Fournier Clémence, Leguillette Clémence, Leblanc Eric, Le Deley Marie-Cécile, Carnot Aurélien, Pasquier David, Escande Alexandre, Taieb Sophie, Ceugnart Luc, Lebellec Loïc
Department of Medical Oncology, Centre Hospitalier de Roubaix, 59100 Roubaix, France.
Clinical Research Department, Centre Oscar Lambret, 59000 Lille, France.
Cancers (Basel). 2023 May 31;15(11):2997. doi: 10.3390/cancers15112997.
After chemotherapy, patients with non-seminomatous germ cell tumors (NSGCTs) with residual masses >1 cm on computed tomography (CT) undergo surgery. However, in approximately 50% of cases, these masses only consist of necrosis/fibrosis. We aimed to develop a radiomics score to predict the malignant character of residual masses to avoid surgical overtreatment. Patients with NSGCTs who underwent surgery for residual masses between September 2007 and July 2020 were retrospectively identified from a unicenter database. Residual masses were delineated on post-chemotherapy contrast-enhanced CT scans. Tumor textures were obtained using the free software LifeX. We constructed a radiomics score using a penalized logistic regression model in a training dataset, and evaluated its performance on a test dataset. We included 76 patients, with 149 residual masses; 97 masses were malignant (65%). In the training dataset ( = 99 residual masses), the best model (ELASTIC-NET) led to a radiomics score based on eight texture features. In the test dataset, the area under the curve (AUC), sensibility, and specificity of this model were respectively estimated at 0.82 (95%CI, 0.69-0.95), 90.6% (75.0-98.0), and 61.1% (35.7-82.7). Our radiomics score may help in the prediction of the malignant nature of residual post-chemotherapy masses in NSGCTs before surgery, and thus limit overtreatment. However, these results are insufficient to simply select patients for surgery.
化疗后,计算机断层扫描(CT)显示残留肿块大于1 cm的非精原细胞瘤性生殖细胞肿瘤(NSGCT)患者需接受手术治疗。然而,在大约50%的病例中,这些肿块仅由坏死/纤维化组成。我们旨在开发一种放射组学评分系统,以预测残留肿块的恶性特征,避免手术过度治疗。我们从一个单中心数据库中回顾性筛选出2007年9月至2020年7月期间因残留肿块接受手术的NSGCT患者。在化疗后的增强CT扫描上勾勒出残留肿块。使用免费软件LifeX获取肿瘤纹理。我们在训练数据集中使用惩罚逻辑回归模型构建了一个放射组学评分系统,并在测试数据集中评估其性能。我们纳入了76例患者,共149个残留肿块;其中97个肿块为恶性(65%)。在训练数据集(n = 99个残留肿块)中,最佳模型(弹性网络)得出了基于八个纹理特征的放射组学评分。在测试数据集中,该模型的曲线下面积(AUC)、敏感性和特异性分别估计为0.82(95%CI,0.69 - 0.95)、90.6%(75.0 - 98.0)和61.1%(35.7 - 82.7)。我们的放射组学评分系统可能有助于在手术前预测NSGCT化疗后残留肿块的恶性性质,从而限制过度治疗。然而,这些结果尚不足以直接筛选出适合手术的患者。