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Background and objective: Due to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant. Methodology: The study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component. Results: The T1 classifier's accuracy during the training phase was 92.88% whereas the T2 classifier's accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model's accuracy was 82.17% and 81.51%, respectively. The clinical model's receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes. Conclusions: The proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists' involvement, more testing on a larger patient cohort is required to confirm its efficacy.

PurposeThis study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT).MethodsWe analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model.ResultsThe optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets.DiscussionsThe likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose–effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

PurposeIn this study, we aimed to develop a novel Bayesian optimization based multi-stacking deep learning platform for the prediction of radiation-induced dermatitis (grade ≥ two) (RD 2+) before radiotherapy, by using multi-region dose-gradient-related radiomics features extracted from pre-treatment planning four-dimensional computed tomography (4D-CT) images, as well as clinical and dosimetric characteristics of breast cancer patients who underwent radiotherapy.Materials and methodsThe study retrospectively included 214 patients with breast cancer who received radiotherapy after breast surgeries. Six regions of interest (ROIs) were delineated based on three PTV dose -gradient-related and three skin dose-gradient-related parameters (i.e., isodose). A total of 4309 radiomics features extracted from these six ROIs, as well as clinical and dosimetric characteristics, were used to train and validate the prediction model using nine mainstream deep machine learning algorithms and three stacking classifiers (i.e., meta-learners). To achieve the best prediction performance, a Bayesian optimization based multi-parameter tuning technology was adopted for the AdaBoost, random forest (RF), decision tree (DT), gradient boosting (GB) and extra tree (XTree) five machine learning models. The five parameter -tuned learners and the other four learners (i.e., logistic regression (LR), K-nearest neighbors (KNN), linear discriminant analysis (LDA), Bagging) whose parameters cannot be tuned, all as the primary week learners, were fed into the subsequent meta-learners for training and learning the final prediction model.ResultsThe final prediction model included 20 radiomics features and eight clinical and dosimetric characteristics. At the primary learner level, on base of Bayesian parameter tuning optimization, the RF, XGBoost, AdaBoost, GBDT, and LGBM models with the best parameter combinations achieved AUC of 0.82, 0.82, 0.77, 0.80, and 0.80 prediction performance in the verification data set, respectively. In the secondary meta-learner lever, compared with LR and MLP meta-learner, the best predictor of symptomatic RD 2+ for stacked classifiers was the GB meta-learner with an area under the curve (AUC) of 0.97 [95% CI: 0.91-1.0] and an AUC of 0.93 [95% CI: 0.87-0.97] in the training and validation datasets, respectively and the 10 top predictive characteristics were identified.ConclusionA novel multi-region dose-gradient-based Bayesian optimization tunning integrated multi-stacking classifier framework can achieve a high-accuracy prediction of symptomatic RD 2+ in breast cancer patients than any other single deep machine learning algorithm.

As various property studies continue to emerge on high entropy and entropy-stabilized ceramics, we seek a further understanding of the property changes across the phase boundary between “high-entropy” and “entropy-stabilized” phases. The thermal and mechanical properties of bulk ceramic entropy stabilized oxide composition Mg0.2Co0.2Ni0.2Cu0.2Zn0.2O are investigated across this critical transition temperature via the transient plane-source method, temperature-dependent x-ray diffraction, and nano-indentation. The thermal conductivity remains constant within uncertainty across the multi-to-single phase transition at a value of ≈2.5 W/mK, while the linear coefficient of thermal expansion increases nearly 24% from 10.8 to 14.1 × 10−6 K−1. Mechanical softening is also observed across the transition.

PurposeRadiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients.Methods and Materials214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis.ResultsThe best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated.ConclusionsA novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

Abstract Background Whole brain radiotherapy (WBRT) can impair patients’ cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we proposed a credible approach of automatic hippocampal delineation based on convolutional neural networks. Methods Referring to the hippocampus contouring atlas proposed by RTOG 0933, we manually delineated (MD) the hippocampus on the MRI data sets (3-dimensional T1-weighted with slice thickness of 1 mm, n = 175), which were used to construct a three-dimensional convolutional neural network aiming for the hippocampus automatic delineation (AD). The performance of this AD tool was tested on three cohorts: (a) 3D T1 MRI with 1-mm slice thickness (n = 30); (b) non-3D T1-weighted MRI with 3-mm slice thickness (n = 19); (c) non-3D T1-weighted MRI with 1-mm slice thickness (n = 11). All MRIs confirmed with normal hippocampus has not been violated by any disease. Virtual radiation plans were created for AD and MD hippocampi in cohort c to evaluate the clinical feasibility of the artificial intelligence approach. Statistical analyses were performed using SPSS version 23. P

Radiomics, a technology based on multimodal medical image processing and analysis, is able to extract automatically and analyze massive data from computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed tomography (PET/CT) via high-performance computer algorithm in order to pursue early diagnosis of disease, benign and malignant tumor discrimination, dynamic evaluation of disease treatment, and individualized precision therapy. To date, many studies demonstrate that radiomics not only has great potential in early diagnosis of lung cancer and prediction of genotype, treatment efficacy, as well as prognosis but also is based on imaging methods that are noninvasive, inexpensive, and repeatable. It does demonstrate precious values in guiding the clinical diagnosis and treatment of lung cancer, especially in the personalized and precise treatments and researches of lung cancer. However, the consistency and reproducibility of radiomics and the selection of robust characteristics still warrant further researches.

