scholarly journals Multi-Biomarker Panel Prediction Model for Diagnosis of Pancreatic Cancer: a retrospective and multi-center cohort study

2020 ◽  
Author(s):  
Doo-Ho Lee ◽  
Woongchang Yoon ◽  
Areum Lee ◽  
Youngmin Han ◽  
Yoonhyeong Byun ◽  
...  
Keyword(s):  
Prediction Model ◽  
Early Stage ◽  
Risk Groups ◽  
Pancreatic Disease ◽  
Training Data ◽  
Ductal Adenocarcinoma ◽  
Diagnostic Tools ◽  
Enzyme Linked Immunosorbent Assay ◽  
Data Set ◽  
Marker Panel

Abstract Background Early diagnosis is paramount in increasing the survival rate of pancreatic ductal adenocarcinoma (PDAC). However, effective early diagnostic tools are lacking at present. The current study aimed to develop a prediction model using a multi-marker panel (LRG1, TTR, and CA19-9) as a diagnostic screening tool for PDAC. Methods A large multi-center cohort of 1,991 samples were collected from January 2011 to September 2019, of which 609 are normal (NL), 145 are other cancer (OC; colorectal, thyroid, and breast cancer), 314 are pancreatic benign disease (PB), and 923 are PDAC. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers, LRG1, TTR, and CA 19 − 9. Using a logistic regression (LR) model trained on training data set, the predicted values for PDACs were obtained, and the result was classified into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and biomarkers TTR, CA 19 − 9, and LRG1. Results Participants were categorized into four groups as NL (n = 609, 30.6%), OC (n = 145, 7.3%), PB (n = 314, 15.7%), and PDAC (n = 923, 46.4%). The NL, OC, and PB groups were clubbed into the non-PDAC group (n = 1068, 53.6%). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. Conclusions This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing PDAC from normal and benign pancreatic disease states, as well as patients with other cancers. The study indicates that the introduced multi-marker panel prediction model for PDAC diagnosis can help guide medical decisions for patients, including patients with early stage PDAC or with normal levels of CA 19 − 9.

Pancreatic cancer detection using EpiDetect signatures in plasma-derived cell free DNA in high-risk patients with new onset diabetes.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16265-e16265
Author(s):  
Gulfem Guler ◽  
Anna Bergamaschi ◽  
David Haan ◽  
Michael Kesling ◽  
Yuhong Ning ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Stage ◽  
Model Performance ◽  
Training Data ◽  
Diabetes Diagnosis ◽  
Whole Genome ◽  
Data Set ◽  
Independent Validation ◽  
Onset Diabetes ◽  
New Onset

e16265 Background: Pancreatic cancer (PaCa) is the third leading cause of cancer death in the United States despite its low incidence rate, owing to a 5-year survival rate of 10%. It is often asymptomatic in early stage, resulting in the majority of diagnoses occurring when cancer has already metastasized to distant organs. Late diagnosis deprives patients of potentially curative treatments such as surgery and impacts survival rates. Diabetes can be an early symptom of PaCa. Indeed, 25% of PaCa patients had a preceding diabetes diagnosis. Among all people with new onset diabetes (NOD), 0.85% will be diagnosed with PaCa within 3 years, which represents 6-8 fold increased risk for PaCa compared to the general population. Surveillance of the NOD population for PaCa presents an opportunity to shift PaCa diagnosis to earlier stage by finding it sooner. Methods: Whole blood was obtained from a cohort of 117 PaCa patients as well as 800 non-cancer controls with and without NOD. Plasma was processed to isolate cfDNA and 5hmC and low pass whole genome libraries were generated and sequenced. The EpiDetect assay combines 5hmC and whole genome sequencing data and were generated using Bluestar Genomics’s technology platform. Results: To investigate whether PaCa can be detected in plasma, we interrogated plasma-derived cfDNA epigenomic and genomic signal from PaCa patients and non-cancer controls. We first trained stacked ensemble models on PaCa and non-cancer samples utilizing 5hmC, fragmentation and CNV-based biomarkers from cfDNA. These models performed stably with a median of 72.8% sensitivity and 90.1% specificity measured across 25 outer fold iterations using the training data set, which was composed of 50% early stage (Stages I & II) disease. The final binomial ensemble model was trained using all of the training data, yielding an area under the receiver operating characteristic curve (auROC) of 0.9, with 75% sensitivity and 89% specificity. This model was then tested on an independent validation data set from 33 PaCa patients (24 with diabetes, 15 of which was NOD) and 202 non-cancer control patients (76 with diabetes, 51 of which was NOD) and yielded a classification performance auROC of 0.9 with 67% sensitivity at 92% specificity. Lastly, model performance in the subset of patient cohort with NOD only had an auROC of 0.87 with 60% sensitivity at 88% specificity. Conclusions: Our results indicate that 5hmC profiles along with CNV and fragmentation patterns from cfDNA can be used to detect PaCa in plasma-derived cfDNA. Overall, model performance was stable and consistent between the training and independent validation datasets. A larger clinical study is under development to investigate the utility of the model described in this pilot study in identifying occult PaCa within the NOD population, with the aim of shifting diagnosis to early stage and potentially improving patient outcomes.

