Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathological Predictive Power of RhoB in Rectal Cancer Biopsy

Pham, Tuan D.; Ravi, Vinayakumar; Liu, Na; Luo, Bin; Fan, Cheng; Sun, Xiaoying

doi:10.21203/rs.3.rs-957359/v1

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…Deep-learning-based models help in CT scans, chest X-ray image analyses [55], heart disease prediction [56], and the reliable detection of lung diseases from medical images [57]. They can also investigate the predictive factor of RhoB in patients with rectal cancer [58].…”

Section: Xai-based Classification Model For Predicting Cardiovascular...mentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

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Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.

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Section: Xai-based Classification Model For Predicting Cardiovascular...mentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…However, our manual pathologist-based analysis could not confirm the predictive value of RhoB expression in biopsy samples of CRC patients. A recent investigation has reported the use of tensor decomposition of convolutional eigenvalues of RhoB expression in IHC images of biopsy collected from two groups of rectal-cancer patients who had survival rates of less or more than 5 years [17]. By using all IHC biopsy samples, the method of tensor decomposition could differentiate between the factor distributions of the two groups of patients without pRT, who had different survival rates, but the study did not carry out the cross-validation (classification) of the samples.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy

Pham

Ravi

Luo

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Aim: The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer. Methods: This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates. Results: The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%. Conclusions: The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.

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Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathological Predictive Power of RhoB in Rectal Cancer Biopsy

Cited by 2 publications

References 42 publications

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy

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