cases and 1251 glioma cases. Qualitative assessments underscored the accuracy of MMC and ROI-enhanced images in estimating lesion boundaries and capturing internal tumor characteristics. Quantitative analyses revealed strong agreement between MMC and manual delineations. CONCLUSION: This paper introduces a novel analytical algorithm to identify and depict ROI boundaries based on complementary multi-modal 3D medical images. The applicability of the proposed method can extend to both targets and OARs at diverse anatomical sites across multiple image modalities, amplifying its potential for augmenting radiotherapy-related tasks.

Heterogeneity of immune cells and their communications unveiled by transcriptome profiling in acute inflammatory lung injury.

Background: Acute Respiratory Distress Syndrome (ARDS) or its earlier stage Acute lung injury (ALI), is a worldwide health concern that jeopardizes human well-being. Currently, the treatment strategies to mitigate the incidence and mortality of ARDS are severely restricted. This limitation can be attributed, at least in part, to the substantial variations in immunity observed in individuals with this syndrome. Methods: Bulk and single cell RNA sequencing from ALI mice and single cell RNA sequencing from ARDS patients were analyzed. We utilized the Seurat program package in R and cellmarker 2.0 to cluster and annotate the data. The differential, enrichment, protein interaction, and cell-cell communication analysis were conducted. Results: The mice with ALI caused by pulmonary and extrapulmonary factors demonstrated differential expression including Clec4e, Retnlg, S100a9, Coro1a, and Lars2. We have determined that inflammatory factors have a greater significance in extrapulmonary ALI, while multiple pathways collaborate in the development of pulmonary ALI. Clustering analysis revealed significant heterogeneity in the relative abundance of immune cells in different ALI models. The autocrine action of neutrophils plays a crucial role in pulmonary ALI. Additionally, there was a significant increase in signaling intensity between B cells and M1 macrophages, NKT cells and M1 macrophages in extrapulmonary ALI. The CXCL, CSF3 and MIF, TGFß signaling pathways play a vital role in pulmonary and extrapulmonary ALI, respectively. Moreover, the analysis of human single-cell revealed DCs signaling to monocytes and neutrophils in COVID-19-associated ARDS is stronger compared to sepsis-related ARDS. In sepsis-related ARDS, CD8+ T and Th cells exhibit more prominent signaling to B-cell nucleated DCs. Meanwhile, both MIF and CXCL signaling pathways are specific to sepsis-related ARDS. Conclusion: This study has identified specific gene signatures and signaling pathways in animal models and human samples that facilitate the interaction between immune cells, which could be targeted therapeutically in ARDS patients of various etiologies.

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification.

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

miR-451a was selectively sorted into exosomes and promoted the progression of esophageal squamous cell carcinoma through CAB39.

Exosomes are emerging mediators of cell-cell communication, which are secreted from cells and may be delivered into recipient cells in cell biological processes. Here, we examined microRNA (miRNA) expression in esophageal squamous cell carcinoma (ESCC) cells. We performed miRNA sequencing in exosomes and cells of KYSE150 and KYSE450 cell lines. Among these differentially expressed miRNAs, 20 of the miRNAs were detected in cells and exosomes. A heat map indicated that the level of miR-451a was higher in exosomes than in ESCC cells. Furthermore, miRNA pull-down assays and combined exosomes proteomic data showed that miR-451a interacts with YWHAE. Over-expression of YWHAE leads to miR-451a accumulation in the exosomes instead of the donor cells. We found that miR-451a was sorted into exosomes. However, the biological function of miR-451a remains unclear in ESCC. Here, Dual-luciferase reporter assay was conducted and it was proved that CAB39 is a target gene of miR-451a. Moreover, CAB39 is related to TGF-ß1 from RNA-sequencing data of 155 paired of ESCC tissues and the matched tissues. Western Blot and qPCR revealed that CAB39 and TGF-ß1 were positively correlated in ESCC. Over-expression of CAB39 were cocultured with PBMCs from the blood from healthy donors. Flow cytometry assays showed that apoptotic cells were significantly reduced after CAB39 over-expression and significantly increased after treated with TGF-ß1 inhibitors. Thus, our data indicate that CAB39 weakens antitumor immunity through TGF-ß1 in ESCC. In summary, YWHAE selectively sorted miR-451a into exosomes and it can weaken antitumor immunity promotes tumor progression through CAB39.

