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Insights into Imaging
Published in: Insights into Imaging 1/2023

Open Access 01-12-2023 | Kidney Cancer | Original Article

A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma

Authors: Shihui Wang, Chao Zhu, Yidong Jin, Hongqing Yu, Lili Wu, Aijuan Zhang, Beibei Wang, Jian Zhai

Published in: Insights into Imaging | Issue 1/2023

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Abstract

Objectives

This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC).

Methods

In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell’s concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration.

Results

The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912–0.980) and 0.864 (95% CI: 0.734–0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group.

Conclusions

The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC.

Critical relevance statement

We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades.

Key points

1. The multi-model achieved an AUC of 0.864 for assessing pathological grade.
2. The multi-model exhibited an association with survival in ccRCC patients.
3. The high-grade group demonstrated a greater abundance of immune cells.

Graphical Abstract

Appendix
Available only for authorised users
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Metadata
Title
A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma
Authors
Shihui Wang
Chao Zhu
Yidong Jin
Hongqing Yu
Lili Wu
Aijuan Zhang
Beibei Wang
Jian Zhai
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-023-01557-9

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