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BackgroundWe aimed to describe the chest CT findings in sixty-seven patients infected by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MethodsWe retrospectively reviewed 67 patients hospitalized in Ruian People’s Hospital. All the patients received the positive diagnosis of SARS-CoV-2 infection. The CT and clinical data were collected between January 23rd, 2020 and February 10th, 2020. The CT images were analyzed by the senior radiologists.ResultsThere are 54 patients with positive CT findings and 13 patients with negative CT findings. The typical CT findings in hospitalized patients with SARS-CoV-2 infection were ground glass opacities (42/54), lesions located in the peripheral area (50/54), multiple lesions (46/54), and lesions located in the lower lobes (42/54). There were less typical CT findings, including air bronchogram (18/54), pleural thickening or pleural effusion (14/54), consolidation (12/54), lesions in the upper lobes (12/54), interlobular septal thickening (11/54), reversed halo sign (9/54), single lesion (8/54), air cavities (4/54), bronchial wall thickening (3/54), and intrathoracic lymph node enlargement (2/54).ConclusionsCT features can play an important role in the early diagnosis and follow-up of COVID-19 patients.

Purpose: There are very little data available comparing outcomes of intensity-modulated proton therapy (IMPT) to intensity-modulated radiation therapy (IMRT) in patients with locally advanced NSCLC (LA-NSCLC). Methods: Seventy-nine consecutively treated patients with LA-NSCLC underwent definitive IMPT (n = 33 [42%]) or IMRT (n = 46 [58%]) from 2016 to 2018 at our institution. Survival rates were calculated using the Kaplan-Meier method and compared with the log-rank test. Acute and subacute toxicities were graded based on Common Terminology Criteria for Adverse Events, version 4.03. Results: Median follow-up was 10.5 months (range, 1-27) for all surviving patients. Most were stage III (80%), received median radiation therapy (RT) dose of 60 Gy (range, 45-72), and had concurrent chemotherapy (65%). At baseline, the IMPT cohort was older (76 vs 69 years, P

Purpose Approximate dose calculation methods were used in the nominal dose distribution and the perturbed dose distributions due to uncertainties in a commercial treatment planning system (CTPS) for robust optimization in intensity-modulated proton therapy (IMPT). We aimed to investigate whether the approximations influence plan quality, robustness, and interplay effect of the resulting IMPT plans for the treatment of locally advanced lung cancer patients. Materials and methods Ten consecutively treated locally advanced nonsmall cell lung cancer (NSCLC) patients were selected. Two IMPT plans were created for each patient using our in-house developed TPS, named "Solo," and also the CTPS, EclipseTM (Varian Medical Systems, Palo Alto, CA, USA), respectively. The plans were designed to deliver prescription doses to internal target volumes (ITV) drawn by a physician on averaged four-dimensional computed tomography (4D-CT). Solo plans were imported back to CTPS, and recalculated in CTPS for fair comparison. Both plans were further verified for each patient by recalculating doses in the inhalation and exhalation phases to ensure that all plans met clinical requirements. Plan robustness was quantified on all phases using dose-volume-histograms (DVH) indices in the worst-case scenario. The interplay effect was evaluated for every plan using an in-house developed software, which randomized starting phases of each field per fraction and accumulated dose in the exhalation phase based on the patient's breathing motion pattern and the proton spot delivery in a time-dependent fashion. DVH indices were compared using Wilcoxon rank-sum test. Results Compared to the plans generated using CTPS on the averaged CT, Solo plans had significantly better target dose coverage and homogeneity (normalized by the prescription dose) in the worst-case scenario [ITV D95% : 98.04% vs 96.28%, Solo vs CTPS, P = 0.020; ITV D5% -D95% : 7.20% vs 9.03%, P = 0.049] while all DVH indices were comparable in the nominal scenario. On the inhalation phase, Solo plans had better target dose coverage and cord Dmax in the nominal scenario [ITV D95% : 99.36% vs 98.45%, Solo vs CTPS, P = 0.014; cord Dmax : 20.07 vs 23.71 Gy(RBE), P = 0.027] with better target coverage and cord Dmax in the worst-case scenario [ITV D95% : 97.89% vs 96.47%, Solo vs CTPS, P = 0.037; cord Dmax : 24.57 vs 28.14 Gy(RBE), P = 0.037]. On the exhalation phase, similar phenomena were observed in the nominal scenario [ITV D95% : 99.63% vs 98.87%, Solo vs CTPS, P = 0.037; cord Dmax : 19.67 vs 23.66 Gy(RBE), P = 0.039] and in the worst-case scenario [ITV D95% : 98.20% vs 96.74%, Solo vs CTPS, P = 0.027; cord Dmax : 23.47 vs 27.93 Gy(RBE), P = 0.027]. In terms of interplay effect, plans generated by Solo had significantly better target dose coverage and homogeneity, less hot spots, and lower esophageal Dmean , and cord Dmax [ITV D95% : 101.81% vs 98.68%, Solo vs CTPS, P = 0.002; ITV D5% -D95% : 2.94% vs 7.51%, P = 0.002; cord Dmax : 18.87 vs 22.29 Gy(RBE), P = 0.014]. Conclusions Solo-generated IMPT plans provide improved cord sparing, better target robustness in all considered phases, and reduced interplay effect compared with CTPS. Consequently, the approximation methods currently used in commercial TPS programs may have space for improvement in generating optimal IMPT plans for patient cases with locally advanced lung cancer.

