Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.

PURPOSE: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer. METHODS: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score-based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do-calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten-fold cross-validation was used to assess the performance of the models. The prediction results were evaluated using the R-Squared Score (R2 score). RESULTS: The final causal graphical model was formed by two PET-based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R2  = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense. CONCLUSION: The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer.

Pulmonary circulatory system characteristics are associated with future lung cancer risk.

BACKGROUND: Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung cancer and improve the efficiency of LDCT screening programs. OBJECTIVE: This study aims to investigate the association between the morphology of the pulmonary circulatory system (PCS) and lung cancer development using LDCT scans acquired in the screening setting. METHODS: We analyzed the PLuSS cohort of 3635 lung screening patients from 2002 to 2016. Circulatory structures were segmented and quantified from LDCT scans. The time from the baseline CT scan to lung cancer diagnosis, accounting for death, was used to evaluate the prognostic ability (i.e., hazard ratio (HR)) of these structures independently and with demographic factors. Five-fold cross-validation was used to evaluate prognostic scores. RESULTS: Intrapulmonary vein volume had the strongest association with future lung cancer (HR = 0.63, p < 0.001). The joint model of intrapulmonary vein volume, age, smoking status, and clinical emphysema provided the strongest prognostic ability (HR = 2.20, AUC = 0.74). The addition of circulatory structures improved risk stratification, identifying the top 10% with 28% risk of lung cancer within 15 years. CONCLUSION: PCS characteristics, particularly intrapulmonary vein volume, are important predictors of lung cancer development. These factors significantly improve prognostication based on demographic factors and noncirculatory patient characteristics, particularly in the long term. Approximately 10% of the population can be identified with risk several times greater than average.

A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer.

The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables.

Artificial Intelligence Automation of Proptosis Measurement: An Indicator for Pediatric Orbital Abscess Surgery.

INTRODUCTION: To evaluate the ability of artificial intelligence (AI) software to quantify proptosis for identifying patients who need surgical drainage. METHODS: We pursued a retrospective study including 56 subjects with a clinical diagnosis of subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2002 to 2016. AI computer software was developed to perform 3D visualization and quantitative assessment of proptosis from computed tomography (CT) images acquired at the time of hospital admission. The AI software automatically computed linear and volume metrics of proptosis to provide more practice-consistent and informative measures. Two experienced physicians independently measured proptosis using the interzygomatic line method on axial CT images. The AI software and physician proptosis assessments were evaluated for association with eventual treatment procedures as standalone markers and in combination with the standard predictors. RESULTS: To treat the SPOA, 31 of 56 (55%) children underwent surgical intervention, including 18 early surgeries (performed within 24 h of admission), and 25 (45%) were managed medically. The physician measurements of proptosis were strongly correlated (Spearman r = 0.89, 95% CI 0.82-0.93) with 95% limits of agreement of ± 1.8 mm. The AI linear measurement was on average 1.2 mm larger (p = 0.007) and only moderately correlated with the average physicians' measurements (r = 0.53, 95% CI 0.31-0.69). Increased proptosis of both AI volumetric and linear measurements were moderately predictive of surgery (AUCs of 0.79, 95% CI 0.68-0.91, and 0.78, 95% CI 0.65-0.90, respectively) with the average physician measurement being poorly to fairly predictive (AUC of 0.70, 95% CI 0.56-0.84). The AI proptosis measures were also significantly greater in the early as compared to the late surgery groups (p = 0.02, and p = 0.04, respectively). The surgical and medical groups showed a substantial difference in the abscess volume (p < 0.001). CONCLUSION: AI proptosis measures significantly differed from physician assessments and showed a good overall ability to predict the eventual treatment. The volumetric AI proptosis measurement significantly improved the ability to predict the likelihood of surgery compared to abscess volume alone. Further studies are needed to better characterize and incorporate the AI proptosis measurements for assisting in clinical decision-making.

Deep-Masker: A Deep Learning-based Tool to Assess Chord Length from Murine Lung Images.

