Objective Accurate preoperative assessment of muscle invasion in bladder cancer (BCa) guides therapy selection. However, MRI interpretation varies across readers and lesion morphologies. Therefore, we aimed to overcome the morphology-associated diagnostic bias through a deep learning method. Materials and methods This multicenter study included 1374 patients with BCa. An nnU-Net was fine-tuned to assist in lesion segmentation on T2-weighted images, providing inputs for a 2.5D ConvNeXt-tiny model to assess muscle invasion. The performance of the model was compared between pedunculated and sessile lesions. Furthermore, a head-to-head comparison was conducted among the model, a senior radiologist, and a junior radiologist. Results The validation Dice coefficient of nnU-net was 0.834. In the validation and three prospective test sets, the ConvNeXt-tiny model achieved areas under the receiver-operating characteristic curve of 0.915–0.925 for identifying muscle invasion in BCa, with accuracies of 84.9–91.0%, sensitivities of 81.3–96.2%, and specificities of 81.1–93.8%. In the subgroup analysis of pedunculated and sessile lesions, the model’s diagnostic performance showed no significant difference across all datasets. In contrast, the two radiologists’ specificities declined from around 90% in pedunculated lesions to approximately 75% (p = 0.010–0.050) in sessile lesions across the validation set, internal test set, and external test set 1. Therefore, in the head-to-head comparison of sessile lesions, the model demonstrated significantly higher specificities (91.9–96.0%) than the two radiologists (72.8–79.8%, p < 0.001–0.005) across the same datasets. Conclusions The DL model assesses muscle invasion in BCa independently of lesion morphology and holds potential for clinical application, particularly in sessile lesions. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Conventional MRI assessment of muscle invasion risk in pedunculated and sessile bladder cancers may be biased, but clear evidence and potential solutions are still lacking. <Emphasis Type="BoldItalic">Findings</Emphasis> Morphology-associated diagnostic bias indeed exists, mainly as overstaging of sessile bladder cancer, while deep learning assessment of muscle invasion risk is morphology-independent. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The transformer-enhanced convolutional neural network developed in this study effectively reduces overestimation of muscle invasion risk in sessile bladder cancer and may serve as a complementary imaging tool for clinical evaluation. Graphical Abstract

Application of transformer-enhanced convolutional neural network: multicenter MRI assessment of muscle invasion in bladder cancer

期刊论文
发表日期：

2026年4月4日

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Zhichang Fan,
Ding Li,
Wenjing Chen,
Yan Li,
Junting Guo,
Wenqiao Zheng,
Bin Wang,
Yongfang Wang,
Xiaochun Wang

摘要

Objective

Accurate preoperative assessment of muscle invasion in bladder cancer (BCa) guides therapy selection. However, MRI interpretation varies across readers and lesion morphologies. Therefore, we aimed to overcome the morphology-associated diagnostic bias through a deep learning method.

Materials and methods

This multicenter study included 1374 patients with BCa. An nnU-Net was fine-tuned to assist in lesion segmentation on T2-weighted images, providing inputs for a 2.5D ConvNeXt-tiny model to assess muscle invasion. The performance of the model was compared between pedunculated and sessile lesions. Furthermore, a head-to-head comparison was conducted among the model, a senior radiologist, and a junior radiologist.

Results

The validation Dice coefficient of nnU-net was 0.834. In the validation and three prospective test sets, the ConvNeXt-tiny model achieved areas under the receiver-operating characteristic curve of 0.915–0.925 for identifying muscle invasion in BCa, with accuracies of 84.9–91.0%, sensitivities of 81.3–96.2%, and specificities of 81.1–93.8%. In the subgroup analysis of pedunculated and sessile lesions, the model’s diagnostic performance showed no significant difference across all datasets. In contrast, the two radiologists’ specificities declined from around 90% in pedunculated lesions to approximately 75% (p = 0.010–0.050) in sessile lesions across the validation set, internal test set, and external test set 1. Therefore, in the head-to-head comparison of sessile lesions, the model demonstrated significantly higher specificities (91.9–96.0%) than the two radiologists (72.8–79.8%, p < 0.001–0.005) across the same datasets.

Conclusions

The DL model assesses muscle invasion in BCa independently of lesion morphology and holds potential for clinical application, particularly in sessile lesions.

Key Points

Question Conventional MRI assessment of muscle invasion risk in pedunculated and sessile bladder cancers may be biased, but clear evidence and potential solutions are still lacking.

Findings Morphology-associated diagnostic bias indeed exists, mainly as overstaging of sessile bladder cancer, while deep learning assessment of muscle invasion risk is morphology-independent.

Clinical relevance The transformer-enhanced convolutional neural network developed in this study effectively reduces overestimation of muscle invasion risk in sessile bladder cancer and may serve as a complementary imaging tool for clinical evaluation.

Graphical Abstract