Background: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods: Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings: The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation: The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China. Keywords: Oesophageal cancer, Radiomics, Prognostic model, Radiation therapy, Imaging

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from 4-D computed tomography (4DCT), which is applied to patients with lung cancer treated by stereotactic body radiation therapy (SBRT). Seventy seven patients who underwent SBRT followed by 4DCT scans were incorporated in this retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework. For the purpose of comparison, three other IGTVs based on common methods was also delineated. We compared the relative volume difference (RVI), matching index (MI), and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis was performed to assess the tumor volume and motion range as clinical influencing factors in the MI variation. The results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 breath per minute (BPM), the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential solution for an optimal combination to synthesize IGTV in all respiration amplitudes.

Purpose: Radiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiationinduced dermatitis (grade = 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients. Methods and Materials: 214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis. Results: The best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-foldcross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDTmachine learning process were identified and calculated. Conclusions: A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients. [ABSTRACT FROM AUTHOR]

Summary: Renal tubular epithelial cells injury is one of the most important pathological features in hyperuricemic nephropathy (HN). However, the involvement of gasdermin D (GSDMD)-mediated pyroptosis in HN remains obscure. We found GSDMD was upregulated in the kidney tissue of HN mice, which was accompanied by the loss of renal function, renal tubular fibrosis, and reduced body weight. These changes in HN mice were inhibited by GSDMD knockout. Knockdown of GSDMD inhibited the high uric acid-induced injury in cultured cells (NRK-52E). Mechanistically, co-immunoprecipitation showed that RIG-I exist in a complex with caspase-1. Overexpression of RIG-I induced increased expression of caspase-1 protein and caspase-1 activity. Caspase-1 interference significantly reduced the increase of caspase-1 activity and IL-1β production caused by RIG-I overexpression. Knockdown of RIG-I or caspase-1 decreased high uric acid-induced injury in NRK-52E. This work illustrates that targeting the RIG-I/caspase-1/GSDMD may provide potential therapeutic benefits to HN.

The aim of the present study was to develop a statistical model-based method for the optimization of intensity-modulated radiotherapy (IMRT). A prostate cancer IMRT plan was redesigned while retaining the same beam orientation and prescribed dose as the regular plan. A series of dosimetric parameters were generated, and a 4-step protocol was performed to analyze the data: i) The tumor control probability of the target was ensured by setting a number of strict constraint parameters so that much of the target was covered by the 95% isodose line; ii) the parameters for optimization [weight ratio, equivalent characteristic parameter a and maximum equivalent uniform dose of the organ at risk (OAR)] were adjusted; iii) the overall optimization space (OOS) was determined via analysis of the dose-parameter tables based on the correlation factor (CF) and optimization efficiency factor analysis; iv) the OOS in the Pinnacle V7.6 treatment planning system with IMRT function was transposed. A selected optimization phenomenon existed when different optimization methods were used to optimize dose distribution to the targets and OARs, which demonstrates a wide variation in the CFs between the percentage of planning target volume receiving 95% of the prescribed dose and the maximum dose of the bladder, rectum and femur. The OOS used to optimize the randomly selected plan exhibited relatively high efficiency, with benefits for the optimization of IMRT plans. For patients with prostate cancer who require complex IMRT plan optimization, the obtained OOS from the two core analysis techniques is likely to have relatively high efficiency in achieving an optimized plan. These results suggest that the correlation analysis model is a novel method for the optimization of IMRT for prostate cancer. [ABSTRACT FROM AUTHOR]

A new homologous series of asymmetric bent-shaped molecules with two different side wings composed of Schiff base and ester linkage moieties were synthesized. The effects of asymmetric shape on the liquid crystalline properties were investigated and discussed in comparison with the conventional symmetric bent-shaped molecules. [ABSTRACT FROM AUTHOR]

Background/aims: The purpose of this study is to observe and compare the preliminary efficacy and side effects of docetaxel, 5-fluorouracil and leucovorin intravenous chemotherapy in combination with cisplatin hyperthermic intraperitoneal perfusion chemotherapy for the treatment of advanced gastric cancer., Methodology: Retrospectively analyzed 101 patients with advanced gastric cancer receiving docetaxel, 5-fluorouracil, leucovorin and cisplatin intravenous chemotherapy or intravenous administration of docetaxel, 5-fluorouracil and leucovorin combined with cisplatin hyperthermic intraperitoneal perfusion chemotherapy, 49 patients in intravenous chemotherapy (VC) group, 52 patients in hyperthermic intraperitoneal perfusion chemotherapy (HIPEC) group., Results: The response rate was 44.9% (22/49) in VC group and 65.4% (34/52) in HIPEC group, among which there was 1 case of CR, and the difference was statistically significant (p= 0.038). For CBR evaluation, the effective rate was 65.3% (32/49) in VC group and 82.7% (43/52) in HIPEC group, and the difference was statistically significant (p= 0.0458). The median progress free survival time (PFS) was 3.4 months in VC group and 4.6 months in HIPEC group, the difference was statistically significant (p= 0.045). The median overall survival time (OS) was 6.7 months in VC group and 7.5 months in HIPEC group, the difference was not statistically significant (p= 0.201). The main side effects in two groups were well tolerated, and there was no statistically significant difference., Conclusions: The short-term efficacy and PFS of HIPEC plus intravenous chemotherapy were better than single intravenous chemotherapy, and there was no significant improvement in OS, the side effects were similar in two groups with good tolerability.