Optical measurements of dissolved organic matter as proxies for CODMn and BOD5 in plateau lakes.

The presence of organic matter in lakes profoundly impacts drinking water supplies, yet treatment processes involving coagulants and disinfectants can yield carcinogenic disinfection by-products. Traditional assessments of organic matter, such as chemical oxygen demand (CODMn) and biochemical oxygen demand (BOD5), are often time-consuming. Alternatively, optical measurements of dissolved organic matter (DOM) offer a rapid and reliable means of obtaining organic matter composition data. Here we employed DOM optical measurements in conjunction with parallel factor analysis to scrutinize CODMn and BOD5 variability. Validation was performed using an independent dataset encompassing six lakes on the Yungui Plateau from 2014 to 2016 (n = 256). Leveraging multiple linear regressions (MLRs) applied to DOM absorbance at 254 nm (a254) and fluorescence components C1-C5, we successfully traced CODMn and BOD5 variations across the entire plateau (68 lakes, n = 271, R2 > 0.8, P < 0.0001). Notably, DOM optical indices yielded superior estimates (higher R2) of CODMn and BOD5 during the rainy season compared to the dry season and demonstrated increased accuracy (R2 > 0.9) in mesotrophic lakes compared to oligotrophic and eutrophic lakes. This study underscores the utility of MLR-based DOM indices for inferring CODMn and BOD5 variability in plateau lakes and highlights the potential of integrating in situ and remote sensing platforms for water pollution early warning.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma.

To explore the prognostic significance of PET/CT-based radiomics signatures and clinical features for local recurrence-free survival (LRFS) in nasopharyngeal carcinoma (NPC). We retrospectively reviewed 726 patients who underwent pretreatment PET/CT at our center. Least absolute shrinkage and selection operator (LASSO) regression and the Cox proportional hazards model were applied to construct Rad-score, which represented the radiomics features of PET-CT images. Univariate and multivariate analyses were used to establish a nomogram model. The concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability. Receiver operating characteristic analysis was performed to stratify the local recurrence risk of patients. The nomogram was validated by evaluating its discrimination ability and calibration in the validation cohort. A total of eight features were selected to construct Rad-score. A radiomics-clinical nomogram was built after the selection of univariate and multivariable Cox regression analyses, including the Rad-score and maximum standardized uptake value (SUVmax). The C-index was 0.71 (0.67-0.74) in the training cohort and 0.70 (0.64-0.76) in the validation cohort. The nomogram also performed far better than the 8th T-staging system with an area under the receiver operating characteristic curve (AUC) of 0.75 vs. 0.60 for 2 years and 0.71 vs. 0.60 for 3 years. The calibration curves show that the nomogram indicated accurate predictions. Decision curve analysis (DCA) revealed significantly better net benefits with this nomogram model. The log-rank test results revealed a distinct difference in prognosis between the two risk groups. The PET/CT-based radiomics nomogram showed good performance in predicting LRFS and showed potential to identify patients at high-risk of developing NPC.

Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network.

Purpose/Objectives: The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. Materials/Methods: Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05). Results: Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively. Conclusion: In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.

XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers.

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, XBound-Former, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. First, we propose an implicit boundary learner (im-Bound) to constrain the network attention on the points with noticeable boundary variation, enhancing the local context modeling while maintaining the global context. Second, we propose an explicit boundary learner (ex-Bound) to extract the boundary knowledge at multiple scales and convert it into embeddings explicitly. Third, based on the learned multi-scale boundary embeddings, we propose a cross-scale boundary learner (X-Bound) to simultaneously address the problem of ambiguous and multi-scale boundaries by using learned boundary embedding from one scale to guide the boundary-aware attention on the other scales. We evaluate the model on two skin lesion datasets and one polyp lesion dataset, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. All resources could be found in https://github.com/jcwang123/xboundformer.

A generalization performance study on the boosting radiotherapy dose calculation engine based on super-resolution.

