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Original Research

Open Access

A radiomics model derived by combination of radiomics signature and clinical risk factors predict of lymph node metastasis for men renal pelvis urothelial carcinoma

  • Jingyi Huang1
  • Fan Chen1
  • Chengcheng Gao2
  • Zhenyu Shu3
  • Gang Tao4,*,

1Department of Radiology, Zhejiang Medical and Health Group Hangzhou Hospital, 310022 Hangzhou, Zhejiang, China

2Department of Radiology, Hangzhou First People’s Hospital, 310006 Hangzhou, Zhejiang, China

3Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, 314408 Hangzhou, Zhejiang, China

4Department of Oncology, Zhejiang Medical and Health Group Hangzhou Hospital, 310022 Hangzhou, Zhejiang, China

DOI: 10.22514/jomh.2024.072 Vol.20,Issue 5,May 2024 pp.68-75

Submitted: 21 October 2023 Accepted: 21 November 2023

Published: 30 May 2024

*Corresponding Author(s): Gang Tao E-mail: tg@zyjhzyy.com

Abstract

This study aims to propose a radiomics model to identify male patients suffering from renal pelvis urothelial carcinoma (RPUC) with preoperative lymph node (LN) metastasis. In a study involving 133 male RPUC patients, 94 were assigned to a training group and 39 to a testing group. Their arterial-phase computed tomography (CT) images were analyzed to extract radiomics features, which were then refined through data reduction and feature selection. Using the least absolute shrinkage and selection operator (LASSO), a radiomics signature was created, which was then incorporated into a Logistic regression classifier in the training group to predict pathologic lymph node metastases. A comprehensive radiomics model was developed using multivariate logistic regression, integrating clinical risk factors. The model’s efficacy was evaluated in both sets using discrimination, calibration and decision curve analyses in both the training and testing sets. The constructed signature, composed of eight promising imaging-derived features, showed strong discrimination ability in both sets (training: area under the curve (AUC) 0.836 and testing: AUC, 0.817). When combined with CT-reported tumor status, the radiomics model demonstrated excellent calibration and discrimination, achieving an AUC of 0.849 in the training set and 0.851 in the testing set. The radiomics model, incorporating both the radiomics signature and the CT-reported tumor status, could help in the preoperative individualized prediction of LN metastasis in male patients with RPUC.


Keywords

Renal pelvis urothelial carcinoma; Radiomics; Lymph node metastasis; Computed tomography; Men


Cite and Share

Jingyi Huang,Fan Chen,Chengcheng Gao,Zhenyu Shu,Gang Tao. A radiomics model derived by combination of radiomics signature and clinical risk factors predict of lymph node metastasis for men renal pelvis urothelial carcinoma. Journal of Men's Health. 2024. 20(5);68-75.

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