Dataset Information

Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

ABSTRACT: Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence endomicroscopy. In this study we assessed the efficacy of incorporating additional information from label-free fibre-based optical endomicrosopy of the nodule on assessing risk of malignancy. Using image analysis and machine learning approaches, we find that this information does not yield any gain in predictive performance in a cohort of patients. Further advances with pulmonary endomicroscopy will require the addition of molecular tracers to improve information from this procedure.

SUBMITTER: Seth S

PROVIDER: S-EPMC4993998 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

Seth Sohan S Akram Ahsan R AR McCool Paul P Westerfeld Jody J Wilson David D McLaughlin Stephen S Dhaliwal Kevin K Williams Christopher K I CK

Scientific reports 20160823

Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence endomi ...[more]

PMID: 27550539

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Project description:BackgroundThis study proposed a precise diagnostic model for malignant solitary pulmonary nodules (SPNs). This model can be used to identify objective and quantifiable image features and guide the clinical treatment strategy adopted for SPNs. This model will help clinicians optimize management strategies for SPN.MethodsIn this retrospective study, the clinical data of 455 patients of SPN with defined pathological diagnosis between September 2016 and August 2019 were collected and analyzed. The data included pathological diagnosis, preoperative computed tomography (CT) diagnosis, gender, age, smoking history, family history of tumor, previous history, and contact history data. The quantitative image features and radiomic information of the SPNs were provided using computer-aided detection (CAD) "digital lung" software. The Chi-squared test was used to assess the accuracy between CAD and conventional CT in the diagnosis of SPNs. The diagnostic model for benign or malignant SPNs was developed using a multivariate logistic regression analysis that comprises 6 radiomic factors (irregularity, average diameter, COPD910, proportion of emphysema, proportion of fat, and average density of related blood vessels). The area under the receiver operating characteristic curve was used to evaluate the performance of the model in determining SPN risk of malignancy.ResultsThere was a statistical difference in the accuracy of CAD and conventional CT in diagnosing SPNs. According to the golden standard pathological diagnosis, the diagnostic accuracy of CAD (81%) was higher than that of conventional CT (63.7%) (P<0.05). Six variables (i.e., irregularity, the mean diameter, COPD910, the proportion of emphysema, the proportion of fat, and the vascular density) were identified using multivariable logistic regression to establish the diagnostic model for distinguish benign or malignant SPNs. The area under the receiver operating characteristic (ROC) curve (AUC) of the diagnostic model was 0.876 (95% CI: 0.8445-0.9076), and its sensitivity and specificity were 81.25% and 82.56% respectively.ConclusionsThe proposed diagnostic model, which comprises 6 radiomic factors, is accurate and effective at diagnosing benign or malignant SPNs.

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.

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.

Dataset Information

Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

Publications

Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

Similar Datasets

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