PURPOSE: During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution-based deep learning model for fast and accurate dose prediction in clinical practice. METHOD: A Multi-stage Dose Super-Resolution Network (MDSR Net) architecture with sparse masks module and multi-stage progressive dose distribution restoration method were developed to predict high-resolution dose distribution using low-resolution data. A total of 340 VMAT plans from different disease sites were used, among which 240 randomly selected nasopharyngeal, lung, and cervix cases were used for model training, and the remaining 60 cases from the same sites for model benchmark testing, and additional 40 cases from the unseen site (breast and rectum) was used for model generalizability evaluation. The clinical calculated dose with a grid size of 2 mm was used as baseline dose distribution. The input included the dose distribution with 4 mm grid size and CT images. The model performance was compared with HD U-Net and cubic interpolation methods using Dose-volume histograms (DVH) metrics and global gamma analysis with 1%/1 mm and 10% low dose threshold. The correlation between the prediction error and the dose, dose gradient, and CT values was also evaluated. RESULTS: The prediction errors of MDSR were 0.06-0.84% of Dmean indices, and the gamma passing rate was 83.1-91.0% on the benchmark testing dataset, and 0.02-1.03% and 71.3-90.3% for the generalization dataset respectively. The model performance was significantly higher than the HD U-Net and interpolation methods (p < 0.05). The mean errors of the MDSR model decreased (monotonously by 0.03-0.004%) with dose and increased (by 0.01-0.73%) with the dose gradient. There was no correlation between prediction errors and the CT values. CONCLUSION: The proposed MDSR model achieved good agreement with the baseline high-resolution dose distribution, with small prediction errors for DVH indices and high gamma passing rate for both seen and unseen sites, indicating a robust and generalizable dose prediction model. The model can provide fast and accurate high-resolution dose distribution for clinical dose calculation, particularly for the routine practice of OLART.

Auto-segmentation of important centers of growth in the pediatric skeleton to consider during radiation therapy based on deep learning.

BACKGROUND: Routinely delineating of important skeletal growth centers is imperative to mitigate radiation-induced growth abnormalities for pediatric cancer patients treated with radiotherapy. However, it is hindered by several practical problems, including difficult identification, time consumption, and inter-practitioner variability. PURPOSE: The goal of this study was to construct and evaluate a novel Triplet-Attention U-Net (TAU-Net)-based auto-segmentation model for important skeletal growth centers in childhood cancer radiotherapy, concentrating on the accuracy and time efficiency. METHODS: A total of 107 childhood cancer patients fulfilled the eligibility criteria were enrolled in the training cohort (N = 80) and test cohort (N = 27). The craniofacial growth plates, shoulder growth centers, and pelvic ossification centers, with a total of 19 structures in the three groups, were manually delineated by two experienced radiation oncologists on axial, coronal, and sagittal computed tomography images. Modified from U-Net, the proposed TAU-Net has one main branch and two bypass branches, receiving semantic information of three adjacent slices to predict the target structure. With supervised deep learning, the skeletal growth centers contouring of each group was generated by three different auto-segmentation models: U-Net, V-Net, and the proposed TAU-Net. Dice similarity coefficient (DSC) and Hausdorff distance 95% (HD95) were used to evaluate the accuracy of three auto-segmentation models. The time spent on performing manual tasks and manually correcting auto-contouring generated by TAU-Net was recorded. The paired t-test was used to compare the statistical differences in delineation quality and time efficiency. RESULTS: Among the three groups, including craniofacial growth plates, shoulder growth centers, and pelvic ossification centers groups, TAU-Net had demonstrated highly acceptable performance (the average DSC = 0.77, 0.87, and 0.83 for each group; the average HD95 = 2.28, 2.07, and 2.86 mm for each group). In the overall evaluation of 19 regions of interest (ROIs) in the test cohort, TAU-Net had an overwhelming advantage over U-Net (63.2% ROIs in DSC and 31.6% ROIs in HD95, p = 0.001-0.042) and V-Net (94.7% ROIs in DSC and 36.8% ROIs in HD95, p = 0.001-0.040). With an average time of 52.2 min for manual delineation, the average time saved to adjust TAU-Net-generated contours was 37.6 min (p < 0.001), a 72% reduction. CONCLUSIONS: Deep learning-based models have presented enormous potential for the auto-segmentation of important growth centers in pediatric skeleton, where the proposed TAU-Net outperformed the U-Net and V-Net in geometrical precision for the majority status.

Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer.

PURPOSE: Speed and accuracy are two critical factors in dose calculation for radiotherapy. Analytical Anisotropic Algorithm (AAA) is a rapid dose calculation algorithm but has dose errors in tissue margin area. Acuros XB (AXB) has high accuracy but takes long time to calculate. To improve the dose accuracy on the tissue margin area for AAA, we proposed a novel deep learning-based dose accuracy improvement method using Margin-Net combined with Margin-Loss. METHODS: A novel model 'Margin-Net' was designed with a Margin Attention Mechanism to generate special margin-related features. Margin-Loss was introduced to consider the dose errors and dose gradients in tissues margin area. Ninety-five VMAT cervical cancer cases with paired AAA and AXB dose were enrolled in our study: 76 cases for training and 19 cases for testing. Tissues Margin Masks were generated from RT contours with 6 mm extension. Tissues Margin Mask, AAA dose and CTs were input data; AXB dose was used as reference dose for model training and evaluation. Comparison experiments were performed to evaluated effectiveness of Margin-Net and Margin-Loss. RESULTS: Compared to AXB dose, the 3D gamma passing rate (1%/1 mm, 10% threshold) for 19 test cases 95.75 ± 1.05% using Margin-Net with Margin-Loss, which was significantly higher than the original AAA dose (73.64 ± 3.46%). The passing rate reduced to 94.07 ± 1.16% without Margin-Loss and 87.3 ± 1.18% if Margin-Net key structure 'MAM' was also removed. CONCLUSION: The proposed novel tissues margin-based dose conversion method can significantly improve the dose accuracy of Analytical Anisotropic Algorithm to be comparable to AXB algorithm. It can potentially improve the efficiency of treatment planning process with low demanding of computation resources.

Potential markers of cancer stem-like cells in ESCC: a review of the current knowledge.

In patients with esophageal squamous cell carcinoma (ESCC), the incidence and mortality rate of ESCC in our country are also higher than those in the rest of the world. Despite advances in the treatment department method, patient survival rates have not obviously improved, which often leads to treatment obstruction and cancer repeat. ESCC has special cells called cancer stem-like cells (CSLCs) with self-renewal and differentiation ability, which reflect the development process and prognosis of cancer. In this review, we evaluated CSLCs, which are identified from the expression of cell surface markers in ESCC. By inciting EMTs to participate in tumor migration and invasion, stem cells promote tumor redifferentiation. Some factors can inhibit the migration and invasion of ESCC via the EMT-related pathway. We here summarize the research progress on the surface markers of CSLCs, EMT pathway, and the microenvironment in the process of tumor growth. Thus, these data may be more valuable for clinical applications.

Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy.

BACKGROUND AND PURPOSE: Deep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strategy using the patient's previous images and contours to update the model and improve segmentation accuracy and efficiency for MRgART. METHODS AND MATERIALS: A prototype model was trained for each patient using the first set of MRI and corresponding contours as inputs. The patient-specific model was updated after each fraction with all the available fractional MRIs/contours, and then used to predict the segmentation for the next fraction. During model training, a variant was fitted under consistency constraints, limiting the differences in the volume, length and centroid between the predictions for the latest MRI within a reasonable range. The model performance was evaluated for both organ-at-risks and tumors auto-segmentation for a total of 6 abdominal/pelvic cases (each with at least 8 sets of MRIs/contours) underwent MRgART through Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD95), and was compared with deformable image registration (DIR) and frozen DL model (no updating after pre-training). The contouring time was also recorded and analyzed. RESULTS: The proposed model achieved superior performance with higher mean DSC (0.90, 95 % CI: 0.88-0.95), as compared to DIR (0.63, 95 %CI: 0.59-0.68) and frozen DL models (0.74, 95 % CI: 0.71-0.79). As for tumors, the proposed method yielded a median DSC of 0.95, 95 % CI: 0.94-0.97, and a median HD95 of 1.63 mm, 95 % CI: 1.22 mm-2.06 mm. The contouring time was reduced significantly (p < 0.05) using the proposed method (73.4 ± 6.5 secs) compared to the manual process (12 âˆ¼ 22 mins). The online ART time was reduced to 1650 ± 274 seconds with the proposed method, as compared to 3251.8 ± 447 seconds using the original workflow. CONCLUSION: The proposed patient-specific DLAS method can significantly improve the segmentation accuracy and efficiency for longitudinal MRIs, thereby facilitating the routine practice of MRgART.

