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Development And Validation Of A Ct-based Deep Learning Radiomics Nomogram To Predict Muscle Invasion In Bladder Cancer.

Home
Development And Validation Of A Ct-based Deep Learning Radiomics Nomogram To Predict Muscle Invasion In Bladder Cancer.

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Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder

Zongjie Wei¹, Huayun Liu¹, Yingjie Xv¹

¹Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

|February 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new CT-based nomogram combining radiomics and deep learning accurately predicts muscle invasion in bladder cancer. This tool aids in preoperative decision-making for bladder cancer (BCa) patients.

Keywords:

Bladder cancer Computed tomography Deep learning Nomogram Radiomics

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Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Muscle invasion is a critical factor in bladder cancer (BCa) staging and treatment.
Accurate preoperative prediction of muscle invasion is essential for optimal clinical management.

Purpose of the Study:

To develop and validate a nomogram integrating CT-based handcrafted radiomics and deep learning (DL) features for preoperative prediction of muscle invasion in BCa.
To assess the predictive performance and clinical usefulness of the developed nomogram.

Main Methods:

A retrospective study included 323 BCa patients, divided into training, internal validation, and external validation cohorts.
Handcrafted radiomics and DL features were extracted from CT images.
A deep learning radiomics nomogram (DLRN) was constructed using LASSO regression and logistic regression.

Main Results:

The DLRN demonstrated strong predictive performance, with an AUC of 0.884 in the internal validation cohort and 0.862 in the external validation cohort.
The nomogram effectively differentiated non-muscle invasive BCa (NMIBC) from muscle invasive BCa (MIBC).
Decision curve analysis confirmed the clinical utility of the DLRN.

Conclusions:

A CT-based deep learning radiomics nomogram shows promising performance for preoperative prediction of muscle invasion in BCa.
The DLRN may serve as a valuable tool to assist clinicians in the decision-making process for BCa management.