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Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient-specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly.To develop a DVH-based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH-based PSQA.A DL model with a three-dimensional squeeze-and-excitation residual blocks incorporated into a modified U-net was developed to predict the measured PSQA DVHs of 208 head-and-neck (HN) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training.The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for DThe successful prediction of PSQA doses and classification of PoF for HN VMAT PSQA indicating that this DVH-based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

Objectives. To differentiate the primary site of brain metastases (BMs) is of high clinical value for the successful management of patients with BM. The purpose of this study is to investigate a combined radiomics model with computer tomography (CT) and magnetic resonance imaging (MRI) images in differentiating BMs originated from lung and breast cancer. Methods. Pretreatment cerebral contrast enhanced CT and T1-weighted MRI images of 78 patients with 179 BMs from primary lung and breast cancer were retrospectively analyzed. Radiomic features were extracted from contoured BM lesions and selected using the Mann–Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression. Binary logistic regression (BLR) and support vector machine (SVM) models were built and evaluated based on selected radiomic features from CT alone, MRI alone, and combined images to differentiate BMs originated from lung and breast cancer. Results. A total of 10 and 6 optimal radiomic features were screened out of 1288 CT and 1197 MRI features, respectively. The mean area under the curves (AUCs) of the BLR and SVM models using fivefolds cross-validation were 0.703 vs. 0.751, 0.718 vs. 0.754, and 0.781 vs. 0.803 in the training dataset and 0.708 vs. 0.763, 0.715 vs. 0.717, and 0.771 vs. 0.805 in the testing dataset for models with CT alone, MRI alone, and combined CT and MRI radiomic features, respectively. Conclusions. Radiomics model based on combined CT and MRI features is feasible and accurate in the differentiation of the primary site of BMs from lung and breast cancer.

Background Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM. Methods A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM. Results The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC. Conclusions Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.

Strategy for the evaluation of the early diagnostic accuracy of AC- and SCC-specific tumor-derived exosomal miRNAs in a symptomatic set

Identification and quantification of total and tumor-derived exosomes from healthy controls, AC and SCC patients

The raw miRNA-seq data

expression pattern of miR-627-5p, miR-128-3p, miR-101-3p, miR-129-5p (A), miR-1246 and miR-155-5p (C) in AC and health controls or miR-150-5p, miR-4459, miR-6873-3p, miR-9-3p (B), miR-149-5p and miR-1246 (D) in SCC and health controls comparison by evaluating threshold cycle number difference (A, B) or calculating 2ÃÆ'Ã,Æ'ÂÃ,â¢ÃƒÆ'Ã,‚-ÃÆ'Ã,‚ÂÃ,â³Ct(C, D).

mappable miRNA was chosen for further analysis based on differentially expressed gene (DEG) analysis

Noninvasive differentiating thyroid follicular adenoma from carcinoma preoperatively is of great clinical value to decrease the risks resulted from excessive surgery for patients with follicular neoplasm. The purpose of this study is to investigate the accuracy of ultrasound radiomics features integrating with ultrasound features in the differentiation between thyroid follicular carcinoma and adenoma. A total of 129 patients diagnosed as thyroid follicular neoplasm with pathologically confirmed follicular adenoma and carcinoma were enrolled and analyzed retrospectively. Radiomics features were extracted from preoperative ultrasound images with manually contoured targets. Ultrasound features and clinical parameters were also obtained from electronic medical records. Radiomics signature, combined model integrating radiomics features, ultrasound features, and clinical parameters were constructed and validated to differentiate the follicular carcinoma from adenoma. A total of 23 optimal features were selected from 449 extracted radiomics features. Clinical and ultrasound parameters of sex (p = 0.003), interior structure (p = 0.035), edge (p = 0.02), platelets (p = 0.007), and creatinine (p = 0.001) were associated with the differentiation between benign and malignant follicular neoplasm. The values of area under curves (AUCs) of the radiomics signature, clinical model, and combined model were 0.772 (95% CI: 0.707-0.838), 0.792 (95% CI: 0.715-0.869), and 0.861 (95% CI: 0.775-0.909), respectively. A final corrected AUC of 0.844 was achieved for the combined model after internal validation. Radiomics features from ultrasound images combined with ultrasound features and clinical factors are feasible to differentiate thyroid follicular carcinoma from adenoma noninvasive before operation to decrease the unnecessary of diagnostic thyroidectomy for patients with benign follicular adenoma.

Ultrasound (US) imaging has been recognized and widely used as a screening and diagnostic imaging modality for cervical cancer all over the world. However, few studies have investigated the U-net-based automatic segmentation models for cervical cancer on US images and investigated the effects of automatic segmentations on radiomics features. A total of 1102 transvaginal US images from 796 cervical cancer patients were collected and randomly divided into training (800), validation (100) and test sets (202), respectively, in this study. Four U-net models (U-net, U-net with ResNet, context encoder network (CE-net), and Attention U-net) were adapted to segment the target of cervical cancer automatically on these US images. Radiomics features were extracted and evaluated from both manually and automatically segmented area. The mean Dice similarity coefficient (DSC) of U-net, Attention U-net, CE-net, and U-net with ResNet were 0.88, 0.89, 0.88, and 0.90, respectively. The average Pearson coefficients for the evaluation of the reliability of US image-based radiomics were 0.94, 0.96, 0.94, and 0.95 for U-net, U-net with ResNet, Attention U-net, and CE-net, respectively, in their comparison with manual segmentation. The reproducibility of the radiomics parameters evaluated by intraclass correlation coefficients (ICC) showed robustness of automatic segmentation with an average ICC coefficient of 0.99. In conclusion, high accuracy of U-net-based automatic segmentations was achieved in delineating the target area of cervical cancer US images. It is feasible and reliable for further radiomics studies with features extracted from automatic segmented target areas.

ObjectiveTo develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.Material and MethodsIn this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist.ResultsThe All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets.ConclusionThe proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

Objective To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. Methods A total of 536 CC patients with confirmed histological characteristics and lymph node status after radical hysterectomy and pelvic lymphadenectomy were enrolled. Radiomics features were extracted and selected with US images acquired with ATL HDI5000, Voluson E8, MyLab classC, ACUSON S2000, and HI VISION Preirus to build radiomics models for LNM prediction using support vector machine (SVM) and logistic regression, respectively. Results There were 148 patients (training vs validation: 102:46) scanned in machine HDI5000, 75 patients (53:22) in machine Voluson E8, 100 patients (69:31) in machine MyLab classC, 110 patients (76:34) in machine ACUSON S2000, and 103 patients (73:30) in machine HI VISION Preirus, respectively. Few radiomics features were reproducible among different machines. The area under the curves (AUCs) ranged from 0.75 to 0.86, 0.73 to 0.86 in the training cohorts, and from 0.71 to 0.82, 0.70 to 0.80 in the validation cohorts for SVM and logistic regression models, respectively. The highest difference in AUCs for different machines reaches 17.8% and 15.5% in the training and validation cohorts, respectively. Conclusions The performance of radiomics model is dependent on the type of scanner. The problem of scanner dependency on radiomics features should be considered, and their effects should be minimized in future studies for US images.

Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperative predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules. Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by 5 classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured. Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, 7 textural features, 1 shape feature and 1 first-order feature, in which 8 were high-dimensional features. Thus, RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

An accurate and reliable target volume delineation is critical for the safe and successful radiotherapy. The purpose of this study is to develop new 2D and 3D automatic segmentation models based on RefineNet for clinical target volume (CTV) and organs at risk (OARs) for postoperative cervical cancer based on computed tomography (CT) images.A 2D RefineNet and 3D RefineNetPlus3D were adapted and built to automatically segment CTVs and OARs on a total of 44 222 CT slices of 313 patients with stage I-III cervical cancer. Fully convolutional networks (FCNs), U-Net, context encoder network (CE-Net), UNet3D, and ResUNet3D were also trained and tested with randomly divided training and validation sets, respectively. The performances of these automatic segmentation models were evaluated by Dice similarity coefficient (DSC), Jaccard similarity coefficient, and average symmetric surface distance when comparing them with manual segmentations with the test data.The DSC for RefineNet, FCN, U-Net, CE-Net, UNet3D, ResUNet3D, and RefineNet3D were 0.82, 0.80, 0.82, 0.81, 0.80, 0.81, and 0.82 with a mean contouring time of 3.2, 3.4, 8.2, 3.9, 9.8, 11.4, and 6.4 s, respectively. The generated RefineNetPlus3D demonstrated a good performance in the automatic segmentation of bladder, small intestine, rectum, right and left femoral heads with a DSC of 0.97, 0.95, 091, 0.98, and 0.98, respectively, with a mean computation time of 6.6 s.The newly adapted RefineNet and developed RefineNetPlus3D were promising automatic segmentation models with accurate and clinically acceptable CTV and OARs for cervical cancer patients in postoperative radiotherapy.

Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage cervical cancer preoperatively. Methods: US images of 148 cervical cancer patients were collected and manually contoured by two senior radiologists. The four deep learning-based automatic segmentation models, namely U-net, context encoder network (CE-net), Resnet, and attention U-net were constructed to segment the tumor volumes automatically. Radiomics features were extracted and selected from manual and automatically segmented regions of interest (ROIs) to predict the LNM of these cervical cancer patients preoperatively. The reliability and reproducibility of radiomics features and the performances of prediction models were evaluated. Results: A total of 449 radiomics features were extracted from manual and automatic segmented ROIs with Pyradiomics. Features with an intraclass coefficient (ICC) > 0.9 were all 257 (57.2%) from manual and automatic segmented contours. The area under the curve (AUCs) of validation models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net were 0.692, 0.755, 0.696, 0.689, and 0.710, respectively. Attention U-net showed best performance in the LNM prediction model with a lowest discrepancy between training and validation. The AUCs of models with automatic segmentation features from attention U-net, CE-net, Resnet, and U-net were 9.11%, 0.58%, –0.44%, and 2.61% higher than AUC of model with manual contoured features, respectively. Conclusion: The reliability and reproducibility of radiomics features, as well as the performance of radiomics models, were affected by manual segmentation and automatic segmentations.

Multi‐isocenter volumetric modulated arc therapy (VMAT) is recommended for craniospinal irradiation (CSI) to smooth the dose distribution in the junction regions relying solely on inverse optimization. However, few studies have measured the dosimetric impact of setup errors on this multi‐isocenter VMAT in the junction areas. The purpose of this study is to evaluate the impact of positional errors during VMAT CSI with two‐dimension (2D) and three‐dimension (3D) dosimetric measurements. A total of 20 patients treated by three‐isocenter VMAT CSI were retrospectively reviewed and analyzed. A 3D diode array ArcCHECK and radiochromic film EBT3 were applied to measure the percentage gamma passing rates (%GPs) and dose distributions in the junction areas between the cranial/upper‐spinal and the upper/lower‐spinal fields with intentionally introduced setup errors of ± 1 mm, ±2 mm, ±3 mm, ±5 mm, and ± 8 mm, respectively. The length and volume of planning target volume (PTV) for these CSI patients ranged from 50.14 to 80.8 cm, and 1572.3 to 2114.5 cm3, respectively. The %GPs for ±3 mm, ±5 mm, and ±8 mm positional errors were around 95%, 90%, and 85%, respectively, in the junction areas. The dosimetric verification results with EBT3 films indicated that cold and hot areas were observed with the increase of introduced setup errors. In conclusion, the dosimetric verification with intentionally introduced setup errors demonstrated that positional errors within 3 mm have a little impact for VMAT CSI, although setup errors should be minimized. Relying on the inverse optimization of VMAT to smooth the dose distribution in the junction areas is feasible for CSI.

Objective To research and assess automatic volumetric modulated arc therapy (VMAT) planning methods for patients with intraocular cancer. Methods The mdaccAutoPlan system was added to the Pinnacle3 treatment planning system as a plug‐in. Automatic VMAT plans were generated for 10 patients diagnosed with intraocular cancer, and evaluated according to standard dose‐volume histogram parameters. Results The planning target volume of enrolled patients ranges from 14.24 to 50.69 cm3. Both planning methods lead to acceptable planning target volume target coverage with a V95 of 97.9 ± 1.4% and 96.4 ± 1.5% for manual and automatic plans (P = 0.03), respectively. Automatic planning lowered the dose delivered to the ipsilateral lens and optical nerves, but increased the dose to the brainstem compared with manual planning. Automatic planning significantly prolonged VMAT planning time (3.25 ± 0.53 h vs. 1.02 ± 0.69 h, P