Background Esophageal carcinoma is the eighth most common cancer in the world. Volumetric-modulated arc therapy (VMAT) is widely used to treat distal esophageal carcinoma due to high conformality to the target and good sparing of organs at risk (OAR). It is not clear if small-spot intensity-modulated proton therapy (IMPT) demonstrates a dosimetric advantage over VMAT. In this study, we compared dosimetric performance of VMAT and small-spot IMPT for distal esophageal carcinoma in terms of plan quality, plan robustness, and interplay effects. Methods 35 distal esophageal carcinoma patients were retrospectively reviewed; 19 patients received small-spot IMPT and the remaining 16 of them received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTVs) on phase-averaged 4D-CT's. The dose-volume-histogram (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases for each field per fraction. DVH indices were compared using Wilcoxon rank-sum test. For fair comparison, all the treatment plans were normalized to have the same CTVhigh D95% in the nominal scenario relative to the prescription dose. Results In the nominal scenario, small-spot IMPT delivered statistically significantly lower liver Dmean and V30Gy[RBE] , lung Dmean , heart Dmean compared with VMAT. CTVhigh dose homogeneity and protection of other OARs were comparable between the two treatments. In terms of plan robustness, the IMPT and VMAT plans were comparable for kidney V18Gy[RBE] , liver V30Gy[RBE] , stomach V45Gy[RBE] , lung Dmean , V5Gy[RBE] , and V20Gy[RBE] , cord Dmax and D 0.03 c m 3 , liver Dmean , heart V20Gy[RBE] , and V30Gy[RBE] , but IMPT was significantly worse for CTVhigh D95% , D 2 c m 3 , and D5% -D95% , CTVlow D95% , heart Dmean , and V40Gy[RBE] , requiring careful and experienced adjustments during the planning process and robustness considerations. The small-spot IMPT plans still met the standard clinical requirements after interplay effects were considered. Conclusions Small-spot IMPT decreases doses to heart, liver, and total lung compared to VMAT as well as achieves clinically acceptable plan robustness. Our study supports the use of small-spot IMPT for the treatment of distal esophageal carcinoma.

Hepatocellular carcinoma is one of the most common malignant tumors worldwide and the fourth leading cause of cancer-related deaths. The prognosis of hepatocellular carcinoma patients is extremely poor due to the occult onset and high metastasis of hepatocellular carcinoma. Therefore, biomarkers with high specificity and sensitivity are of great importance in early screening, diagnosis prognosis, and treatment of hepatocellular carcinoma patients. Exosomes are tiny vesicles secreted by various types of cells, which can serve as mediators of intercellular communication to regulate the tumor microenvironment, and play a key role in the occurrence, development, prognosis, monitor and treatment of hepatocellular carcinoma. As microRNA deliverer, exosomes are involved in multiple life activities by regulating target genes of recipient cells such as proliferation, invasion, metastasis and apoptosis of cancer cells. In this review, we summarized the composition, active mechanism and function of exosomal microRNAs in hepatocellular carcinoma, and elaborated on their potential application value of early diagnosis and treatment in hepatocellular carcinoma.

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.

