Abstract：
OBJECTIVE: To assess the efficacy of a preoperative contrast-enhanced CT (CECT)-based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroperitoneal well-differentiated liposarcomas (WDLPS) and lipomas.
METHODS: This retrospective multi-center study included 167 patients (training/external test cohort, 104/63) with MDM2-positive WDLPS or MDM2-negative lipomas. Clinical data and CECT features were independently measured and analyzed by two radiologists. A clinico-radiological model, radiomics signature (RS), deep learning and radiomics signature (DLRS), and a DLRN incorporating radiomics and deep learning features were developed to differentiate between WDLPS and lipoma. The model utility was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA).
RESULTS: The DLRN showed good performance for distinguishing retroperitoneal lipomas and WDLPS in the training (AUC, 0.981; accuracy, 0.933) and external validation group (AUC, 0.861; accuracy, 0.810). The DeLong test revealed the DLRN was noticeably better than clinico-radiological and RS models (training: 0.981 vs. 0.890 vs. 0.751; validation: 0.861 vs. 0.724 vs. 0.700; both P < 0.05); however, no discernible difference in performance was seen between the DLRN and DLRS (training: 0.981 vs. 0.969; validation: 0.861 vs. 0.837; both P > 0.05). The calibration curve analysis and DCA demonstrated that the nomogram exhibited good calibration and offered substantial clinical advantages.
CONCLUSIONS: The DLRN exhibited strong predictive capability in predicting WDLPS and retroperitoneal lipomas preoperatively, making it a promising imaging biomarker that can facilitate personalized management and precision medicine.
METHODS: This retrospective multi-center study included 167 patients (training/external test cohort, 104/63) with MDM2-positive WDLPS or MDM2-negative lipomas. Clinical data and CECT features were independently measured and analyzed by two radiologists. A clinico-radiological model, radiomics signature (RS), deep learning and radiomics signature (DLRS), and a DLRN incorporating radiomics and deep learning features were developed to differentiate between WDLPS and lipoma. The model utility was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA).
RESULTS: The DLRN showed good performance for distinguishing retroperitoneal lipomas and WDLPS in the training (AUC, 0.981; accuracy, 0.933) and external validation group (AUC, 0.861; accuracy, 0.810). The DeLong test revealed the DLRN was noticeably better than clinico-radiological and RS models (training: 0.981 vs. 0.890 vs. 0.751; validation: 0.861 vs. 0.724 vs. 0.700; both P < 0.05); however, no discernible difference in performance was seen between the DLRN and DLRS (training: 0.981 vs. 0.969; validation: 0.861 vs. 0.837; both P > 0.05). The calibration curve analysis and DCA demonstrated that the nomogram exhibited good calibration and offered substantial clinical advantages.
CONCLUSIONS: The DLRN exhibited strong predictive capability in predicting WDLPS and retroperitoneal lipomas preoperatively, making it a promising imaging biomarker that can facilitate personalized management and precision medicine.
摘要：
目的：评估基于术前对比增强CT（CECT）的深度学习影像组学列线图（DLRN）预测小鼠双分2（MDM2）基因扩增的有效性，以区分腹膜后高分化脂肪肉瘤（WDLPS）和脂肪瘤。
方法：这项回顾性多中心研究包括167名患者（训练/外部测试队列，104/63）患有MDM2阳性WDLPS或MDM2阴性脂肪瘤。临床数据和CECT特征由两名放射科医生独立测量和分析。临床放射模型,放射组学签名(RS)，深度学习和影像组学签名(DLRS)，并开发了包含影像组学和深度学习功能的DLRN以区分WDLPS和脂肪瘤。根据受试者工作特征曲线下面积（AUC）评估模型效用，准确度,校正曲线,和决策曲线分析(DCA)。
结果：DLRN在训练中显示出很好的区分腹膜后脂肪瘤和WDLPS的表现（AUC，0.981；精度，0.933）和外部验证组（AUC，0.861；准确度，0.810)。DeLong测试显示DLRN明显优于临床放射学和RS模型（训练：0.981vs.0.890vs.0.751；验证：0.861与0.724vs.0.700；两者P<0.05)；然而，DLRN和DLRS之间的表现没有明显差异(训练：0.981vs.0.969；验证：0.861与0.837；均P>0.05）。校准曲线分析和DCA表明，列线图显示出良好的校准效果，并具有明显的临床优势。
结论：DLRN在术前预测WDLPS和腹膜后脂肪瘤方面表现出较强的预测能力，使其成为有前途的成像生物标志物，可以促进个性化管理和精准医疗。
方法：这项回顾性多中心研究包括167名患者（训练/外部测试队列，104/63）患有MDM2阳性WDLPS或MDM2阴性脂肪瘤。临床数据和CECT特征由两名放射科医生独立测量和分析。临床放射模型,放射组学签名(RS)，深度学习和影像组学签名(DLRS)，并开发了包含影像组学和深度学习功能的DLRN以区分WDLPS和脂肪瘤。根据受试者工作特征曲线下面积（AUC）评估模型效用，准确度,校正曲线,和决策曲线分析(DCA)。
结果：DLRN在训练中显示出很好的区分腹膜后脂肪瘤和WDLPS的表现（AUC，0.981；精度，0.933）和外部验证组（AUC，0.861；准确度，0.810)。DeLong测试显示DLRN明显优于临床放射学和RS模型（训练：0.981vs.0.890vs.0.751；验证：0.861与0.724vs.0.700；两者P<0.05)；然而，DLRN和DLRS之间的表现没有明显差异(训练：0.981vs.0.969；验证：0.861与0.837；均P>0.05）。校准曲线分析和DCA表明，列线图显示出良好的校准效果，并具有明显的临床优势。
结论：DLRN在术前预测WDLPS和腹膜后脂肪瘤方面表现出较强的预测能力，使其成为有前途的成像生物标志物，可以促进个性化管理和精准医疗。
