Use of artificial intelligence to characterize renal tumors

Kim, Hokun; Hong, Sung Hoo

doi:10.4111/icu.20220051

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…CT texture analysis is utilized by ML and DL algorithms to differentiate various renal masses [43][44][45]. ML and DL algorithms can predict nuclear class and identify specific genetic mutations, which affect the prediction of prognosis, recurrence, and survival outcomes [58].…”

Section: Differentiation Of Rcc Typesmentioning

confidence: 99%

Artificial Intelligence in Urooncology: What We Have and What We Expect

Froń,

Semianiuk,

Lazuk

et al. 2023

Cancers

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Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

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Section: Differentiation Of Rcc Typesmentioning

confidence: 99%

Artificial Intelligence in Urooncology: What We Have and What We Expect

Froń,

Semianiuk,

Lazuk

et al. 2023

Cancers

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“…The extrapolation of the quantitative data from the imaging tests cannot be achieved through only human interpretation [4]. These data can later be used to build algorithms that allow the differentiation of tissue characteristics, which can help, for example, in the differentiation of tumor lesions between benign and malignant tumors [11].…”

Section: Radiomics and Texture Analysismentioning

confidence: 99%

“…Figure 1 shows a summary of how the texture analysis is performed. After image features analysis using radiomics, it is possible to use AI (via machine learning and deep learning algorithms) to analyze these data, and create models that can help in treatment decisions [11].…”

Section: Radiomics and Texture Analysismentioning

confidence: 99%

“…Different kidney lesions can vary between malignant and benign tumors, and the differentiation of these main groups is essential to making a correct treatment decision. Although MR and CT are used to assess tumor characteristics and help to distinguish After image features analysis using radiomics, it is possible to use AI (via machine learning and deep learning algorithms) to analyze these data, and create models that can help in treatment decisions [11].…”

Section: Radiomics In Renal Mass Characterizationmentioning

confidence: 99%

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The Use of Radiomic Tools in Renal Mass Characterization

Gutiérrez Hidalgo,

Gómez Rivas,

de la Parra

et al. 2023

Diagnostics

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The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.

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“…Differential diagnosis through computed tomography (CT), which was solely dependent on the radiologist, has recently entered a new phase with the advent of artificial intelligence (AI) technology. Recent radiologic analysis using AI has potential [6,7]. We have glimpsed this possibility, and hence, we have already attempted to diagnose small renal tumors using a convolutional neural network (CNN) model [8].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics

Bang,

Wang,

Kim

et al. 2023

Applied Sciences

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Objectives: To develop and validate a machine learning-based CT radiomics classification model for distinguishing benign renal tumors from malignant renal tumors. Methods: We reviewed 499 patients who underwent nephrectomy for solid renal tumors at our institution between 2003 and 2021. In this retrospective study, patients who had undergone a computed tomography (CT) scan within 3 months before surgery were included. We randomly divided the dataset in a stratified manner as follows: 75% as the training set and 25% as the test set. By using various feature selection methods and a dimensionality reduction method exclusively for the training set, we selected 160 radiomic features out of 1,288 radiomic features to classify malignant renal tumors. Results: The training set included 396 patients, and the test set included 103 patients. The percentage of extracted radiomic features from patients was 32% (385/1218) after the reproducibility test. In terms of the average Area Under the Receiver Operating Characteristic Curve (AU-ROC) and the average Area Under the Precision-Recall Curve (AU-PRC), the Random Forest model achieved better performance (AU-ROC = 0.725; AU-PRC = 0.899). An average accuracy of 0.778 was obtained on evaluation with the hold-out test set. At the optimal threshold, the Random Forest model showed an F1 score of 0.746, precision of 0.862, sensitivity of 0.657, specificity of 0.651, and Negative Predictive Value (NPV) of 0.364. Conclusions: Our machine learning-based CT radiomics classification model performed well for the independent test set, indicating that it could be a useful tool for discriminating between malignant and benign solid renal tumors.

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Use of artificial intelligence to characterize renal tumors

Cited by 5 publications

References 6 publications

Artificial Intelligence in Urooncology: What We Have and What We Expect

Artificial Intelligence in Urooncology: What We Have and What We Expect

The Use of Radiomic Tools in Renal Mass Characterization

Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics

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