CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PurposeTo establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.MethodsThis study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.ResultsA total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1–3 years AUC:0.774–0.837; validation cohort 1–3 years AUC:0.754–0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.ConclusionsThe CR model could predict the OS of the HCC patients after hepatectomy.     

Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PurposeTo develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.MethodsT1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell''s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).ResultsThe nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.ConclusionsUnlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.     

Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model

BackgroundWorkers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker’s background.MethodsProspective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients’ data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests.ResultsAt 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate.ConclusionsNon-RTW may be predicted with a simple model constructed with variables independent of the patient’s education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.     

DEMAT: A multi-institutional dosimetry audit of rotational and static intensity-modulated radiotherapy

PurposeStatic beam intensity-modulated-radiation-therapy (IMRT) and/or Volumetric-Modulated-Arc-Therapy (VMAT) are now available in many regional radiotherapy departments. The aim of this multi-institutional audit was to design a new methodology based on radiochromic films to perform an independent quality control.MethodsA set of data were sent to all participating centres for two clinical localizations: prostate and Head and Neck (H&N) cancers. The agreement between calculations and measurements was verified in the Octavius phantom (PTW) by point measurements using ionization chambers and by 2D measurements using EBT3 radiochromic films. Due to uncertainties in the whole procedure, criteria were set to 5% and 3% in local dose and 3 mm in distance excluding doses lower than 10% of the maximum doses. No normalization point or area was used for the quantitative analysis.Results13 radiotherapy centres participated in this audit involving 28 plans (12 IMRT, 16 VMAT). For point measurements, mean errors were −0.18 ± 1.54% and 0.00 ± 1.58% for prostate and H&N cases respectively. For 2D measurements with 5%/3 mm criteria, gamma map analysis showed a pixel pass rate higher than 95% for prostate and H&N. Mean gamma index was lower than 0.4 for prostate and 0.5 for H&N. Both techniques yielded similar results.ConclusionThis study showed the feasibility of an independent quality control by peers for conventional IMRT and VMAT. Results from all participating centres were found to be in good agreement. This regional study demonstrated the feasibility of our new methodology based on radiochromic films without dose normalization on a specific point.     

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria

PurposeTo establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria.MethodsIn total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves.ResultsThe risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78–0.92) and 0.85 (95% CI: 0.74–0.95), and those for the Rad-score were 0.82 (95% CI: 0.73–0.90) and 0.80 (95% CI: 0.67–0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81–0.94) in the training group and 0.89 (95% CI: 0.81–0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits.ConclusionsThe nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.     

Development of novel DNAJB6-KIAA1522-p-mTOR three-protein prognostic prediction models for CRC

BackgroundTo evaluate the prognostic value of DNAJB6, KIAA1522, and p-mTOR expression for colorectal cancer (CRC) and to develop effective prognostic models for CRC patients.MethodsThe expression of DNAJB6, KIAA1522, and p-mTOR (Ser2448) was detected using immunohistochemistry in 329 CRC specimens. The prognostic values of the three proteins in the training cohort were assessed using Kaplan-Meier curves and univariate and multivariate Cox proportional hazards models. Prediction nomogram models integrating the three proteins and TNM stage were constructed. Subsequently, calibration curves, receiver operating characteristic (ROC) curves, the concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of the nomograms in the training and validation cohorts.ResultsThe three proteins DNAJB6, KIAA1522, and p-mTOR were significantly overexpressed in CRC tissues (each P < 0.01), and their expression was an independent prognostic factor for overall survival (OS) and disease-free survival (DFS) (each P < 0.05). The area under the ROC curves (AUC) and C-index values were approximately 0.7. Additionally, the calibration curves showed that the predicted values and the actual values fit well. Furthermore, DCA curves indicated that the clinical value of the nomogram models was higher than that of TNM stage. Overall, the novel prediction models have good discriminability, sensitivity, specificity and clinical utility.ConclusionThe nomograms containing DNAJB6, KIAA1522, and p-mTOR may be promising models for predicting postoperative survival in CRC.     