scholarly journals Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA

2018 ◽  
Author(s):  
Francois Collin ◽  
Yuhong Ning ◽  
Tierney Phillips ◽  
Erin McCarthy ◽  
Aaron Scott ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Stage ◽  
Training Data ◽  
Ductal Adenocarcinoma ◽  
Data Sets ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Pancreatic Cancers ◽  
Free Dna ◽  
Circulating Cell Free Dna

AbstractPancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylation cytosine status (5hmC) of circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). We found that 5hmC sites are enriched in a disease and stage specific manner in exons, 3’UTRs and transcription termination sites. Our data show that 5hmC density is reduced in promoters and histone H3K4me3-associated sites with progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes points to biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in statistically filtered genes or a comprehensive set of highly variable 5hmC counts in genes and performed with an AUC = 0.94-0.96 on training data. We were able to test the ability to classify PDAC and non-cancer samples with the Elastic net and Lasso models on two external pancreatic cancer 5hmC data sets and found validation performance to be AUC = 0.74-0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with high fidelity and are worthy of further investigation on larger cohorts of patient samples.

scholarly journals Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 1623-1630
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Naive Bayes ◽  
Early Stage ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Wide Range

As huge amount of data accumulating currently, Challenges to draw out the required amount of data from available information is needed. Machine learning contributes to various fields. The fast-growing population caused the evolution of a wide range of diseases. This intern resulted in the need for the machine learning model that uses the patient's datasets. From different sources of datasets analysis, cancer is the most hazardous disease, it may cause the death of the forbearer. The outcome of the conducted surveys states cancer can be nearly cured in the initial stages and it may also cause the death of an affected person in later stages. One of the major types of cancer is lung cancer. It highly depends on the past data which requires detection in early stages. The recommended work is based on the machine learning algorithm for grouping the individual details into categories to predict whether they are going to expose to cancer in the early stage itself. Random forest algorithm is implemented, it results in more efficiency of 97% compare to KNN and Naive Bayes. Further, the KNN algorithm doesn't learn anything from training data but uses it for classification. Naive Bayes results in the inaccuracy of prediction. The proposed system is for predicting the chances of lung cancer by displaying three levels namely low, medium, and high. Thus, mortality rates can be reduced significantly.

scholarly journals Biomarkers of iron metabolism facilitate clinical diagnosis in Mycobacterium tuberculosis infection

Thorax ◽  
2019 ◽  
Vol 74 (12) ◽  
pp. 1161-1167 ◽  
Author(s):  
Youchao Dai ◽  
Wanshui Shan ◽  
Qianting Yang ◽  
Jiubiao Guo ◽  
Rihong Zhai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Iron Homeostasis ◽  
Serum Levels ◽  
Training Data ◽  
Lung Damage ◽  
Mycobacterium Tuberculosis Infection ◽  
Data Set ◽  
Patient Status ◽  
Latent Tb Infection ◽  
Tb Diagnostics

BackgroundPerturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker.MethodsWe recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model.ResultsWe observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity.ConclusionsThe established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics.

scholarly journals Congestion Prediction in FPGA Using Regression Based Learning Methods

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1995
Author(s):  
Pingakshya Goswami ◽  
Dinesh Bhatia
Keyword(s):  
Prediction Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Physical Design ◽  
Design Flow ◽  
Training Data ◽  
Regression Problem ◽  
Data Set ◽  
Congestion Prediction ◽  
Routing Congestion

Design closure in general VLSI physical design flows and FPGA physical design flows is an important and time-consuming problem. Routing itself can consume as much as 70% of the total design time. Accurate congestion estimation during the early stages of the design flow can help alleviate last-minute routing-related surprises. This paper has described a methodology for a post-placement, machine learning-based routing congestion prediction model for FPGAs. Routing congestion is modeled as a regression problem. We have described the methods for generating training data, feature extractions, training, regression models, validation, and deployment approaches. We have tested our prediction model by using ISPD 2016 FPGA benchmarks. Our prediction method reports a very accurate localized congestion value in each channel around a configurable logic block (CLB). The localized congestion is predicted in both vertical and horizontal directions. We demonstrate the effectiveness of our model on completely unseen designs that are not initially part of the training data set. The generated results show significant improvement in terms of accuracy measured as mean absolute error and prediction time when compared against the latest state-of-the-art works.

scholarly journals Established and Validated a Novel Nomogram for Predicting Prognosis of Post-mastectomy pN0-1 Breast Cancer Without Adjuvant Radiotherapy

2020 ◽  
Author(s):  
Wei-Xiang Qi ◽  
Cao Lu ◽  
Cheng Xu ◽  
shengguang zhao ◽  
Jiayi Chen
Keyword(s):  
Breast Cancer ◽  
Adjuvant Radiotherapy ◽  
Early Stage ◽  
External Validation ◽  
Training Data ◽  
Phase Iii ◽  
Lasso Regression ◽  
Cancer Specific Survival ◽  
Data Set ◽  
Free Survival

Abstract BackgroundThe present study aims to establish and validate a nomogram for predicting prognosis of breast cancer with pN0-1 who are treated with mastectomy and without adjuvant radiotherapy. Material and methods Between Jan 2009 and Dec 2015, a total of 1879 breast cancer without adjuvant radiotherapy were used for nomogram development. The model was externally validated in an independent cohort of 1356 patients from one phase III trial (NCT00041119). The least absolute shrinkage and selection operator (LASSO) regression were performed to identify predictors of breast cancer specific survival (BCSS), local regional recurrence (LRR), and distant metastasis (DM). Results The 5-year BCSS, LRR and DM rates for the entire cohort was 98% ,2% and 4%, respectively. LASSO regression analysis found that pathological T stage, number of positive LN, grade and Ki-67 were significant predictors for both BCSS and DM-free survival in post-mastectomy breast cancer with pN0-1, while number of resected LN and PR status were predictors for DM-free survival. In addition, number of positive LN was the only significant predictor for developing LRR. The C-indexes for the 5-year BCSS and DM nomograms were 0.81 and 0.78 in the training data set, 0.65 and 0.70 in the testing set, and 0.72 and 0.69 in the external validation set, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 5-year BCSS and DM-free survival.Conclusion Our prognostic nomograms could accurately predict 5-year BCSS and DM-free survival in post-mastectomy early stage breast cancer without adjuvant radiotherapy, which provide a useful tool to identify high-risk patients who could benefit from additional adjuvant therapy.

scholarly journals Role of 3D Volumetric and Perfusion Imaging for Detecting Early Changes in Pancreatic Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Syed Rahmanuddin ◽  
Ronald Korn ◽  
Derek Cridebring ◽  
Erkut Borazanci ◽  
Jordyn Brase ◽  
...  
Keyword(s):  
Early Detection ◽  
Pancreatic Adenocarcinoma ◽  
Perfusion Imaging ◽  
Early Stage ◽  
Risk Groups ◽  
Volumetric Analysis ◽  
Detection Methods ◽  
Ductal Adenocarcinoma ◽  
Imaging Biomarkers ◽  
3D Volume