Evaluation Exploration of Atlas-Based and Deep Learning-Based Automatic Contouring for Nasopharyngeal Carcinoma.

Purpose: The purpose of this study was to evaluate and explore the difference between an atlas-based and deep learning (DL)-based auto-segmentation scheme for organs at risk (OARs) of nasopharyngeal carcinoma cases to provide valuable help for clinical practice. Methods: 120 nasopharyngeal carcinoma cases were established in the MIM Maestro (atlas) database and trained by a DL-based model (AccuContour®), and another 20 nasopharyngeal carcinoma cases were randomly selected outside the atlas database. The experienced physicians contoured 14 OARs from 20 patients based on the published consensus guidelines, and these were defined as the reference volumes (Vref). Meanwhile, these OARs were auto-contoured using an atlas-based model, a pre-built DL-based model, and an on-site trained DL-based model. These volumes were named Vatlas, VDL-pre-built, and VDL-trained, respectively. The similarities between Vatlas, VDL-pre-built, VDL-trained, and Vref were assessed using the Dice similarity coefficient (DSC), Jaccard coefficient (JAC), maximum Hausdorff distance (HDmax), and deviation of centroid (DC) methods. A one-way ANOVA test was carried out to show the differences (between each two of them). Results: The results of the three methods were almost similar for the brainstem and eyes. For inner ears and temporomandibular joints, the results of the pre-built DL-based model are the worst, as well as the results of atlas-based auto-segmentation for the lens. For the segmentation of optic nerves, the trained DL-based model shows the best performance (p < 0.05). For the contouring of the oral cavity, the DSC value of VDL-pre-built is the smallest, and VDL-trained is the most significant (p < 0.05). For the parotid glands, the DSC of Vatlas is the minimum (about 0.80 or so), and VDL-pre-built and VDL-trained are slightly larger (about 0.82 or so). In addition to the oral cavity, parotid glands, and the brainstem, the maximum Hausdorff distances of the other organs are below 0.5 cm using the trained DL-based segmentation model. The trained DL-based segmentation method behaves well in the contouring of all the organs that the maximum average deviation of the centroid is no more than 0.3 cm. Conclusion: The trained DL-based segmentation performs significantly better than atlas-based segmentation for nasopharyngeal carcinoma, especially for the OARs with small volumes. Although some delineation results still need further modification, auto-segmentation methods improve the work efficiency and provide a level of help for clinical work.

Dosimetric predictors and Lyman normal tissue complication probability model of hematological toxicity in cervical cancer patients with treated with pelvic irradiation.

PURPOSE: To identify dosimetric parameters associated with acute hematological toxicity (HT) and identify the corresponding normal tissue complication probability (NTCP) model in cervical cancer patients receiving helical tomotherapy (Tomo) or fixed-field intensity-modulated radiation therapy (ff-IMRT) in combination with chemotherapy, that is, concurrent chemoradiotherapy (CCRT) using the Lyman-Kutcher-Burman normal tissue complication probability (LKB-NTCP) model. METHODS: Data were collected from 232 cervical cancer patients who received Tomo or ff-IMRT from 2015 to 2018. The pelvic bone marrow (PBM) (including the ilium, pubes, ischia, acetabula, proximal femora, and lumbosacral spine) was contoured from the superior boundary (usually the lumbar 5 vertebra) of the planning target volume (PTV) to the proximal end of the femoral head (the lower edge of the ischial tubercle). The parameters of the LKB model predicting ≥grade 2 hematological toxicity (Radiation Therapy Oncology Group [RTOG] grading criteria) (TD50 (1), m, and n) were determined using maximum likelihood analyses. Univariate and multivariate logistic regression analyses were used to identify correlations between dose-volume parameters and the clinical factors of HT. RESULTS: In total, 212 (91.37%) patients experienced ≥grade 2 hematological toxicity. The fitted normal tissue complication probability model parameters were TD50 (1) = 38.90 Gy (95%CI, [36.94, 40.96]), m = 0.13 (95%CI [0.12, 0.16]), and n = 0.04 (95%CI [0.02, 0.05]). Per the univariate analysis, the NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023), maximal PBM dose (p = 0.01), mean PBM dose (p = 0.021), radiation dose (p = 0.001), and V16-53 (p < 0. 05) were associated with ≥grade 2 HT. The NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023; AUC = 0.87), V16, V17, and V18 ≥ 79.65%, 75.68%, and 72.65%, respectively (p < 0.01, AUC = 0.66∼0.68), V35 and V36 ≥ 30.35% and 28.56%, respectively (p < 0.05; AUC = 0.71), and V47 ≥ 13.43% (p = 0.045; AUC = 0.80) were significant predictors of ≥grade 2 hematological toxicity from the multivariate logistic regression analysis. CONCLUSIONS: The volume of the PBM of patients treated with concurrent chemoradiotherapy and subjected to both low-dose (V16-18 ) and high-dose (V35,36 and V47 ) irradiation was associated with hematological toxicity, depending on the fractional volumes receiving the variable degree of dosage. The NTCP were stronger predictors of toxicity than V16-18 , V35, 36 , and V47 . Hence, avoiding radiation hot spots on the PBM could reduce the incidence of severe HT.