It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM). The purpose of this study is to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with BM based on radiomics from brain contrast-enhanced computer tomography (CECT) images. A total of 144 BM patients (94 male, 50 female) were enrolled in this study with 102 with primary lung AD and 42 with SCC, respectively. Radiomics features from manually contoured tumors were extracted using python. Mann–Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select relative radiomics features. Binary logistic regression and support vector machines (SVM) were applied to build models with radiomics features alone and with radiomics features plus age and sex. Fourteen features were selected from a total of 105 radiomics features for the final model building. The area under the curves (AUCs) and accuracy of SVM and binary logistic regression models were 0.765 vs. 0.769, 0.795 vs.0.828, and 0.716 vs. 0.726, 0.768 vs. 0.758, respectively, for models with radiomics features alone and models with radiomics features plus sex and age. Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC. • It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM) to define the prognosis and treatment. • Few studies had investigated the feasibility and accuracy of differentiating the pathological subtypes of primary non-small-cell lung cancer between adenocarcinoma (AD) and squamous cell carcinoma (SCC) for patients with BM based on radiomics from brain contrast-enhanced CT (CECT) images, although CECT images are often the initial imaging modality to screen for metastases and are recommended on equal footing with MRI for the detection of cerebral metastases. • Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC with a highest area under the curve (AUC) of 0.828 and an accuracy of 0.758, respectively.

Background Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules. Methods Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured. Results Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features. Conclusions RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.

Purpose To predict the voxel-based dose distribution for postoperative cervical cancer patients underwent volumetric modulated arc therapy using deep learning models. Method A total of 254 patients with cervical cancer received volumetric modulated arc therapy in authors’ hospital from January 2018 to September 2021 were enrolled in this retrospective study. Two deep learning networks (3D deep residual neural network and 3DUnet) were adapted to train (203 cases) and test (51 cases) the feasibility and effectiveness of the prediction method. The performance of deep learning models was evaluated by comparing the results with those of treatment planning system according to metrics of dose-volume histogram of target volumes and organs at risk. Results The dose distributions predicted by deep learning models were clinically acceptable. The automatic dose prediction time was around 5 to 10 min, which was about one-eighth to one-tenth of the manual optimization time. The maximum dose difference was observed in D98 of rectum with a | δD| of 5.00 ± 3.40% and 4.88 ± 3.99% for Unet3D and ResUnet3D, respectively. The minimum difference was observed in the D2 of clinical target volume with a |δD| of 0.53 ± 0.45% and 0.83 ± 0.45% for ResUnet3D and Unet3D, respectively. Conclusion The 2 deep learning models adapted in the study showed the feasibility and reasonable accuracy in the voxel-based dose prediction for postoperative cervical cancer underwent volumetric modulated arc therapy. Automatic dose distribution prediction of volumetric modulated arc therapy with deep learning models is of clinical significance for the postoperative management of patients with cervical cancer.

To investigate the feasibility and accuracy of radiomics models based on contrast-enhanced CT (CECT) in the prediction of perineural invasion (PNI), so as to stratify high-risk recurrence and improve the management of patients with gastric cancer (GC) preoperatively.Total of 154 GC patients underwent D2 lymph node dissection with pathologically confirmed GC and preoperative CECT from an open-label, investigator-sponsored trial (NCT01711242) were enrolled. Radiomics features were extracted from contoured images and selected using Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) after inter-class correlation coefficient (ICC) analysis. Models based on radiomics features (R), clinical factors (C) and combined parameters (R + C) were built and evaluated using Support Vector Machine (SVM) and logistic regression to predict the PNI for patients with GC preoperatively.Total of 11 radiomics features were selected for final analysis, along with two clinical factors. The area under curve (AUC) of models based on R, C, and R + C with logistic regression and SVM were 0.77 vs. 0.83, 0.71 vs.0.70, 0.86 vs. 0.90, and 0.73 vs.0.80, 0.62 vs. 0.64, 0.77 vs. 0.82 in the training and testing cohorts, respectively. SVM(R + C) achieved a best AUC of 0.82(0.69-0.94) in the test cohorts with a sensitivity, specificity and accuracy of 0.63, 0.91, and 0.77, respectively.The performance of these models indicates that radiomics features alone or combined with clinical factors provide a feasible way to classify patients preoperatively and improve the management of patients with GC.

Globally, colorectal cancer (CRC) is one of the leading causes of cancer-related deaths. Oxaliplatin based treatments are frequently used as chemotherapeutic methods for CRC, however, associated side effects and drug resistance often limit their clinical application. Dihydroartemisinin (DHA) induces apoptosis in various cancer cells by increasing reactive oxygen species (ROS) production. However, the direct target of DHA and underlying molecular mechanisms in oxaliplatin-mediated anti-tumor activities against CRC are unclear.We used 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT), flow cytometry, and colony formation assays to investigate cell phenotype alterations and ROS generation. We also used quantitative Real-Time PCR (qRT-PCR) and western blotting to measure relative gene and protein expression. Finally, an in vivo mouse xenograft model was used to assess the anti-tumor activity of oxaliplatin and DHA alone, and combinations.DHA synergistically enhanced the anti-tumor activity of oxaliplatin in colon cancer cells by regulating ROS-mediated ER stress, signal transducer and activator of transcription 3 (STAT3), C-Jun-amino-terminal kinase (JNK), and p38 signaling pathways. Mechanistically, DHA increased ROS levels by inhibiting peroxiredoxin 2 (PRDX2) expression, and PRDX2 knockdown sensitized DHA-mediated cell growth inhibition and ROS production in CRC cells. A mouse xenograft model showed strong anti-tumor effects from combination treatments when compared with single agents.We demonstrated an improved therapeutic strategy for CRC patients by combining DHA and oxaliplatin treatments.

PURPOSE Thecurrent practice in intensity-modulated radiation therapy (IMRT) planning almost always includes different dose calculation strategies for plan optimization and final dose verification. The accurate Monte Carlo (MC) dose algorithm is considered to be time-consuming for the optimization. Thus a fast, simplified dose algorithm is used in the optimization. The significant differences between the optimized dose and the delivered dose lead to tediously planning loops and potentially suboptimal solutions. This work aims to develop an IMRT optimization algorithm to minimize the dose discrepancy so that the delivered dose can be optimized in a holistic, end-to-end manner. METHODS The proposed algorithm, namely NeuralDAO, integrates a neural dose network into the column generation (CG) direct aperture optimization (DAO) formulation for step-and-shoot IMRT planning. The neural dose network is designed and trained to produce doses of MC-level accuracy within few milliseconds. Its differentiability is fully exploited to compute gradients for identifying potential aperture shapes. A prototype of NeuralDAO was developed in PyTorch and available to the public. Five lung patient cases have been studied. Dosimetric accuracy was compared with the MC dose. Plan quality and time were compared with a state-of-the-art (SoA) dose-correct algorithm. Statistical analysis was performed by Wilcoxon signed-rank test. RESULTS The average gamma passing rate at 2 mm/2% is 99.7% between the optimized and delivered doses. The convergence process produced by NeuralDAO is virtually identical to that produced by an MC-based DAO. The average dose calculation time is 12.1 ms for an aperture on GPU. One session of optimization took 10-36 min. Compared with the SoA, better conformity index and homogeneity index were observed for the target. The esophagus was significantly spared. Significant reductions were observed for the replanning number and the planning time. CONCLUSIONS A new DAO algorithm based on the neural dose network has been developed. The results suggest that this algorithm minimizes the discrepancy between the optimized and delivered doses, which offers a promising approach to reduce the time and effort required in IMRT planning. This work demonstrates the possibility of applying the neural network in IMRT optimization. It is of great potential to extend this algorithm to other treatment modalities.

