Ye Lei, Wang Yayi, Xiang Wanxin, Yao Jin, Liu Jiaming, Song Bin
Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, China.
J Clin Med. 2023 Sep 11;12(18):5900. doi: 10.3390/jcm12185900.
We explored the added value of a radiomic strategy based on quantitative transverse relaxation (T2) mapping and conventional magnetic resonance imaging (MRI) to evaluate the histologic grade of bladder cancer (BCa) preoperatively. Patients who were suspected of BCa underwent pelvic MRI (including T2 mapping and diffusion-weighted imaging (DWI) before any treatment. All patients with histological-proved urothelial BCa were included. We constructed different prediction models using the mean signal values and radiomic features from both T2 mapping and apparent diffusion coefficient (ADC) maps. The diagnostic performance of each model or parameter was assessed using receiver operating characteristic curves. In total, 92 patients were finally included (training cohort, = 64; testing cohort, = 28); among these, 71 had high-grade BCa. In the testing cohort, the T2-mapping radiomic model achieved the highest prediction performance (area under the curve (AUC), 0.87; 95% confidence interval (CI), 0.73-1.0) compared with the ADC radiomic model (AUC, 0.77; 95%CI, 0.56-0.97), and the joint radiomic model of 0.78 (95%CI, 0.61-0.96). Our results demonstrated that radiomic mapping could provide more information than direct evaluation of T2 and ADC values in differentiating histological grades of BCa. Additionally, among the radiomic models, the T2-mapping radiomic model outperformed the ADC and joint radiomic models.
我们探讨了基于定量横向弛豫（T2）成像和传统磁共振成像（MRI）的放射组学策略在术前评估膀胱癌（BCa）组织学分级方面的附加价值。疑似患有BCa的患者在接受任何治疗前均接受盆腔MRI检查（包括T2成像和扩散加权成像（DWI））。所有经组织学证实为尿路上皮BCa的患者均被纳入研究。我们使用T2成像和表观扩散系数（ADC）图的平均信号值及放射组学特征构建了不同的预测模型。使用受试者操作特征曲线评估每个模型或参数的诊断性能。最终共纳入92例患者（训练队列，n = 64；测试队列，n = 28）；其中71例为高级别BCa。在测试队列中，与ADC放射组学模型（曲线下面积（AUC），0.77；95%置信区间（CI），0.56 - 0.97）及联合放射组学模型（AUC，0.78；95%CI，0.61 - 0.96）相比，T2成像放射组学模型具有最高的预测性能（AUC，0.87；95%CI，0.73 - 1.0）。我们的结果表明，在鉴别BCa组织学分级方面，放射组学成像比直接评估T2和ADC值可提供更多信息。此外，在放射组学模型中，T2成像放射组学模型优于ADC和联合放射组学模型。