Hepatocellular carcinoma (HCC) is one of the most common clinically malignant tumors of the digestive system. It ranks the sixth most common malignant tumor in the world and ranks fourth among cancer-related death worldwide. At present, early diagnosis and prognosis monitoring of hepatocellular carcinoma mainly use alpha-fetoprotein combined with ultrasonography, which leads to clinical frequently missed diagnosis or even misdiagnosis. Therefore, seeking specific diagnostic and monitoring molecules of hepatocellular carcinoma are still hot topics in contemporary medical practice. MicroRNA is an endogenous non-coding small RNA that regulates the expression of the target molecule and participates in various biological processes in vivo. The miR-200 family, the most common celebrity family of microRNAs, is commonly lower expression in a variety of cancers and is closely associated with tumorigenesis and outcome, especially hepatocellular carcinoma. This review mainly discusses the expression changes, specific molecular mechanisms, biological functions and clinical values of miR-200 family in hepatocellular carcinoma. Moreover, we highlighted utilization of miR-200 family as molecular biomarkers for early diagnosis, prognostic monitoring and appropriate therapy in hepatocellular carcinoma.

Rationale: Many clinical studies have focused on the epidemiological and clinical characteristics of inpatients with coronavirus disease (COVID-19). However, there are few reports about the clinical follow-up of discharged patients. Objectives: To describe the follow-up of patients with COVID-19 in Wenzhou City, Zhejiang, China. Methods: We retrospectively reviewed 4-week follow-ups in patients with COVID-19, including computed tomographic (CT) chest scanning, blood testing, and oropharyngeal-swab testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ribonucleic acid. The chest CT scans and blood tests were performed on the last day before discharge and 2 weeks and 4 weeks after discharge. The oropharyngeal-swab tests were performed at both 1 week and 2 weeks after discharge. Fifty-one patients with common COVID-19 were enrolled in the study. All the CT and clinical data were collected between January 23 and March 28, 2020. Results: Compared with the abnormalities found on the the last CT scans before discharge, the abnormalities in the lungs at the first and second follow-ups after discharge had been gradually absorbed. The cases with focal ground-glass opacity were reduced from 17.7% to 9.8% of cases. The cases with multiple ground-glass opacities decreased from 80.4% to 23.5%. The cases with consolidation were reduced from 49.0% to 2.0%. The cases with interlobular septal thickening were reduced from 80.4% to 35.3%. The cases with subpleural lines were reduced from 29.4% to 7.8%. The cases with irregular lines were reduced from 41.2% to 15.7%. The lung lesions of 25.5% patients were shown to be fully absorbed on the first CT scans after discharge, and the rate of lung recovery increased to 64.7% after the second follow-up. Nucleic-acid test results became recurrently positive in 17.6% of discharged patients, of whom only 33.3% complained of clinical symptoms. There were no differences in the characteristics of the last CT scans before discharge between the patients with recurrently positive test results and the patients with negative test results. The lung damage was fully absorbed in 55.6% of discharged patients with recurrence of positive test results for SARS-CoV-2 ribonucleic acid. Conclusions: The lung damage due to COVID-19 could be reversible for patients with common COVID-19. A few cases showed recurring positive results of nucleic-acid tests after discharge.

Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. Results and conclusions The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

Purpose To compare dosimetric performance of volumetric-modulated arc therapy (VMAT) and small-spot intensity-modulated proton therapy for stage III non-small-cell lung cancer (NSCLC). Methods and materials A total of 24 NSCLC patients were retrospectively reviewed; 12 patients received intensity-modulated proton therapy (IMPT) and the remaining 12 received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTV) on averaged 4D-CTs. The dose-volume-histograms (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases of each field per fraction. DVH indices were compared using Wilcoxon rank sum test. Results Compared with VMAT, IMPT delivered significantly lower cord Dmax , heart Dmean , and lung V5 Gy[ RBE ] with comparable CTV dose homogeneity, and protection of other OARs. In terms of plan robustness, the IMPT plans were statistically better than VMAT plans in heart Dmean , but were statistically worse in CTV dose coverage, cord Dmax , lung Dmean , and V5 Gy[ RBE ] . Other DVH indices were comparable. The IMPT plans still met the standard clinical requirements with interplay effects considered. Conclusions Small-spot IMPT improves cord, heart, and lung sparing compared to VMAT and achieves clinically acceptable plan robustness at least for the patients included in this study with motion amplitude less than 11 mm. Our study supports the usage of IMPT to treat some lung cancer patients.

