Diagnosis of solitary lung nodules using the local form of Ripley's K function applied to three-dimensional CT data

Comparing the diagnostic value of 18F-FDG-PET/CT versus CT for differentiating benign and malignant solitary pulmonary nodules: a meta-analysis

Background

This quantitative meta-analysis was conducted to provide an indirect comparison of the diagnostic value of computed tomography (CT) with positron emission tomography (PET)/CT for differentiating benign and malignant solitary pulmonary nodules (SPNs).

Methods

PubMed, Embase, and the Cochrane Library were searched to identify eligible studies throughout November 2018, which differentiated benign and malignant SPNs using CT or PET/CT. The summary sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC) were calculated using bivariate generalized linear mixed model and random-effects model. The diagnostic value of CT with PET/CT was indirectly evaluated using the ratio for diagnostic parameters.

Results

The sensitivity, specificity, PLR, NLR, DOR, and AUC for CT were 0.94 [95% confidence interval (CI): 0.87-0.97], 0.73 (95% CI: 0.64-0.80), 3.45 (95% CI: 2.60-4.58), 0.09 (95% CI: 0.04-0.17), 32.01 (95% CI: 15.10-67.86), and 0.89 (95% CI: 0.86-0.91), respectively. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC for PET/CT were 0.89 (95% CI: 0.85-0.92), 0.78 (95% CI: 0.66-0.86), 3.97 (95% CI: 2.57-6.13), 0.15 (95% CI: 0.10-0.20), 24.04 (95% CI: 12.71-45.48), and 0.91 (95% CI: 0.89-0.94), respectively. No significant differences were observed between CT and PET/CT for sensitivity, specificity, PLR, NLR, DOR, and AUC.

Conclusions

This study used both CT and PET/CT with a moderate-to-high diagnostic value for differentiating benign and malignant SPNs and showed no significant differences in diagnostic parameters between CT and PET/CT.

Assessment of lung involvement in sarcoidosis - the use of an open-source software to quantify data from computed tomography

Computed tomography (CT) plays a pivotal role in the initial evaluation of patients suspected of sarcoidosis. Although it has significant limitations associated with radiation exposure, CT scanning is also occasionally used to follow-up patients with sarcoidosis. Hitherto, no widely accepted method of quantitative assessment of pulmonary involvement in sarcoidosis has been established. The aims of the study were as follows: (1) to assess the utility of the open-source, free of charge DICOM Viewer software in quantitative analysis of pulmonary involvement in sarcoidosis; (2) to compare the parameters of quantitative CT analysis with the results of pulmonary function tests (PFTs). We included contrast-enhanced thorax CT examinations of 80 patients with sarcoidosis. Post-processing analysis of CT data was carried out using OsiriX Lite software (Pixmeo, Switzerland). Following densitometric parameters were measured: CT-derived lung volume (CT-LV), mean lung attenuation (MLA), kurtosis, skewness and standard deviation of lung radiodensity (SD_LR). Kurtosis was significantly lower in patients with lung fibrosis comparing to those with mediastinal and/or hilar lymphadenopathy (MHL) and pulmonary involvement (median 1.49 vs 1.93). Furthermore, SD_LR was significantly higher in patients with lung fibrosis comparing to those with isolated MHL and MHL with pulmonary involvement (median 163.6 vs 137.4). Also, significant correlations between densitometric parameters and the results of PFTs were demonstrated, including correlation between CT-LV and TLC (R=0.7). Our study showed that post-processing of the CT data with the use of Osirix Lite DICOM Viewer might be a valuable method of quantitative analysis of pulmonary involvement in sarcoidosis. (Sarcoidosis Vasc Diffuse Lung Dis 2017; 34: 315-325).

Diagnostic value of computed tomography scanning in differentiating malignant from benign solitary pulmonary nodules: a meta-analysis

An early diagnosis of lung cancer is crucial for early treatment and management. The objective of this systematic review was to assess the overall diagnostic accuracy of chest computed tomography (CT) scanning in differentiating malignant from benign solitary pulmonary nodules (SPNs) with meta-analysis. The PubMed and China National Knowledge Infrastructure (CNKI) database were searched for eligible studies published up to March 2014. The sensitivity, specificity, and other measures of accuracy of chest CT scanning in the diagnosis of SPNs were pooled along with 95 % confidence intervals (CI). Summary receiver operating characteristic (ROC) curves were used to summarize overall test performance. Thirty-two studies met our inclusion criteria. The summary estimates for chest CT scanning in the diagnosis of SPNs in the meta-analysis were as follows: pooled sensitivity, 0.89 (95 % CI, 0.88 to 0.91); pooled specificity, 0.70 (95 % CI, 0.68 to 0.73); positive likelihood ratio, 2.88 (95 % CI, 2.46 to 3.37); negative likelihood ratio, 0.16 (95 % CI, 0.12 to 0.21); and diagnostic odds ratio, 23.83 (95 % CI, 16.18 to 35.11). The results indicate that CT scanning has relatively high sensitivity and moderate specificity for the diagnosis of SPNs. Given the low cost and growing prevalence of the technology, CT scanning should be recommended as the initial test for the evaluation of SPNs.

Development of quantitative computed tomography lung protocols

The purpose of this review article is to review the process of developing optimal computed tomography (CT) protocols for quantitative lung CT (QCT). In this review, we discuss the following important topics: QCT-derived metrics of lung disease; QCT scanning protocols; quality control; and QCT image processing software. We will briefly discuss several QCT-derived metrics of lung disease that have been developed for the assessment of emphysema, small airway disease, and large airway disease. The CT scanning protocol is one of the most important elements in a successful QCT. We will provide a detailed description of the current move toward optimizing the QCT protocol for the assessment of chronic obstructive pulmonary disorder and asthma. Quality control of CT images is also a very important part of the QCT process. We will discuss why it is necessary to use CT scanner test objects (phantoms) to provide frequent periodic checks on the CT scanner calibration to ensure precise and accurate CT numbers. We will discuss the use of QCT image processing software to segment the lung and extract the desired QCT metrics of lung disease. We will discuss the practical issues of using this software. The data obtained from the image processing software are then combined with those from other clinical examinations, health status questionnaires, pulmonary physiology, and genomics to increase our understanding of obstructive lung disease and improve our ability to design new therapies for these diseases.

Spatial Point Pattern Analysis of Neurons Using Ripley's K-Function in 3D

The aim of this paper is to apply a non-parametric statistical tool, Ripley's K-function, to analyze the 3-dimensional distribution of pyramidal neurons. Ripley's K-function is a widely used tool in spatial point pattern analysis. There are several approaches in 2D domains in which this function is executed and analyzed. Drawing consistent inferences on the underlying 3D point pattern distributions in various applications is of great importance as the acquisition of 3D biological data now poses lesser of a challenge due to technological progress. As of now, most of the applications of Ripley's K-function in 3D domains do not focus on the phenomenon of edge correction, which is discussed thoroughly in this paper. The main goal is to extend the theoretical and practical utilization of Ripley's K-function and corresponding tests based on bootstrap resampling from 2D to 3D domains.