Dai Bo, Han Dong, Miao Yufei, Zhou Yong, Hajiarbabi Mohammadreza, Wang Yiqing, Butch Christopher J, Cai Huiming, Hu Jian
Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China.
Department of Thoracic Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Transl Lung Cancer Res. 2024 Apr 29;13(4):885-900. doi: 10.21037/tlcr-24-168. Epub 2024 Apr 24.
In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%.
In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis.
The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases.
Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.
在肺腺癌（LADC）的外科手术干预中，精确确定LADC浸润范围对外科医生制定手术策略起着关键作用。目前的诊断标准是在术中快速对LADC浸润区域进行病理诊断。然而，当前方法依赖于专业病理学家对组织图像的视觉解读，误差幅度高达15.6%。
在本研究中，我们研究了显微拉曼技术在人LADC分离标本上的应用，目的是制定并验证一种针对不同浸润程度LADC的病理诊断工作流程。我们的策略包括对LADC进行全面的病理特征分析，涵盖不同组织类型和浸润水平。通过将拉曼光谱与先进的深度学习模型相结合进行同步诊断，该方法提供了一种快速、精确且与临床相关的分析手段。
发现卷积神经网络（CNN）模型与显微拉曼技术相结合的诊断性能卓越且一致，超过了传统支持向量机（SVM）模型。CNN模型在有效区分正常组织与LADC方面的曲线下面积（AUC）值为96.1%，在辨别LADC不同浸润程度方面高达99.0%。为全面评估其临床实用性，将术中快速病理诊断有误患者的拉曼数据集用作测试对象并输入已建立的CNN模型。结果强调了显微拉曼技术的强大校正能力，所有病例的误诊校正率均超过96%。
最终，我们的发现突出了显微拉曼技术在提高不同浸润水平LADC术中诊断精度方面的潜力。与传统SVM模型相比，CNN模型在诊断不同浸润水平时具有更好的泛化能力。该方法为外科医生提供了客观依据，以便他们就后续手术计划做出明智决策。