While convolutional neural networks (CNNs) have become the first choice for the medical image segmentation, they still lack the critical ingredient of incorporating priors, such as smoothness and boundary shapes. The authors tackle the limitation by developing a novel prior that is boundary-aware in two ways: promoting smoothness without blurring object boundaries and punishing prediction errors according to boundary shapes. They bring the boundary-aware property into effect by weighting the prediction gradients and errors with the distance map. Their prior differs from previous approaches that either over-smooth boundaries or tend to produce rough boundaries. They evaluate their prior alongside the cross-entropy (CE) on a cardiac MRI dataset. Compared to CE alone, their prior improves the Dice score by 1.5% and Hausdorff distance by 53%. It also yielded a faster and more stable learning process.

Purpose As affordable equipment, electronic portal imaging devices (EPIDs) are wildly used in radiation therapy departments to verify patients' positions for accurate radiotherapy. However, these devices tend to produce visually ambiguous and low-contrast planar digital radiographs under megavoltage x ray (MV-DRs), which poses a tremendous challenge for clinicians to perform multimodal registration between the MV-DRs and the kilovoltage digital reconstructed radiographs (KV-DRRs) developed from the planning computed tomography. Furthermore, the existent of strong appearance variations also makes accurate registration beyond the reach of current automatic algorithms. Methods We propose a novel modality conversion approach to this task that first synthesizes KV images from MV-DRs, and then registers the synthesized and real KV-DRRs. We focus on the synthesis technique and develop a conditional generative adversarial network with information bottleneck extension (IB-cGAN) that takes MV-DRs and nonaligned KV-DRRs as inputs and outputs synthesized KV images. IB-cGAN is designed to address two main challenges in deep-learning-based synthesis: (a) training with a roughly aligned dataset suffering from noisy correspondences; (b) making synthesized images have real clinical meanings that faithfully reflects MV-DRs rather than nonaligned KV-DRRs. Accordingly, IB-cGAN employs (a) an adversarial loss to provide training supervision at semantic level rather than the imprecise pixel level; (b) an IB to constrain the information from the nonaligned KV-DRRs. Results We collected 2698 patient scans to train the model and 208 scans to test its performance. The qualitative results demonstrate realistic KV images can be synthesized allowing clinicians to perform the visual registration. The quantitative results show it significantly outperforms current nonmodality conversion methods by 22.37% (P = 0.0401) in terms of registration accuracy. Conclusions The modality conversion approach facilitates the downstream MV-KV registration for both clinicians and off-the-shelf registration algorithms. With this approach, it is possible to benefit the developing countries where inexpensive EPIDs are widely used for the image-guided radiation therapy.

Background Controversial conclusions had been reported in studies trying to confirm the impact of heart dose on overall survival (OS) reported in RTOG 0167 for non-small cell lung cancer (NSCLC) patients who underwent radiotherapy (RT). The purpose of this study is to investigate the association of lung and heart dosimetric parameters with OS in NSCLC patients treated by volumetric modulated arc therapy (VMAT). Methods Inoperable NSCLC patients treated by VMAT from March 2012 to December 2015 were retrospectively reviewed. OS and progression-free survival (PFS) were estimated with the Kaplan-Meier method. Univariate and multivariate analyses were conducted with Cox proportional hazards model. Multivariate model building was conducted using stepwise regression for variables with p-value smaller than 0.2 in the univariate analysis. Results There were 130 NSCLC patients enrolled in this study with a median age of 63 years (range from 34 to 82 y). The median prescription dose for these patients was 56 Gy (range 40-70 Gy) with a mean heart and lung dose of 14.8±8.5 Gy and 13.6±4.4 Gy, respectively. The rates of patients with above grade III radiation pneumonitis (RP) and fibrosis were 8.5% and 8.5%, respectively. The 2-year PFS and OS of these patients were 15.2% and 39.8%, with a median PFS and OS of 7.2 and 18.8 months, respectively. RP was correlated with OS (p=0.048) and lung V20 was associated with PFS (p=0.04) according to the univariate analysis. Multivariate analysis demonstrated that RP (HR 1.39, 95%CI 1.010-1.909, p=0.043) and heart V15 (HR 1.02, 95%CI 1.006-1.025 p=0.002) were progression factors of OS, and no factor was associated with PFS. Conclusions RP and heart V15 were associated with OS for patients with stage III NSCLC who underwent VMAT. Heart and lung dosimetric parameters were highly correlated with each other, sparing of heart and lung should be considered equally during the treatment planning.

Background: Published data on the effects and toxicities of volumetric modulated arc therapy (VMAT) in the management of inoperable lung cancer are scarce. Materials and methods: The clinical outcomes and pulmonary toxicities of 134 patients with consecutive inoperable lung cancer who underwent VMAT from March 2011 to September 2016 were retrospectively reviewed. The dosimetric and characteristic factors associated with acute radiation pneumonitis (RP) and pulmonary fibrosis were evaluated with univariate and multivariate analysis. Results: The average prescription doses to these 134 patients were 57.07±6.27 Gy (range 52-64 Gy). The overall median follow-up time was 18.6 months (range, 2-45 mo), with a median follow-up time for the surviving patients of 20 months (range, 7-45 mo). The 2-year progression-free survival (PFS) and overall survival (OS) for all patients were 18.2% and 38.4%, with a median PFS and OS of 7.6 months and 18.6 months, respectively. The percent of patients with grade III/higher RP and pulmonary fibrosis were 10.5% and 9.0%, respectively. V13 (p=0.02) and age (p=0.02) were independently associated with acute RP according to multivariate analysis. The constraints for lung dosimetric metrics V10,V13,V20 and V30 were approximately 49%,41%,26% and 17% in VMAT treatment of lung cancer to limit the RP rate < 10%. Conclusion: VMAT can be delivered safely with acceptable acute and late toxicities for lung cancer patients. Lung dosimetric metrics were valuable in predicting acute RP. A lung V13 constraint of 40% was helpful to limit the RP rate < 10% in VMAT treatment of lung cancer patients.

