Multidetector CT of pulmonary cavitation: filling in the holes

Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans

OBJECTIVE

Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteristics. A deep learning-based model to automatically detect, segment, and quantify the region of cavity lesion on CT scans has potential in clinical diagnosis, monitoring, and treatment efficacy assessment.

METHODS

A weakly-supervised deep learning-based method named CSA2-ResNet was proposed to quantitatively characterize cavity lesions in this paper. The lung parenchyma was firstly segmented using a pretrained 2D segmentation model, and then the output with or without cavity lesions was fed into the developed deep neural network containing hybrid attention modules. Next, the visualized lesion was generated from the activation region of the classification network using gradient-weighted class activation mapping, and image processing was applied for post-processing to obtain the expected segmentation results of cavity lesions. Finally, the automatic characteristic measurement of cavity lesions (e.g., area and thickness) was developed and verified.

RESULTS

the proposed weakly-supervised segmentation method achieved an accuracy, precision, specificity, recall, and F1-score of 98.48%, 96.80%, 97.20%, 100%, and 98.36%, respectively. There is a significant improvement (P < 0.05) compared to other methods. Quantitative characterization of morphology also obtained good analysis effects.

CONCLUSIONS

The proposed easily-trained and high-performance deep learning model provides a fast and effective way for the diagnosis and dynamic monitoring of pulmonary cavity lesions in clinic. Clinical and Translational Impact Statement: This model used artificial intelligence to achieve the detection and quantitative analysis of pulmonary cavity lesions in CT scans. The morphological features revealed in experiments can be utilized as potential indicators for diagnosis and dynamic monitoring of patients with cavity lesions.

Non-surgical management of an abrupt cavitation and large oval-shaped lung abscess secondary to acute thromboembolic pulmonary infarction: a case report

Infected cavitating pulmonary infarction is a rare complication of pulmonary embolism with a high mortality rate. Surgical excision for this complication has been used in past decades. Abrupt cavitation and a large oval-shaped lung abscess caused by acute thromboembolic pulmonary infarction during anticoagulation are rare. We present a 70-year-old man who suffered from pleuritic pain and breathlessness, accompanied by nausea and vomiting for 1 day. A physical examination showed tachycardia and tachypnea with moist rales in the left upper chest. High D-dimer levels, leukocytosis, respiratory failure and left upper lobe consolidation were found on plain computed tomography (CT). CT pulmonary angiography was performed 2 days after the previous CT scan because pulmonary embolism was suspected. This scan showed emboli in the main, right upper, middle, lower and left upper pulmonary arteries with deteriorated left upper lobe consolidation and cavitation. Thromboembolic pulmonary infarction and an abscess were diagnosed. Enoxaparin 60 mg was administered every 12 hours for 10 days, followed by rivaroxaban, antibiotics and drainage of the hydrothorax. The patient improved after the strategy of non-surgical treatment and was discharged approximately 1 month later. The patient had an uneventful course during rivaroxaban 20 mg once daily for 1 year.

The value of bronchial and cavity contraction rates in differentiating benign and malignant pulmonary cavities

BACKGROUND

The present study aimed to assess the value of bronchial and cavity contraction percentages in differentiating benign and malignant pulmonary cavities.

METHODS

Forty-two patients with pulmonary cavities were scanned by dual-phase computed tomography (CT). Then, the cavity and bronchial contraction percentages were respectively measured, the differences between the benign and malignant cavities were compared, and the best diagnostic critical point for differentiating benign and malignant cavities was obtained through the receiver operator characteristic (ROC) curve of the diagnostic test.

RESULTS

The contraction percentage of the bronchial end with benign cavities was significantly higher than that of the bronchial end with malignant cavities (P < 0.001). The contraction percentage was significantly higher in the benign group than in the malignant group (P < 0.001). The ROC analysis revealed that the sensitivity and specificity of the bronchial contraction percentage was 90.50 and 86.40%, respectively, while the sensitivity and specificity of the cavity contraction percentage was 90.50 and 90.90%, respectively.

CONCLUSION

The dual-phase CT scanning of the bronchial and cavity contraction percentage can distinguish between benign and malignant cavities.