A Novel Nomogram for Predicting the Overall Survival in Patients with Unresectable HCC after TACE plus Hepatic Arterial Infusion Chemotherapy

Background and aimTransarterial chemoembolization combined with hepatic arterial infusion chemotherapy (TACE-HAIC) has shown encouraging efficacy in the treatment of unresectable hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict overall survival (OS) of patients with unresectable HCC treated with TACE-HAIC.MethodsA total of 591 patients with unresectable HCC treated with TACE-HAIC between May 2009 and September 2020 were enrolled. These patients were randomly divided into training and validation cohorts. The independent prognostic factors were identified with Cox proportional hazards model. The model's discriminative ability and accuracy were validated using concordance index (C-index), calibration plots, the area under the time-dependent receiver operating characteristic curve (AUC) and decision curve analyses (DCAs).ResultsThe median OS was 15.6 months. A nomogram was established based on these factors, including tumor size, vein invasion, extrahepatic metastasis, tumor number, alpha fetoprotein (AFP), and albumin-bilirubin (ALBI), to predict OS for patients with unresectable HCC treated with TACE-HAIC. The C-index of the nomogram were 0.717 in the training cohort and 0.724 in validation cohort. The calibration plots demonstrated good agreement between the predicted outcomes and the actual observations. The AUC values were better than those of three conventional staging systems. The results of DCA indicated that the nomogram may have clinical usefulness. The patients in the low-risk group had a longer OS than those in intermediate-risk and high-risk groups (P<0.001).ConclusionA prognostic nomogram was developed and validated to assist clinicians in accurately predicting the OS of patients with unresectable HCC after TACE-HAIC.     

Modeling of couch transmission in the RayStation treatment planning system

PurposeTo present our methods and results regarding the modeling of a carbon fiber couch (Varian Exact IGRT) in the RayStation treatment planning system (TPS).MethodsThree geometrical-models (GMs) were implemented in the TPS to represent the three different regions of the couch (thick, medium and thin). The materials and densities of each GM component were tuned to maximize the agreement between measured and calculated attenuations. Moreover, a couch computed-tomography (CT) scan was acquired and dosimetrically compared with the GMs. For validation, plan-specific quality assurance (QA) of VMAT plans (TG-119 cases, 5 prostate and 5 H&N clinical cases) was performed by comparing measured dose distributions with doses computed with and without including the GMs in the TPS.ResultsCouch attenuations up to 4.3% were measured (energy: 6MV). Compared to couch CT, GMs could be modified to optimize the agreement with measurements and reduce dependence on the dose grid resolution. For both couch CT and GM, absolute deviations between measured and calculated attenuations were within 1.0%. When including the GMs in plan-specific QA, global 2%/2 mm γ-pass rates showed an average improvement of 4.8% (p-value < 0.001, max +18.6%). The couch reduced the mean dose to targets by up to 2.4% of the prescribed dose for prostate cases and up to 1.4% for H&N cases.ConclusionsRayStation accurately considers the implemented couch GMs replicating measured attenuations within an uncertainty of 1.0%. Materials and densities are proposed for the Varian Exact IGRT couch. The results obtained justify introducing couch GMs in clinical routine.     

心力衰竭患者AKI发生的临床预测模型构建

摘要 目的：构建心力衰竭患者AKI（acute kidney injury）发生的临床预测模型,对早期高危患者识别提供依据。方法：回顾性分析新疆医科大学第一附属医院2018年1月至2020年12月明确诊断心力衰竭患者350例,其中AKI患者104名（29.7%）,非AKI患者246名（70.3%）,将其按7:3 比例随机分为建模队列(n=245)和验证队列(n=105)。构建 LASSO回归分析建模队列,基于 logistic 回归结果构建HF-AKI（heart failure-acute kidney injury）患者的诺顿图,同时对模型进行校准,同时验证模型效益。结果：单因素分析得到25个差异变量,LASSO回归、多因素逐步logistics 回归,最终得到5个差异变量：年龄、住院天数、入院肌酐、射血分数、是否使用抗生素。构建HF-AKI 患者的临床预测模型并绘制成诺顿图。构建训练组和验证组诺顿图的 ROC曲线 AUC大小分别为 0.730和 0.794,通过Hosmer-Lemeshow检验,验证组虽然没有训练组的拟合优度优异,但P＞0.05,表明该诺顿图模型同样具有良好的校准度。结论：本研究成功构建了HF-AKI的临床预测模型,经过系列验证提示该模型的训练组和验证组均具有净收益范围,具有一定的临床价值。     

Clinicopathologic predictors of early relapse in advanced epithelial ovarian cancer: development of prediction models using nationwide data

