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. 2023 Jan 20:14:1093452.
doi: 10.3389/fendo.2023.1093452. eCollection 2023.

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy

Xiachuan Qin  1   2 , Linlin Xia  1 , Xiaomin Hu  2 , Weihan Xiao  2 , Xian Huaming  3 , Xie Xisheng  3 , Chaoxue Zhang  1
Affiliations

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy

Xiachuan Qin et al. Front Endocrinol (Lausanne). .

Abstract

Objective: We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy.

Methods: Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.

Results: The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram.

Conclusion: ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.

Keywords: IgA nephropathy; crescents; machine learning; nomogram; radiomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SF declared a shared parent affiliation with the authors XQ, LX, CZ to the handling editor at the time of review.

Figures

Figure 1
Figure 1
The flow diagram of the study.
Figure 2
Figure 2
The radiomics flow chart of the study.
Figure 3
Figure 3
The receiver operating characteristic (ROC) curves of the three ML models. (A) Three ML model ROC curves in the training cohort. (B) Three model ML ROC curves in the testing cohort.
Figure 4
Figure 4
The clinical radiomics nomogram. The values of clinical characteristics and rad score can be converted into quantitative values according to the points axis. After summing the individual points to achieve the final sum shown on the total points axis, The evaluation of this crescent is shown.
Figure 5
Figure 5
The calibration curve of the clinical radiomics model. (A) The calibration plot also showed good agreement between the transition probabilities predicted by the nomogram in the training cohort; (B) The calibration plot also showed good agreement in the testing cohort.
Figure 6
Figure 6
The receiver operating characteristic (ROC) curves and decision curve analysis (DCA) of the three models of the three models. (A) Three model ROC curves in the training cohort. (B) Three model ROC curves in the testing cohort. (C) Three DCA models in the training cohort. (D) Three DCA models in the testing cohort.
See this image and copyright information in PMC

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