Chord length is an indirect measure of alveolar size and a critical endpoint in animal models of chronic obstructive pulmonary disease (COPD). In assessing chord length, the lumens of nonalveolar structures are eliminated from measurement by various methods, including manual masking. However, manual masking is resource intensive and can introduce variability and bias. We created a fully automated deep learning-based tool to mask murine lung images and assess chord length to facilitate mechanistic and therapeutic discovery in COPD called Deep-Masker (available at http://47.93.0.75:8110/login). We trained the deep learning algorithm for Deep-Masker using 1,217 images from 137 mice from 12 strains exposed to room air or cigarette smoke for 6 months. We validated this algorithm against manual masking. Deep-Masker demonstrated high accuracy with an average difference in chord length compared with manual masking of -0.3 ± 1.4% (rs = 0.99) for room-air-exposed mice and 0.7 ± 1.9% (rs = 0.99) for cigarette-smoke-exposed mice. The difference between Deep-Masker and manually masked images for change in chord length because of cigarette smoke exposure was 6.0 ± 9.2% (rs = 0.95). These values exceed published estimates for interobserver variability for manual masking (rs = 0.65) and the accuracy of published algorithms by a significant margin. We validated the performance of Deep-Masker using an independent set of images. Deep-Masker can be an accurate, precise, fully automated method to standardize chord length measurement in murine models of lung disease.

CT-derived body composition associated with lung cancer recurrence after surgery.

OBJECTIVES: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence. METHODS: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence. RESULTS: Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years. CONCLUSIONS: Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.

BACKGROUND: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. METHODS: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. RESULTS: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. CONCLUSIONS: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Super U-Net: a modularized generalizable architecture.

Objective: To develop and validate a novel convolutional neural network (CNN) termed "Super U-Net" for medical image segmentation. Methods: Super U-Net integrates a dynamic receptive field module and a fusion upsampling module into the classical U-Net architecture. The model was developed and tested to segment retinal vessels, gastrointestinal (GI) polyps, skin lesions on several image types (i.e., fundus images, endoscopic images, dermoscopic images). We also trained and tested the traditional U-Net architecture, seven U-Net variants, and two non-U-Net segmentation architectures. K-fold cross-validation was used to evaluate performance. The performance metrics included Dice similarity coefficient (DSC), accuracy, positive predictive value (PPV), and sensitivity. Results: Super U-Net achieved average DSCs of 0.808±0.0210, 0.752±0.019, 0.804±0.239, and 0.877±0.135 for segmenting retinal vessels, pediatric retinal vessels, GI polyps, and skin lesions, respectively. The Super U-net consistently outperformed U-Net, seven U-Net variants, and two non-U-Net segmentation architectures (p < 0.05). Conclusion: Dynamic receptive fields and fusion upsampling can significantly improve image segmentation performance.

Circulating 1,3-Beta-D-Glucan is Associated with Lung Function, Respiratory Symptoms, and Mediators of Matrix Degradation in Chronic Obstructive Pulmonary Disease.

Introduction: Factors beyond cigarette smoke likely contribute to chronic obstructive pulmonary disease (COPD) pathogenesis. Prior studies demonstrate fungal colonization of the respiratory tract and increased epithelial barrier permeability in COPD. We sought to determine whether 1,3-beta-d-glucan (BDG), a polysaccharide component of the fungal cell wall, is detectable in the plasma of individuals with COPD and associates with clinical outcomes and matrix degradation proteins. Methods: BDG was measured in the plasma of current and former smokers with COPD. High BDG was defined as a value greater than the 95th percentile of BDG in smokers without airflow obstruction. Pulmonary function, emphysema, and symptoms were compared between COPD participants with high versus low BDG. The relationship between plasma BDG, matrix metalloproteinases (MMP) 1, 7, and 9, and tissue inhibitor of matrix metalloproteinases (TIMP) 1, 2, and 4 was assessed adjusting for age, sex, and smoking status. Results: COPD participants with high BDG plasma levels (19.8%) had lower forced expiratory volume in 1 second to forced vital capacity ratios (median 31.9 versus 39.3, p=0.025), higher St George's Respiratory Questionnaire symptom scores (median 63.6 versus 57.4, p=0.016), and greater prevalence of sputum production (69.4% versus 52.0%) and exacerbations (69.4% versus 48%) compared to COPD participants with low BDG. BDG levels directly correlated with MMP1 (r=0.27, p<0.001) and TIMP1 (r=0.16, p=0.022) in unadjusted and adjusted analyses. Conclusions: Elevated plasma BDG levels correlate with worse lung function, greater respiratory morbidity, and circulating markers of matrix degradation in COPD. These findings suggest that targeting dysbiosis or enhancing epithelial barrier integrity may have disease-modifying effects in COPD.

