目的:本研究旨在建立多模态深度学习网络模型,以增强良恶性肺磨玻璃结节[GGNs]的诊断。
方法:从中国多个中心收集肺GGNs的回顾性数据,包括北方,东北,西北,南,和中国西南部。以8:2的比率将数据分成训练集和验证集。此外,我们还从我们的医院数据库获得了一个GGN数据集,并将其用作测试集.所有患者均行胸部计算机断层扫描[CT],结节的最终诊断基于术后病理报告。残差网络[ResNet]用于提取成像数据,用于语义信息抽取的Word2Vec方法,以及结合影像特征和患者数据构建多模态分类模型的自注意方法。然后,将提出的多模式模型的诊断效率与现有的ResNet和VGG模型以及放射科医师的诊断效率进行了比较。
结果:多中心数据集包含1020个GNS,包括265个良性结节和755个恶性结节,测试数据集包括204个GGN,67个良性结节和137个恶性结节。在验证集中,提出的多模态模型达到了90.2%的精度,灵敏度为96.6%,特异性为75.0%,超过了VGG[73.1%,76.7%,和66.5%]和ResNet[78.0%,83.3%,和65.8%]模型诊断良性和恶性结节。在测试集中,多模式模型准确诊断125个[91.18%]恶性结节,优于放射科医生[80.37%的准确率]。此外,多模态模型正确识别54[准确性,80.70%]良性结节,相比之下,放射科医生的准确率为85.47%。将放射科医师的诊断结果与多模式模型的结果与术后病理相关的一致性检验显示出很强的一致性,多模式模型显示与金标准病理结果更接近[Kappa=0.720,P<0.01]。
结论:多模态深度学习网络模型在区分良性和恶性GGN方面表现出了有希望的诊断有效性,因此,作为辅助放射科医生提高GGN诊断准确性的参考工具具有潜力,有可能提高他们在临床环境中的工作效率。
OBJECTIVE: This study aimed to establish a multimodal deep-learning network model to enhance the diagnosis of benign and malignant pulmonary ground glass nodules [GGNs].
METHODS: Retrospective data on pulmonary GGNs were collected from multiple centers across China, including North, Northeast, Northwest, South, and Southwest China. The data were divided into a training set and a validation set in an 8:2 ratio. In addition, a GGN dataset was also obtained from our hospital database and used as the test set. All patients underwent chest computed tomography [CT], and the final diagnosis of the nodules was based on postoperative pathological reports. The Residual Network [ResNet] was used to extract imaging data, the Word2Vec method for semantic information extraction, and the Self Attention method for combining imaging features and patient data to construct a multimodal classification model. Then, the diagnostic efficiency of the proposed multimodal model was compared with that of existing ResNet and VGG models and radiologists.
RESULTS: The multicenter dataset comprised 1020 GGNs, including 265 benign and 755 malignant nodules, and the test dataset comprised 204 GGNs, with 67 benign and 137 malignant nodules. In the validation set, the proposed multimodal model achieved an accuracy of 90.2%, a sensitivity of 96.6%, and a specificity of 75.0%, which surpassed that of the VGG [73.1%, 76.7%, and 66.5%] and ResNet [78.0%, 83.3%, and 65.8%] models in diagnosing benign and malignant nodules. In the test set, the multimodal model accurately diagnosed 125 [91.18%] malignant nodules, outperforming radiologists [80.37% accuracy]. Moreover, the multimodal model correctly identified 54 [accuracy, 80.70%] benign nodules, compared to radiologists\' accuracy of 85.47%. The consistency test comparing radiologists\' diagnostic results with the multimodal model\'s results in relation to postoperative pathology showed strong agreement, with the multimodal model demonstrating closer alignment with gold standard pathological findings [Kappa=0.720, P<0.01].
CONCLUSIONS: The multimodal deep learning network model exhibited promising diagnostic effectiveness in distinguishing benign and malignant GGNs and, therefore, holds potential as a reference tool to assist radiologists in improving the diagnostic accuracy of GGNs, potentially enhancing their work efficiency in clinical settings.