PDF(Pubmed)
Abstract：
BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment.
METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA).
RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy.
CONCLUSIONS: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).
METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA).
RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy.
CONCLUSIONS: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).
摘要：
背景：手术联合放疗可大幅增加早期宫颈鳞状细胞癌（ESCSCC）并发症的可能性。我们的目的是探讨基于深度学习的肿瘤内和瘤周MRI影像影像组学的可行性，以预测ESCSCC辅助放疗的病理特征，并最大限度地减少与治疗相关的不良事件的发生。
方法：从2019年1月至2022年4月接受根治性子宫切除术和盆腔淋巴结清扫术的289例患者获得了包含MR图像的数据集。该数据集以4:1的比例随机分为两组。术后放疗方案按照Peter/Sedlis标准进行评估。我们提取了临床特征，以及肿瘤内和瘤周的影像学特征，使用最小绝对收缩和选择算子(LASSO)回归。我们构建了临床签名(Clinic_Sig)，Radiomics签名(Rad_Sig)和深度变换器学习签名(DTL_Sig)。此外,我们将Rad_Sig与DTL_Sig融合以创建深度学习辐射组学签名(DLR_Sig)。我们使用曲线下面积(AUC)评估了模型的预测性能，校正曲线,和决策曲线分析(DCA)。
结果：DLR_Sig显示出高水平的准确性和预测能力，训练队列的曲线下面积（AUC）为0.98（95％CI：0.97-0.99），测试队列为0.79（95％CI：0.67-0.90）。此外,Hosmer-Lemeshow测试,它为训练队列提供了0.87的p值，为测试队列提供了0.15的p值，分别,表示很适合。DeLong检验表明，DLR_Sig的预测效果明显优于Clinic_Sig（训练和测试队列均P<0.05）。DLR_Sig的校准图表明，实际概率和预测概率之间具有极好的一致性，而DCA曲线表明在预测辅助放疗的病理特征方面具有更大的临床实用性。
结论：基于瘤内和瘤周MRI图像的DLR_Sig在术前预测早期宫颈鳞状细胞癌（ESCSCC）辅助放疗的病理特征方面具有潜力。
方法：从2019年1月至2022年4月接受根治性子宫切除术和盆腔淋巴结清扫术的289例患者获得了包含MR图像的数据集。该数据集以4:1的比例随机分为两组。术后放疗方案按照Peter/Sedlis标准进行评估。我们提取了临床特征，以及肿瘤内和瘤周的影像学特征，使用最小绝对收缩和选择算子(LASSO)回归。我们构建了临床签名(Clinic_Sig)，Radiomics签名(Rad_Sig)和深度变换器学习签名(DTL_Sig)。此外,我们将Rad_Sig与DTL_Sig融合以创建深度学习辐射组学签名(DLR_Sig)。我们使用曲线下面积(AUC)评估了模型的预测性能，校正曲线,和决策曲线分析(DCA)。
结果：DLR_Sig显示出高水平的准确性和预测能力，训练队列的曲线下面积（AUC）为0.98（95％CI：0.97-0.99），测试队列为0.79（95％CI：0.67-0.90）。此外,Hosmer-Lemeshow测试,它为训练队列提供了0.87的p值，为测试队列提供了0.15的p值，分别,表示很适合。DeLong检验表明，DLR_Sig的预测效果明显优于Clinic_Sig（训练和测试队列均P<0.05）。DLR_Sig的校准图表明，实际概率和预测概率之间具有极好的一致性，而DCA曲线表明在预测辅助放疗的病理特征方面具有更大的临床实用性。
结论：基于瘤内和瘤周MRI图像的DLR_Sig在术前预测早期宫颈鳞状细胞癌（ESCSCC）辅助放疗的病理特征方面具有潜力。
