Liu Zheng, Gu Wei-Jie, Wan Fang-Ning, Chen Zhang-Zhe, Kong Yun-Yi, Liu Xiao-Hang, Ye Ding-Wei, Dai Bo
Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
BMC Cancer. 2025 May 27;25(1):953. doi: 10.1186/s12885-025-14354-y.
Diffusion-weighted imaging with higher b-value improves detection rate for prostate cancer lesions. However, obtaining high b-value DWI requires more advanced hardware and software configuration. Here we use a novel deep learning network, NAFNet, to generate a deep learning reconstructed (DLR) images from 800 b-value to mimic 1500 b-value images, and to evaluate its performance and lesion detection improvements based on whole-slide images (WSI).
We enrolled 303 prostate cancer patients with both 800 and 1500 b-values from Fudan University Shanghai Cancer Centre between 2017 and 2020. We assigned these patients to the training and validation set in a 2:1 ratio. The testing set included 36 prostate cancer patients from an independent institute who had only preoperative DWI at 800 b-value. Two senior radiology doctors and two junior radiology doctors read and delineated cancer lesions on DLR, original 800 and 1500 b-values DWI images. WSI were used as the ground truth to assess the lesion detection improvement of DLR images in the testing set.
After training and generating, within junior radiology doctors, the diagnostic AUC based on DLR images is not inferior to that based on 1500 b-value images (0.832 (0.788-0.876) vs. 0.821 (0.747-0.899), P = 0.824). The same phenomenon is also observed in senior radiology doctors. Furthermore, in the testing set, DLR images could significantly enhance junior radiology doctors' diagnostic performance than 800 b-value images (0.848 (0.758-0.938) vs. 0.752 (0.661-0.843), P = 0.043).
DLR DWIs were comparable in quality to original 1500 b-value images within both junior and senior radiology doctors. NAFNet based DWI enhancement can significantly improve the image quality of 800 b-value DWI, and therefore promote the accuracy of prostate cancer lesion detection for junior radiology doctors.
采用更高b值的扩散加权成像可提高前列腺癌病灶的检出率。然而，获取高b值扩散加权成像需要更先进的硬件和软件配置。在此，我们使用一种新型深度学习网络——NAFNet，从800 b值生成深度学习重建（DLR）图像，以模拟1500 b值图像，并基于全视野图像（WSI）评估其性能及对病灶检测的改善情况。
我们纳入了2017年至2020年间来自复旦大学附属肿瘤医院的303例同时具有800和1500 b值的前列腺癌患者。我们将这些患者按2:1的比例分配到训练集和验证集。测试集包括来自一家独立机构的36例前列腺癌患者，他们仅在术前有800 b值的扩散加权成像。两名资深放射科医生和两名初级放射科医生在DLR图像、原始的800和1500 b值扩散加权成像上读取并勾勒出癌灶。全视野图像被用作金标准，以评估测试集中DLR图像对病灶检测的改善情况。
经过训练和生成后，在初级放射科医生中，基于DLR图像的诊断曲线下面积（AUC）不低于基于1500 b值图像的诊断曲线下面积（0.832（0.788 - 0.876）对0.821（0.747 - 0.899），P = 0.824）。在资深放射科医生中也观察到了同样的现象。此外，在测试集中，与800 b值图像相比，DLR图像可显著提高初级放射科医生的诊断性能（0.848（0.758 - 0.938）对0.752（0.661 - 0.843），P = 0.043）。
在初级和资深放射科医生中，DLR扩散加权成像在质量上与原始的1500 b值图像相当。基于NAFNet的扩散加权成像增强可显著提高800 b值扩散加权成像的图像质量，从而提高初级放射科医生对前列腺癌病灶检测的准确性。