Medical physicists are a tiny part of the medical staff group. Medical physics activities are categorized into administrative, clinical services, education, quality assurance (QA), and radiation safety. Medical physicists in radiation oncology departments perform the acceptance and commissioning of new equipment, dosimetry services, treatment planning, patient chart review, patient-specific QA, etc. Due to their professional characteristics, medical physicists cannot go to the front line in the battle of COVID-19. The outbreak of the pandemic in late 2019 is a global disaster and its great impact in Asia-Pacific has been unmasked in economic and health crises. COVID-19 can create devastating social, economic, and political crises that will leave deep scars. Asia-Pacific’s governments and societies should seize the historic opportunity to strengthen public health systems, make online education safer and more accessible, address poverty and social exclusion, tackle violence against children, and create greener, more sustainable economies.

ObjectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated.MethodsRadiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC.ResultsEight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97.ConclusionsNomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.

Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based on multiple U-net models and their effects on radiomics features from US images for patients with ovarian cancer. A total of 469 US images from 127 patients were collected and randomly divided into three groups: training sets (353 images), validation sets (23 images), and test sets (93 images) for automatic segmentation models building. Manual segmentation of target volumes was delineated as ground truth. Automatic segmentations were conducted with U-net, U-net++, U-net with Resnet as the backbone (U-net with Resnet), and CE-Net. A python 3.7.0 and package Pyradiomics 2.2.0 were used to extract radiomic features from the segmented target volumes. The accuracy of automatic segmentations was evaluated by Jaccard similarity coefficient (JSC), dice similarity coefficient (DSC), and average surface distance (ASD). The reliability of radiomics features were evaluated by Pearson correlation and intraclass correlation coefficients (ICC). CE-Net and U-net with Resnet outperformed U-net and U-net++ in accuracy performance by achieving a DSC, JSC, and ASD of 0.87, 0.79, 8.54, and 0.86, 0.78, 10.00, respectively. A total of 97 features were extracted from the delineated target volumes. The average Pearson correlation was 0.86 (95% CI, 0.83–0.89), 0.87 (95% CI, 0.84–0.90), 0.88 (95% CI, 0.86–0.91), and 0.90 (95% CI, 0.88–0.92) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. The average ICC was 0.84 (95% CI, 0.81–0.87), 0.85 (95% CI, 0.82–0.88), 0.88 (95% CI, 0.85–0.90), and 0.89 (95% CI, 0.86–0.91) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. CE-Net based segmentation achieved the best radiomics reliability. In conclusion, U-net based automatic segmentation was accurate enough to delineate the target volumes on US images for patients with ovarian cancer. Radiomics features extracted from automatic segmented targets showed good reproducibility and for reliability further radiomics investigations.

The increase of proton beam number might provide higher degrees of freedom in the optimization of intensity-modulated proton therapy planning. In this study, we aimed to quantitatively explore the potential benefits of the increased beam number, including dose volume histogram (DVH), linear energy transfer volume histogram, and DVH bandwidth metrics. Twelve patients with lung cancer are retrospectively selected. Four plans were created based on internal target volume (ITV) robust optimization for each patient using the RayStation treatment planning system. Four plans were generated using different numbers (three, five, seven, and nine) of evenly separated coplanar beams. The three-beam plan was considered as the reference plan. Biologically equivalent doses were calculated using both constant relative biological effectiveness (RBE) and variable RBE models, respectively. To evaluate plan quality, DVH metrics in the target [ITV: D2%, CI, HI] and organs-at-risk [Lung: V5Gy[RBE], V20Gy[RBE], V30Gy[RBE]; Heart D2%; Spinal cord D2%] were calculated using both RBE models. To evaluate LET distributions, LET volume histogram metrics [ITV LETmean and LET2%; Lung LETmean and LET2%; Heart LET2%; Spinal cord LET2%] were quantified. To evaluate plan robustness, the metrics using DVH bandwidth [ITV: D2%, D99%; Lung: V5Gy[RBE], V20Gy[RBE], V30Gy[RBE]; Heart D2%; Spinal cord D2%] were also reported. For plan quality, the increase of proton beam number resulted in fewer target hot spots, improved target dose conformity, improved target dose homogeneity, lower median-dose lung volume, and fewer hot spots in spinal cord. As to LET distributions, target mean LET increased significantly as the beam number increased to seven or more. Lung LET hot spots were significantly reduced with the increase of proton beams. With respect to plan robustness, the robustness of target dose coverage, target hot spots, and low-dose lung volume were improved, while the robustness of heart hot spots became worse as the beam number increased to nine. The robustness of cord hot spots became worse using five and seven beams compared to that using three beams. As the proton beam number increased, plan quality and LET distributions were comparable or significantly improved. The robustness of target dose coverage, target dose hot spots, and low-dose lung volume were significantly improved.

It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM). The purpose of this study is to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with BM based on radiomics from brain contrast-enhanced computer tomography (CECT) images.A total of 144 BM patients (94 male, 50 female) were enrolled in this study with 102 with primary lung AD and 42 with SCC, respectively. Radiomics features from manually contoured tumors were extracted using python. Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select relative radiomics features. Binary logistic regression and support vector machines (SVM) were applied to build models with radiomics features alone and with radiomics features plus age and sex.Fourteen features were selected from a total of 105 radiomics features for the final model building. The area under the curves (AUCs) and accuracy of SVM and binary logistic regression models were 0.765 vs. 0.769, 0.795 vs.0.828, and 0.716 vs. 0.726, 0.768 vs. 0.758, respectively, for models with radiomics features alone and models with radiomics features plus sex and age.Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC.• It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM) to define the prognosis and treatment. • Few studies had investigated the feasibility and accuracy of differentiating the pathological subtypes of primary non-small-cell lung cancer between adenocarcinoma (AD) and squamous cell carcinoma (SCC) for patients with BM based on radiomics from brain contrast-enhanced CT (CECT) images, although CECT images are often the initial imaging modality to screen for metastases and are recommended on equal footing with MRI for the detection of cerebral metastases. • Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC with a highest area under the curve (AUC) of 0.828 and an accuracy of 0.758, respectively.

