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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Mri-based Radiomics Machine Learning Model To Differentiate Non-clear Cell Renal Cell Carcinoma From Benign Renal Tumors.

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Mri-based Radiomics Machine Learning Model To Differentiate Non-clear Cell Renal Cell Carcinoma From Benign Renal Tumors.

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MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal

Ruiting Wang^1,2, Lianting Zhong³, Pingyi Zhu^1,2

¹Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

European Journal of Radiology Open

|November 11, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed an MRI-based radiomics model to accurately differentiate non-clear cell renal cell carcinoma (non-ccRCC) from benign renal tumors. The combined logistic regression model achieved high accuracy, improving preoperative diagnosis.

Keywords:

Benign renal tumors Machine learning Magnetic resonance imaging Radiomics Renal cell carcinoma

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Area of Science:

Radiology
Oncology
Machine Learning

Background:

Accurate preoperative differentiation of renal tumors is crucial for treatment planning.
Non-clear cell renal cell carcinoma (non-ccRCC) and benign renal tumors often present similar imaging features.
Improving diagnostic accuracy can prevent unnecessary surgeries for benign conditions.

Purpose of the Study:

To develop and validate an MRI-based radiomics model for differentiating non-ccRCC from benign renal tumors.
To enhance the accuracy of preoperative diagnosis in renal mass evaluation.
To explore the utility of machine learning in classifying renal tumors.

Main Methods:

Retrospective analysis of 195 patients with pathologically confirmed renal tumors.
Development of radiomics models using MRI data and machine learning classifiers (SVM, LR).

Feature selection using LASSO and evaluation of model performance via AUC and accuracy.

Main Results:

The combined logistic regression (LR) model demonstrated the highest differentiation efficiency.
The model achieved an AUC of 0.964 and accuracy of 0.919 in the training set.
In the test set, the model achieved an AUC of 0.936 and accuracy of 0.864.

Conclusions:

MRI-based radiomics machine learning is a feasible approach for differentiating non-ccRCC from benign renal tumors.
This method can significantly improve the accuracy of clinical diagnosis.
Radiomics holds promise for non-invasive preoperative tumor characterization.