Percutaneous pulmonary puncture guided by computed tomography (CT) is one of the most effective tools for obtaining lung tissue and diagnosing lung cancer. Path planning is an important procedure to avoid puncture complications and reduce patient pain and puncture mortality. In this work, a path planning method for lung puncture is proposed based on multi-level constraints. A digital model of the chest is firstly established using patient's CT image. A Fibonacci lattice sampling is secondly conducted on an ideal sphere centered on the tumor lesion in order to obtain a set of candidate paths. Finally, by considering clinical puncture guidelines, an optimal path can be obtained by a proposed multi-level constraint strategy, which is combined with oriented bounding box tree (OBBTree) algorithm and Pareto optimization algorithm. Results of simulation experiments demonstrated the effectiveness of the proposed method, which has good performance for avoiding physical and physiological barriers. Hence, the method could be used as an aid for physicians to select the puncture path.
Humans ; Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Punctures ; Thorax ; Tomography, X-Ray Computed

This paper reviews the current state of the technique of multislice CT (MSCT) scan and imaging postprocessing of CT pulmonary angiography, as well as the CT features of different pulmonary arterial diseases. Investigations demonstrate that MSCT and CTA are available means to visualize pulmonary artery structures and the anatomic correlation between pulmonary artery and adjacent structures. They are fast, effective and noninvasive methods for diagnosing pulmonary arterial diseases.
Humans ; Hypertension, Pulmonary ; diagnostic imaging ; Lung Diseases ; diagnostic imaging ; Lung Neoplasms ; blood supply ; Pulmonary Artery ; diagnostic imaging ; Pulmonary Embolism ; diagnostic imaging ; Tomography, Spiral Computed

Carcinoma, Non-Small-Cell Lung ; diagnostic imaging ; Humans ; Lung ; diagnostic imaging ; Lung Neoplasms ; diagnostic imaging ; Perfusion Imaging ; Radiotherapy, Intensity-Modulated ; methods ; Tomography, Emission-Computed, Single-Photon ; methods

Humans ; Lung Neoplasms ; diagnostic imaging ; pathology ; Tomography, X-Ray Computed

The availability of medical big data and the rapid development of computer software and hardware have greatly promoted the advancement of intelligent medical healthcare. Artificial intelligence (AI) has been successfully applied in many fields of medicine, especially in lung cancer. The performance of AI in some specific tasks has surpassed that of humans. Several AI software has been deeply used in clinical practice to help decision-making, which is producing a profound influence on clinicians. At present, the application of AI in the field of lung cancer mainly includes detection, segmentation, classification, prognosis prediction, efficacy evaluation, and so on. AI faces certain challenges and opportunities in the era of big data in terms of data acquisition, annotation and interpretability. Researchers have conducted deep and extensive studies using AI in the field of lung cancer, and AI is expected to become a powerful assistant in the prevention and treatment of lung cancer. AI is bringing an unprecedented revolution to radiologists, but the role of radiologists is crucial in the development of AI.
Artificial Intelligence ; Humans ; Lung Neoplasms/diagnostic imaging* ; Prognosis

With the progress of targeted therapy and immunotherapy for lung cancer, the clinical demand for lung biopsy is increasing. An ideal biopsy specimen can be used not only for histopathological diagnosis, but also for biomarker detection. The ideal biopsy specimen should meet two requirements, including more than 60 mm2 of tumor tissue and containing more than 20% of tumor cells. In order to obtain ideal lung cancer biopsy specimens, advanced imaging techniques are needed to help. In this article, we reviewed the requirements for biopsy specimens based on biomarker detection, as well as the current status and research progress of using imaging techniques for preoperative planning and intraoperative real time guidance of lung cancer biopsy.
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Humans ; Lung Neoplasms/diagnostic imaging* ; Biopsy ; Biomarkers ; Immunotherapy

Adult ; Hamartoma ; diagnosis ; diagnostic imaging ; Humans ; Lung Neoplasms ; diagnosis ; diagnostic imaging ; Male ; Radiography

Humans ; Lung Neoplasms ; complications ; diagnostic imaging ; Male ; Pneumoconiosis ; complications ; diagnostic imaging ; Radiography ; Retrospective Studies

Carcinoid Tumor ; diagnostic imaging ; Female ; Humans ; Lung Neoplasms ; diagnostic imaging ; Tomography, X-Ray Computed ; Young Adult

OBJECTIVETo assess the feasibility of endobronchial ultrasonography (EBUS) in the differential diagnosis of malignant and benign lesions based on the two characteristic echo features of malignancy.

METHODSEBUS images from 102 patients undergoing bronchoscopy for peripheral lung lesions were analyzed. The sensitivity and specificity were determined for each echo feature, namely the halo sign and low-level echoes that indicated malignancy, or their combination in diagnosing malignant and benign lesions.

RESULTSLow-level echoes showed a sensitivity of 89.46% and a specificity of 83% in the diagnosis of malignancy, both higher than those of the halo sign (69.51% and 65%, respectively). The presence of either of the two echo features had a diagnostic sensitivity of 94.6% for malignant lesions, and the coexistence of the two features had a specificity of 93% for a diagnosis of malignant lesions.

CONCLUSIONEBUS is a useful adjunctive modality for lung cancer diagnosis, especially in cases where peripheral lung lesions are invisible in conventional bronchoscopy.