Radiation therapy is an effective method in the management of esophageal cancer. MicroRNAs (miRNAs) have been reported to play an important role in tumorigenesis. However, the roles of specific miRNAs in radioresistant esophageal cancer remain to be investigated. In present study, the relative expression level of miR-20b-5p and miR-125a-5p were evaluated by quantitative Real-time polymerase chain reaction. Cell counting Kit-8 assay, wound-healing assay, transwell assay were used to assess cell proliferation, cell migration and cell invasion. TUNEL and Annexin V-FITC assays were applied to evaluate cell apoptosis. Dual-luciferase reporter gene assay was conducted to identify direct targets of miRNAs. The protein expression level was assessed by Western blot. The results indicated that miR-20b-5p was increased in radioresistant KYSE-150R cells compared with KYSE-150 cells, whereas miR-125a-5p was downregulated. MiR-20b-5p upregulation promoted cell proliferation, migration, invasion, and the EMT process, and decreased apoptosis by negatively regulating PTEN. MiR-125a-5p inhibited cell proliferation, migration, invasion, the EMT process and it induced apoptosis by negatively regulating IL6R. These data indicate that miR-20b-5p and miR-125a-5p promote tumorigenesis in radioresistant KYSE-150R cells and have the potential to be used as novel therapeutic targets for the treatment of esophageal cancer.

The aim of this study was to investigate the feasibility and sensitivity of using individual volume-based 3D gamma indices for composite dose-volume histogram (DVH)-based intensity-modulated radiation therapy (IMRT) quality assurance (QA). Composite IMRT QA for 15 cervical cancer patients was performed with ArcCHECK. The percentage dosimetric errors (%DEs) of DVH metrics when comparing treatment planning system and QA-reconstructed dose distribution, percentage gamma passing rates (%GPs) with different criteria for individual volumes and global gamma indices were evaluated, as well as their correlations. Receiver operating characteristic (ROC) curves were applied in order to study the sensitivities of the global and individual volume gamma indices. Most %DEs of the DVH metrics were within 3%. The γPTV and γrectum were

Purpose: To investigate the feasibility of partial arc volumetric modulated arc therapy (VMAT) in lung cancer stereotactic body radiotherapy (SBRT), as well the volumetric and dosimetric effects of different internal target volume (ITV) definitions with 4D CT. Methods: Fourteen patients with primary and metastatic lung cancer underwent SBRT were enrolled. Full and partial arc VMAT plans were generated with four different ITVs: ITVall, ITVMIP, ITVAIP and ITV2phases, representing ITVs generated from all 10 respiratory phases, maximum intensity projection (MIP), average intensity projection (AIP), and 2 extreme respiratory phases. Volumetric and dosimetric differences, as well as MU and delivery time were investigated. Results: Partial arc VMAT irradiated more dose at 2 cm away from planning target volume (PTV) (P = 0.002), however, it achieved better protection on mean lung dose , lung V5, spinal cord, heart and esophagus compared with full arc VMAT. The average MU and delivery time of partial arc VMAT were 240 and 1.6 min less than those of full arc VMAT. There were no significant differences on target coverage and organ at risks (OARs) sparing among four ITVs. The average percent volume differences of ITVMIP, ITVAIP and ITV2phases to ITVall were 8.6%, 13.4%, and 25.2%, respectively. Conclusions: Although partial arc VMAT delivered more dose 2 cm out of PTV, it decreases the dose to lung, spinal cord, and esophagus, as well decreased the total MU and delivery time compared with full arc VMAT without sacrificing target coverage. Partial arc VMAT was feasible and more efficient for lung SBRT.

Multimodal medical image registration remains a challenging problem when strong appearance variations and imprecise alignment exist in images. Previous deep network approaches cannot handle such a high degree of variability and prohibit the use of strong geometric constraints. The authors introduce a novel deep architecture that not only produces image representations that are well-suited for this challenging task, but also leverages knowledge of the geometry constraints for robust registration. By enforcing the representations of different modalities living in a common semantic space, they obtain convolutional features tending to respond to object parts consistently across modality. This yields a unique description for all object fragments and allows the end user to know the model's decision process, whereas most existing models remain unclear and difficult to explain. By using the differentiable spatial transformer to compensate transform, they integrate geometric consensus into the cost function to enable end-to-end model optimisation which has not yet been exploited before. The authors evaluate their method on a very challenging medical image dataset. Experiments demonstrate that the proposed method provides a plausible representation and outperforms state-of-the-art approaches by a significant margin.

PURPOSE/OBJECTIVE(S) Clinical evidence demonstrated that combination of radiotherapy and immunotherapy may be more effective than either treatment alone. However, the efficacy of combined treatment was limited due to the low response rate of immune checkpoints inhibit (ICIs). Pyrolysis is a lytic proinflammatory cell death mediated by the gasdermin family of pore-forming proteins. Studies indicated that combining radiotherapy with pyroptosis greatly enhance the body's anti-tumor immunity and radiotherapy efficacy. In order to precisely control the location and intensity of cell scorch, an accurate and efficient cancer immunotherapy system was constructed by organically combining radiotherapy and pyrolysis with nanomaterials with infrared light and pH responsive regulatory protein expression. MATERIALS/METHODS An upconverting nanoparticles with integrated azobenzene-modified mesoporous silica was constructed to realize the controlled release of the drug (decitabine (DAC)) under the control of infrared light. Firstly, synthesis of UCNPS@mSiO2-azo. NaYF4: Tm, Yb@NaYF4 nanocrystals (UCNPS) was prepared by solvothermal method, and then UCNPS@mSiO2 was prepared by using cetyl trimethyl ammonium bromide (CTAB) as template and tetraethyl orthosilicate (TEOS) as silicon source. Finally, mesopores were formed by ion exchange method. UCNPS@mSiO2-azo is prepared by mixing UCNP@mSiO2 and N-(3-triethoxysilyl) propyl-4-phenylazobenzamide in ethanol at 80°C for 1 hour. Secondly, Synthesis of UCNPS@mSiO2-azo@DAC@HSA, (human serum albumin (HSA)). Disperse UCNPS@mSiO2-azo in PBS, add a certain amount of DAC and stir for 24h, add the polymerized HSA and react at 37°C for 3h to obtain UCNPS@mSiO2-azo@DAC@HSA. RESULTS The prepared UCNPS@mSiO2 with good up-conversion efficiency, which can convert infrared light into visible light and ultraviolet light (fig 1.a,c,d). At the same time, it was found that UCNPS@mSiO2-azo can effectively load the DAC, and at the same time it has good stability, and can controllably release the DAC under the control of infrared light (fig 1.b). More importantly, it was found through cell experiments that the cells would not die after adding materials and performing infrared light. However, once further low-dose radiotherapy treatment, the cells would experience obvious pyrosis. CONCLUSION A specific and highly sensitive radiotherapy immunosensitization system that can effectively enhance the therapeutic effect of tumors was constructed. It can regulate the specific protein content of tumor location in vivo by means of both internal and external sources of pH and infrared light with the combination of radiotherapy. This work provides a new treatment strategy for radiotherapy by changing the way of cell death to enhance the body's anti-tumor immunity after local treatment to effectively reduce the damage of normal cells and to effectively change the tumor suppression microenvironment and enhance its own anti-tumor immunity to completely eliminate tumors.

