Comparison of machine learning algorithms for the classification of spinal cord tumor

Garg, Sheetal; Raghavan, Bhagyashree

doi:10.1007/s11845-023-03487-3

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…ML algorithms used were also provided by the scikit-learn and XGBoost libraries. All of the algorithms consist of supervised machine learning algorithms, and are commonly used for classifications of tumors 34,35 . Further, L2 regularization has been considered to provide improved accuracy of the ML algorithms 36 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Alsharoh

2023

Preprint

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Pancreatic cancer (PC) is associated with high mortality overall. Recent literature has focused on investigating long noncoding RNAs (lncRNAs) in several cancers, but studies on their functions in PC are lacking. The purpose of this study was to identify novel lncRNAs and utilize machine learning to techniques to predict metastatic cases of PC using the identified lncRNAs. To identify significantly altered expression of lncRNA in PC, data was collected from The Cancer Genome Atlas (TCGA) and RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas were extracted for differential gene expression analysis. To assess the contribution of these lncRNAs to metastatic progression, different ML algorithms were used, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC) and eXtreme Gradient Boosting Classifier (XGBC). To improve the predictive accuracy of these models, hyperparameter tuning was performed, in addition to reducing bias through the synthetic minority oversampling technique. Out of 60,660 gene transcripts shared between 151 PC patients, 38 lncRNAs that were significantly differentially expressed were identified. To further investigate the functions of the novel lncRNAs, gene set enrichment analysis (GSEA) was performed on the population lncRNA panel. GSEA results revealed enrichment of several terms implicated in proliferation. Moreover, using the 4 ML algorithms to predict metastatic progression returned 76% accuracy for both SVM and RFC, explicitly based on the novel lncRNA panel. To the best of my knowledge, this is the first study of its kind to identify this lncRNA panel to differentiate between non-metastatic PC and metastatic PC, with many novel lncRNAs previously unmapped to PC. The ML accuracy score reveals important involvement of the detected RNAs. Based on these findings, I suggest further investigations of this lncRNA panelin vitroandin vivo, as they could be targeted for improved outcomes in PC patients, as well as assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors.

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Section: Methodsmentioning

confidence: 99%

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Alsharoh

2023

Preprint

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MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning

Liu,

Zhou,

Wang

2024

Journal of X-Ray Science and Technology: Clinical Applications

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Backgroud Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging characteristics. Objective The objective of this study is to facilitate early diagnosis of patients and reduce clinician stress by constructing a deep learning-based classification model for automatic diagnosis of schwannoma and meningiomas using magnetic resonance images (MRI). Methods We retrospectively collected MRI images of 74 patients with pathologically confirmed schwannoma and meningiomas from 2015 to 2020 at a local hosipital, and constructed a CNN model based on the PyTorch's deep learning framework for the discrimination between the two. First, a modified feature fusion CNN model (ResNet34-SKConv) was trained by introducing a selective convolutional kernel module into the original CNN model. The introduction of the selective convolutional kernel module enhances the network's focus on tumor features and effectively improves the network's performance. Finally, the trained model was used to process all the MRI image slices to achieve the classification of SCH and MEN patients by the voting prediction method. Results Using the 5-fold cross-validation method, this new ResNet34-SKConv model achieves a classification accuracy of 92.32%, a specificity of 95.87%, and a F1-score of 93.54, respectively. Conclusion This study demonstrated that a classification model using a deep learning network can be effective in achieving differential diagnosis of SCH and MEN. Thus, the new method has great potential for developing new computer-aided diagnosis and applications with future clinical practice.

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Comparison of machine learning algorithms for the classification of spinal cord tumor

Cited by 2 publications

References 26 publications

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning

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