Risk Factors and Predictive Score Model for Early Recurrence After Curative Surgery in Patients With Poorly Differentiated Gastrointestinal Neuroendocrine Neoplasms

Purpose: Studies on early recurrence in gastrointestinal neuroendocrine carcinoma (NEC) and mixed adenoneuroendocrine carcinoma (MANEC) are lacking and risk factors related to early recurrence are not clear. We evaluated risk factors for early recurrence in such patients and developed a predictive scoring model.Methods: Patients undergoing curative surgery for GI-NEC or MANEC between January 2010 and January 2019 were included. Early recurrence was defined as recurrence within 12 months after surgery. Risk factors for early recurrence were identified using logistic regression.Results: Of the 80 included patients, 27 developed early recurrence and 53 had no early recurrence. Independent risk factors associated with early recurrence included tumor location in the midgut/hindgut [odds ratio (OR) = 5.077, 95% confidence interval (CI) 1.058–24.352, p = 0.042], alkaline phosphatase (ALP) >80 (OR = 5.331, 95% CI 1.557–18.258, p = 0.008), and lymph node ratio (LNR) >0.25 (OR = 6.578, 95% CI 1.971–21.951, p = 0.002). Risk scores were assigned to tumor location (foregut, 0; midgut/hindgut, 1), ALP (≤80, 0; >80, 1), and LNR (≤0.25, 0; >0.25, 1). Patients with a high risk (score 2–3) for early recurrence had significantly shorter disease-free survival and overall survival than those with low- (score 0) and intermediate risks (score 1) (both p < 0.001). The novel scoring model had superior predictive efficiency for early recurrence over TNM staging (area under the curve 0.795 vs. 0.614, p = 0.003).Conclusion: Tumor location, preoperative ALP, and LNR were independent factors associated with early recurrence after curative surgery for GI-NEC or MANEC. The risk scoring model developed based on these three factors shows superior predictive efficiency.

Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency. Methods: lasso regression analysis followed by univariate cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package “clusterProfiler” was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by ssGSEA of R package “GSVA”. Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition. Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.

Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma

PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. −1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.

Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

Background Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. Methods Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots. Results Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications. Conclusion A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Purpose To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). Methods A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900–0.964), 0.901 (0.840–0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions. Conclusion This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

Comprehensive analysis of DNA methylation and gene expression reveals specific predictive biomarkers for renal cell carcinoma

Background We aimed to identify methylation-driven genes (MDGs) and predict the prognosis of clear cell renal cell carcinoma (ccRCC) based on several cohorts with high-throughput data.Methods The transcriptome profiles, 450K methylated data and corresponding clinical information were extracted from the cancer genome atlas (TCGA) database. MethylMix package was used to screen aberrant methylation events. Next, the Cox regression models were applied via survival package. Functional analysis was mainly performed based on ConsensusPathDB database. Besides, “mimifi” R package were adopted to analyze methylated alterations from three Gene Expression Omnibus (GEO) datasets. The predictive efficiency of constructed MDGs signature was assessed and validated in TCGA-KIRC and ICGC-RCC cohort, respectively. Unsupervised clustering analysis was conducted via ConsensusClusterPlus package. Moreover, MDGs-nomogram for OS prediction was conducted via glm and survival packages. Results Totally, we collected We finally identified 761 samples from TCGA-KIRC, ICGC-RCC, GSE61441, GSE105260 and GSE105261. We combined the expression data and 450K methylated data to find 5 hub prognostic MDGs (TAGLN2, PDK2, HHLA2, HOXA2, XAF1). We used the TCGA-KIRC cohort as the training dataset to verify the superior predictive significance of the 5 MDGs signature (AUC = 0.713), and validated it in ICGC-RCC cohort with AUC = 0.769. Kaplan-Meier analysis suggested that patients with high MDGs levels suffered from worse suvival outcomes. Besides, we further conducted the unsupervised clustering analysis in the whole ccRCC patients and identified the sub-cluster with the worst prognosis, indicating the MDGs could be used as efficient molecular classifier for ccRCC. We also identified 32 prognostic risk loci associated with hub MDGs in KIRC. Superior predictive efficiency was found in the MDGs-nomogram [Area Under Curve (AUC) of 3-year: 0.842, AUC of 5-year: 0.862], compared with traditional independent feature such as TNM stage (AUC of 3-year: 0.759, AUC of 5-year: 0.717). The 5 MDGs signature were mainly associated with oxygen metabolism, glycometabolism, even the HIF-1 signaling pathway. Conclusions Collectively, this study indicated several hub-MDGs and explored the prognostic value in ccRCC, which would provide new insights on the exploration of epigenetic pathogenesis and therapeutic targets for ccRCC.

Modeling Transient Liquid Slosh Behavior at Variable Operating Speeds Induced by Intermittent Motions in Packaging Machines

The present paper deals with the problem of modeling liquid slosh occurring in the packaging process of containers filled with liquid. Sloshing effects are induced by one-dimensional intermittent motions and are undesired due to the necessity of quality control processes, such as weighing. Therefore, motion optimizations are often applied with the intention to minimize the residual vibrations. Valid process models are required to do so. The aim of this paper is to derive models for describing the liquid slosh behavior for different motions and for common practical circumstances, e.g., different container geometries as well as machine operating speeds, and to state the model’s limits of use. Known model approaches are discussed, and their assumptions are reviewed experimentally. This leads to a set of limited ranges of operating speeds in which the applied models’ assumptions are valid. The models are derived for these sets from experimental data, and a comparison is executed that enables the determination of the models’ validity concerning their operating speed dependency. Finally, the validity of the derived models is investigated by comparing their predictive efficiency of describing the vibration for different motion profiles.

An Intelligent High Performance Automatic Sentiment Analysis Model Creation using Deep Convolution Neural Network

With the advancement of data and communications technology, social media platforms and small news blogs serve as significant sources of data. In a small blogging forum, people can share their opinions, complaints, feelings and behaviors about the topic, current problems, and products. Emotional examination is an significant examination area in natural language processing that intends to target the emotion of the source material. Twitter is a well-liked stage where people around the globe can interrelate through user-produced messages. Data received from Twitter can give out as a primary source for many applications, together with event recognition, news recommendations as well as emergency supervision. In the categorization of emotions, recognition of suitable sub feature set acts an significant role. LIWC (Linguistic Inquiry and Word Count) is a research program for text examination to retrieve psychometric features from text documents. In this article this work present a psychometric method called the intelligent high performance automatic sentiment analysis model (IHPASAM) for Twitter emotion analysis. In this scheme, this work employed two main types of LIWC (linguistic processes along with psychological) as feature sets. To discover the predictive efficiency of dissimilar feature engineering systems, five supervised learning techniques (Naïve Bayes, logistic regression, k-nearest neighbor algorithm, support vector machines as well as convolution neural network) along with proposed Intelligent Deep Convolution Neural Network (IDCNN) are employed. Investigational outcome show that the ensemble feature sets provides a superior predictive efficiency than the individual set.