scholarly journals Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0210976 ◽  
Author(s):  
Zaki Hasnain ◽  
Jeremy Mason ◽  
Karanvir Gill ◽  
Gus Miranda ◽  
Inderbir S. Gill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

2021 ◽  
Vol 11 (6) ◽  
pp. 2835
Author(s):  
Jacopo Troisi ◽  
Angelo Colucci ◽  
Pierpaolo Cavallo ◽  
Sean Richards ◽  
Steven Symes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Partial Least Square ◽  
Least Square ◽  
High Morbidity ◽  
Low Grade ◽  
Learning Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Models

Bladder cancer has a high incidence and is marked by high morbidity and mortality. Early diagnosis is still challenging. The objective of this study was to create a metabolomics-based profile of bladder cancer in order to provide a novel approach for disease screening and stratification. Moreover, the study characterized the metabolic changes associated with the disease. Serum metabolomic profiles were obtained from 149 bladder cancer patients and 81 healthy controls. Different ensemble machine learning models were built in order to: (1) differentiate cancer patients from controls; (2) stratify cancer patients according to grading; (3) stratify patients according to cancer muscle invasiveness. Ensemble machine learning models were able to discriminate well between cancer patients and controls, between high grade (G3) and low grade (G1-2) cancers and between different degrees of muscle invasivity; ensemble model accuracies were ≥80%. Relevant metabolites, selected using the partial least square discriminant analysis (PLS-DA) algorithm, were included in a metabolite-set enrichment analysis, showing perturbations primarily associated with cell glucose metabolism. The metabolomic approach may be useful as a non-invasive screening tool for bladder cancer. Furthermore, metabolic pathway analysis can increase understanding of cancer pathophysiology. Studies conducted on larger cohorts, and including blind trials, are needed to validate results.

scholarly journals Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6013
Author(s):  
Hyun-Soo Park ◽  
Kwang-sig Lee ◽  
Bo-Kyoung Seo ◽  
Eun-Sil Kim ◽  
Kyu-Ran Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Cancer Patients ◽  
Low Dose ◽  
Random Forest Model ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Forest Model ◽  
Machine Learning Models

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Prediction of the prognosis of oesophageal cancer: Systematic review & meta-analysis of machine learning solutions- Study protocol

2021 ◽  
Author(s):  
Ali Haider Bangash
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cancer Patients ◽  
Oesophageal Cancer ◽  
Study Protocol ◽  
Meta Analysis ◽  
Learning Models ◽  
Collaborative Project ◽  
International Collaborative ◽  
Machine Learning Models

In an international collaborative project, we shall be exploring the features of machine learning models that predict the outcome & prognosis of oesophageal cancer patients.

Automated identification of immune related adverse events in oncology patients using machine learning.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1551-1551
Author(s):  
Wenxin Xu ◽  
Alexander Gusev ◽  
Stefan Groha ◽  
Osama E. Rahma ◽  
Deborah Schrag ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Adverse Events ◽  
Cancer Patients ◽  
Gold Standard ◽  
Cancer Center ◽  
Learning Models ◽  
Major Barrier ◽  
Patient Level ◽  
Machine Learning Models

1551 Background: Immune related adverse events (irAEs) are a major cause of morbidity among cancer patients treated with immune checkpoint inhibitors (ICIs). irAEs are difficult to identify systematically, which represents a major barrier to the conduct and reproducibility of irAE research. Automated approaches would facilitate cohort identification and understanding of risk factors for irAEs following ICI therapy. Methods: Patients treated with one or more ICIs at a single tertiary cancer center were identified. Patients who received ICIs outside the clinical trial context were used as a development cohort. For each date containing clinical documentation, proxy outcomes expected to correlate with grade 2+ irAEs including irAE related diagnosis codes, key laboratory values, prescriptions for topical and systemic steroids, and irAE keywords were extracted. Intermediate machine learning models were trained to predict the presence of each proxy outcome using structured and unstructured patient data. We used clinical trial irAEs extracted from adverse event tables found in the electronic health record as the “gold standard” outcome for a final training and evaluation cohort. A logistic regression model was used to combine predictions from each intermediate model and generate an overall probability score for each irAE type on a given encounter date. Ten-fold cross-validation was used to evaluate the final machine learning model on a held-out sample of clinical trial patients. Encounter level models were evaluated for predicting the onset of a given irAE on a given date, and patient level models for predicting irAE onset within 6 months of ICI initiation. Results: We identified 3,765 patients treated with ICIs off-trial and 1106 patients treated on ICI clinical trials. Among trial patients, overall incidence of any grade 2+ irAE was 21%. The combined irAE models were able to predict prospective gold standard irAE labels with accurate discrimination at both the encounter and patient level (Table). Conclusions: Machine learning models can identify irAEs among cancer patients in an automated manner, which may facilitate research to mitigate toxicities and optimize clinical outcomes. Validation of these methods in an external institutional cohort is underway.[Table: see text]

