Death Anxiety During COVID-19 and its Related Factors among Chinese Elderly People.

This study aimed at investigating death anxiety and its related factors in Chinese elderly people during COVID-19. This study totally interviewed 264 participants from four cities in different regions of China. Death anxiety scale (DAS), NEO-Five-Factor Inventory (Neo-FFI) and Brief COPE were scored on the basis of one-on-one interviews. Quarantine experience didn't make significant difference in death anxiety among the elderly; Elderly people with high death anxiety had higher scores of neuroticism, and were more likely to use a Behavior Disengagement coping strategy; Multiple linear regression analysis showed that neuroticism, openness and COVID impact predicted 44.6% of the variance in the death anxiety among elderly people. The results support both theories of vulnerability-stress model and terror management theory (TMT). In the post-epidemic era, we suggest to pay attention to the mental health status of elderly people with personality susceptibility to handling the stress of infection badly.

Identification of genes of four malignant tumors and a novel prediction model development based on PPI data and support vector machines.

Triple-negative breast cancer (TNBC), colon adenocarcinoma (COAD), ovarian cancer (OV), and glioblastoma multiforme (GBM) are common malignant tumors, in which significant challenges are still faced in early diagnosis, treatment, and prognosis. Therefore, further identification of genes related to those malignant tumors is of great significance for the improvement of management of the diseases. The database of the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository was used as the data source of gene expression profiles in this study. Malignant tumors genes were selected using a feature selection algorithm of maximal relevance and minimal redundancy (mRMR) and the protein-protein interaction (PPI) network. And finally selected 20 genes as potential related genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the potential related genes, and different tumor-specific genes and similarities and differences between network modules and pathways were analyzed. Further, using the potential cancer-related genes found above in this study as features, a support vector machine (SVM) model was developed to predict high-risk malignant tumors. As a result, the prediction accuracy reached more than 85%, indicating that such a model can effectively predict the four types of malignant tumors. It is demonstrated that such genes found above in this study indeed play important roles in the differentiation of the four types of malignant tumors, providing basis for future experimental biological validation and shedding some light on the understanding of new molecular mechanisms related to the four types of tumors.

Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines.

Triple-negative breast cancer (TNBC) is a special subtype of breast cancer that is difficult to treat. It is crucial to identify breast cancer-related genes that could provide new biomarkers for breast cancer diagnosis and potential treatment goals. In the development of our new high-risk breast cancer prediction model, seven raw gene expression datasets from the NCBI gene expression omnibus (GEO) database (GSE31519, GSE9574, GSE20194, GSE20271, GSE32646, GSE45255, and GSE15852) were used. Using the maximum relevance minimum redundancy (mRMR) method, we selected significant genes. Then, we mapped transcripts of the genes on the protein-protein interaction (PPI) network from the Search Tool for the Retrieval of Interacting Genes (STRING) database, as well as traced the shortest path between each pair of proteins. Genes with higher betweenness values were selected from the shortest path proteins. In order to ensure validity and precision, a permutation test was performed. We randomly selected 248 proteins from the PPI network for shortest path tracing and repeated the procedure 100 times. We also removed genes that appeared more frequently in randomized results. As a result, 54 genes were selected as potential TNBC-related genes. Using 14 out the 54 genes, which are potential TNBC associated genes, as input features into a support vector machine (SVM), a novel model was trained to predict high-risk breast cancer. The prediction accuracy of normal tissues and TNBC tissues reached 95.394%, and the predictions of Stage II and Stage III TNBC reached 86.598%, indicating that such genes play important roles in distinguishing breast cancers, and that the method could be promising in practical use. According to reports, some of the 54 genes we identified from the PPI network are associated with breast cancer in the literature. Several other genes have not yet been reported but have functional resemblance with known cancer genes. These may be novel breast cancer-related genes and need further experimental validation. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to appraise the 54 genes. It was indicated that cellular response to organic cyclic compounds has an influence in breast cancer, and most genes may be related with viral carcinogenesis.