PurposeThere is a major shortage of reliable early detection methods for pancreatic cancer in high-risk groups. The focus of this preliminary study was to use Time Intensity-Density Curve (TIDC) and Marley Equation analyses, in conjunction with 3D volumetric and perfusion imaging to demonstrate their potential as imaging biomarkers to assist in the early detection of Pancreatic Ductal Adenocarcinoma (PDAC).Experimental DesignsA quantitative retrospective and prospective study was done by analyzing multi-phase Computed Tomography (CT) images of 28 patients undergoing treatment at different stages of pancreatic adenocarcinoma using advanced 3D imaging software to identify the perfusion and radio density of tumors.ResultsTIDC and the Marley Equation proved useful in quantifying tumor aggressiveness. Perfusion delays in the venous phase can be linked to Vascular Endothelial Growth Factor (VEGF)-related activity which represents the active part of the tumor. 3D volume analysis of the multiphase CT scan of the patient showed clear changes in arterial and venous perfusion indicating the aggressive state of the tumor.ConclusionTIDC and 3D volumetric analysis can play a significant role in defining the response of the tumor to treatment and identifying early-stage aggressiveness.

scholarly journals A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients in China

2021 ◽  
Author(s):  
Dong Wang ◽  
JinBo Li ◽  
Yali Sun ◽  
Xianfei Ding ◽  
Xiaojuan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Secondary Analysis ◽  
Adverse Outcomes ◽  
Training Data ◽  
Medical Systems ◽  
Validation Data ◽  
Data Set ◽  
Icu Patients

Abstract Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early.Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis.Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%.Conclusions: The newly established model can accurately predict the onset of sepsis in ICU patients in clinical settings as early as possible. Prospective studies are necessary to determine the clinical utility of the proposed sepsis prediction model.

scholarly journals Prognostic Impact of a Lymphocyte Activation-Associated Gene Signature in GBM based on Transcriptome Analysis

2020 ◽  
Author(s):  
Yujia Lan ◽  
Erjie Zhao ◽  
Xinxin Zhang ◽  
Xiaojing Zhu ◽  
Linyun Wan ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Lymphocyte Activation ◽  
Risk Groups ◽  
Gene Signature ◽  
Cox Proportional Hazards ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression

Abstract Background Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor that has poor prognosis. Lymphocyte activation played important roles in cancer and therapy. Objective To identify an efficient lymphocyte activation-associated gene signature that could predict the progression and prognosis of GBM. Methods We used univariate Cox proportional hazards regression and stepwise regression algorithm to develop a lymphocyte activation-associated gene signature in the training data set (TCGA, n = 525). Then, the signature was validated in two data sets, including GSE16011 (n = 150) and GSE13041 (n = 191) by the Kaplan Meier method. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. Results In the training data set, we identified a lymphocyte activation-associated gene signature (TCF3, IGFBP2, TYRO3 and NOD2), which classified the patients into high-risk and low-risk groups with significant differences in overall survival (median survival 15.33 months vs 12.57 months, HR = 1.55, 95% CI = 1.28-1.87, logrank test P < 0.001). In the other two data sets, this signature showed similar prognostic values. Further, univariate and multivariate Cox proportional hazards regression models analysis indicated that the signature was an independent prognostic factor for GBM patients.Moreover, we found that there were differences in lymphocyte activity between the high- and low-risk groups of GBM patients among all data sets. Furthermore, by longitudinal analysis, the lymphocyte activationassociated gene signature could significantly predict the survival of patients with some features, such as IDH-wildtype patients and patients with radiotherapy. In addition, the signature could improve prognostic power of age. Conclusions In summary, our results suggested that the lymphocyte activation-associated gene signature is a promising factor for the survival of patients, which may be helpful for diagnosis, prognosis, and treatment of GBM patients.

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