Purpose/Objective(s) We previously identified changes of immune cells in the cervical cancer microenvironment following concurrent chemoradiotherapy (CCRT) at single cell resolution. However, changes of other cells in the tumor microenvironment are unknown; thus, we further investigated CCRT-induced landscape alteration of all cells in the tumor microenvironment of locally advanced cervical cancer (LACC) using single-cell RNA sequencing. Materials/Methods We performed single-cell RNA sequencing of 5 paired of tumor biopsies before and during CCRT in LACC patients and identified the subsets of epithelial cells, cancer-associated fibroblasts, immune cells, and endothelial cells and their alterations following CCRT. We also used immunostaining to validate our findings. Results Seven clusters of epithelial cells, 2 clusters of mCAF and 7 clusters of vCAF, 7 clusters of myeloid cells, 10 clusters of T cells and 2 clusters of NK cells, and 3 clusters of endothelial cells were identified in cervical cancer. Epithelial cells, mCAF and myeloid cells dramatic changed following CCRT. The proportion of a highly proliferate cluster of epithelial cells was decreased induced by CCRT, while a pre-malignant cluster of epithelial cells was predominant during CCRT. CCRT induced the transition of mCAF from one subset to another. For T cells, CCL5+CD8+ T cells, exhausted CD8+ T cells, and proliferate T cells were predominant before CCRT, but not during CCRT; cytotoxic CD8+ T cells were mainly existed in the tumor environment during CCRT. Conclusion Our work reveals the landscape alteration of tumor microenvironment induced by CCRT in LACC patients at single-cell resolution.

Purpose/Objective(s) Microbiome in cervical cancer tissue remains largely unknown, although cervicovaginal microbiome was investigated in various studies. We investigated the microbiome in cancer tissues and vagina, and its impact on tumor response after concurrent chemoradiotherapy (CCRT) in locally advanced cervical cancer (LACC) patients. Materials/Methods We prospectively enrolled 63 LACC patients who received CCRT and collected their tumor biopsies and vaginal swabs before and during CCRT. 16S rRNA gene sequencing was used to evaluated the microbiome in samples. Tumor response was evaluated based on RECIST 1.1 guidelines at one month after CCRT. Results Complete response (CR) was found in 50 (79%) patients, while partial response (PR) or stable disease (SD) was shown in 13 (21%) patients after CCRT. Patients with CR showed higher beta-diversity (P = 0.021) but not alpha-diversity (P > 0.05) in pretreatment tumor tissues than patients with PR/SD. Different microbiome composition in tumor tissues and vaginal swabs was observed between patients with CR and PR/SD at the two time points. KEGG showed significantly increased metabolic pathways, such as amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism, in patients with CR than with PR/SD. Interestingly, alpha-diversity was significantly higher in cervical cancer tissues than in vaginal swabs during CCRT (P 0.05). Conclusion Our work reveals the microbiome in tumor microenvironment and vagina and its correction with tumor response after CCRT in LACC patients. Further study was warranted to explore which microbe regulates tumor response by which metabolic pathway. Author Disclosure C. Liu: None. A. Li: None. C. Wang: None. W. Zou: None. J. Yu: None. L. Zhang: None. X. Zhang: None. J. Yue: None.

Purpose: There is an emerging interest of applying magnetic resonance imaging (MRI) to radiotherapy (RT) due to its superior soft tissue contrast for accurate target delineation as well as functional information for evaluating treatment response. MRI-based RT planning has great potential to enable dose escalation to tumors while reducing toxicities to surrounding normal tissues in RT treatments of nasopharyngeal carcinoma (NPC). Our study aims to generate synthetic CT from T2-weighted MRI using a deep learning algorithm. Methods: Thirty-three NPC patients were retrospectively selected for this study with local IRB's approval. All patients underwent clinical CT simulation and 1.5T MRI within the same week in our hospital. Prior to CT/MRI image registration, we had to normalize two different modalities to a similar intensity scale using the histogram matching method. Then CT and T2 weighted MRI were rigidly and deformably registered using intensity-based registration toolbox elastix (version 4.9). A U-net deep learning algorithm with 23 convolutional layers was developed to generate synthetic CT (sCT) using 23 NPC patients' images as the training set. The rest 10 NPC patients were used as the test set (~1/3 of all datasets). Mean absolute error (MAE) and mean error (ME) were calculated to evaluate HU differences between true CT and sCT in bone, soft tissue and overall region. Results: The proposed U-net algorithm was able to create sCT based on T2-weighted MRI in NPC patients, which took 7 s per patient on average. Compared to true CT, MAE of sCT in all tested patients was 97 ± 13 Hounsfield Unit (HU) in soft tissue, 131 ± 24 HU in overall region, and 357 ± 44 HU in bone, respectively. ME was −48 ± 10 HU in soft tissue, −6 ± 13 HU in overall region, and 247 ± 44 HU in bone, respectively. The majority soft tissue and bone region was reconstructed accurately except the interface between soft tissue and bone and some delicate structures in nasal cavity, where the inaccuracy was induced by imperfect deformable registration. One patient example was shown with almost no difference in dose distribution using true CT vs. sCT in the PTV regions in the sinus area with fine bone structures. Conclusion: Our study indicates that it is feasible to generate high quality sCT images based on T2-weighted MRI using the deep learning algorithm in patients with nasopharyngeal carcinoma, which may have great clinical potential for MRI-only treatment planning in the future.