ObjectiveTo identify clinicopathologic factors predictive of early relapse (platinum-free interval (PFI) of ≤6 months) in advanced epithelial ovarian cancer (EOC) in first-line treatment, and to develop and internally validate risk prediction models for early relapse.MethodsAll consecutive patients diagnosed with advanced stage EOC between 01-01-2008 and 31-12-2015 were identified from the Netherlands Cancer Registry. Patients who underwent cytoreductive surgery and platinum-based chemotherapy as initial EOC treatment were selected. Two prediction models, i.e. pretreatment and postoperative, were developed. Candidate predictors of early relapse were fitted into multivariable logistic regression models. Model performance was assessed on calibration and discrimination. Internal validation was performed through bootstrapping to correct for model optimism.ResultsA total of 4,557 advanced EOC patients were identified, including 1,302 early relapsers and 3,171 late or non-relapsers. Early relapsers were more likely to have FIGO stage IV, mucinous or clear cell type EOC, ascites, >1 cm residual disease, and to have undergone NACT-ICS. The final pretreatment model demonstrated subpar model performance (AUC = 0.64 [95 %-CI 0.62−0.66]). The final postoperative model based on age, FIGO stage, pretreatment CA-125 level, histologic subtype, presence of ascites, treatment approach, and residual disease after debulking, demonstrated adequate model performance (AUC = 0.72 [95 %-CI 0.71−0.74]). Bootstrap validation revealed minimal optimism of the final postoperative model.ConclusionA (postoperative) discriminative model has been developed and presented online that predicts the risk of early relapse in advanced EOC patients. Although external validation is still required, this prediction model can support patient counselling in daily clinical practice.     

A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria

PurposeThe prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients.MethodsIn total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets.ResultsUnivariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC.ConclusionsThe nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.     

Proof-of-Concept Study of Using Supervised Machine Learning Algorithms to Predict Self-Care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy

ObjectiveUsing supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within 6 months.MethodsThis was a single-center retrospective chart review of 100 adult type 1 diabetes mellitus patients on insulin pump therapy (≥6 months). Three SMLAs were deployed: multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included area under the curve-Receiver of characteristics for discrimination and Brier scores for calibration.ResultsVariables predictive of adherence with IPSMB criteria were baseline hemoglobin A1c, continuous glucose monitoring, and sex. The models had comparable discriminatory power (LR = 0.74; RF = 0.74; k-NN = 0.72), with the RF model showing better calibration (Brier = 0.151). Predictors of the good glycemic response included baseline hemoglobin A1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR = 0.81, RF = 0.80, k-NN = 0.78) but the RF model being better calibrated (Brier = 0.099).ConclusionThese proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within 6 months. Subject to further study, nonlinear prediction models may perform better.     

Development and Validation of a Nomogram for Predicting Overall Survival in Pancreatic NeuroendocrineTumors

BACKGROUND: The objective of current study was to develop and validate a nomogram to predict overall survival in pancreatic neuroendocrine tumors (PNETs). METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for patients with PNETs between 2004 and 2015. Patients were randomly separated into the training set and the validation set. Cox regression model was used in training set to obtain independent prognostic factors to develop a nomogram for predicting overall survival (OS). The discrimination and calibration plots were used to evaluate the predictive accuracy of the nomogram. RESULTS: A total of 3142 patients with PNETs were collected from the SEER database. Sex, age, marital status, primary site, TNM stage, tumor grade, and therapy were associated with OS in the multivariate models. A nomogram was constructed based on these variables. The nomogram for predicting OS displayed better discrimination power than the Tumor-Node-Metastasis (TNM) stage systems 7th edition in the training set and validation set. The calibration curve indicated that the nomogram was able to accurately predict 3- and 5-year OS. CONCLUSIONS: The nomogram which could predict 3- and 5-year OS were established in this study. Our nomogram showed a good performance, suggesting that it could be served as an effective tool for prognostic evaluation of patients with PNETs.     

Alterations in peripheral joint muscle force control in adults with musculoskeletal disease,injury, surgery,or arthroplasty: A systematic review and meta-analysis