Although gamma analysis is still a widely accepted quantitative tool to analyze and report patient‐specific QA for intensity‐modulated radiotherapy (IMRT) and volumetric‐modulated arc therapy (VMAT), the correlation between the 2D percentage gamma passing rate (%GP), and the clinical dosimetric difference for IMRT and VMAT has been questioned. The purpose of this study was to investigate the feasibility of individual volume‐based 3D gamma indices for pretreatment VMAT QA. Percentage dosimetric errors (%DE) of dose‐volume histogram metrics (includes target volumes and organ at risks) between the treatment planning system and QA‐reconstructed dose distribution, %GPs for individual volume and global gamma indices, as well their correlations and sensitivities were investigated for one‐ and two‐arc VMAT plans. The %GPs of individual volumes had a higher percent of correlation with individual 15 %DE metrics compared with global %GPs. For two‐arc VMAT at 2%/2 mm, 3%/3 mm, and 4%/4 mm criteria, individual volume %GPs were correlated with 9, 12, and 9 out of 15 %DE metrics, while global %GPs were correlated with only 2 out of 15 %DE metrics, respectively. For one‐arc VMAT at 2%/2 mm, 3%/3 mm, and 4%/4 mm criteria, individual volume %GPs were correlated with 18, 16, and 13 out of 23 %DE metrics, and global %GPs were correlated with 19, 12, and 1 out 23 %DE metrics, respectively. The area under curves (AUC) of individual volume %GPs were higher than those of global %GPs for two‐arc VMAT plans, but with mixed results for one‐arc VMAT plans. In a conclusion, the idea of individual volume %GP was created and investigated to better serve for VMAT QA and individual volume‐based %GP had a higher percent of correlation with DVH 15 %DE metrics compared with global %GP for both one‐ and two‐arc VMAT plans.

Background: Although superior clinical benefits of first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in the treatment of advanced non-small-cell lung cancer (NSCLC) had been reported with different sensitivity, the sensitivity of second-line TKIs in NSCLC patients with different EFGR mutations was unknown. The purpose of this study is to investigate the clinical outcome of second-line EGFR-TKIs in the treatment of NSCLC patients according to different EGFR genotypes. Methods: The treatment outcomes of 166 NSCLC patients with different EGFR mutations treated by second-line TKIs were retrospectively reviewed. The efficacy was evaluated with Pearson chi-square or Fisher's exact tests, Log-rank test and Cox proportional hazards model. Results: The disease control rate (DCR) and objective response rate (ORR) of enrolled NSCLC patients were 77.7% and 11.4%, respectively. The exon 19 deletion group had a significantly longer median progression-free survival (PFS) (6.7 vs. 4.5 months, P=0.002) and overall survival (OS) (13.7 vs. 11.7 months, P=0.02) compared with the exon 19 L858R mutation group for NSCLC patients, as well for patients with brain metastasis [PFS: (6.7 vs. 3.9 months, p

Background: The rates of locoregional and distant recurrence for esophageal squamous cell carcinoma (ESCC) patients underwent radical esophagectomy remain high. The purpose of this study is to explore an optimal postoperative therapeutic modality by investigating the efficacy of various adjuvant therapies in the treatment of ESCC. Methods: We retrospectively reviewed 408 ESCC patients underwent thoracic esophagectomy and 3-field lymph node dissection from 2010 to 2015. Patients were classified into surgery alone (Group S), adjuvant chemotherapy (Group CT) and postoperative chemotherapy plus radiotherapy (Group CRT), respectively. Univariate and multivariate Cox regression analyses were used to analyze prognostic factors and survival. Results: The overall survival (OS) and disease-free survival (DFS) were similar among groups. Postoperative CT and CRT both were beneficial for patients with positive lymph nodes, particularly for those with 3 or more lymph nodes involvement and metastasis in the middle thoracic segment compared with surgery alone. The 3-year OS and DFS for patients with 3 or more lymph nodes involvement were 30.8%, 53.7%, 50.5% and 19.9%, 41.6%, 34.0% for Group S, CT, and CRT, respectively (p=0.04; p=0.004, respectively). There was no notable difference in OS and DFS between the adjuvant Group CT and CRT (p=0.42; p=0.49, respectively). Postoperative CRT significantly reduced the rates of distant metastasis and overall recurrence for patients with positive lymph nodes (p=0.042; p=0.01, respectively). Number of metastatic lymph nodes, extent of resection, and AJCC stage were independent predictors of survival. Grade 1-2 myelosuppression was experienced significantly more frequently by patients in Group CRT than those in Group CT (P=0.03). Late toxicities were rare and manageable overall. Conclusions: Postoperative CT and CRT both were associated with better survival for patients with positive lymph nodes, particularly for those with 3 or more lymph nodes involvement and metastasis in the middle thoracic segment. Postoperative CRT was significantly more effective at reducing the rates of distant metastasis and overall recurrence for patients with positive lymph nodes.

Cervical cancer is the most commonly diagnosed cancer among women. Early diagnosis and prediction will greatly improve the treatment outcome. Many clinical parameters have been used as diagnostic and prognostic factors for cervical cancer patients, including tumor stage, histological type, lymph node status, but with limitations in prediction accuracy. The development of noninvasive biomarker with the potential to provide more specific tumor characterization before treatment begins or during therapy is urgent needed, which may permit clinicians to administer a more individualized anti-cancer treatment. Radiomics is a mathematical-statistical procedure extracting information from medial images, which has the potential for prediction of staging, histological type, node status, relapse and survival in patients with cervical cancer. In this manuscript, we reviewed recent clinical studies and future potential for the application of radiomics in the treatment of patients with cervical cancer, and discussed the current challenges and limitations of radiomics for oncology.