Thoracic Organ Doses and Cancer Risk from Low Pitch Helical 4-Dimensional Computed Tomography Scans.

PURPOSE: To investigate the dose depositions to organs at risk (OARs) and associated cancer risk in cancer patients scanned with 4-dimensional computed tomography (4DCT) as compared with conventional 3DCT. METHODS AND MATERIALS: The radiotherapy treatment planning CT image and structure sets of 102 patients were converted to CT phantoms. The effective diameters of those patients were computed. Thoracic scan protocols in 4DCT and 3DCT were simulated and verified with a validated Monte Carlo code. The doses to OARs (heart, lungs, esophagus, trachea, spinal cord, and skin) were calculated and their correlations with patient effective diameter were investigated. The associated cancer risk was calculated using the published models in BEIR VII reports. RESULTS: The average of mean dose to thoracic organs was in the range of 7.82-11.84 cGy per 4DCT scan and 0.64-0.85 cGy per 3DCT scan. The average dose delivered per 4DCT scan was 12.8-fold higher than that of 3DCT scan. The organ dose was linearly decreased as the function of patients' effective diameter. The ranges of intercept and slope of the linear function were 17.17-30.95 and -0.0278--0.0576 among patients' 4DCT scans, and 1.63-2.43 and -0.003--0.0045 among patients' 3DCT scans. Relative risk of cancer increased (with a ratio of 15.68:1) resulting from 4DCT scans as compared to 3DCT scans. CONCLUSIONS: As compared to 3DCT, 4DCT scans deliver more organ doses, especially for pediatric patients. Substantial increase in lung cancer risk is associated with higher radiation dose from 4DCT and smaller patients' size as well as younger age.

Plan quality and robustness in field junction region for craniospinal irradiation with VMAT.

PURPOSE: To propose a "staggered overlap" technique in volumetric modulated arc therapy (VMAT) for craniospinal irradiation (CSI) and compare the dose distribution and plan robustness with "overlap" technique and "gradient optimization" approach. METHODS AND MATERIALS: 6 patients previously treated in our clinic were retrospectively selected. 9 VMAT plans of each patient were optimized with "staggered overlap", "overlap" and "gradient optimization" in overlapping region of 3 cm, 6 cm, and 9 cm separately. For the "staggered overlap" plan, adjacent field sets were intentionally overlapped by staggering field edges in an appropriate step size to avoid sharp dose gradient. Evaluation metrics including V95%, D2%, D98%, conformity number (CN) and homogeneity index (HI) were employed to evaluate the dose distribution. Moreover, shifts of the upper spinal field isocenter in each direction were performed to simulate junction errors for robustness analysis. RESULTS: The CN and HI of VMAT plans with "staggered overlap" were 0.82 (0.811-0.822) and 0.113 (0.112-0.114), while they were 0.778 (0.776-0.782) and 0.131 (0.130-0.131) for plans with "gradient optimization". In the robustness study, <3% dose deviations were found for 5 mm shifts in lateral and vertical directions with all techniques. In cranial-caudal direction, "overlap" technique created hot spots (D2% > 170%) and cold spots (D98% < 44%) in the junction region with 10 mm shifts. The dose deviations were decreased to 22% for plans with "staggered overlap" and 9 cm overlapping region. CONCLUSION: "Staggered overlap" technique provides better plan quality as compared to "gradient optimization" approach and makes the plan more robust against junction errors as compared to "overlap" technique.

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

Clinical and radiobiological evaluation of a method for planning target volume generation dependent on organ-at-risk exclusions in magnetic resonance imaging-based prostate radiotherapy.