Machine Learning based Tissue Analysis Reveals Brachyury has a Diagnosis Value in Breast Cancer

2020 ◽  
Author(s):  
Kaichun Li ◽  
Qiaoyun Wang ◽  
Yanyan Lu ◽  
Xiaorong Pan ◽  
Long Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Cancer Patients ◽  
Decision Tree Model ◽  
Superior Performance ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Tree Model ◽  
Machine Learning Models

Abstract Background The aim of this study was to confirm the role of Brachyury in breast cells and to establish and verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected a total of 303 patients for research and implemented four machine learning prediction algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).Conclusions Brachyury is highly expressed in breast cancer and indicates that the patient had a poor chance of survival. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients. This indicates that machine learning can thus be applied in a wide range of clinical studies.

scholarly journals Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning

2021 ◽  
Vol 13 (10) ◽  
pp. 5438
Author(s):  
Roya Etminani-Ghasrodashti ◽  
Chen Kan ◽  
Muhammad Arif Qaisrani ◽  
Omer Mogultay ◽  
Houliang Zhou
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Built Environment ◽  
Cancer Patients ◽  
The United States ◽  
The Self ◽  
Learning Models ◽  
Health Related ◽  
Machine Learning Models

Despite accumulative evidence regarding the impact of the physical environment on health-related outcomes, very little is known about the relationships between built environment characteristics and the quality of life (QoL) of cancer patients. This study aims to investigate the association between the built environment and QoL by using survey data collected from cancer patients within the United States in 2019. To better understand the associations, we controlled the effects from sociodemographic attributes and health-related factors along with the residential built environment, including density, diversity, design, and distance to transit and hospitals on the self-reported QoL in cancer patients after treatment. Furthermore, machine learning models, i.e., logistic regression, decision tree, random forest, and multilayer perceptron neural network, were employed to evaluate the contribution of these features in predicting the QoL. The results from machine learning models indicated that the travel distance to the closest large hospital, perceived accessibility, distance to transit, and population density were among the most significant predictors of the cancer patients’ QoL. Additionally, the health insurance status, age, and education of patients are associated with QoL. The adverse effects of density on the self-reported QoL in this study can be addressed by individuals’ emotions towards negative aspects of density. Given the strong association between QoL and urban sustainability, consideration should be given to the side effects of urban density on cancer patients’ perceived wellbeing.

scholarly journals Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer

2021 ◽  
Author(s):  
Kaichun Li ◽  
Qiaoyun Wang ◽  
Yanyan Lu ◽  
Xiaorong Pan ◽  
Long Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Cancer Patients ◽  
Decision Tree Model ◽  
Superior Performance ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Tree Model ◽  
Machine Learning Models

Background The aim of this study was to confirm the role of Brachyury in breast cancer and to verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.</p>  Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected  303 patients for research and implemented four machine learning algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.</p>  Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).</p>  Conclusions Brachyury is highly expressed in breast cancer and indicates that patients had a poor prognosis. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients.

Analysis of Machine Learning Models Predicting Quality of Life for Cancer Patients

2021 ◽  
Author(s):  
Miloš Savić ◽  
Vladimir Kurbalija ◽  
Mihailo Ilić ◽  
Mirjana Ivanović ◽  
Dušan Jakovetić ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Cancer Patients ◽  
Learning Models ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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