Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.
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ABSTRACT:
SUBMITTER: Seth S
PROVIDER: S-EPMC4993998 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Malignant solitary pulmonary nodules (SPN) have become more prevalent, with upper lobes predilection. Probe-based confocal laser endomicroscopy (pCLE) provides in-vivo imaging of SPN. However, the stiffness of the 1mm confocal probe (AlveoFlex) causes difficult accessibility to the upper lobes. A thinner 600?m probe designed for bile duct exploration (CholangioFlex) has the potential to reach the upper lobes.To examine the accessibility of malignant SPNs in all segments of the lungs using either the 0.6mm or 1.4 mm probe and to assess the quality and inter observer interpretation of SPN confocal imaging obtained from either miniprobes.Radial(r)-EBUS was used to locate and sample the SPN. In-vivo pCLE analysis of the SPN was performed using either CholangioFlex (apical and posterior segments of the upper lobes) or AlveoFlex (other segments) introduced into the guide sheath before sampling. pCLE features were compared between the two probes.Fourty-eight patients with malignant SPN were included (NCT01931579). The diagnostic accuracy for lung cancer using r-EBUS coupled with pCLE imaging was 79.2%. All the SPNs were successfully explored with either one of the probes (19 and 29 subjects for CholangioFlex and AlveoFlex, respectively). A specific solid pattern in the SPN was found in 30 pCLE explorations. Comparison between the two probes found no differences in the axial fibers thickness, cell size and specific solid pattern in the nodules. Extra-alveolar microvessel size appeared larger using CholangioFlex suggesting less compression effect. The kappa test for interobserver agreement for the identification of solid pattern was 0.74 (p = 0.001).This study demonstrates that pCLE imaging of SPNs is achievable in all segments of both lungs using either the 0.6mm or 1.4mm miniprobe.
| S-EPMC5739462 | biostudies-literature
Project description:PurposeTo quantitatively assess the value of dual-energy CT (DECT) in differentiating malignancy and benignity of solitary pulmonary nodules.Materials and methodsSixty-three patients with solitary pulmonary nodules detected by CT plain scan underwent contrast enhanced CT scans in arterial phase (AP) and venous phase (VP) with spectral imaging mode for tumor type differentiation. The Gemstone Spectral Imaging (GSI) viewer was used for image display and data analysis. Region of interest was placed on the relatively homogeneous area of the nodule to measure iodine concentration (IC) on iodine-based material decomposition images and CT numbers on monochromatic image sets to generate spectral HU curve. Normalized IC (NIC), slope of the spectral HU curve (?HU) and net CT number enhancement on 70keV images were calculated. The two-sample t-test was used to compare quantitative parameters. Receiver operating characteristic curves were generated to calculate sensitivity and specificity.ResultsThere were 63 nodules, with 37 malignant nodules (59%) and 26 benign nodules (41%). NIC, ?HU and net CT number enhancement on 70keV images for malignant nodules were all greater than those of benign nodules. NIC and ?HU had intermediate to high performances to differentiate malignant nodules from benign ones with the areas under curve of 0.89 and 0.86 respectively in AP, 0.96 and 0.89 respectively in VP. Using 0.30 as a threshold value for NIC in VP, one could obtain sensitivity of 93.8% and specificity of 85.7% for differentiating malignant from benign solitary pulmonary nodules. These values were statistically higher than the corresponding values of 74.2% and 53.8% obtained with the conventional CT number enhancement.ConclusionsDECT imaging with GSI mode provides more promising value in quantitative way for distinguishing malignant nodules from benign ones than CT enhancement numbers.
