scholarly journals Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

2020 ◽  
Vol 9 (3) ◽  
pp. 724 ◽  
Author(s):  
Georgios A. Kaissis ◽  
Sebastian Ziegelmayer ◽  
Fabian K. Lohöfer ◽  
Felix N. Harder ◽  
Friederike Jungmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Learning Algorithm ◽  
Molecular Subtype ◽  
Characteristic Curve ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Learning Approaches ◽  
Imaging Data ◽  
Log Rank Test

To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.

Clinical Characteristics and Overall Survival in Patients with Anaplastic Pancreatic Cancer

2014 ◽  
Vol 80 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Clancy J. Clark ◽  
Janani S. Arun ◽  
Rondell P. Graham ◽  
Lizhi Zhang ◽  
Michael Farnell ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Tumor Stage ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Poor Overall Survival ◽  
Perioperative Morbidity ◽  
Log Rank Test ◽  
Kaplan Meier

Anaplastic pancreatic cancer (APC) is a rare undifferentiated variant of pancreatic ductal adenocarcinoma with poor overall survival (OS). The aim of this study was to evaluate the clinical outcomes of APC compared with differentiated pancreatic ductal adenocarcinoma. We conducted a retrospective review of all patients treated at the Mayo Clinic with pathologically confirmed APC from 1987 to 2011. After matching with control subjects with pancreatic ductal adenocarcinoma, OS was evaluated using Kaplan-Meier estimates and log-rank test. Sixteen patients were identified with APC (56.3% male, median age 57 years). Ten patients underwent exploration of whom eight underwent pancreatectomy. Perioperative morbidity was 60 per cent with no mortality. The median OS was 12.8 months. However, patients with APC who underwent resection had longer OS compared with those who were not resected, 34.1 versus 3.3 months ( P = 0.001). After matching age, sex, tumor stage, and year of operation, the median OS was similar between patients with APC and those with ductal adenocarcinoma treated with pancreatic resection, 44.1 versus 39.9 months, ( P = 0.763). Overall survival for APC is poor; however, when resected, survival is similar to differentiated pancreatic ductal adenocarcinoma.

scholarly journals CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yinghao Meng ◽  
Hao Zhang ◽  
Qi Li ◽  
Fang Liu ◽  
Xu Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Predictive Value ◽  
Tumor Stroma ◽  
Ductal Adenocarcinoma ◽  
Gradient Boosting ◽  
Rank Test ◽  
Training Set ◽  
Extreme Gradient Boosting ◽  
Validation Set

PurposeTo develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor–stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsIn this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.ResultsWe observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.ConclusionsThe CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

scholarly journals Machine Learning-based Approaches for Identifying Human Cells Harboring Fetal Chromatin Domain Ablations

2021 ◽  
Author(s):  
Yi Li ◽  
Shadi Zaheri ◽  
Khai Nguyen ◽  
Li Liu ◽  
Fatemeh Hassanipour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Morphological Changes ◽  
Regulatory Element ◽  
Characteristic Curve ◽  
Globin Gene ◽  
Human Cells ◽  
Chromatin Domain ◽  
Learning Approaches ◽  
Imaging Data ◽  
Genetic Modifications

Abstract Two common hemoglobinopathies, sickle cell disease (SCD) and β-thalassemia, arise from genetic mutations within the β-globin gene. A 500-bp motif termed Fetal Chromatin Domain (FCD), upstream of human ϒ-globin locus, may function as a transcriptional regulatory element driving inhibition of the ϒ-globin gene. Here, we hypothesize that the removal of this motif using CRISPR technology may reactivate the expression of ϒ-globin and subsequently restore fetal hemoglobin functionality. In this work we present two different cell morphology-based machine learning approaches that can be used identify cells that harbor FCD genetic modifications. Three candidate models from the first, which uses multilayer perceptron algorithm (MLP 20–26, MLP26-18, and MLP 30 − 26) and flow cytometry-derived cellular data, yielded 0.83 precision, 0.80 recall, 0.82 accuracy, and 0.90 area under the ROC (receiver operating characteristic) curve when predicting the edited cells. In comparison, the candidate model from the second approach, which uses deep learning (T2D5) and DIC microscopy-derived imaging data, performed with less accuracy (0.80) and ROC AUC (0.87). We envision both assays could be valuable and complementary to currently available genotyping protocols for specific genetic modifications which result in morphological changes in human cells.

scholarly journals Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network

2020 ◽  
Vol 17 (3) ◽  
pp. 740
Author(s):  
Hsiang-Yuan Yeh ◽  
Chia-Ter Chao ◽  
Yi-Pei Lai ◽  
Huei-Wen Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Structural Information ◽  
Characteristic Curve ◽  
Learning Approaches ◽  
Activity Prediction ◽  
Topological Features ◽  
Better Than

