Yuan Yuan, Ren Shengnan, Lu Haidi, Chen Fangying, Xiang Lei, Chamberlain Ryan, Shao Chengwei, Lu Jianping, Shen Fu, Chen Luguang
Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China.
Department of Nuclear Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, 200434, China.
BMC Med Imaging. 2025 Jul 1;25(1):259. doi: 10.1186/s12880-025-01775-1.
To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, compared with conventional MR images without DLR.
Images of high-resolution T2-weighted, diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) from patients with pathologically diagnosed rectal cancer were retrospectively processed with and without DLR and assessed by five readers. The first two readers measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the lesions. The overall image quality and lesion display performance for each sequence with and without DLR were independently scored using a five-point scale, and the TN stage of rectal cancer lesions was evaluated by the other three readers. Fifty of the patients were randomly selected to further make a comparison between DLR and traditional denoising filter. Deep learning classification models were developed and compared for the TN stage. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the proposed model.
Overall, 178 patients were evaluated. The SNR and CNR of the lesion on images with DLR were significantly higher than those without DLR, for T2WI, DWI and CE-T1WI, respectively (p < 0.0001). A significant difference was observed in overall image quality and lesion display performance between images with and without DLR (p < 0.0001). The image quality scores, SNR, and CNR values of DLR image set were significantly larger than those of original and filter enhancement image sets (all p values < 0.05) for all the three sequences, respectively. The deep learning classification models with DLR achieved good discrimination of the TN stage, with area under the curve (AUC) values of 0.937 (95% CI 0.839-0.977) and 0.824 (95% CI 0.684-0.913) in the test sets, respectively.
Deep learning reconstruction and classification models could improve the image quality of rectal MRI images and enhance the diagnostic performance for determining the TN stage of patients with rectal cancer.
确定深度学习重建（DLR）是否能提高直肠癌磁共振成像（MRI）的图像质量，并与未进行DLR的传统MRI图像相比，探讨不同阅片者和深度学习分类模型对直肠癌TN分期的鉴别能力。
对经病理诊断为直肠癌患者的高分辨率T2加权成像、扩散加权成像（DWI）和对比增强T1加权成像（CE-T1WI）图像，分别进行有无DLR的回顾性处理，并由5名阅片者进行评估。前两名阅片者测量病变的信噪比（SNR）和对比噪声比（CNR）。对有无DLR的每个序列的整体图像质量和病变显示性能采用五分制独立评分，另外三名阅片者评估直肠癌病变的TN分期。随机选择50例患者进一步比较DLR与传统去噪滤波器。开发深度学习分类模型并比较其对TN分期的诊断性能。采用受试者操作特征（ROC）曲线分析和决策曲线分析（DCA）评估所提模型的诊断性能。
共评估178例患者。对于T2WI、DWI和CE-T1WI，有DLR图像上病变的SNR和CNR分别显著高于无DLR图像（p<0.0001）。有无DLR的图像在整体图像质量和病变显示性能上存在显著差异（p<0.0001）。对于所有三个序列，DLR图像集的图像质量评分、SNR和CNR值分别显著大于原始图像集和滤波增强图像集（所有p值<0.05）。采用DLR的深度学习分类模型对TN分期具有良好的鉴别能力，测试集中曲线下面积（AUC）值分别为0.937（95%CI 0.839-0.977）和0.824（95%CI 0.684-0.913）。
深度学习重建和分类模型可提高直肠癌MRI图像质量，增强对直肠癌患者TN分期的诊断性能。
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