A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer

Background Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC. Methods We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan–Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal. Results An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). Conclusions We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

Short range SW monsoon rainfall forecasting over India using neural networks

Feedforward Neural Networks are used for daily precipitation forecast using several test stations all over India. The six year European Centre of Medium Range Weather Forecasting (ECMWF) data is used with the training set consisting of the four year data from 1985-1988 and validation set consisting of the data from 1989-1990. Neural networks are used to develop a concurrent relationship between precipitation and other atmospheric variables. No attempt is made to select optimal variables for this study and the inputs are chosen to be same as the ones obtained earlier at National Center for Medium Range Weather Forecasting (NCMRWF) in developing a linear regression model. Neural networks are found to yield results which are atleast as good as linear regression and in several cases yield 10 - 20 % improvement. This is encouraging since the variable selection has so far been optimized for linear regression.

Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays

Classification of drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) from chest radiographs remains an open problem. Our previous cross validation performance on publicly available chest X-ray (CXR) data combined with image augmentation, the addition of synthetically generated and publicly available images achieved a performance of 85% AUC with a deep convolutional neural network (CNN). However, when we evaluated the CNN model trained to classify DR-TB and DS-TB on unseen data, significant performance degradation was observed (65% AUC). Hence, in this paper, we investigate the generalizability of our models on images from a held out country’s dataset. We explore the extent of the problem and the possible reasons behind the lack of good generalization. A comparison of radiologist-annotated lesion locations in the lung and the trained model’s localization of areas of interest, using GradCAM, did not show much overlap. Using the same network architecture, a multi-country classifier was able to identify the country of origin of the X-ray with high accuracy (86%), suggesting that image acquisition differences and the distribution of non-pathological and non-anatomical aspects of the images are affecting the generalization and localization of the drug resistance classification model as well. When CXR images were severely corrupted, the performance on the validation set was still better than 60% AUC. The model overfitted to the data from countries in the cross validation set but did not generalize to the held out country. Finally, we applied a multi-task based approach that uses prior TB lesions location information to guide the classifier network to focus its attention on improving the generalization performance on the held out set from another country to 68% AUC.

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Hypopharyngeal Squamous Cell Carcinomas: A Population-Based Study

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion

BackgroundDetection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.ObjectiveTo train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.Materials and MethodsThe model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.ResultsFeature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.ConclusionBiomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.

Development of a Drum Tower Severity Scoring (DTSS) system for pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome

Background and aims There has been no reliable severity system based on the prognosis to guide therapeutic strategies for patients with pyrrolizidine alkaloid (PA)-induced hepatic sinusoidal obstruction syndrome (HSOS). We aimed to create a novel Drum Tower Severity Scoring (DTSS) system for these patients to guide therapy. Methods 172 Patients with PA-HSOS who received supportive care and anticoagulation therapy in Nanjing Drum Tower Hospital from January 2008 to December 2020 were enrolled and analyzed retrospectively. These patients were randomized into a training or validation set in a 3:1 ratio. Next, we established and validated the newly developed DTSS system. Results Analysis identified a predictive formula: logit (P) = 0.004 × aspartate aminotransferase (AST, U/L) + 0.019 × total bilirubin (TB, μmol/L) − 0.571 × fibrinogen (FIB, g/L) − 0.093 × peak portal vein velocity (PVV, cm/s) + 1.122. Next, we quantified the above variables to establish the DTSS system. For the training set, the area under the ROC curve (AUC) (n = 127) was 0.787 [95% confidence interval (CI) 0.706–0.868; p < 0.001]. With a lower cut-off value of 6.5, the sensitivity and negative predictive value for predicting no response to supportive care and anticoagulation therapy were 94.7% and 88.0%, respectively. When applying a high cut-off value of 10.5, the specificity was 92.9% and the positive predictive value was 78.3%. For the validation set, the system performed stable with an AUC of 0.808. Conclusions The DTSS system can predict the outcome of supportive care and anticoagulation in PA-HSOS patients with satisfactory accuracy by evaluating severity, and may have potential significance for guiding therapy.