Purpose We propose linear energy transfer (LET)-guided robust optimization in intensity modulated proton therapy for head and neck cancer. This method simultaneously considers LET and physical dose distributions of tumors and organs at risk (OARs) with uncertainties. Methods and Materials Fourteen patients with head and neck cancer were included in this retrospective study. Cord, brain stem, brain, and oral cavity were considered. Two algorithms, voxel-wise worst-case robust optimization and LET-guided robust optimization (LETRO), were used to generate intensity modulated proton therapy plans for each patient. The latter method directly optimized LET distributions rather than indirectly as in previous methods. LET–volume histograms (LETVHs) were generated, and high LET was redistributed from nearby OARs to tumors in a user-defined way via LET–volume constraints. Dose–volume histogram indices, such as clinical target volume (CTV) D98% and D2%-D98%, cord Dmax, brain stem Dmax, brain Dmax, and oral cavity Dmean, were calculated. Plan robustness was quantified using the worst-case analysis method. LETVH indices analogous to dose–volume histogram indices were used to characterize LET distributions. The Wilcoxon signed rank test was performed to measure statistical significance. Results In the nominal scenario, LETRO provided higher LET distributions in the CTV (unit: keV/μm; CTV LET98%: 1.18 vs 1.08, LETRO vs RO, P = .0031) while preserving comparable physical dose and plan robustness. LETRO achieved significantly reduced LET distributions in the cord, brain stem, and oral cavity compared with RO (cord LETmax: 7.20 vs 8.20, P = .0010; brain stem LETmax: 10.95 vs 12.05, P = .0007; oral cavity LETmean: 2.11 vs 3.12, P = .0052) and had comparable physical dose and plan robustness in all OARs. In the worst-case scenario, LETRO achieved significantly higher LETmean in the CTV, reduced LETmax in the brain, and was comparable to other LETVH indices (CTV LETmean: 3.26 vs 3.35, P = .0012; brain LETmax: 24.80 vs 22.00, P = .0016). Conclusions LETRO robustly optimized LET and physical dose distributions simultaneously. It redistributed high LET from OARs to targets with slightly modified physical dose and plan robustness.

Background: Thyroid nodules are commonly found at palpation amounting to 4–7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions. Methods: In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition. Results: In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively. Conclusions: The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.

A method based on intelligent video processing is introduced here to objectively assess the intensity and energy expenditure of popular indoor workouts, including sit up, push up, jumping jack, and squat. The method is based a smart phone camera, which can count the repetition, assess the intensity level, and quantify the energy expenditure of the workouts. It uses an algorithm that analyzes body movement and body’s effort to overcome gravity in hierarchical layers, each with an increasing level of details. The energy expenditure values for different types of workouts at different intensities determined by the method are compared with a gold standard indirect calorimetry apparatus worn by the study subject.

Dehydration is known to lead to serious health conditions and diseases, and a convenient way to check one’s hydration level may help reduce risk of dehydration. A widely used method by clinicians is skin turgor test, which is based on the visual inspection of skin mechanical properties. The test does not require special equipment, but it lacks quantification and is hard to practice by non-experts. Here, we demonstrate the detection of hydration state based on analyzing skin mechanical properties with a mobile phone camera. The camera captures and quantifies the dynamic processes of skin stretching and relaxation related to hydration using two methods: 1) tracking of a mark drawn on the skin and 2) the natural skin texture change during the turgor test. Tests on a small cohort of human subjects have been carried to validate the methods.