PurposeTo systematically review and analyse whether musculoskeletal conditions affect peripheral joint muscle force control (i.e. magnitude and/or complexity of force fluctuations).MethodsA literature search was conducted using MEDLINE, CINAHL and SPORTDiscus databases (from inception-8th April 2021) for studies involving: 1) participants with musculoskeletal disease, injury, surgery, or arthroplasty in the peripheral joints of the upper/lower limb; 2) comparison with an unaffected control group or unaffected contralateral limb; and 3) measures of the magnitude and/or complexity of force fluctuations during targeted isometric contractions. The methodological quality of studies was evaluated using a modified Downs and Black Quality Index. Studies were combined using the standardized mean difference (SMD) in a random-effects model.Results14 studies (investigating 694 participants) were included in the meta-analysis. There was a significant effect of musculoskeletal conditions on peripheral joint muscle force coefficient of variation (CV; SMD = 0.19 [95 % CI 0.06, 0.32]), whereby individuals with musculoskeletal conditions exhibited greater CV than controls. Subgroup analyses revealed that CV was only greater: 1) when comparison was made between symptomatic and asymptomatic individuals (rather than between affected and contralateral limbs; SMD = 0.22 [95 % CI 0.07, 0.38]); 2) for conditions of the knee (SMD = 0.29 [95 % CI 0.14, 0.44]); and 3) for ACL injury post-surgery (SMD = 0.56 [95 % CI 0.36, 0.75]).ConclusionMusculoskeletal conditions result in an increase in peripheral joint muscle force CV, with this effect dependent on study design, peripheral joint, and surgical status. The greater force CV is indicative of decreased force steadiness and could have implications for long-term tissue health/day-to-day function.     

Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer

PurposeTo derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up.Methods and materialsLyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined.ResultsThe α/βs obtained with different models were 2.7–3.2 Gy. The thresholds and optimal doses curves were EUDs of 3.2–7.8 Gy and 15.2–18.1 Gy with LEUD, LogEUD and RS models, and μ_d of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients’ values of EUD and μ_d.ConclusionsThe models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.     

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

BackgroundGastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.MethodsFrom January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.ResultsThere was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.     

The effect of beam interruption during VMAT delivery on the delivered dose distribution

PurposeThe aim of this study is to investigate the effect of beam interruptions during delivery of volumetric modulated arc therapy (VMAT) on delivered dose distributions.MethodsTen prostate and ten head and neck (H&N) VMAT plans were retrospectively selected. Each VMAT plan was delivered using Trilogy™ without beam interruption, and with 4 and 8 intentional beam interruptions per a single arc. Two-dimensional global and local gamma evaluations with a diode array were performed with gamma criteria of 3%/3 mm, 2%/2 mm, 1%/2 mm and 2%/1 mm for each VMAT plan with and without beam interruptions. The VMAT plans were reconstructed with log files recorded during delivery and the dose-volumetric parameters were calculated for each reconstructed plan. The differences among dose-volumetric parameters due to the beam interruptions were calculated.ResultsThe changes in global gamma passing rates with various gamma criteria were less than 1.6% on average, while the changes in local gamma passing rates were less than 5.3% on average. The dose-volumetric parameter changes for the target volumes of prostate and H&N VMAT plans due to beam interruptions were less than 0.72% and 1.5% on average, respectively.ConclusionThe delivered dose distributions with up to 8 beam interruptions per an arc were clinically acceptable, showing minimal changes in both gamma passing rates and dose-volumetric parameters.     

Safety and immunogenicity of heterologous boost immunization with an adenovirus type-5-vectored and protein-subunit-based COVID-19 vaccine (Convidecia/ZF2001): A randomized,observer-blinded,placebo-controlled trial

