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Related Experiment Video

Updated: May 3, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

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Published on: March 30, 2015

Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid

Yaohai Wu¹, Fei Cao¹, Hanqi Lei¹

¹Department of Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.

Abdominal Radiology (New York)

|May 11, 2024

Summary

Machine learning models using contrast-enhanced CT (CECT) effectively distinguish benign from malignant renal tumors. The excretory phase (EP) model showed the best performance in differentiating tumor types.

Keywords:

Computed tomography Radiomics Random forest Renal tumors SHapley Additive exPlanations

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Distinguishing benign from malignant renal tumors is crucial for appropriate patient management.
Contrast-enhanced CT (CECT) is a key imaging modality for renal tumor characterization.

Purpose of the Study:

To develop and compare machine learning models for differentiating benign and malignant renal tumors using triphasic CECT.
To evaluate the performance of models based on single and combined CECT phases.

Main Methods:

Radiomic features were extracted from corticomedullary (CP), nephrographic (NP), and excretory (EP) phases of CECT.
Random forest (RF) models were trained using single-phase and all-phase (TP) features.
Models were validated internally and externally, with SHapley Additive exPlanations (SHAP) used for interpretation.

Main Results:

RF models achieved high AUCs in both training and validation sets.
The excretory phase (EP) model demonstrated the highest AUC (0.930) in the training set and strong performance (0.921) in the validation set.
The 'original_shape_Flatness' feature was identified as most important for the EP model's predictions.

Conclusions:

Machine learning models based on triphasic CECT are effective for differentiating renal tumors.
The EP feature-based RF model exhibited superior performance for benign vs. malignant tumor classification.