Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai, 200032, People's Republic of China.
Department of Radiology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, No. 241, West Huaihai Rd, Shanghai, 200030, People's Republic of China.
Cancer Imaging. 2024 Nov 28;24(1):163. doi: 10.1186/s40644-024-00808-2.
It is difficult for radiologists, especially junior radiologists with limited experience to make differential diagnoses between mediastinal lymphomas and thymic epithelial tumors (TETs) due to the overlapping imaging features. The purpose of this study was to develop and validate a CT-based clinico-radiomics model for differentiating lymphomas from TETs and to investigate whether a human-machine hybrid system can assist junior radiologists in improving their diagnostic performance.
The patients who underwent contrast-enhanced chest CT and pathologically confirmed with lymphoma or TET at two centers from January 2011 to December 2019 and from January 2017 to December 2021 were retrospectively included and split as training/validation set and external test set, respectively. Clinical and radiomic signatures were pre-selected by elastic-net, and the models were established with the selected signatures using ensemble learning. Three radiologists independently reviewed CT images and assessed each case of the external test set with knowledge of the relevant clinical information. The diagnoses of reader 1, reader 2, and reader 3 were compared with those of the models in the external test set and further separately input to the model's ensemble process as a human-machine system to make final decisions in the external test set. The improvement of diagnostic performance of radiologists by human-machine system was evaluated by the area under the receiver operating characteristic curve and increase rate.
A total of 95 patients (51 with lymphomas and 44 with TETs) at Center 1 and 94 (52 with lymphomas and 42 with TETs) at Center 2 were enrolled and divided into training/validation sets and external test set, respectively. The diagnostic performance of the clinico-radiomics model has outperformed the junior radiologists and senior radiologist in AUC (clinico-radiomics model: 0.85 (0.76,0.92); reader 2: 0.70 (0.60,0.80); reader 3: 0.60 (0.49,0.71), reader 1: 0.76 (0.66,0.86), respectively) in the external test set. The human-machine hybrid system demonstrated significant increases in AUC (reader 1 + model: 0.87 (0.79,0.94), an increase of 14%; reader 2 + model: 0.86 (0.77,0.93), an increase of 23%; reader 3 + model: 0.84 (0.76,0.91), an increase of 40%), compared to the human performance alone.
The clinico-radiomics model outperformed three radiologists in differentiating lymphomas from TETs on CT. The use of the human-machine hybrid system significantly improved the performance of radiologists, especially junior radiologists. It provides a real-time decision tool to reduce bias and mistakes in radiologist diagnosis and enhances the diagnostic confidence of junior radiologists. This attempt may lead to more human-machine hybrid systems being explored in the diagnosis of different diseases to drive future clinical applications.
由于纵隔淋巴瘤和胸腺癌（TET）的影像学特征重叠，对于经验有限的放射科医生，尤其是初级放射科医生来说，进行鉴别诊断具有一定难度。本研究旨在建立并验证一种基于 CT 的临床-放射组学模型，用于区分淋巴瘤和 TET，并探讨人机混合系统是否可以帮助初级放射科医生提高诊断性能。
回顾性纳入 2011 年 1 月至 2019 年 12 月和 2017 年 1 月至 2021 年 12 月在两个中心接受增强胸部 CT 检查且经病理证实为淋巴瘤或 TET 的患者，并将其分别分为训练/验证集和外部测试集。采用弹性网络进行临床和放射组学特征的预筛选，然后使用集成学习方法从选定的特征中建立模型。三位放射科医生分别独立阅片，并在了解相关临床信息的情况下对外部测试集的每个病例进行评估。将读者 1、读者 2 和读者 3 的诊断与外部测试集中模型的诊断进行比较，并将读者 1、读者 2 和读者 3 的诊断结果分别输入到模型的集成过程中，作为人机系统在外部测试集中做出最终决策。通过接受者操作特征曲线下面积（AUC）和增长率评估人机系统对放射科医生诊断性能的提高程度。
在中心 1 共纳入 95 例患者（51 例淋巴瘤，44 例 TET），在中心 2 共纳入 94 例患者（52 例淋巴瘤，42 例 TET），分别将其分为训练/验证集和外部测试集。在外部测试集中，临床-放射组学模型的诊断性能优于初级放射科医生和高级放射科医生（AUC：临床-放射组学模型：0.85（0.76，0.92）；读者 2：0.70（0.60，0.80）；读者 3：0.60（0.49，0.71）；读者 1：0.76（0.66，0.86））。人机混合系统在 AUC 方面的表现显著提高（读者 1+模型：0.87（0.79，0.94），提高 14%；读者 2+模型：0.86（0.77，0.93），提高 23%；读者 3+模型：0.84（0.76，0.91），提高 40%），优于单独使用人类表现。
在 CT 上，临床-放射组学模型在区分纵隔淋巴瘤和胸腺癌方面优于三位放射科医生。人机混合系统的使用显著提高了放射科医生，尤其是初级放射科医生的性能。它提供了一个实时的决策工具，可以减少放射科医生诊断中的偏差和错误，并增强初级放射科医生的诊断信心。这种尝试可能会促使更多的人机混合系统在不同疾病的诊断中得到探索，从而推动未来的临床应用。