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone. METHODS: A dataset of chest computed tomography scans containing lung nodules was collected with their pathologic diagnosis from several sources. The dataset was split randomly into training (70%), internal validation (15%), and independent test sets (15%) at the patient level. Two machine learning algorithms were developed, trained, and validated. The first algorithm used the support vector machine model, and the second used deep learning technology: a convolutional neural network. Receiver operating characteristic analysis was used to evaluate the performance of the classification on the test dataset. RESULTS: The support vector machine/convolutional neural network-based models classified nodules into 6 categories resulting in an area under the curve of 0.59/0.65 when differentiating atypical adenomatous hyperplasia versus adenocarcinoma in situ, 0.87/0.86 with minimally invasive adenocarcinoma versus invasive adenocarcinoma, 0.76/0.72 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma, 0.89/0.87 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma + invasive adenocarcinoma, and 0.93/0.92 atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma. Classifying benign versus atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma resulted in a micro-average area under the curve of 0.93/0.94 for the support vector machine/convolutional neural network models, respectively. The convolutional neural network-based methods had higher sensitivities than the support vector machine-based methods but lower specificities and accuracies. CONCLUSIONS: The machine learning algorithms demonstrated reasonable performance in differentiating benign versus preinvasive versus invasive adenocarcinoma from computed tomography images alone. However, the prediction accuracy varies across its subtypes. This holds the potential for improved diagnostic capabilities with less-invasive means.

Macrovasculature and positron emission tomography (PET) standardized uptake value in patients with lung cancer.

PURPOSE: To investigate the relationship between macrovasculature features and the standardized uptake value (SUV) of positron emission tomography (PET), which is a surrogate for the metabolic activity of a lung tumor. METHODS: We retrospectively analyzed a cohort of 90 lung cancer patients who had both chest CT and PET-CT examinations before receiving cancer treatment. The SUVs in the medical reports were used. We quantified three macrovasculature features depicted on CT images (i.e., vessel number, vessel volume, and vessel tortuosity) and several tumor features (i.e., volume, maximum diameter, mean diameter, surface area, and density). Tumor size (e.g., volume) was used as a covariate to adjust for possible confounding factors. Backward stepwise multiple regression analysis was performed to develop a model for predicting PET SUV from the relevant image features. The Bonferroni correction was used for multiple comparisons. RESULTS: PET SUV was positively correlated with vessel volume (R = 0.44, p < 0.001) and vessel number (R = 0.44, p < 0.001) but not with vessel tortuosity (R = 0.124, p > 0.05). After adjusting for tumor size, PET SUV was significantly correlated with vessel tortuosity (R = 0.299, p = 0.004) and vessel number (R = 0.224, p = 0.035), but only marginally correlated with vessel volume (R = 0.187, p = 0.079). The multiple regression model showed a performance with an R-Squared of 0.391 and an adjusted R-Squared of 0.355 (p < 0.001). CONCLUSIONS: Our investigations demonstrate the potential relationship between macrovasculature and PET SUV and suggest the possibility of inferring the metabolic activity of a lung tumor from chest CT images.

Automated delineation of orbital abscess depicted on CT scan using deep learning.

OBJECTIVES: To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT). METHODS: We retrospectively collected orbital CT scans acquired on 67 pediatric subjects with a confirmed orbital abscess in the setting of infectious orbital cellulitis. A context-aware convolutional neural network (CA-CNN) was developed and trained to automatically segment orbital abscess. To reduce the requirement for a large dataset, transfer learning was used by leveraging a pre-trained model for CT-based lung segmentation. An ophthalmologist manually delineated orbital abscesses depicted on the CT images. The classical U-Net and the CA-CNN models with and without transfer learning were trained and tested on the collected dataset using the 10-fold cross-validation method. Dice coefficient, Jaccard index, and Hausdorff distance were used as performance metrics to assess the agreement between the computerized and manual segmentations. RESULTS: The context-aware U-Net with transfer learning achieved an average Dice coefficient and Jaccard index of 0.78 ± 0.12 and 0.65 ± 0.13, which were consistently higher than the classical U-Net or the context-aware U-Net without transfer learning (P < 0.01). The average differences of the abscess between the computerized results and the experts in terms of volume and Hausdorff distance were 0.10 ± 0.11 mL and 1.94 ± 1.21 mm, respectively. The context-aware U-Net detected all orbital abscess without false positives. CONCLUSIONS: The deep learning solution demonstrated promising performance in detecting and segmenting orbital abscesses on CT images in strong agreement with a human observer.

