PDF(Pubmed)
Abstract：
OBJECTIVE: This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by radiologists, limiting their practical applicability.
METHODS: The deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy.
RESULTS: In both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model.
CONCLUSIONS: The deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes.
CONCLUSIONS: • This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively.
METHODS: The deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy.
RESULTS: In both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model.
CONCLUSIONS: The deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes.
CONCLUSIONS: • This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively.
摘要：
目的：本研究旨在开发一种创新的，使用术前CT图像进行胸腺瘤风险分层的深层模型。当前的算法主要集中在影像组学特征或2D深层特征，并且需要放射科医生手动进行肿瘤分割。限制其实际适用性。
方法：在包含147名患者（82名女性；平均年龄，54岁±10），接受手术切除并接受随后的病理确认。根据CT扫描时间将符合条件的参与者分为训练队列（117名患者）和测试队列（30名患者）。该模型包括两个阶段：3D肿瘤分割和风险分层。构建放射学模型和深度模型(2D)进行比较分析。通过骰子系数评估模型性能，曲线下面积（AUC），和准确性。
结果：在培训和测试队列中，深度模型在区分胸腺瘤风险方面表现更好，AUC分别为0.998和0.893。将其与放射组学模型(AUC为0.773和0.769)和深度模型(2D)(AUC为0.981和0.760)进行比较。值得注意的是,深层模型能够同时识别病变，分割感兴趣区域(ROI)，并在动脉期CT图像上区分胸腺瘤的风险。其诊断能力优于基线模型。
结论：深度模型具有作为创新决策工具的潜力，协助临床预后评估和识别不同胸腺瘤病理亚型的合适治疗方法。
结论：•本研究纳入了肿瘤分割和风险分层。•深度模型，使用临床和3D深层特征，有效预测胸腺瘤风险。•与放射学模型和深度模型（2D）相比，深度模型分别将AUC提高了16.1pt和17.5pt。
方法：在包含147名患者（82名女性；平均年龄，54岁±10），接受手术切除并接受随后的病理确认。根据CT扫描时间将符合条件的参与者分为训练队列（117名患者）和测试队列（30名患者）。该模型包括两个阶段：3D肿瘤分割和风险分层。构建放射学模型和深度模型(2D)进行比较分析。通过骰子系数评估模型性能，曲线下面积（AUC），和准确性。
结果：在培训和测试队列中，深度模型在区分胸腺瘤风险方面表现更好，AUC分别为0.998和0.893。将其与放射组学模型(AUC为0.773和0.769)和深度模型(2D)(AUC为0.981和0.760)进行比较。值得注意的是,深层模型能够同时识别病变，分割感兴趣区域(ROI)，并在动脉期CT图像上区分胸腺瘤的风险。其诊断能力优于基线模型。
结论：深度模型具有作为创新决策工具的潜力，协助临床预后评估和识别不同胸腺瘤病理亚型的合适治疗方法。
结论：•本研究纳入了肿瘤分割和风险分层。•深度模型，使用临床和3D深层特征，有效预测胸腺瘤风险。•与放射学模型和深度模型（2D）相比，深度模型分别将AUC提高了16.1pt和17.5pt。