Purpose To investigate how spot size and spacing affect plan quality, robustness, and interplay effects of robustly optimized intensity modulated proton therapy (IMPT) for lung cancer. Methods and Materials Two robustly optimized IMPT plans were created for 10 lung cancer patients: first by a large-spot machine with in-air energy-dependent large spot size at isocenter (σ: 6-15 mm) and spacing (1.3 σ), and second by a small-spot machine with in-air energy-dependent small spot size (σ: 2-6 mm) and spacing (5 mm). Both plans were generated by optimizing radiation dose to internal target volume on averaged 4-dimensional computed tomography scans using an in-house–developed IMPT planning system. The dose–volume histograms band method was used to evaluate plan robustness. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effects with randomized starting phases for each field per fraction. Patient anatomy voxels were mapped phase-to-phase via deformable image registration, and doses were scored using in-house–developed software. Dose–volume histogram indices, including internal target volume dose coverage, homogeneity, and organs at risk (OARs) sparing, were compared using the Wilcoxon signed-rank test. Results Compared with the large-spot machine, the small-spot machine resulted in significantly lower heart and esophagus mean doses, with comparable target dose coverage, homogeneity, and protection of other OARs. Plan robustness was comparable for targets and most OARs. With interplay effects considered, significantly lower heart and esophagus mean doses with comparable target dose coverage and homogeneity were observed using smaller spots. Conclusions Robust optimization with a small spot-machine significantly improves heart and esophagus sparing, with comparable plan robustness and interplay effects compared with robust optimization with a large-spot machine. A small-spot machine uses a larger number of spots to cover the same tumors compared with a large-spot machine, which gives the planning system more freedom to compensate for the higher sensitivity to uncertainties and interplay effects for lung cancer treatments.

Background Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish nodule from normal thyroid tissue. The pixel intensity in normal thyroid tissues is homogeneous and smooth. In the benign or malignant nodules, the pixel intensity is heterogeneous. Several studies have shown that the first order features in ultrasound image can be used as imaging biomarkers in nodule recognition. Methods In this paper, we investigate the feasibility of utilizing the first order texture features to identify nodule from normal thyroid tissue in CT image. A total of 284 thyroid CT images from 113 patients were collected in this study. We used 150 healthy controlled thyroid CT images from 55 patients and 134 nodule images (50 malignant and 84 benign nodules) from 58 patients who have undergone thyroid surgery. The final diagnosis was confirmed by histopathological examinations. In the presented method, first, regions of interest (ROIs) from axial non-enhancement CT images were delineated manually by a radiologist. Second, average, median, and wiener filter were applied to reduce photon noise before feature extraction. The first-order texture features, including entropy, uniformity, average intensity, standard deviation, kurtosis and skewness were calculated from each ROI. Third, support vector machine analysis was applied for classification. Several statistical values were calculated to evaluate the performance of the presented method, which includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area of under receiver operating characteristic curve (AUC). Results The entropy, uniformity, mean intensity, standard deviation, skewness (P

We present a video-based method to monitor blood pressure by analyzing pulse transit time (PTT). The PTT is determined from ballistocardiogram (BCG) and photoplethysmogram (PPG), which are recorded simultaneously using a single camera. The measured blood pressure changes are compared with those measured with a cuff-based reference blood pressure monitor. In addition to blood pressure, premature ventricular contraction (PVC) is also observed from both PPG and BCG waveforms. The method provides a non-contact solution towards real-time blood pressure and abnormal heart rhythm monitoring of an individual under normal living conditions without his/her conscious attention.

We present optical imaging-based methods to measure vital physiological signals, including breathing frequency (BF), exhalation flow rate, heart rate (HR), and pulse transit time (PTT). The breathing pattern tracking was based on the detection of body movement associated with breathing using a differential signal processing approach. A motion-tracking algorithm was implemented to correct random body movements that were unrelated to breathing. The heartbeat pattern was obtained from the color change in selected region of interest (ROI) near the subject's mouth, and the PTT was determined by analyzing pulse patterns at different body parts of the subject. The measured BF, exhaled volume flow rate and HR are consistent with those measured simultaneously with reference technologies (r = 0.98, for HR; r = 0.93, for breathing rate), and the measured PTT difference (30-40 ms between mouth and palm) is comparable to the results obtained with other techniques in the literature. The imaging-based methods are suitable for tracking vital physiological parameters under free-living condition and this is the first demonstration of using noncontact method to obtain PTT difference and exhalation flow rate.

