Bayesian Classifier for Predicting Malignant Renal Cysts on MDCT: Early Clinical Experience

Lee, Youngjoo; Kim, Namkug; Cho, Kyoung‐Sik; Kang, Suk‐Ho; Kim, Dae Yoon; Jung, Yoon Young; Kim, Jeong Kon

doi:10.2214/ajr.08.1858

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…There are two general approaches to creating BN models: first, inputting data for the BN to "learn" and, second, building them based on data in the literature, expert knowledge, or both. In the first approach, the parameters of the BN are learned by mining a large dataset of cases in which the diagnoses of the cases are established [9]. This approach requires a large dataset for training and could be impractical because it requires thousands of annotated cases.…”

Section: Discussionmentioning

confidence: 99%

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A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features

Liu

Kamaya²,

Desser³

et al. 2011

American Journal of Roentgenology

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Section: Discussionmentioning

confidence: 99%

“…These models have been used in many areas of medicine to estimate the probability of disease, such as diagnosing renal cystic lesions, predicting breast cancer risk, and identifying deep venous thrombosis [6][7][8][9].…”

mentioning

confidence: 99%

A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features

Liu

Kamaya²,

Desser³

et al. 2011

American Journal of Roentgenology

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show abstract

“…In fact, from the articles analyzed, it emerges that, according to the CT acquisition phase taken into consideration, the results obtained change; specifically, the most used phase is the corticomedullary phase [30]. Furthermore, as regards the use of CT for the extraction of characteristic features, the literature considers the three-dimensional use of CT to be better and more representative [117], but in the research identified [27][28][29][30][31][32][33][34][35][36], to reduce the workload of manual segmentation and facilitate the repeatability of this operation, a limited number of slices or only the two-dimensional slice containing the largest portion of the mass considered is used. In addition to how CT is used, it is also important to control the method by which features are extracted; in some research [28,29,[31][32][33][34][35][36], radiomic features are used, after manual segmentation by at least one experienced physician, to classify tumors.…”

Section: Discussionmentioning

confidence: 99%

“…To face this challenge, modern ML techniques have been employed to process image data, proving to help physicians in making a more precise and accurate diagnosis. To classify and distinguish between malignant and benign masses, [27] some use a Bayesian classifier [28], a learning algorithm based on the statistical relationship between radiomics features (relational functional gradient boosting), and [29] an algorithm based on CT texture analysis. Many works focus on the analysis of renal cell carcinoma (RCC), which is the cause of 80% of kidney cancer deaths [25], either to distinguish different types of RCCs or to differentiate them from benign tumors.…”

Section: Kidney Massesmentioning

confidence: 99%

Machine Learning for Renal Pathologies: An Updated Survey

Magherini

Mussi

Volpe

et al. 2022

Sensors

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Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

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“…Many earlier studies have shown that machine learning approaches can be used to stratify CRL ( 23 , 24 ). However, only a few studies rely on pathology as the diagnostic criteria ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Tan

Liao

et al. 2022

Front. Oncol.

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Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature’s reproducibility. Pearson correlation coefficients for normal distribution features and Spearman’s rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

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Bayesian Classifier for Predicting Malignant Renal Cysts on MDCT: Early Clinical Experience

Abstract: ). G e n it o u r i n a r y I m ag i ng • O r ig i n a l R e s e a rc hWEB This is a Web exclusive article.

Cited by 13 publications

References 26 publications

A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features

A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features

Machine Learning for Renal Pathologies: An Updated Survey

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

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