Background Controversial conclusions had been reported in studies trying to confirm the impact of heart dose on overall survival (OS) reported in RTOG 0167 for non-small cell lung cancer (NSCLC) patients who underwent radiotherapy (RT). The purpose of this study is to investigate the association of lung and heart dosimetric parameters with OS in NSCLC patients treated by volumetric modulated arc therapy (VMAT). Methods Inoperable NSCLC patients treated by VMAT from March 2012 to December 2015 were retrospectively reviewed. OS and progression-free survival (PFS) were estimated with the Kaplan–Meier method. Univariate and multivariate analyses were conducted with Cox proportional hazards model. Multivariate model building was conducted using stepwise regression for variables with p-value smaller than 0.2 in the univariate analysis. Results There were 130 NSCLC patients enrolled in this study with a median age of 63 years (range from 34 to 82 y). The median prescription dose for these patients was 56 Gy (range 40–70 Gy) with a mean heart and lung dose of 14.8±8.5 Gy and 13.6±4.4 Gy, respectively. The rates of patients with above grade III radiation pneumonitis (RP) and fibrosis were 8.5% and 8.5%, respectively. The 2-year PFS and OS of these patients were 15.2% and 39.8%, with a median PFS and OS of 7.2 and 18.8 months, respectively. RP was correlated with OS (p=0.048) and lung V20 was associated with PFS (p=0.04) according to the univariate analysis. Multivariate analysis demonstrated that RP (HR 1.39, 95%CI 1.010–1.909, p=0.043) and heart V15 (HR 1.02, 95%CI 1.006–1.025 p=0.002) were progression factors of OS, and no factor was associated with PFS. Conclusions RP and heart V15 were associated with OS for patients with stage III NSCLC who underwent VMAT. Heart and lung dosimetric parameters were highly correlated with each other, sparing of heart and lung should be considered equally during the treatment planning.

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images. One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann–Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models. A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p

This study investigated the impact of beam complexities on planar quality assurance and plan quality robustness by introducing MLC errors in intensity‐modulate radiation therapy. Forty patients' planar quality assurance (QA) plans were enrolled in this study, including 20 dynamic MLC (DMLC) IMRT plans and 20 static MLC (SMLC) IMRT plans. The total beam numbers were 150 and 160 for DMLC and SMLC, respectively. Six different magnitudes of MLC errors were introduced to these beams. Gamma pass rates were calculated by comparing error‐free fluence and error‐induced fluence. The plan quality variation was acquired by comparing PTV coverage. Eight complexity scores were calculated based on the beam fluence and the MLC sequence. The complexity scores include fractal dimension, monitor unit, modulation index, fluence map complexity, weighted average of field area, weighted average of field perimeter, and small aperture ratio (

To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning. The radiomic features and dosimetric parameters of 94 EC patients were extracted and modeled using Support Vector Classification (SVM) and Extreme Gradient Boosting algorithm (XGBoost). The 94-sample dataset was randomly divided into a 70-sample training subset and a 24-sample independent test set while keeping the class proportions intact via stratification. A receiver operating characteristic (ROC) curve was used to assess the performance of models using radiomic features alone and using combined radiomic features and dosimetric parameters. A total of 42 radiomic features and 18 dosimetric parameters plus the patients’ characteristic parameters were extracted for these 94 cases (58 responders and 36 non-responders). XGBoost plus principal component analysis (PCA) achieved an accuracy and area under the curve of 0.708 and 0.541, respectively, for models with radiomic features combined with dosimetric parameters, and 0.689 and 0.479, respectively, for radiomic features alone. Image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the model. The dosimetric parameters of gross tumor volume (GTV) homogeneity index (HI), Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model. The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomic features alone in predicting the treatment response of patients with EC who underwent CRT. • The model with radiomic features combined with dosimetric parameters is promising in predicting the treatment response of patients with EC who underwent CRT. • The model with radiomic features combined with dosimetric parameters (prediction accuracy of 0.708 and AUC of 0.689) outperforms that with radiomic features alone (best prediction accuracy of 0.625 and AUC of 0.412). • The image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the treatment response prediction model. The dosimetric parameters of GTV HI, Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.

Background In the era of intensity-modulated radiotherapy (IMRT), the role of additional concurrent chemotherapy (CC) to radiotherapy (RT) after induction chemotherapy (IC) compared to IC followed by RT alone remains unclear for stage II-IVB nasopharyngeal carcinoma (NPC) patients. The aim of this study was to evaluate the efficacy and toxicities of IC/RT and IC/CCRT in the treatment of NPC with volumetric modulated arc therapy (VMAT). Methods From January 2012 to March 2016, a total of 217 NPC patients were retrospectively assessed. Of the 217 patients, 139 patients received IC followed by VMAT alone and 78 patients received IC plus CCRT. Overall survival (OS), progression-free survival (PFS) and toxicities were assessed. Results The 5-year OS, PFS rates were 57.5%, 41.8% and 47.8%, 38.4% for the IC/RT and IC/CCRT arms, respectively, without significant difference in survival between the two groups (both p > 0.05). Multivariate analysis indicated that treatment modality (IC/RT vs. IC/CCRT) was not an independent prognostic factor for OS or PFS. Grade 3–4 leukopenia/neutropenia (3.60% vs. 20.51%, p

Background and Objectives: Colorectal cancer is one of the most common cancers and the leading cause of cancer-related death worldwide. The impact of the primary tumor location on the prognosis of patients with colorectal cancer has long been a concern, but studies have led to conflicting conclusions. Methods: In total, 465 colorectal cancer patients who received radical surgery were reviewed in this study. Enrolled patients were divided into two groups according to the tumor location. Disease-free survival (DFS) and overall survival (OS) were analyzed via the Kaplan-Meier method. A Cox regression model was employed to evaluate the independent prognostic factors for DFS and OS. Results: The right colorectal cancer (RCC) and left colorectal cancer (LCC) groups comprised 202 and 140 patients, respectively. Univariate and multivariate analyses revealed that the tumor location and TNM stage were independent predictors of DFS and OS. Subgroup analyses by stage demonstrated that there were significant differences in DFS and OS between patients with stage II and III RCC and LCC, but not for those with stage I colorectal cancer. Conclusions: Patients with stage II and III LCC had better survival than those with RCC. However, this improvement in DFS and OS was not observed in patients with stage I colorectal cancer.