BackgroundHeterologous boost vaccination has been proposed as an option to elicit stronger and broader, or longer-lasting immunity. We assessed the safety and immunogenicity of heterologous immunization with a recombinant adenovirus type-5-vectored Coronavirus Disease 2019 (COVID-19) vaccine (Convidecia, hereafter referred to as CV) and a protein-subunit-based COVID-19 vaccine (ZF2001, hereafter referred to as ZF).Methods and findingsWe conducted a randomized, observer-blinded, placebo-controlled trial, in which healthy adults aged 18 years or older, who have received 1 dose of Convidecia, with no history of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, were recruited in Jiangsu, China. Sixty participants were randomly assigned (2:1) to receive either 1 dose of ZF2001 or placebo control (trivalent inactivated influenza vaccine (TIV)) administered at 28 days after priming, and received the third injection with ZF2001 at 5 months, referred to as CV/ZF/ZF (D0-D28-M5) and CV/ZF (D0-M5) regimen, respectively. Sixty participants were randomly assigned (2:1) to receive either 1 dose of ZF2001 or TIV administered at 56 days after priming, and received the third injection with ZF2001 at 6 months, referred to as CV/ZF/ZF (D0-D56-M6) and CV/ZF (D0-M6) regimen, respectively. Participants and investigators were masked to the vaccine received but not to the boosting interval. Primary endpoints were the geometric mean titer (GMT) of neutralizing antibodies against wild-type SARS-CoV-2 and 7-day solicited adverse reactions. The primary analysis was done in the intention-to-treat population. Between April 7, 2021 and May 6, 2021, 120 eligible participants were randomly assigned to receive ZF2001/ZF2001 (n = 40) or TIV/ZF2001 (n = 20) 28 days and 5 months post priming, and receive ZF2001/ZF2001 (n = 40) or TIV/ZF2001 (n = 20) 56 days and 6 months post priming. Of them, 7 participants did not receive the third injection with ZF2001. A total of 26 participants (21.7%) reported solicited adverse reactions within 7 days post boost vaccinations, and all the reported adverse reactions were mild, with 13 (32.5%) in CV/ZF/ZF (D0-D28-M5) regimen, 7 (35.0%) in CV/ZF (D0- M5) regimen, 4 (10.0%) in CV/ZF/ZF (D0-D56-M6) regimen, and 2 (10.0%) in CV/ZF (D0-M6) regimen, respectively. At 14 days post first boost, GMTs of neutralizing antibodies in recipients receiving ZF2001 at 28 days and 56 days post priming were 18.7 (95% CI 13.7 to 25.5) and 25.9 (17.0 to 39.3), respectively, with geometric mean ratios of 2.0 (1.2 to 3.5) and 3.4 (1.8 to 6.4) compared to TIV. GMTs at 14 days after second boost of neutralizing antibodies increased to 107.2 (73.7 to 155.8) in CV/ZF/ZF (D0-D28-M5) regimen and 141.2 (83.4 to 238.8) in CV/ZF/ZF (D0-D56-M6) regimen. Two-dose schedules of CV/ZF (D0-M5) and CV/ZF (D0-M6) induced antibody levels comparable with that elicited by 3-dose schedules, with GMTs of 90.5 (45.6, 179.8) and 94.1 (44.0, 200.9), respectively. Study limitations include the absence of vaccine effectiveness in a real-world setting and current lack of immune persistence data.ConclusionsHeterologous boosting with ZF2001 following primary vaccination with Convidecia is more immunogenic than a single dose of Convidecia and is not associated with safety concerns. These results support flexibility in cooperating viral vectored and recombinant protein vaccines.Trial registrationStudy on Heterologous Prime-boost of Recombinant COVID-19 Vaccine (Ad5 Vector) and RBD-based Protein Subunit Vaccine; ClinicalTrial.gov {"type":"clinical-trial","attrs":{"text":"NCT04833101","term_id":"NCT04833101"}}NCT04833101.

In a randomized controlled trial, Pengfei Jin and colleagues assess the safety and immunogenicity of heterologous immunization with a recombinant adenovirus type-5-vectored COVID-19 vaccine and a protein-subunit-based COVID-19 vaccine.     

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

BackgroundOsteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.MethodWe studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsAge, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.ConclusionWe established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.     

Effects of remedies made in patient setup process on residual setup errors and margins in head and neck cancer radiotherapy based on 2D image guidance

AimPatient setup errors were aimed to be reduced in radiotherapy (RT) of head-and-neck (H&N) cancer. Some remedies in patient setup procedure were proposed for this purpose.BackgroundRT of H&N cancer has challenges due to patient rotation and flexible anatomy. Residual position errors occurring in treatment situation and required setup margins were estimated for relevant bony landmarks after the remedies made in setup process and compared with previous results.Materials and methodsThe formation process for thermoplastic masks was improved. Also image matching was harmonized to the vertebrae in the middle of the target and a 5 mm threshold was introduced for immediate correction of systematic errors of the landmarks. After the remedies, residual position errors of bony landmarks were retrospectively determined from 748 orthogonal X-ray images of 40 H&N cancer patients. The landmarks were the vertebrae C1–2, C5–7, the occiput bone and the mandible. The errors include contributions from patient rotation, flexible anatomy and inter-observer variation in image matching. Setup margins (3D) were calculated with the Van Herk formula.ResultsSystematic residual errors of the landmarks were reduced maximally by 49.8% (p ≤ 0.05) and the margins by 3.1 mm after the remedies. With daily image guidance the setup margins of the landmarks were within 4.4 mm, but larger margins of 6.4 mm were required for the mandible.ConclusionsRemarkable decrease in the residual errors of the bony landmarks and setup margins were achieved through the remedies made in the setup process. The importance of quality assurance of the setup process was demonstrated.