利用表观扩散系数图和影像组学预测上尿路尿路上皮癌的病理分级
Using apparent diffusion coefficient maps and radiomics to predict pathological grade in upper urinary tract urothelial carcinoma.
作者信息
Nai Rile, Wang Kexin, Ma Shuai, Xi Zuqiang, Zhang Yaofeng, Zhang Xiaodong, Wang Xiaoying
机构信息
Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
School of Basic Medical Sciences, Capital Medical University, 10, Xitoutiao, Youanmenwai, Fengtai District, Beijing, 100069, China.
出版信息
BMC Med Imaging. 2024 Dec 30;24(1):355. doi: 10.1186/s12880-024-01540-w.
BACKGROUND
The apparent diffusion coefficient (ADC) has been reported as a quantitative biomarker for assessing the aggressiveness of upper urinary tract urothelial carcinoma (UTUC), but it has typically been used only with mean ADC values. This study aims to develop a radiomics model using ADC maps to differentiate UTUC grades by incorporating texture features and to compare its performance with that of mean ADC values.
METHODS
A total of 215 patients with histopathologically confirmed UTUC were enrolled retrospectively and divided into training and test sets. The optimum cutoff value for the mean ADC was derived using the receiver operating characteristic (ROC) curve. Radiomics features based on ADC maps were extracted and screened, and then a radiomics model was constructed. Both mean ADC values and the radiomics model were tested on the training and test sets. ROC curve and DeLong test were used to assess the diagnostic performance.
RESULTS
The training set consisted of 151 patients (median age: 68.0, IQR: [63.0, 75.0] years; 80 males), whereas the test set consisted of 64 patients (median age: 68.0, IQR: [61.0, 72.3] years; 31 males). The ADC values were significantly lower in high-grade versus low-grade UTUC (1310 × 10mm/s vs. 1480 × 10mm/s, p < 0.001). The area under the curve (AUC) values of the mean ADC values in the training and test sets were 0.698 [95% confidence interval [CI]: 0.625-0.772] and 0.628 [95% CI: 0.474-0.782], respectively. Compared with the mean ADC values, the ADC-based radiomics model, which incorporates features such as log-sigma-1-0-mm-3D_glcm_ClusterProminence and wavelet-LLL_firstorder_10Percentile, obtained a significantly greater AUC in the training set (AUC: 1.000, 95% CI: 1.000-1.000, p < 0.001), and a trend towards statistical significance in the test set (AUC: 0.786, 95% CI: 0.651-0.921, p = 0.071).
CONCLUSIONS
The ADC-based radiomics model showed promising potential in predicting the pathological grade of UTUC, outperforming the mean ADC values in classification accuracy. Further studies with larger sample sizes and external validation are necessary to confirm its clinical utility and generalizability.
CLINICAL TRIAL NUMBER
Not applicable.
背景
表观扩散系数（ADC）已被报道为评估上尿路尿路上皮癌（UTUC）侵袭性的定量生物标志物，但通常仅使用平均ADC值。本研究旨在开发一种利用ADC图的放射组学模型，通过纳入纹理特征来区分UTUC分级，并将其性能与平均ADC值进行比较。
方法
回顾性纳入215例经组织病理学确诊的UTUC患者，并分为训练集和测试集。使用受试者操作特征（ROC）曲线得出平均ADC的最佳截断值。提取并筛选基于ADC图的放射组学特征，然后构建放射组学模型。在训练集和测试集上对平均ADC值和放射组学模型进行测试。使用ROC曲线和DeLong检验评估诊断性能。
结果
训练集包括151例患者（中位年龄：68.0岁，四分位间距：[63.0，75.0]岁；80例男性），而测试集包括64例患者（中位年龄：68.0岁，四分位间距：[61.0，72.3]岁；31例男性）。高级别UTUC的ADC值显著低于低级别UTUC（1310×10⁻³mm²/s对1480×10⁻³mm²/s，p<0.001）。训练集和测试集中平均ADC值的曲线下面积（AUC）分别为0.698[95%置信区间（CI）：0.625 - 0.772]和0.628[95%CI：0.474 - 0.782]。与平均ADC值相比，纳入诸如对数标准差-1-0-mm-3D_glcm_簇突出度和小波-LLL_一阶_10百分位数等特征的基于ADC的放射组学模型在训练集中获得了显著更高的AUC（AUC：1.000，95%CI：1.000 - 1.000，p<0.001），在测试集中有统计学意义的趋势（AUC：0.786，95%CI：0.651 - 0.921，p = 0.071）。
结论
基于ADC的放射组学模型在预测UTUC病理分级方面显示出有前景的潜力，在分类准确性方面优于平均ADC值。需要进一步进行更大样本量的研究和外部验证以确认其临床实用性和可推广性。
临床试验编号
不适用。
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