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The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

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Small Renal Masses: Developing a Robust Radiomic Signature.

Michele Maddalo¹, Lorenzo Bertolotti², Aldo Mazzilli¹

¹Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy.

|September 28, 2023

Summary

This study developed a machine learning model using radiomic features from CT scans to differentiate small renal masses (SRMs), successfully distinguishing between cancerous (RCC) and benign renal tumors.

Keywords:

benign characterization kidney cancer malignant oncocytoma radiomics renal cell carcinoma small renal masses

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Area of Science:

Radiology
Oncology
Artificial Intelligence

Background:

Small renal masses (SRMs) require accurate differentiation between benign and malignant types.
Distinguishing renal cell carcinoma (RCC) from benign lesions can be challenging.
Radiomics offers potential for non-invasive characterization of renal tumors.

Purpose of the Study:

To develop and validate a machine learning model using radiomic features to differentiate benign from malignant SRMs.
To assess the performance of radiomics in classifying small renal masses.

Main Methods:

Retrospective analysis of 85 patients with SRMs (<4 cm) undergoing surgery.
Extraction of 108 radiomic features from abdominal CT scans using 3D-Slicer.
Development of a machine learning pipeline including feature selection and model validation on unseen data.

Main Results:

The study included 51 RCCs and 34 benign lesions (oncocytomas, angiomyolipomas, leiomyomas).
A radiomic signature of 10 features was identified.
The model achieved an average ROC-AUC of 0.79, accuracy of 0.73, sensitivity of 0.78, and specificity of 0.63 on unseen data.

Conclusions:

A robust radiomics pipeline can effectively distinguish RCCs from benign renal tumors.
Radiomic features show promise as a tool for non-invasive diagnosis of SRMs.
Further validation is warranted for clinical application.