Small cell lung cancer (SCLC) is an aggressive malignancy that typically presents as a centrally located mass with early metastatic spread and only rarely manifests as a solitary pulmonary nodule (SPN) without lymphadenopathy. We report a 63-year-old active smoker whose low-dose computed tomography (LDCT) screening identified a 1.6 cm solitary nodule in the right lower lobe. Positron emission tomography (PET) confirmed metabolic activity without evidence of metastasis, although the nodule grew to 2.2 cm over six months. Robotic-assisted bronchoscopy with endobronchial ultrasound-guided biopsy revealed a high-grade neuroendocrine carcinoma. Immunohistochemical staining confirmed SCLC (TTF-1 positive, synaptophysin positive, p40 negative, Napsin A negative, and Ki-67 >80%). The tumor was staged as IA (T1c N0 M0), and the patient was considered a candidate for curative surgical resection. However, she succumbed to severe cardiovascular comorbidities prior to intervention. This case illustrates the "solitary but sinister" presentation of SCLC as a SPN and underscores the importance of maintaining a high index of suspicion, prompt tissue diagnosis, and expedited management in high-risk patients to avoid missing a narrow window for potentially curative treatment.

Pulmonary carcinoid (PC) is a relatively rare neuroendocrine tumor, accounting for approximately 1%-2% of all lung tumors. According to histopathological characteristics, it can be categorized into low-grade typical carcinoid (TC) and intermediate-grade atypical carcinoid (AC), with AC being less common, representing about 10% of all pulmonary carcinoids. The clinical symptoms and imaging manifestations of AC lack specificity, with solitary pulmonary nodules as the major radiological feature. Cases of interstitial lung disease (ILD) combined with AC are extremely rare. Clinicians have limited awareness of this comorbidity, and AC nodules occurring against a background of ILD are easily confused with fibrotic or inflammatory lesions, leading to a high rate of clinical misdiagnosis and missed diagnosis. Herein, we report a case from our institution: the patient was found to have bilateral pulmonary interstitial changes and nodules on routine chest CT, and was initially diagnosed with connective tissue disease-associated interstitial lung disease (CTD-ILD) based on pathological and ancillary examinations. Several years later, the patient presented with dizziness related to brain metastases and was finally diagnosed with AC. By analyzing the entire diagnosis and treatment process of this case, we aim to improve clinicians' ability to recognize such rare cases and provide clinical guidance for their early diagnosis and appropriate treatment.

Lung nodule evaluation is a vital practice in pulmonary medicine. While biopsy remains the diagnostic gold standard, it carries procedural risks. In regions endemic for fungal disease, low-cost, noninvasive testing may help reduce unnecessary procedures.

A 58-year-old man had undergone surgery for prostate cancer 2 years previously. He was admitted to the respiratory department because of a solitary nodule in the right lung. He underwent bronchoscopy and was diagnosed with prostate cancer recurrence. This case highlights the importance of active bronchoscopy.

For pulmonary nodules in computed tomography (CT) images, which exhibit complex morphology and blurred boundaries, existing segmentation methods still fall short in modelling cross-level dependencies of multi-scale features, thereby limiting their performance in pulmonary nodule segmentation tasks. To address these challenges, this paper proposes a semantic segmentation method for pulmonary nodules based on multiscale feature interaction and cross-level coordinate attention (MFI-CLCA). This U-shaped network incorporated three architectures: a convolutional neural network (CNN), a Transformer, and Mamba. During the encoding phase, combining CNN and Mamba learning paradigms capured both global and local information in the input data. The convolutional component extracted complex boundary features of the target by combining multi-scale convolutional operations with adaptive fusion operations. Global and local multi-head attention mechanisms were introduced in the bottleneck layer and decoding phase respectively to model these hierarchical feature dependencies. The skip-connection section incorporated a multi-level coordinate attention module to adaptively focus on the information being passed through. Experimental results on the Lung Image Database Consortium (LIDC) dataset demonstrated that this approach achieved Dice scores of 90.52% and sensitivity of 91.93%, which outperforms existing state-of-the-art methods and validates its effectiveness for lung nodule segmentation tasks.

Solitary pulmonary nodule (SPN) in a heavy smoker patient is commonly considered a lung cancer. However, in endemic regions, differential diagnosis should include pulmonary hydatid cyst. We present a 65-year-old heavy smoker male with a 5 cm SPN. He had negative serology, and inconclusive PET-CT. So, it mimicked malignancy. Video-assisted thoracoscopic surgery (VATS) confirmed hydatid cyst, with successful management. This case shows the importance of considering parasitic disease in the differential diagnosis of SPN, even with negative serology and typical risk factors for lung cancer.