Yu Na, Wang Baoping, Ren Jialiang, Wu Hui, Gao Yang, Liu Aishi, Niu Guangming
Department of Imaging Diagnosis, The Fifth People's Hospital of Datong, Datong, Shanxi Province, China.
General Electric Pharmaceutical (Shanghai), Beijing, China.
J Int Med Res. 2021 Jun;49(6):3000605211014301. doi: 10.1177/03000605211014301.
Three models were used to evaluate prostate cancer after androgen deprivation therapy (ADT) and to determine the value of detecting residual lesions after treatment.
We retrospectively analysed patients with prostate cancer who received ADT from January 2018 to June 2019. Patients were divided into ADT responder and ADT non-responder groups, and clinical risk factors were determined. Regions of interest were manually contoured on each slice on fat-saturated-T2-weighted imaging, and radiomic features were extracted. Uni- and multivariate logistic regression were used to establish radiomics, clinical and combined models.
There were 23 ADT non-responders and 20 ADT responders. In the clinical model, total prostate-specific antigen concentration and T stage were independent predictors of efficacy (area under the curve (AUC) = 0.774). The characteristics, MinIntensity and Correlation_ angle135_offset4 indicated an effective clinical model (AUC = 0.807). GLCMEntropy_ AllDirection_offset1_SD was the best feature to differentiate residual lesions from the central gland (CG) (Lesion-CG model, AUC = 0.955). Correlation_angle135_offset4, GLCMEntropy_ AllDirection_offset4_SD and GLCMEntropy_AllDirection_offset7_SD differentiated residual lesions from the peripheral zone (PZ) (Lesion-PZ model, AUC = 0.855). The AUC for the combined model was 0.904.
Our models can guide the clinical treatment of patients with different ADT responses. Furthermore, the radiomics model can detect prostate cancer that is non-responsive to ADT.
使用三种模型评估去势雄激素治疗（ADT）后的前列腺癌，并确定治疗后检测残留病灶的价值。
我们回顾性分析了2018年1月至2019年6月接受ADT的前列腺癌患者。将患者分为ADT反应者和ADT无反应者组，并确定临床危险因素。在脂肪饱和T2加权成像的每个切片上手动勾勒感兴趣区域，并提取放射组学特征。使用单变量和多变量逻辑回归建立放射组学、临床和联合模型。
有23例ADT无反应者和20例ADT反应者。在临床模型中，总前列腺特异性抗原浓度和T分期是疗效的独立预测因素（曲线下面积（AUC）=0.774）。特征MinIntensity和Correlation_angle135_offset4表明是有效的临床模型（AUC=0.807）。GLCMEntropy_AllDirection_offset1_SD是区分中央腺体（CG）残留病灶的最佳特征（病灶-CG模型，AUC=0.955）。Correlation_angle135_offset4、GLCMEntropy_AllDirection_offset4_SD和GLCMEntropy_AllDirection_offset7_SD区分外周区（PZ）残留病灶（病灶-PZ模型，AUC=0.855）。联合模型的AUC为0.904。
我们的模型可以指导不同ADT反应患者的临床治疗。此外，放射组学模型可以检测对ADT无反应的前列腺癌。