Natural products are the most important and commonly used in Traditional Chinese Medicine (TCM) for healthcare and disease prevention in East-Asia. Although the Meridian system of TCM was established several thousand years ago, the rationale of Meridian classification based on the ingredient compounds remains poorly understood. A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical compound. Therefore, we apply a cost-sensitive graph convolutional neural network model to learn local and global topological features of chemical compounds, and discover the associations between TCM and their Meridians. In the experiments, we find that the performance of our approach with the area under the receiver operating characteristic curve (ROC-AUC) of 0.82 which is better than the traditional machine learning algorithm and also obtains 8%–13% improvement comparing with the state-of-the-art methods. We investigate the powerful ability of deep learning approach to learn the proper molecular descriptors for Meridian prediction and to provide novel insights into the complementary and alternative medicine of TCM.

scholarly journals Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters

2020 ◽  
Vol 9 (5) ◽  
pp. 1250
Author(s):  
Georgios A. Kaissis ◽  
Friederike Jungmann ◽  
Sebastian Ziegelmayer ◽  
Fabian K. Lohöfer ◽  
Felix N. Harder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Molecular Subtype ◽  
Cox Proportional Hazards ◽  
Ductal Adenocarcinoma ◽  
Imaging Biomarkers ◽  
Lymph Node Status ◽  
Survival Prediction ◽  
Imaging Features ◽  
True Value

Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.

scholarly journals Dynamic Detection of Delayed Cerebral Ischemia Using Machine Learning

2020 ◽  
Author(s):  
Murad Megjhani ◽  
Kalijah Terilli ◽  
Ayham Alkhachroum ◽  
David J. Roh ◽  
Sachin Agarwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Ischemia ◽  
Characteristic Curve ◽  
Delayed Cerebral Ischemia ◽  
Risk Scores ◽  
Support Vector ◽  
Model Parameters ◽  
Learning Approaches ◽  
Physiologic Data ◽  
Over Time

AbstractObjectiveTo develop a machine learning based tool, using routine vital signs, to assess delayed cerebral ischemia (DCI) risk over time.MethodsIn this retrospective analysis, physiologic data for 540 consecutive acute subarachnoid hemorrhage patients were collected and annotated as part of a prospective observational cohort study between May 2006 and December 2014. Patients were excluded if (i) no physiologic data was available, (ii) they expired prior to the DCI onset window (< post bleed day 3) or (iii) early angiographic vasospasm was detected on admitting angiogram. DCI was prospectively labeled by consensus of treating physicians. Occurrence of DCI was classified using various machine learning approaches including logistic regression, random forest, support vector machine (linear and kernel), and an ensemble classifier, trained on vitals and subject characteristic features. Hourly risk scores were generated as the posterior probability at time t. We performed five-fold nested cross validation to tune the model parameters and to report the accuracy. All classifiers were evaluated for good discrimination using the area under the receiver operating characteristic curve (AU-ROC) and confusion matrices.ResultsOf 310 patients included in our final analysis, 101 (32.6%) patients developed DCI. We achieved maximal classification of 0.81 [0.75-0.82] AU-ROC. We also predicted 74.7 % of all DCI events 12 hours before typical clinical detection with a ratio of 3 true alerts for every 2 false alerts.ConclusionA data-driven machine learning based detection tool offered hourly assessments of DCI risk and incorporated new physiologic information over time.

scholarly journals Machine learning approaches revealed metabolic signatures of incident chronic kidney disease in persons with pre-and type 2 diabetes

2020 ◽  
Author(s):  
Ada Admin ◽  
Jialing Huang ◽  
Cornelia Huth ◽  
Marcela Covic ◽  
Martina Troll ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Population Based ◽  
Learning Approaches ◽  
Clinical Variables

Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk of progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in persons with pre- and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals Liver Metastases in Newly Diagnosed Pancreatic Ductal Adenocarcinoma: A Population Based Analysis

2020 ◽  
Author(s):  
Guoyi Wu ◽  
Xiaoben Pan ◽  
Baohua Wang ◽  
Xiaolei Zhu ◽  
Jing Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Metastases ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Tumor Size ◽  
Cox Regression ◽  
Population Based ◽  
The Body ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Factors Associated

Abstract Background Estimates of the incidence and prognosis of developing liver metastases at the pancreatic ductal adenocarcinoma (PDAC) diagnosis are lacking.Methods In this study, we analyzed the association of liver metastases and the PDAC patients outcome. The risk factors associated with liver metastases in PDAC patients were analyzed using multivariable logistic regression analysis. The overall survival (OS) was estimated using Kaplan-Meier curves and log-rank test. Cox regression was performed to identify factors associated with OS.Results Patients with primary PDAC in the tail of the pancreas had a higher incidence of liver metastases (62.2%) than those with PDAC in the head (28.6%). Female gender, younger age, primary PDAC in the body or tail of the pancreas, and larger primary PDAC tumor size were positively associated with the occurrence of liver metastases. The median survival of patients with liver metastases was significantly shorter than that of patients without liver metastases. Older age, unmarried status, primary PDAC in the tail of the pancreas, and tumor size ≥4 cm were risk factors for OS in the liver metastases cohort.Conclusions Population-based estimates of the incidence and prognosis of PDAC with liver metastases may help decide whether diffusion-weighted magnetic resonance imaging should be performed in patients with primary PDAC in the tail or body of the pancreas. The location of primary PDAC should be considered during the diagnosis and treatment of primary PDAC.

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