Predicting Cancer Using Supervised Machine Learning: Mesothelioma

• URL: http://arxiv.org/abs/2111.01912v1
• Date: Sun, 31 Oct 2021 16:49:59 GMT
• Title: Predicting Cancer Using Supervised Machine Learning: Mesothelioma
• Authors: Avishek Choudhury
• Abstract summary: Pleural Mesothelioma accounts for about 75% of all Mesothelioma diagnosed yearly in the U.S. We use artificial intelligence algorithms recommending the best fit model for early diagnosis of MPM.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Pleural Mesothelioma (PM) is an unusual, belligerent tumor that rapidly develops into cancer in the pleura of the lungs. Pleural Mesothelioma is a common type of Mesothelioma that accounts for about 75% of all Mesothelioma diagnosed yearly in the U.S. Diagnosis of Mesothelioma takes several months and is expensive. Given the risk and constraints associated with PM diagnosis, early identification of this ailment is essential for patient health. Objective: In this study, we use artificial intelligence algorithms recommending the best fit model for early diagnosis and prognosis of MPM. Methods: We retrospectively retrieved patients clinical data collected by Dicle University, Turkey, and applied multilayered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested using paired T-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers characteristic curve (ROC), and precision-recall curve (PRC). Results: In phase-1, SGD, AdaBoost. M1, KLR, MLP, VFDT generate optimal results with the highest possible performance measures. In phase 2, AdaBoost, with a classification accuracy of 71.29%, outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate Mesothelioma. Conclusion: This study confirms that data obtained from Biopsy and imagining tests are strong predictors of Mesothelioma but are associated with a high cost; however, they can identify Mesothelioma with optimal accuracy.

Related papers

• Biomarker based Cancer Classification using an Ensemble with Pre-trained Models [2.2436844508175224]
  We propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks. We leverage a meta-trained Hyperfast model for classifying cancer, accomplishing the highest AUC of 0.9929. We also propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks, achieving an incremental increase in accuracy (0.9464)
  arXiv  Detail & Related papers  (2024-06-14T14:43:59Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks [33.51490233427579]
  Two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
  arXiv  Detail & Related papers  (2023-04-18T10:14:45Z)
• Regression-based Deep-Learning predicts molecular biomarkers from pathology slides [40.24757332810004]
  We developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
  arXiv  Detail & Related papers  (2023-04-11T11:43:51Z)
• Deep Learning for Predicting Metastasis on Melanoma WSIs [1.4724454726700604]
  Northern Europe has the second highest mortality rate of melanoma globally. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This paper presents a convolutional neural network (CNN) method based on VGG16 to predict melanoma prognosis as the presence of metastasis within five years.
  arXiv  Detail & Related papers  (2023-03-10T07:40:09Z)
• Improving the diagnosis of breast cancer based on biophysical ultrasound features utilizing machine learning [0.0]
  We propose a biophysical feature based machine learning method for breast cancer detection. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve.
  arXiv  Detail & Related papers  (2022-07-13T23:53:09Z)
• Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning [0.0]
  It is estimated that annually over 13,000 deaths occur in the US due to Glioblastoma Multiforme (GBM) It is important to identify the MGMT promoter status through non-invasive magnetic resonance imaging (MRI) based machine learning (ML) models. We developed four primary models - two radiomic models and two CNN models - each solving the binary classification task with progressive improvements.
  arXiv  Detail & Related papers  (2022-01-12T11:04:34Z)
• The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients [31.567542945171834]
  We describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge. BraTS-Reg is the first public benchmark environment for deformable registration algorithms. The aim of BraTS-Reg is to continue to serve as an active resource for research.
  arXiv  Detail & Related papers  (2021-12-13T19:25:16Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images [0.3120960917423201]
  Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods. In this study, we explore a new approach to build an optimal machine learning model using small and imbalanced image datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:53:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.