Remote photoplethysmography (rPPG) is attractive for tracking a subject’s physiological parameters without wearing a device. However, rPPG is known to be prone to body movement-induced artifacts, making it unreliable in realistic situations. Here we report a method to minimize the movement-induced artifacts. The method selects an optimal region of interest (ROI) automatically, prunes frames in which the ROI is not clearly captured (e.g., subject moves out of the view), and analyzes rPPG using an algorithm in CIELab color space, rather than the widely used RGB color space. We show that body movement primarily affects image intensity, rather than chromaticity, and separating chromaticity from intensity in CIELab color space thus helps achieve effective reduction of the movement-induced artifacts. We validate the method by performing a pilot study including 17 people with diverse skin tones.

We present a noncontact method to measure ballistocardiogram (BCG) and photoplethysmogram (PPG) simultaneously using a single camera. The method tracks the motion of facial features to determine displacement BCG, and extracts the corresponding velocity and acceleration BCGs by taking first and second temporal derivatives from the displacement BCG, respectively. The measured BCG waveforms are consistent with those reported in the literature and also with those recorded with an accelerometer-based reference method. The method also tracks PPG based on the reflected light from the same facial region, which makes it possible to track both BCG and PPG with the same optics. We verify the robustness and reproducibility of the noncontact method with a small pilot study with 23 subjects. The presented method is the first demonstration of simultaneous BCG and PPG monitoring without wearing any extra equipment or marker by the subject.

159 Background: Intensity-modulated proton therapy (IMPT) with small spot size has the potential to reduce dose to normal tissues but also introduces new confounding factors such as setup uncertainty, range uncertainty, and interplay effects due to breathing motion. We investigated plan quality and robustness of IMPT with small spot size for distal esophageal adenocarcinoma (DEA). Methods: 19 patients with DEA treated by IMPT were retrospectively evaluated. Spot sizes ranged from 2 to 6mm, with spot spacing of 5mm. All plans were generated using a commercial treatment planning system. In-house-developed dose-evaluation software modeled time-dependent spot delivery to incorporate interplay effects. Dose-volume-histogram (DVH) indices were used to evaluate plan quality and robustness was evaluated using the DVH band method. Results: For plan quality, median values of clinical target volume D95% and D5%-D95% (normalized to the prescribed doses), total lung mean dose, heart mean dose, and cord Dmax were 1.014, 0.035, 3.82Gy[RBE], 7.73Gy[RBE], 39.16Gy[RBE], respectively. For plan robustness, median band widths of the aforementioned DVH indices were 0.018, 0.048, 0.60Gy[RBE], 4.17Gy[RBE], and 2.36Gy[RBE], respectively. For interplay effects, median values of the aforementioned DVH indices were 0.99, 0.061, 4.07Gy[RBE], 7.87Gy[RBE], and 39.66Gy[RBE], respectively. Seven patients underwent esophagectomy after neoadjuvant chemoradiation, and five, three, and one of them achieved downstaging, near complete response, and pathologic complete response, respectively; R0 resection was achieved in all cases. For all patients, acute side effects were mostly limited to grades 1-2 only. The only grade 3 adverse events were related to feeding tube placement (8 patients); there were no RT-induced pneumonitis or other lung-related toxicities. Conclusions: IMPT plans with small spot size for DEA have good target dose coverage, homogeneity and normal tissue protection. Treatment plans are also robust to uncertainties and interplay effects. Early clinical results demonstrate low acute toxicity and encouraging clinical and pathologic response.

We present a noncontact method to monitor blood oxygen saturation (SpO2). The method uses a CMOS camera with a trigger control to allow recording of photoplethysmography (PPG) signals alternatively at two particular wavelengths, and determines the SpO2 from the measured ratios of the pulsatile to the nonpulsatile components of the PPG signals at these wavelengths. The signal-to-noise ratio (SNR) of the SpO2 value depends on the choice of the wavelengths. We found that the combination of orange $(\lambda = 611\, {\rm nm})$ and near infrared $(\lambda = 880\, {\rm nm})$ provides the best SNR for the noncontact video-based detection method. This combination is different from that used in traditional contact-based SpO 2 measurement since the PPG signal strengths and camera quantum efficiencies at these wavelengths are more amenable to SpO2 measurement using a noncontact method. We also conducted a small pilot study to validate the noncontact method over an SpO2 range of 83%–98%. This study results are consistent with those measured using a reference contact SpO2 device ( $r = 0.936$ , $p ). The presented method is particularly suitable for tracking one's health and wellness at home under free-living conditions, and for those who cannot use traditional contact-based PPG devices.