Risk-Adjusted Mortality Rates as a Quality Proxy Outperform Volume in Surgical Oncology—A New Perspective on Hospital Centralization Using National Population-Based Data

PURPOSE Despite a long-known association between annual hospital volume and outcome, little progress has been made in shifting high-risk surgery to safer hospitals. This study investigates whether the risk-standardized mortality rate (RSMR) could serve as a stronger proxy for surgical quality than volume. METHODS We included all patients who underwent complex oncologic surgeries in Germany between 2010 and 2018 for any of five major cancer types, splitting the data into training (2010-2015) and validation sets (2016-2018). For each surgical group, we calculated annual volume and RSMR quintiles in the training set and applied these thresholds to the validation set. We studied the overlap between the two systems, modeled a market exit of low-performing hospitals, and compared effectiveness and efficiency of volume- and RSMR-based rankings. We compared travel distance or time that would be required to reallocate patients to the nearest hospital with low-mortality ranking for the specific procedure. RESULTS Between 2016 and 2018, 158,079 patients were treated in 974 hospitals. At least 50% of high-volume hospitals were not ranked in the low-mortality group according to RSMR grouping. In an RSMR centralization model, an average of 32 patients undergoing complex oncologic surgery would need to relocate to a low-mortality hospital to save one life, whereas 47 would need to relocate to a high-volume hospital. Mean difference in travel times between the nearest hospital to the hospital that performed surgery ranged from 10 minutes for colorectal cancer to 24 minutes for pancreatic cancer. Centralization on the basis of RSMR compared with volume would ensure lower median travel times for all cancer types, and these times would be lower than those observed. CONCLUSION RSMR is a promising proxy for measuring surgical quality. It outperforms volume in effectiveness, efficiency, and hospital availability for patients.

A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

The Combination of Inflammation and Nutrition Factors Reinforces the Prognostic Prediction for Stage III Colorectal Cancer Patients After Curative Resection

Purpose Inflammation and nutritional status are known to be associated with the prognosis of several malignancies. Herein, we attempted to develop inflammation–nutrition scores and predict the prognosis of stage III colorectal cancer (CRC). Methods This retrospective study included 262 patients with stage III CRC who underwent curative surgery and were divided into two groups: a training set (TS) of 162 patients and a validation set (VS) of 100 patients. In the TS, clinicopathological factors were tested using a Cox regression model, and the Kansai prognostic score (KPS) was assessed by 1 point each for <3.5 g/dL albumin level, >450 monocyte counts, and <1.65 × 105 platelet counts, which were associated with disease-free survival (DFS). Using KPS, DFS and overall survival (OS) were validated in VS. Results The C-indices of KPS to predict DFS and OS in TS were 0.707 and 0.772. It was validated in VS that the C-indices of KPS to predict DFS and OS were 0.618 and 0.708, respectively. A high KPS was a significant predictor of DFS and OS. Conclusion KPS serves as a new model for the prognosis of patients with stage III CRC.

Deep Neural Networks for Aerosol Optical Depth Retrieval

Aerosol Optical Depth (AOD) is a measure of the extinction of solar radiation by aerosols in the atmosphere. Understanding the variations of global AOD is necessary for precisely determining the role of aerosols. Arctic warming is partially caused by aerosols transported from vast distances, including those released during biomass burning events (BBEs). However, measuring AODs is challenging, typically requiring active LIDAR systems or passive sun photometers. Both are limited to cloud-free conditions; sun photometers provide only point measurements, thus requiring more spatial coverage. A more viable method to obtain accurate AOD may be found through machine learning. This study uses DNNs to estimate Svalbard’s AODs using a minimal set of meteorological parameters (temperature, air mass, water vapor, wind speed, latitude, longitude, and time of year). The mean absolute error (MAE) between predicted and true data was 0.00401 for the entire set and 0.0079 for the validation set. It was then shown that the inclusion of BBE data improves predictions by 42.167%. It was demonstrated that AODs may be accurately estimated without the use of expensive instrumentation, using machine learning and minimal data. Similar models may be developed for other regions, allowing immediate improvement of current meteorological models.