Abstract
Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019–9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.
Highlights
Lung cancer is one of the leading causes of cancer-related death worldwide[1]
Materials In this study, we retrospectively reviewed patients with benign and pulmonary adenocarcinoma pleural effusion cases diagnosed by interventional fine-needle aspiration from March 2018 to January 2020 at Shanghai Pulmonary Hospital
To the best of our knowledge, this is the first study with the largest dataset that applied weakly supervised Artificial intelligence (AI) to cytological pleural effusion image evaluation and compared predictions from AI with diagnoses by cytopathologists at different whole-slide images (WSIs) levels
Summary
Lung cancer is one of the leading causes of cancer-related death worldwide[1]. With the improvement of computed tomography screening technology, small lesions could be detected, making it possible for more patients in the early stage to be cured. How to precisely treat this group of patients and improve their disease-free survival and overall survival is key to the diagnosis and treatment of advanced lung cancer[4]. Patients with advanced lung cancer are often in poor physical condition and can hardly tolerate invasive examinations. This is because bleeding or other complications could occur during the procedure due to the large tumor loads. It is of great benefit to patients to diagnose, classify (e.g. to distinguish between small cell and non-small cell carcinomas) and stage tumors through minimally invasive approaches, and this remains a popular research direction towards precision therapy[5,6]. Whilst histological biopsy and liquid-based cytological test (LCT) are both minimally invasive approaches for diagnosing lung cancer, the latter is even less invasive, since the specimens are harvested through sputum, pleural effusion, endobronchial ultrasoundguided transbronchial needle aspiration, bronchoscopy brush examination, bronchoalveolar lavage fluid, etc.[7,8]
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