BACKGROUND AND PURPOSE: Due to a smaller target volume when delineating prostate on magnetic resonance imaging (MRI), margins may be too tight as compared to computed tomography (CT) delineation, potentially reducing tumor control probability (TCP) in prostate radiotherapy. This study evaluated a clinically implemented MRI-based target expansion method to provide adequate margins yet limit organ-at-risk (OAR) dose as compared to CT-based delineation. METHODS AND MATERIALS: Patients in this study were treated to 79.2 Gy in 44 fractions via intensity modulated radiotherapy using an MRI-based expansion method, which excluded OARs when performing a 5 mm isotropic (except 4 mm posterior) expansion from gross tumor volume to clinical target volume (CTV), followed by an isotropic 5 mm expansion to generate the planning target volume (PTV). Ten cases were re-planned using CT-delineated prostate with CTV-to-PTV expansion of isotropic 8 mm, except for a 5 mm posterior expansion, with comparison of PTV volumes, TCP and normal tissue complication probability (NTCP) to the MRI-based method. Under IRB approved protocol, we retrospectively evaluated 51 patients treated with the MRI-based method for acute bladder and rectal toxicity with CTC-AE version 4.0 used for scoring. RESULTS: MRI-based PTV volume differed by 4% compared to CT-based PTV volume. Radiobiological calculated TCP of the MRI-based method was found comparable to CT-based methods with an average equivalent uniform dose of 80.5 Gy and 80.1 Gy respectively. Statistically significant decrease in bladder NTCP (toxicity Grade 2 and above for 5% complications within 5 years post radiotherapy) was observed in the MRI-based method. Outcomes data collected showed 65% and 100% of patients studied experienced Grade 0/1 bladder and rectal acute toxicity respectively. Grade 2 bladder toxicity was indicated in the remaining 35% of patients studied with no Grade 3 toxicity reported. CONCLUSIONS: Results showed comparable PTV volume with MRI-based method, and NTCP was reduced while maintaining TCP. Clinically, bladder and rectal toxicities were observed to be minimal.

A novel automatic quantification method for high-content screening analysis of DNA double strand-break response.

High-content screening is commonly used in studies of the DNA damage response. The double-strand break (DSB) is one of the most harmful types of DNA damage lesions. The conventional method used to quantify DSBs is γH2AX foci counting, which requires manual adjustment and preset parameters and is usually regarded as imprecise, time-consuming, poorly reproducible, and inaccurate. Therefore, a robust automatic alternative method is highly desired. In this manuscript, we present a new method for quantifying DSBs which involves automatic image cropping, automatic foci-segmentation and fluorescent intensity measurement. Furthermore, an additional function was added for standardizing the measurement of DSB response inhibition based on co-localization analysis. We tested the method with a well-known inhibitor of DSB response. The new method requires only one preset parameter, which effectively minimizes operator-dependent variations. Compared with conventional methods, the new method detected a higher percentage difference of foci formation between different cells, which can improve measurement accuracy. The effects of the inhibitor on DSB response were successfully quantified with the new method (p = 0.000). The advantages of this method in terms of reliability, automation and simplicity show its potential in quantitative fluorescence imaging studies and high-content screening for compounds and factors involved in DSB response.

Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

BACKGROUND: Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. OBJECTIVE: According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. CONCLUSION: We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes.

Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method.

AIM AND OBJECTIVE: Lysine acetylation, as one type of post-translational modifications (PTM), plays key roles in cellular regulations and can be involved in a variety of human diseases. However, it is often high-cost and time-consuming to use traditional experimental approaches to identify the lysine acetylation sites. Therefore, effective computational methods should be developed to predict the acetylation sites. In this study, we developed a position-specific method for epsilon lysine acetylation site prediction. MATERIAL AND METHODS: Sequences of acetylated proteins were retrieved from the UniProt database. Various kinds of features such as position specific scoring matrix (PSSM), amino acid factors (AAF), and disorders were incorporated. A feature selection method based on mRMR (Maximum Relevance Minimum Redundancy) and IFS (Incremental Feature Selection) was employed. RESULTS: Finally, 319 optimal features were selected from total 541 features. Using the 319 optimal features to encode peptides, a predictor was constructed based on dagging. As a result, an accuracy of 69.56% with MCC of 0.2792 was achieved. We analyzed the optimal features, which suggested some important factors determining the lysine acetylation sites. CONCLUSION: We developed a position-specific method for epsilon lysine acetylation site prediction. A set of optimal features was selected. Analysis of the optimal features provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation.