| S-EPMC4739615 | biostudies-literature
Project description:We present the first endoscopic Doppler optical coherence tomography and co-registered autofluorescence imaging (DOCT-AFI) of peripheral pulmonary nodules and vascular networks in vivo using a small 0.9 mm diameter catheter. Using exemplary images from volumetric data sets collected from 31 patients during flexible bronchoscopy, we demonstrate how DOCT and AFI offer complementary information that may increase the ability to locate and characterize pulmonary nodules. AFI offers a sensitive visual presentation for the rapid identification of suspicious airway sites, while co-registered OCT provides detailed structural information to assess the airway morphology. We demonstrate the ability of AFI to visualize vascular networks in vivo and validate this finding using Doppler and structural OCT. Given the advantages of higher resolution, smaller probe size, and ability to visualize vasculature, DOCT-AFI has the potential to increase diagnostic accuracy and minimize bleeding to guide biopsy of pulmonary nodules compared to radial endobronchial ultrasound, the current standard of care.
| S-EPMC4605074 | biostudies-other
Project description:BackgroundComputed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes.MethodsFrom May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs.ResultsIn cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001).ConclusionsCT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.
| S-EPMC10895103 | biostudies-literature
Project description:Ground-glass nodules (GGNs), which are possible precursors of lung cancer, attract increasing attention. Many studies have attempted to identify the characteristic imaging features of GGNs for their qualitative diagnosis; however, the comprehension of GGNs remains controversial. We performed this study to identify imaging characteristics helpful to the differential diagnosis of solitary GGNs.We retrospectively evaluated 112 solitary GGNs resected from 112 patients, pathologically examined after surgical resection. Imaging features of the GGNs, such as size, shape, a solid component, lobulation, spiculation, vascular convergence sign, pleural tag, and air cavity density, were assessed. Differences between malignant and benign nodules were analyzed using binary logistic regression analysis.Of the 112 GGNs, 82 were malignant and 30 were benign. A solid component, vascular convergence sign, and a larger diameter were risk factors for malignancy, with a sensitivity, specificity, and accuracy of 93.9%, 60.0%, and 84.8%, respectively. Lobulation, spiculation, air cavity densities, and pleural tags were also important indicators of malignancy, with positive predictive values of 93.5%, 83.3%, 91.7%, and 87.2%, respectively.GGNs with a solid component, vascular convergence sign, and a larger diameter are highly suggestive of malignancy. The possibility of a neoplasm should also be considered in the case of GGNs that show lobulation, spiculation, air cavity densities, or pleural tags. To obtain a comprehensive and accurate analysis of the nodules, three-dimensional reconstruction is highly recommended.
| S-EPMC4718137 | biostudies-other
Project description:BackgroundAt present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm.MethodIn this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA).ResultA total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice.ConclusionWe developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.
| S-EPMC10545867 | biostudies-literature
Project description:A serum miRNA combination could be a favourable classifier to differentiate malignant pulmonary nodules from benign pulmonary nodules.
2023-08-27 | GSE157074 | GEO
Project description:Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.
| S-EPMC7312740 | biostudies-literature
Project description:There are no large samples or exact prediction models for assessing the cancer risk factors of solitary pulmonary nodules (SPNs) in the Chinese population. We retrospectively analyzed the clinical and imaging data of patients with SPNs who underwent computer tomography guided needle biopsy in our hospital from Jan 1st of 2011 to March 30th of 2016. These patients were divided into a development data set and a validation data set. These groups included 1078 and 344 patients, respectively. A prediction model was developed from the development data set and was validated with the validation data set using logistic regression. The predictors of cancer in our model included female gender, age, pack-years of smoking, a previous history of malignancy, nodule size, lobulated and spiculated edges, lobulation alone and spiculation alone. The Area Under the Curves, sensitivity and specificity of our model in the development and validation data sets were significantly higher than those of the Mayo model and VA model (p < 0.001). We established the largest sampling risk prediction model of SPNs in a Chinese cohort. This model is particularly applicable to SPNs > 8 mm in size. SPNs in female patients, as well as SPNs featuring a combination of lobulated and spiculated edges or lobulated edges alone, should be evaluated carefully due to the probability that they are malignant.
| S-EPMC5438732 | biostudies-literature
Project description:ObjectiveTo explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).MethodsA total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results.ResultsAttenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively.ConclusionIt is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.
| S-EPMC8719667 | biostudies-literature