Adult community-acquired pneumonia (ACAP) is the most prevalent pulmonary infectious disease that may be asymptomatic or have varying clinical presentations. Patients with ACAP often present with enlarged mediastinal lymph nodes on their chest computed tomography images. However, large irregular swollen lymph nodes are rarely reported in ACAP, and may therefore be confused with enlarged lymph node masses. In the present case report, the patient presented with lymph node masses, which were ameliorated to their normal size following antimicrobial treatment. The patient was 24 years old and otherwise healthy, which led to a pronounced and excessive immune response to pneumonia in the lymph nodes. Atypical pneumonia is difficult to diagnose based on imaging features. The present case report demonstrates that patients with pneumonia may present with unusually enlarged mediastinal lymph nodes, which are most likely, a result of a strong immune response to pneumonia.
Background Different pathological subtypes of lung adenocarcinoma lead to different treatment decisions and prognoses, and it is clinically important to distinguish invasive lung adenocarcinoma from preinvasive adenocarcinoma (adenocarcinoma in situ and minimally invasive adenocarcinoma). This study aims to investigate the performance of the deep learning approach on the classification of tumor invasiveness and compare it with the performances of currently available approaches. Methods In this study, we propose a deep learning approach based on 3D conventional networks to automatically predict the invasiveness of pulmonary nodules. A total of 901 early-stage non-small cell lung cancer patients who underwent surgical treatment at Shanghai Chest Hospital between November 2015 and March 2017 were retrospectively included and randomly assigned to a training set (n = 814) or testing set 1 (n = 87). We subsequently included 116 patients who underwent surgical treatment and intraoperative frozen section between April 2019 and January 2020 to form testing set 2. We compared the performance of our deep learning approach in predicting tumor invasiveness with that of intraoperative frozen section analysis and human experts (radiologists and surgeons). Results The deep learning approach yielded an area under the receiver operating characteristic curve (AUC) of 0.946 for distinguishing preinvasive adenocarcinoma from invasive lung adenocarcinoma in testing set 1, which is significantly higher than the AUCs of human experts (P < 0.05). In testing set 2, the deep learning approach distinguished invasive adenocarcinoma from preinvasive adenocarcinoma with an AUC of 0.862, which is higher than that of frozen section analysis (0.755, P = 0.043), senior thoracic surgeons (0.720, P = 0.006), radiologists (0.766, P > 0.05) and junior thoracic surgeons (0.768, P > 0.05). Conclusions We developed a deep learning approach that achieved comparable performance to intraoperative frozen section analysis in determining tumor invasiveness. The proposed method may contribute to clinical decisions related to the extent of surgical resection.
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