Artificial intelligence-based framework in evaluating intrafraction motion for liver cancer robotic stereotactic body radiation therapy with fiducial tracking.

PURPOSE: This study aimed to design a fully automated framework to evaluate intrafraction motion using orthogonal x-ray images from CyberKnife. METHODS: The proposed framework includes three modules: (a) automated fiducial marker detection, (b) three-dimensional (3D) position reconstruction, and (c) intrafraction motion evaluation. A total of 5927 images from real patients treated with CyberKnife fiducial tracking were collected. The ground truth was established by labeling coarse bounding boxes manually, and binary mask images were then obtained by applying a binary threshold and filter. These images and labels were used to train a detection model using a fully convolutional network (fCN). The output of the detection model can be used to reconstruct the 3D positions of the fiducial markers and then evaluate the intrafraction motion via a rigid transformation. For a patient test, the motion amplitudes, rotations, and fiducial cohort deformations were calculated used the developed framework for 13 patients with a total of 52 fractions. RESULTS: The precision and recall of the fiducial marker detection model were 98.6% and 95.6%, respectively, showing high model performance. The mean (±SD) centroid error between the predicted fiducial markers and the ground truth was 0.25 ± 0.47 pixels on the test data. For intrafraction motion evaluation, the mean (±SD) translations in the superior-posterior (SI), left-right (LR), and anterior-posterior (AP) directions were 13.1 ± 2.2 mm, 2.0 ± 0.4 mm, and 5.2 ± 1.4 mm, respectively, and the mean (±SD) rotations in the roll, pitch and yaw directions were 2.9 ± 1.5°, 2.5 ± 1.5°, and 3.1 ± 2.2°. Seventy-one percent of the fractions had rotations larger than the system limitations. With rotation correction during rigid registration, only 2 of the 52 fractions had residual errors larger than 2 mm in any direction, while without rotation correction, the probability of large residual errors increased to 46.2%. CONCLUSION: We developed a framework with high performance and accuracy for automatic fiducial marker detection, which can be used to evaluate intrafraction motion using orthogonal x-ray images from CyberKnife. For liver patients, most fractions have fiducial cohort rotations larger than the system limitations; however, the fiducial cohort deformation is small, especially for the scenario with rotation correction.

Decreasing underwater ultraviolet radiation exposure strongly driven by increasing ultraviolet attenuation in lakes in eastern and southwest China.

