Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Pathologic T3a Upstaging in Clinical T1 RCC

<b>Background/Objectives</b>: To develop a nomogram for the preoperative prediction of pathologic T3a (pT3a) upstaging in patients with clinical T1(cT1) renal cell carcinoma (RCC). <b>Methods</b>: A total of 169 cT1 patients with RCC with preoperative contrast-enhanced CT (CECT) and clinical data were enrolled in this study. Afterwards, the sample was split randomly into training and testing sets in a 7:3 ratio. Radiomics features were extracted and selected from the whole primary tumor on CECT images to develop radiomics signatures. The nomogram was constructed using the obtained radiomics signature and clinical risk factors. The predictive performance of different models was evaluated and visualized using receiver operator characteristic (ROC) curves. <b>Results</b>: In total, 26 radiomics features were selected for the radiomics signature construction. The radiomics signature yielded area under the curve (AUC) values of 0.945 and 0.873 in the training and testing sets, respectively. The nomogram integrating radiomics signature and predictive clinical factors, including tumor size and neutrophil-lymphocyte ratio (NLR), achieved higher predictive performance in the training [AUC, 0.958; 95% confidence interval (CI): 0.921, 0.995] and testing (AUC, 0.913; 95% CI: 0.814, 1.000) sets. Good calibration was achieved for the nomogram in both the training and testing sets (Brier score = 0.082 and 0.098). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful in predicting pT3a upstaging, with a corresponding net benefit of 0.378. <b>Conclusions</b>: The proposed nomogram can preoperatively predict pT3a upstaging in cT1 RCC and serve as a non-invasive imaging marker to guide individualized treatment.

Authors

Institutions

Publication Details

Journal
Diagnostics
Published
2025-02-12
DOI
https://doi.org/10.3390/diagnostics15040443
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Pathologic T3a Upstaging in Clinical T1 RCC

Keruo Wang, Hongyi Xu, Yang Zhao, Dengyi Duan et al.
1 links
Diagnostics
Radiomics and Machine Learning in Medical Imaging
article

Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Pathologic T3a Upstaging in Clinical T1 RCC

Keruo Wang, Hongyi Xu, Yang Zhao, Dengyi Duan, Yiming Li, Wenyi Zhang, Zheng-Yang Li, Baoxin Qian, Yunfei Guo, Di Yin
article en

Abstract

<b>Background/Objectives</b>: To develop a nomogram for the preoperative prediction of pathologic T3a (pT3a) upstaging in patients with clinical T1(cT1) renal cell carcinoma (RCC). <b>Methods</b>: A total of 169 cT1 patients with RCC with preoperative contrast-enhanced CT (CECT) and clinical data were enrolled in this study. Afterwards, the sample was split randomly into training and testing sets in a 7:3 ratio. Radiomics features were extracted and selected from the whole primary tumor on CECT images to develop radiomics signatures. The nomogram was constructed using the obtained radiomics signature and clinical risk factors. The predictive performance of different models was evaluated and visualized using receiver operator characteristic (ROC) curves. <b>Results</b>: In total, 26 radiomics features were selected for the radiomics signature construction. The radiomics signature yielded area under the curve (AUC) values of 0.945 and 0.873 in the training and testing sets, respectively. The nomogram integrating radiomics signature and predictive clinical factors, including tumor size and neutrophil-lymphocyte ratio (NLR), achieved higher predictive performance in the training [AUC, 0.958; 95% confidence interval (CI): 0.921, 0.995] and testing (AUC, 0.913; 95% CI: 0.814, 1.000) sets. Good calibration was achieved for the nomogram in both the training and testing sets (Brier score = 0.082 and 0.098). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful in predicting pT3a upstaging, with a corresponding net benefit of 0.378. <b>Conclusions</b>: The proposed nomogram can preoperatively predict pT3a upstaging in cT1 RCC and serve as a non-invasive imaging marker to guide individualized treatment.

DiagnosticsVol. 15(4)
Second Hospital of Tianjin Medical University (CN), Tianjin Medical University (CN)
National Natural Science Foundation of China, Science Fund for Distinguished Young Scholars of Tianjin
Openalex Percentile: Top 96%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.