To develop and evaluate deep learning models for the detection and semiquantitative analysis of cardiomegaly, pneumothorax, and pleural effusion on chest radiographs. Materials and Methods:In this retrospective study, models were trained for lesion detection or for lung segmentation. The first dataset for lesion detection consisted of 2838 chest radiographs from 2638 patients (obtained between November 2018 and January 2020) containing findings positive for cardiomegaly, pneumothorax, and pleural effusion that were used in developing Mask region-based convolutional neural networks plus Point-based Rendering models. Separate detection models were trained for each disease. The second dataset was from two public datasets, which included 704 chest radiographs for training and testing a U-Net for lung segmentation. Based on accurate detection and segmentation, semiquantitative indexes were calculated for cardiomegaly (cardiothoracic ratio), pneumothorax (lung compression degree), and pleural effusion (grade of pleural effusion). Detection performance was evaluated by average precision (AP) and free-response receiver operating characteristic (FROC) curve score with the intersection over union greater than 75% (AP75; FROC score75). Segmentation performance was evaluated by Dice similarity coefficient. Results:The detection models achieved high accuracy for detecting cardiomegaly (AP75, 98.0%; FROC score75, 0.985), pneumothorax (AP75, 71.2%; FROC score75, 0.728), and pleural effusion (AP75, 78.2%; FROC score75, 0.802), and they also weakened boundary aliasing. The segmentation effect of the lung field (Dice, 0.960), cardiomegaly (Dice, 0.935), pneumothorax (Dice, 0.827), and pleural effusion (Dice, 0.826) was good, which provided important support for semiquantitative analysis. Conclusion:The developed models could detect cardiomegaly, pneumothorax, and pleural effusion, and semiquantitative indexes could be calculated from segmentations.
Rationale:Small cell lung cancer accounts for 15-20% of all lung cancers and is the most common pulmonary neuroendocrine neoplasm. Most small cell lung cancers arise from lobar or main bronchi, the most common manifestations of small cell lung cancer is a large mass centrally located within the lung parenchyma or a mediastinal mass involving the hilus. Small cell lung cancer is easily ignored by clinicians without lung parenchyma and hilus involvement. Here, we report a case of small cell lung cancer, which was misdiagnosed as the lymphoma in contrast enhanced CT and 68Ga-DOTA-NOC PET/CT imagings.Patient concerns:A 49-year-old male with chief complaint of discontinuous cough for 1 month.Diagnoses:Small cell lung cancer.Interventions:Radiotherapy and chemotherapy were given thereafter.Outcomes:The case had multiple enlarged lymph nodes due to tumor progression.Lessons:Small cell lung cancer is a malignant and progressive disease, and easy to be ignored in clinical. The case of small cell lung cancer without parenchyma and hilus involvement has never been reported before. Here, we report it and hope it provides a differential diagnosis for clinicians in the following similar cases.
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