Lesion-Mapping Software for the Measurement of Subperiosteal Abscess Volume for Pediatric Orbital Cellulitis: A Retrospective Chart Review.

INTRODUCTION: Our objective was to assess the utility of using lesion-mapping software to calculate precise orbital volumes to predict patients who would benefit from early surgical intervention. METHODS: We retrospectively reviewed patients diagnosed with subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2005 to 2016. Diagnoses were confirmed by CT scans. Lesion-mapping software was used to measure SPOA volume using initial CT images. Data collected included patient demographics, length of hospital stay, and subsequent medical or surgical treatment. RESULTS: Thirty-three (52%) patients ultimately underwent surgical intervention, while 30 (48%) were managed medically. Between the surgical and medical groups, there were no differences in gender, age, or comorbidities. The surgical group had larger abscess volumes than the medically managed group (0.94 mL vs. 0.31 mL, p < 0.01). Overall, increased SPOA volume was associated with increased age (Pearson's coefficient = 0.374, p ≤ 0.01) and increased total days of intravenous (IV) antibiotic administration (Pearson's coefficient = 0.260, p = 0.039). Patients who underwent surgery on the day of admission had 25% shorter hospital stay than patients who had delayed surgery (p < 0.01). Our calculated sensitivity-optimized SPOA volume cutoff of 0.231 mL yielded sensitivity of 90.9% and specificity of 70.0%. CONCLUSIONS: This is the first study to use lesion-mapping software for precise calculation of SPOA volumes, which can help refine indications for early surgical intervention and help decrease length of hospital stay.

2D CNN versus 3D CNN for false-positive reduction in lung cancer screening.

Purpose: To clarify whether and to what extent three-dimensional (3D) convolutional neural network (CNN) is superior to 2D CNN when applied to reduce false-positive nodule detections in the scenario of low-dose computed tomography (CT) lung cancer screening. Approach: We established a dataset consisting of 1600 chest CT examinations acquired on different subjects from various sources. There were in total 18,280 candidate nodules in these CT examinations, among which 9185 were nodules and 9095 were not nodules. For each candidate nodule, we extracted a number of cubic subvolumes with a dimension of 72 × 72 × 72 mm 3 by rotating the CT examinations randomly for 25 times prior to the extraction of the axis-aligned subvolumes. These subvolumes were split into three groups in a ratio of 8 ∶ 1 ∶ 1 for training, validation, and independent testing purposes. We developed a multiscale CNN architecture and implemented its 2D and 3D versions to classify pulmonary nodules into two categories, namely true positive and false positive. The performance of the 2D/3D-CNN classification schemes was evaluated using the area under the receiver operating characteristic curves (AUC). The p -values and the 95% confidence intervals (CI) were calculated. Results: The AUC for the optimal 2D-CNN model is 0.9307 (95% CI: 0.9285 to 0.9330) with a sensitivity of 92.70% and a specificity of 76.21%. The 3D-CNN model with the best performance had an AUC of 0.9541 (95% CI: 0.9495 to 0.9583) with a sensitivity of 89.98% and a specificity of 87.30%. The developed multiscale CNN architecture had a better performance than the vanilla architecture did. Conclusions: The 3D-CNN model has a better performance in false-positive reduction compared with its 2D counterpart; however, the improvement is relatively limited and demands more computational resources for training purposes.

Loss of skin elasticity is associated with pulmonary emphysema, biomarkers of inflammation, and matrix metalloproteinase activity in smokers.

BACKGROUND: Elastin breakdown and the resultant loss of lung elastic recoil is a hallmark of pulmonary emphysema in susceptible individuals as a consequence of tobacco smoke exposure. Systemic alterations to the synthesis and degradation of elastin may be important to our understanding of disease phenotypes in chronic obstructive pulmonary disease. We investigated the association of skin elasticity with pulmonary emphysema, obstructive lung disease, and blood biomarkers of inflammation and tissue protease activity in tobacco-exposed individuals. METHODS: Two hundred and thirty-six Caucasian individuals were recruited into a sub-study of the University of Pittsburgh Specialized Center for Clinically Orientated Research in chronic obstructive pulmonary disease, a prospective cohort study of current and former smokers. The skin viscoelastic modulus (VE), a determinant of skin elasticity, was recorded from the volar forearm and facial wrinkling severity was determined using the Daniell scoring system. RESULTS: In a multiple regression analysis, reduced VE was significantly associated with cross-sectional measurement of airflow obstruction (FEV1/FVC) and emphysema quantified from computed tomography (CT) images, ß = 0.26, p = 0.001 and ß = 0.24, p = 0.001 respectively. In emphysema-susceptible individuals, elasticity-determined skin age was increased (median 4.6 years) compared to the chronological age of subjects without emphysema. Plasma biomarkers of inflammation (TNFR1, TNFR2, CRP, PTX3, and SAA) and matrix metalloproteinase activity (MMP1, TIMP1, TIMP2, and TIMP4) were inversely associated with skin elasticity. CONCLUSIONS: We report that an objective non-invasive determinant of skin elasticity is independently associated with measures of lung function, pulmonary emphysema, and biomarkers of inflammation and tissue proteolysis in tobacco-exposed individuals. Loss of skin elasticity is a novel observation that may link the common pathological processes that drive tissue elastolysis in the extracellular matrix of the skin and lung in emphysema-susceptible individuals.

Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models.

INTRODUCTION: Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives. METHODS: In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort. RESULTS: Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p<0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules. DISCUSSION: LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.

Computerized identification of the vasculature surrounding a pulmonary nodule.

OBJECTIVES: The idea of inferring the prognosis of lung tumor via its surrounding vasculature is novel, but not supported by available technology. In this study, we described and validated a computerized method to identify the vasculature surrounding a pulmonary nodule depicted on low-dose computed tomography (LDCT). MATERIALS AND METHODS: The proposed computerized scheme identified the vessels surrounding a lung nodule by using novel computational geometric solutions and quantified them by decomposing the vessels into independent vessel branches. We validated this scheme by testing it on a dataset consisting of 100 chest CT examinations, with 50-paired benign and malignant nodules. Two experienced pulmonologists were asked to measure the vessel branches associated with a nodule under the aid of a visualization tool. We used the Bland-Altman plots and the concordance correlation coefficient (CCC) to assess the agreement between the results of the computer algorithm and two experienced pulmonologists. RESULTS: Bland-Altman different analysis demonstrated a mean bias of 0.61 ± 4.17 in terms of vessel branches between the computer results and the human experts, while the inter-rater mean bias was -0.61 ± 1.60. The CCC-based agreements between the computer and the two raters were 0.90 / 0.86, 0.79 / 0.83 for benign and malignant nodules, respectively. CONCLUSION: The small width of the limits of agreement between the computer algorithm and the human experts suggests that the results from the computer and the pulmonologist experts were relatively consistent, namely the computerized scheme is capable of reliably identifying the vasculature surrounding a nodule.

Vasculature surrounding a nodule: A novel lung cancer biomarker.

PURPOSE: To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules. MATERIALS AND METHODS: We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features. RESULTS: The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit.

Preoperative (3-dimensional) computed tomography lung reconstruction before anatomic segmentectomy or lobectomy for stage I non-small cell lung cancer.

OBJECTIVES: Accurate cancer localization and negative resection margins are necessary for successful segmentectomy. In this study, we evaluate a newly developed software package that permits automated segmentation of the pulmonary parenchyma, allowing 3-dimensional assessment of tumor size, location, and estimates of surgical margins. METHODS: A pilot study using a newly developed 3-dimensional computed tomography analytic software package was performed to retrospectively evaluate preoperative computed tomography images of patients who underwent segmentectomy (n = 36) or lobectomy (n = 15) for stage 1 non-small cell lung cancer. The software accomplishes an automated reconstruction of anatomic pulmonary segments of the lung based on bronchial arborization. Estimates of anticipated surgical margins and pulmonary segmental volume were made on the basis of 3-dimensional reconstruction. RESULTS: Autosegmentation was achieved in 72.7% (32/44) of preoperative computed tomography images with slice thicknesses of 3 mm or less. Reasons for segmentation failure included local severe emphysema or pneumonitis, and lower computed tomography resolution. Tumor segmental localization was achieved in all autosegmented studies. The 3-dimensional computed tomography analysis provided a positive predictive value of 87% in predicting a marginal clearance greater than 1 cm and a 75% positive predictive value in predicting a margin to tumor diameter ratio greater than 1 in relation to the surgical pathology assessment. CONCLUSIONS: This preoperative 3-dimensional computed tomography analysis of segmental anatomy can confirm the tumor location within an anatomic segment and aid in predicting surgical margins. This 3-dimensional computed tomography information may assist in the preoperative assessment regarding the suitability of segmentectomy for peripheral lung cancers.