Underwater light attenuation plays an important role in modulating aquatic ecosystems and is considered a sentinel of climate change and human activity. However, knowledge of the long-term exposure of underwater ultraviolet radiation (UVR) in aquatic ecosystem is still very limited. We carried out extensive UVR measurements in different seasons in five lakes at different altitudes, collected long-term Secchi disk depth (SDD) data, developed the models between UVR diffuse attenuation coefficient (Kd) and SDD, and further assessed the long-term underwater UVR exposure. Observation results from five lakes including 259 samples showed large spatial variabilities of Kd(313) (UVB) from 0.83 to 5.91 m-1 and Kd(340) (UVA) from 0.51 to 4.67 m-1. Chromophoric dissolved organic matter (CDOM) absorption coefficients were significantly correlated with Kd(313) and Kd(340). Thus, the effects of climate change and human activity on CDOM abundance, source and composition may significantly alter UVR attenuation in aquatic environments. The long-term underwater UVR exposure, which was estimated from significant positive correlations between 1/SDD and Kd(313) and Kd(340), and incident UVR, significantly decreased in Lake Fuxianhu, Lake Erhai, and Lake Qiandaohu. The regime shift from clear water state to turbid state in Lake Erhai around 2001-2003 dramatically decreased underwater UVR exposure. In conclusion, increasing UVR attenuation played a more important role in determining underwater UVR exposure than decreasing incident UVR with the relative contributions of 89.9% and 87.7% in Lake Fuxianhu, 98.0% and 97.7% in Lake Erhai, 94.4% and 92.5% in Lake Qiandaohu for UVB and UVA exposure, respectively. This is the first study to elucidate the long-term trend of underwater UVR exposure considering both increasing UVR attenuation and decreasing incident UVR.

Overexpression of the regulator of G-protein signaling 5 reduces the survival rate and enhances the radiation response of human lung cancer cells.

Regulator of G protein signaling 5 (RGS5) belongs to the R4 subfamily of RGS proteins, a family of GTPase activating proteins, which is dynamically regulated in various biological processes including blood pressure regulation, smooth muscle cell pathology, fat metabolism and tumor angiogenesis. Low-expression of RGS5 was reported to be associated with tumor progression in lung cancer. In the present study, we examined the potential roles of RGS5 in human lung cancer cells by overexpressing RGS5 in the cancer cells and further explored the underlying molecular mechanisms. The RGS5 gene was cloned and transfected into the human lung cancer cell lines A549 and Calu-3. The cells were tested for apoptosis with flow cytometry, for viability with MTT, for mobility and adhesion capacity. The radiosensitization effect of RGS5 was measured by a colony formation assay. The mechanisms of RGS5 functioning was also investigated by detection of protein expression with western blot analysis, including PARP, caspase 3 and 9, bax, bcl2, Rock1, Rock2, CDC42, phospho-p53 (Serine 15) and p53. The present study demonstrated that RGS5 overexpression remarkably induced apoptosis in human lung cancer cells, which was suggested to be through mitochondrial mechanisms. Overexpression of RGS5 resulted in significantly lower adhesion and migration abilities of the lung cancer cells (P<0.01). Furthermore, overexpression of RGS5 sensitized the lung cancer cells to radiation. In conclusion, the present study showed that RGS5 played an inhibitory role in human lung cancer cells through induction of apoptosis. Furthermore, RGS5 enhanced the cytotoxic effect of radiation in the human lung cancer cells. Our results indicated that RGS5 may be a potential target for cancer therapy.

Phase II trial of recombinant human endostatin in combination with concurrent chemoradiotherapy in patients with stage III non-small-cell lung cancer.

PURPOSE: The objective of this study was to evaluate the efficacy and safety of Endostar combined with concurrent chemoradiotherapy (CCRT) in patients with stage III non-small-cell lung cancer (NSCLC). METHODS: Patients with unresectable stage III NSCLC were treated with Endostar (7.5mg/m(2)/d) for 7days at weeks 1, 3, 5, and 7, while two cycles of docetaxel (65mg/m(2)) and cisplatin (65mg/m(2)) were administered on days 8 and 36, with concurrent thoracic radiation to a dose of 60-66Gy. Primary end points were short-term efficacy and treatment-related toxicity. RESULTS: Fifty patients were enrolled into the study, and 48 were assessable. Of the 48 patients, 83% had stage IIIB and 65% had N3 disease. Median follow-up was 25.0months. Overall response rate was 77%. The estimated median progression-free survival (PFS) was 9.9months, and the estimated median overall survival (OS) was 24.0months. The 1-, 2-, and 3-year local control rates were 75%, 67%, and 51%, PFS rates were 48%, 27%, and 16%, and OS rates were 81%, 50%, and 30%, respectively. All toxicities were tolerable with proper treatment. CONCLUSIONS: The combination of Endostar with CCRT for locally advanced NSCLC patients was feasible and showed promising survival and local control rates.