An Expanded Multi-scale Monte Carlo Simulation Method for Personalized Radiobiological Effect Estimation in Radiotherapy: a feasibility study.

A novel and versatile "bottom-up" approach is developed to estimate the radiobiological effect of clinic radiotherapy. The model consists of multi-scale Monte Carlo simulations from organ to cell levels. At cellular level, accumulated damages are computed using a spectrum-based accumulation algorithm and predefined cellular damage database. The damage repair mechanism is modeled by an expanded reaction-rate two-lesion kinetic model, which were calibrated through replicating a radiobiological experiment. Multi-scale modeling is then performed on a lung cancer patient under conventional fractionated irradiation. The cell killing effects of two representative voxels (isocenter and peripheral voxel of the tumor) are computed and compared. At microscopic level, the nucleus dose and damage yields vary among all nucleuses within the voxels. Slightly larger percentage of cDSB yield is observed for the peripheral voxel (55.0%) compared to the isocenter one (52.5%). For isocenter voxel, survival fraction increase monotonically at reduced oxygen environment. Under an extreme anoxic condition (0.001%), survival fraction is calculated to be 80% and the hypoxia reduction factor reaches a maximum value of 2.24. In conclusion, with biological-related variations, the proposed multi-scale approach is more versatile than the existing approaches for evaluating personalized radiobiological effects in radiotherapy.

A measurement-based generalized source model for Monte Carlo dose simulations of CT scans.

The goal of this study is to develop a generalized source model for accurate Monte Carlo dose simulations of CT scans based solely on the measurement data without a priori knowledge of scanner specifications. The proposed generalized source model consists of an extended circular source located at x-ray target level with its energy spectrum, source distribution and fluence distribution derived from a set of measurement data conveniently available in the clinic. Specifically, the central axis percent depth dose (PDD) curves measured in water and the cone output factors measured in air were used to derive the energy spectrum and the source distribution respectively with a Levenberg-Marquardt algorithm. The in-air film measurement of fan-beam dose profiles at fixed gantry was back-projected to generate the fluence distribution of the source model. A benchmarked Monte Carlo user code was used to simulate the dose distributions in water with the developed source model as beam input. The feasibility and accuracy of the proposed source model was tested on a GE LightSpeed and a Philips Brilliance Big Bore multi-detector CT (MDCT) scanners available in our clinic. In general, the Monte Carlo simulations of the PDDs in water and dose profiles along lateral and longitudinal directions agreed with the measurements within 4%/1 mm for both CT scanners. The absolute dose comparison using two CTDI phantoms (16 cm and 32 cm in diameters) indicated a better than 5% agreement between the Monte Carlo-simulated and the ion chamber-measured doses at a variety of locations for the two scanners. Overall, this study demonstrated that a generalized source model can be constructed based only on a set of measurement data and used for accurate Monte Carlo dose simulations of patients' CT scans, which would facilitate patient-specific CT organ dose estimation and cancer risk management in the diagnostic and therapeutic radiology.

Radiation-induced heart disease in lung cancer radiotherapy: A dosimetric update.

BACKGROUND: Radiation-induced heart disease (RIHD), which affects the patients' prognosis with both acute and late side effects, has been published extensively in the radiotherapy of breast cancer, lymphoma and other benign diseases. Studies on RIHD in lung cancer radiotherapy, however, are less extensive and clear even though the patients with lung cancer are delivered with higher doses to the heart during radiation treatment. METHODS: In this article, after extensive literature search and analysis, we reviewed the current evidence on RIHD in lung cancer patients after their radiation treatments and investigated the potential risk factors for RIHD as compared to other types of cancers. RESULT: Cardiac toxicity has been found highly relevant in lung cancer radiotherapy. So far, the crude incidence of cardiac complications in the lung cancer patients after radiotherapy has been up to 33%. CONCLUSION: The dose to the heart, the lobar location of tumor, the treatment modality, the history of heart and pulmonary disease and smoking were considered as potential risk factors for RIHD in lung cancer radiotherapy. As treatment techniques improve over the time with better prognosis for lung cancer survivors, an improved prediction model can be established to further reduce the cardiac toxicity in lung cancer radiotherapy.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors.

