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Prediction Study Of Surrounding Tissue Invasion In Clear Cell Renal Cell Carcinoma Based On Multi-phase Enhanced Ct Radiomics.

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Prediction Study Of Surrounding Tissue Invasion In Clear Cell Renal Cell Carcinoma Based On Multi-phase Enhanced Ct Radiomics.

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Prediction study of surrounding tissue invasion in clear cell renal cell carcinoma based on multi-phase enhanced CT

Mengwei Wu¹, Hanlin Zhu², Zhijiang Han³

¹The Quzhou Affiliated Hospital of Wenzhou Medical University, (Quzhou People's Hospital), Quzhou, China.

Abdominal Radiology (New York)

|November 25, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new nomogram combining CT imaging and clinical data accurately predicts surrounding tissue invasion in clear cell renal cell carcinoma (ccRCC) patients, aiding preoperative assessment.

Keywords:

Clear cell renal cell carcinoma Computed tomography Nomograph Prediction model Radiomics Stage

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Area of Science:

Radiology
Oncology
Medical Imaging Analysis

Background:

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer.
Accurate preoperative prediction of surrounding tissue invasion (STI) in ccRCC is crucial for surgical planning and patient outcomes.
Current methods for assessing STI preoperatively have limitations.

Purpose of the Study:

To evaluate the effectiveness of a nomogram model incorporating clinical-image features and CT radiomics for predicting STI in ccRCC patients.
To develop a reliable preoperative tool for assessing the risk of STI in ccRCC.

Main Methods:

Retrospective analysis of 248 ccRCC patients' postoperative pathological data from two centers.
Construction of a clinical model using univariate and multivariate regression analyses.

Extraction of radiomics features from CT scans (tumoral, intratumor, peritumoral regions).

Development of a nomogram integrating the clinical model with an optimal radiomics signature.

Interpretation of feature importance using Shapley Additive Explanations (SHAP) values.

Main Results:

The combined nomogram model achieved high AUC values: 0.890 (training), 0.886 (internal validation), and 0.826 (external validation).
The radiomics signature, particularly ROI4 in NP, showed superior predictive performance compared to other radiomics approaches.
Key radiomics features identified by SHAP analysis included Small Dependence Low Gray Level Emphasis, Maximum 3D Diameter, and Maximum Probability.

Conclusions:

A nomogram integrating preoperative CT radiomics and clinical-image features is a reliable tool for predicting STI in ccRCC.
This nomogram can aid in preoperative risk stratification and surgical decision-making for ccRCC patients.
SHAP value analysis facilitates the understanding and potential clinical adoption of this predictive tool.