利用快速现场评估载玻片和血清生物标志物进行自动肺癌亚型分类。
Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.
机构信息
Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
出版信息
Respir Res. 2024 Oct 29;25(1):391. doi: 10.1186/s12931-024-03021-8.
BACKGROUND
Rapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.
METHODS
The hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS model. The area under the curve (AUC) and accuracy were calculated to assess the performance of the model, and Cohen's kappa (κ) was calculated to measure the agreement between the model and the annotation. The HMLCS model was also compared with professional staff.
RESULTS
The HMLCS model achieved AUC values of 0.9540 (95% confidence interval [CI]: 0.9257-0.9823) in malignant/benign classification, 0.9126 (95% CI: 0.8756-0.9365) in malignancy subtyping (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC], or other malignancies), and 0.9297 (95% CI: 0.9026-0.9603) in NSCLC subtyping (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], or NSCLC not otherwise specified [NSCLC-NOS]), respectively. In total, the model achieved an AUC of 0.8721 (95% CI: 0.7714-0.9258) and an accuracy of 0.7184 in the six-class classification task (benign, LUAD, LUSC, NSCLC-NOS, SCLC, or other malignancies). In addition, the model demonstrated a κ value of 0.6183 with the annotation, which was comparable to cytopathologists and superior to trained bronchoscopists and technicians.
CONCLUSION
The HMLCS model showed promising performance in the multiclassification of lung lesions or intrathoracic lymphadenopathy, with potential application to provide real-time feedback regarding preliminary diagnoses of specimens during transbronchial sampling procedures.
CLINICAL TRIAL NUMBER
Not applicable.
背景
实时现场评估（ROSE）在经支气管采样过程中发挥着重要作用，可为术中细胞病理学评估提供支持。然而，细胞病理学家的短缺限制了其广泛应用。本研究旨在开发一种深度学习模型，以自动分析 ROSE 细胞学图像。
方法
我们提出了一种分层多标签肺癌亚型（HMLCS）模型，该模型结合了 ROSE 切片的全玻片图像和血清生物标志物，用于区分良恶性病变并识别不同类型的肺癌。回顾性收集了 2019 年 7 月至 2020 年 11 月期间 811 张 ROSE 切片和配对的血清生物标志物数据，并将其随机分为训练、验证和测试集，以训练、验证和测试 HMLCS 模型。计算曲线下面积（AUC）和准确率来评估模型的性能，并计算科恩κ（κ）值以衡量模型与标注之间的一致性。还将 HMLCS 模型与专业人员进行了比较。
结果
HMLCS 模型在恶性/良性分类中的 AUC 值为 0.9540（95%置信区间[CI]：0.9257-0.9823），在恶性肿瘤亚型分类（非小细胞肺癌[NSCLC]、小细胞肺癌[SCLC]或其他恶性肿瘤）中的 AUC 值为 0.9126（95%CI：0.8756-0.9365），在 NSCLC 亚型分类（肺腺癌[LUAD]、肺鳞状细胞癌[LUSC]或非特指型 NSCLC[NSCLC-NOS]）中的 AUC 值为 0.9297（95%CI：0.9026-0.9603）。总的来说，该模型在六分类任务（良性、LUAD、LUSC、NSCLC-NOS、SCLC 或其他恶性肿瘤）中的 AUC 值为 0.8721（95%CI：0.7714-0.9258），准确率为 0.7184。此外，模型与标注之间的κ值为 0.6183，与细胞病理学家相当，优于经过培训的支气管镜医生和技术员。
结论
HMLCS 模型在肺病变或胸内淋巴结病变的多分类中表现出良好的性能，有望在经支气管采样过程中为初步诊断标本提供实时反馈。
临床试验编号
不适用。
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