Purpose:To investigate the feasibility of utilizing texture features to classify nodule from normal thyroid tissue in Computed Tomography (CT) images. Materials and Methods: Group A (negative) includes 152 normal thyroid CT images from 55 patients healthy controls enrolled in the study. Group B (positive) includes 134 thyroid images with nodules (50 malignant, 84 benign) of 58 patients undergone thyroid surgery and final diagnoses were confirmed by histopathology. Regions of interest (ROIs) from axial noncontrast CT images were delineated manually and 31 texture features including the gray level co-occurrence matrix (GLCM), the gray level gradient co-occurrence matrix (GLGCM), average intensity, contrast and coherence were extracted. Support Vector Machine (SVM) was used in data classification. Leave one out cross validation (LOOCV) strategy was utilized to take full advantage of the samples. To evaluate the performance of the proposed method, accuracyrate, sensitivity, specificity and area of under receiver operating characteristic (ROC) curve (AUC) etc. were calculated. Results: the accuracy-rate, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.8895±0.0186, 0.8265±0.0448, 0.9467±0.0141, 0.9340±0.0146 and 0.8586±0.0300 respectively, and the AUC is 0.9520±0.0089. Conclusion: Texture features can help radiologists to classify the nodule from normal thyroid tissue.

We present an imaging-based method for noncontact spirometry. The method tracks the subtle respiratory-induced shoulder movement of a subject, builds a calibration curve, and determines the flow-volume spirometry curve and vital respiratory parameters, including forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow rate. We validate the accuracy of the method by comparing the data with those simultaneously recorded with a gold standard reference method and examine the reliability of the noncontact spirometry with a pilot study including 16 subjects. This work demonstrates that the noncontact method can provide accurate and reliable spirometry tests with a webcam. Compared to the traditional spirometers, the present noncontact spirometry does not require using a spirometer, breathing into a mouthpiece, or wearing a nose clip, thus making spirometry test more easily accessible for the growing population of asthma and chronic obstructive pulmonary diseases.

Glioma is the primary tumor in the central nervous system, and poses one of the greatest challenges in clinical treatment. MIB-1 and p53 are the most useful biomarkers for gliomas and could help neurosurgeons establish a therapeutic schedule. However, these biomarkers are commonly detected with the help of immunohistochemistry (IHC), which wastes time and energy and is often influenced by subjective factors. To reduce the subjective factors and improve the efficiency in the judgment of IHC, a novel magnetic resonance image (MRI) analysis method is proposed in the present study to detect the expression status of MIB-1 and p53 in IHC. The proposed method includes two kinds of MRI acquisition (FLAIR and T1 FLAIR images), regions of interest (ROIs) selection, texture features (i.e. the gray level gradient co-occurrence matrix (GLGCM), Minkowski functions (MFs), etc) extraction in ROIs, and classification with a support vector machine in a leave-one-out cross validation strategy. By classifying the ROIs, the performance of the method was evaluated by accuracy, area under ROC curve (AUC), etc. A high accuracy (0.7640 ± 0.0225) and AUC (0.7873 ± 0.0377) for MIB-I detection were achieved. In terms of the texture features, 0.7621 ± 0.0199, 0.7666 ± 0.0365 and 0.7426 ± 0.0451 AUC can be obtained using only GLCM, RLM or GLGCM for MIB-1 detection, respectively. In all, the experimental results demonstrated that MR image texture features are associated with the expression status of MIB-1 and p53. The proposed method has the potential to realize high accuracy and robust detection for MIB-I expression status, which makes it promising for clinical glioma diagnosis and prognosis.

The ±500kV Xiluodu-Guangdong HVDC system in China is a 6400MW HVDC transmission in double bipole scheme. It is the new HVDC project designed in the scheme of two bipoles with common earth electrode and four DC transmission lines erected on the single tower. In contrast to the traditional bipole HVDC project, this double bipole project obviously has further requirement for the control and protection system. First, a detail research on operating modes is presented in this paper which fulfills both the flexibility and reliability requirements of the double bipole scheme. Then, the well-designed hierarchical configuration of the control and protection system is put forward, as well as the control function configuration which based on this system configuration. A complete backup control concept is introduced in this paper which substantially increases the reliability of the DC transmission. The two bipolar coordinated control strategies and the proper protection strategies are studied carefully and the effective principles are proposed. Some simulation results are presented to confirm the conclusion using the closed-loop simulation system which consists of the real-time digital simulator (RTDS) and the control and protection equipment that applied in site for this project.