Lung nodule pre-diagnosis and insertion path planning for chest CT images.

Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.

Quantitative Airway Assessment of Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH) on CT as a Novel Biomarker.

Objectives: Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia (DIPNECH) occurs due to abnormal proliferation of pulmonary neuroendocrine cells. We hypothesized that performing a quantitative analysis of airway features on chest CT may reveal differences to matched controls, which could ultimately help provide an imaging biomarker. Methods: A retrospective quantitative analysis of chest CTs in patients with DIPNECH and age matched controls was carried out using semi-automated post-processing software. Paired segmental airway and artery diameters were measured for each bronchopulmonary segment, and the airway:artery (AA) ratio, airway wall thickness:artery ratio (AWTA ratio) and wall area percentage (WAP) calculated. Nodule number, size, shape and location was recorded. Correlation between CT measurements and pulmonary function testing was performed. Results: 16 DIPNECH and 16 control subjects were analysed (all female, mean age 61.7 +/− 11.8 years), a combined total of 425 bronchopulmonary segments. The mean AwtA ratio, AA ratio and WAP for the DIPNECH group was 0.57, 1.18 and 68.8%, respectively, compared with 0.38, 1.03 and 58.3% in controls (p < 0.001, <0.001, 0.03, respectively). DIPNECH patients had more nodules than controls (22.4 +/− 32.6 vs. 3.6 +/− 3.6, p = 0.03). AA ratio correlated with FVC (R2 = 0.47, p = 0.02). A multivariable model incorporating nodule number, AA ratio and AWTA-ratio demonstrated good performance for discriminating DIPNECH and controls (AUC 0.971; 95% CI: 0.925−1.0). Conclusions: Quantitative CT airway analysis in patients with DIPNECH demonstrates increased airway wall thickness and airway:artery ratio compared to controls. Advances in knowledge: Quantitative CT measurement of airway wall thickening offers a potential imaging biomarker for treatment response.

Bioinformatics analysis of differentially expressed proteins in human lung adenocarcinoma / 中国医师杂志

Objective To investigate the biological significance of differentially expressed proteins from human primary lung adenocarcinoma with lymph node metastasis adenocarcinoma (LNM AdC) and without metastasis (non-LNM AdC) according to clinical diagnosis of lymph node metastasis and distant metastasis,with bioinformatics approach.Methods Cytoscape software was used to analyze a functional enrichment analysis and a protein-protein interaction network from differentially expressed proteins from LNM AdC and non-LNM AdC.Results The top biological processes were related to glucose catabolic process,hexose catabolic process,monosaccharide catabolic process,alcohol catabolic process,and cellular carbohydrate catabolic process.The top molecular functions were related to phospholipase inhibitor activity,lipase inhibitor activity,calcium-dependent phospholipid binding,phosphlipase A2 inhibitor activity,and lipid binding.A protein-protein interaction network of differentially expressed proteins was generated with literature data.Conclusions This bioinformatics analysis demonstrated that glucose catabolic process,alcohol catabolic process,calcium-dependent phospholipid binding,phosphlipase A2 inhibitor activity,ACTB,ANXA1,ANXA2,ANXA3,VCP,NPM1,KRT1,and SUMO4 are significantly associated with a lung adenocarcinoma.These network data provide new insights into the metastasis mechanisms of human lung adenocarcinoma.

Serologic proteome analysis of human lung adenocarcinoma / 中国医师杂志

Objective To identify lung adenocarcinoma-associated antigens by using Serologic Proteome Analysis(SERPA),an approach which combined conventional proteome analysis with serological screening.Methods SERPA of four human lung adenocarcinoma tissues were performed.The Western blot imaging films which reacted with autologous patient serum and with control normal serum were obtained and the differential reacting protein spots were recognized.Results Well-resolved,reproducible 2-DE Western blot imaging films of human lung adenocarcinoma reacted with autologous patient sera and the control sera were obtained.Totally(27?5) differentially expressed proteins which only were reactive with lung adenocarcinoma patient sera were found.Some differentially expressed proteins were identified by peptide mass fingerprint(PMF).Some of the proteins were the products of oncogenes,and others were involved in the regulation of cell cycle and signal transduction.Conclusion These results will provide scientific foundation on screening the molecular biomarker for the diagnosis,treatment,and prognosis of the lung adenocarcinoma.