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns.

Distribution of scattered image patterns hinges on morphological and optical characteristics of cells. This paper applied a numerical method to simulate scattered images of real cell morphologies, which were reconstructed from confocal image stacks dyed by fluorescent stains. Two approaches, contourlet transform (CT) and gray level co-occurrence matrix (GLCM), were then used to analyze the simulated scattered images. The results showed that features extracted using GLCM contained more information than those extracted using CT. Higher classification accuracy could be achieved with a single GLCM parameter than CT and GLCM could achieve higher accuracy with fewer parameters than CT when using multiple parameters. Meanwhile, GLCM requires less computational cost. Thus, GLCM is more suitable and efficient than CT for the analysis of cell-scattered images.

Original Research: Label-free detection for radiation-induced apoptosis in glioblastoma cells.

Current flow cytometry (FCM) requires fluorescent dyes labeling cells which make the procedure costly and time consuming. This manuscript reports a feasibility study of detecting the cell apoptosis with a label-free method in glioblastoma cells. A human glioma cell line M059K was exposed to 8 Gy dose of radiation, which enables the cells to undergo radiation-induced apoptosis. The rates of apoptosis were studied at different time points post-irradiation with two different methods: FCM in combination with Annexin V-FITC/PI staining and a newly developed technique named polarization diffraction imaging flow cytometry. Totally 1000 diffraction images were acquired for each sample and the gray level co-occurrence matrix (GLCM) algorithm was used in morphological characterization of the apoptotic cells. Among the feature parameters extracted from each image pair, we found that the two GLCM parameters of angular second moment (ASM) and sum entropy (SumEnt) exhibit high sensitivities and consistencies as the apoptotic rates (Pa) measured with FCM method. In addition, no significant difference exists between Pa and ASM_S, Pa and SumEnt_S, respectively (P > 0.05). These results demonstrated that the new label-free method can detect cell apoptosis effectively. Cells can be directly used in the subsequent biochemical experiments as the structure and function of cells and biomolecules are well-preserved with this new method.

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images.

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after radiotherapy (R(2) = 0.83) than the 4 GLCM parameters (R(2) = 0.63, 0.73, 0.59 and 0.75 for Energy, Contrast, Local Homogeneity and Entropy respectively). The new model of the H index has the capacity to characterize the intratumor heterogeneity feature from 3D [18]F-FDG PET image data. As a single parameter with an intuitive definition, the H index offers potential for clinical applications.

Energy Modulated Photon Radiotherapy: A Monte Carlo Feasibility Study.

A novel treatment modality termed energy modulated photon radiotherapy (EMXRT) was investigated. The first step of EMXRT was to determine beam energy for each gantry angle/anatomy configuration from a pool of photon energy beams (2 to 10 MV) with a newly developed energy selector. An inverse planning system using gradient search algorithm was then employed to optimize photon beam intensity of various beam energies based on presimulated Monte Carlo pencil beam dose distributions in patient anatomy. Finally, 3D dose distributions in six patients of different tumor sites were simulated with Monte Carlo method and compared between EMXRT plans and clinical IMRT plans. Compared to current IMRT technique, the proposed EMXRT method could offer a better paradigm for the radiotherapy of lung cancers and pediatric brain tumors in terms of normal tissue sparing and integral dose. For prostate, head and neck, spine, and thyroid lesions, the EMXRT plans were generally comparable to the IMRT plans. Our feasibility study indicated that lower energy (<6 MV) photon beams could be considered in modern radiotherapy treatment planning to achieve a more personalized care for individual patient with dosimetric gains.