Computed Tomography Phenotypes Research Articles

-AI-assisted diagnostic potential of CT in bone oncology and its impact on clinical decision-making for intensive care

ObjectiveThis study evaluates the AI-assisted diagnostic potential of computed tomography (CT) for bone cancer and its influence on patient care during the pre- and post-treatment phases. It compares patient management approaches based on CT severity levels and identifies distinct CT phenotypes linked to disease severity. MethodologyWe retrospectively examined 50 patients diagnosed with bone cancer between December 2022 and June 2023. The CT scans were analyzed according to the Radiological Society of North America (RSNA) guidelines. This study was performed using the deep convolutional neutral network (DCNN) model to assist doctors in diagnosing bone tumors through CT scanning. Patients’ management approaches were compared based on the severity levels indicated by CT scans. ResultsFifty patients participated in this study, with a median age of 67.2 years, ranging from 32 to 89 years. Of them, 38 % were female and 62 % were male. In 2022, 19 individuals (13 males and 6 females, ages 32 to 84) were assessed, with a mean age of 59.9 years. In 2023, 31 individuals, aged 54 to 89 with a mean age of 71.6 years, were assessed; among them were 18 men and 13 women. SPECT scans revealed the following key diagnostic features: 85.9 % of patients exhibited bone lesions with ground-glass opacities, 88 % had multipolar involvement, 92.8 % had bilateral involvement, and 92.8 % showed peripheral involvement. The severity scores based on CT scans were significantly higher in patients requiring intensive care, with scores above 14 being more common in this group. ConclusionDistinct CT findings during the AI-assisted diagnosis and treatment of bone cancer provided prompt and sensitive examination capabilities. Notably, two CT phenotypes emerged, associated with large consolidation patterns and high severity scores, offering crucial insights into disease severity and aiding in clinical decision-making for intensive care requirements. The study underscores the importance of CT in the effective monitoring and management of bone cancer pre- and post-treatment.

Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

BackgroundHigh-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.MethodsOur study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.ResultsBy integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.ConclusionsThis study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

Plasma metabolomics and quantitative interstitial abnormalities in ever-smokers

BackgroundQuantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality.MethodsIn 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography–mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini–Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst.ResultsWe found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways.ConclusionsMetabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers.

Relationship between computed tomography and histological features of gastrointestinal stromal tumors

Background and Aim: Histomorphological and immunohistochemical (IHC) properties of gastrointestinal stromal tumors (GISTs) allow for accurate diagnosis and determine the prognosis. We aimed to evaluate the relationship between the computed tomography (CT) features, histomorphological properties, and IHC markers. Materials and Methods: This retrospective study comprised patients with pathologically confirmed GISTs between 2016 and 2021. The predefined CT characteristics comprised tumor size, hemorrhage and calcification, CT-growth pattern (exophytic/endophytic), and contrast enhancement pattern of the solid component (homogeneous/heterogeneous). The GISTs were divided into groups according to the National Institutes of Health risk category, cell type, presence of necrosis, CD117 and α-SMA positivity, and Ki-67 index. The frequencies of CT phenotypes were compared between groups. Results: Overall 24 (14 [58.3%] males) patients with a median age of 64 (59.5–75.5) having 25 GISTs were included. Of 25 GISTs, 16 (64%) were gastric and 9 (36%) were intestinal in origin. Among CT features, the maximum diameter was higher in epithelioid, infiltrative, a mitotic count ≥5/50, necrotic, high-risk GISTs (P < 0.05 for all). The median tumor size was higher in Ki-67 >8 than Ki-67 <6 subjects (112.5 [39.25–153.75] vs. 22.5 [16.75–57.5] mm, P = 0.014). A heterogeneous enhancement was also more frequent in Ki-67 >8 tumors (P = 0.04). The enhancement pattern did not differ according to CD-117 or SMA positivity. Logistic regression analysis revealed that the only independent predictor of a Ki-67 >8 status was the tumor size (odds ratio: 1.02, 95% confidence interval: 1.001–1.046, P = 0.04). Conclusions: Heterogeneously enhanced large GISTs at CT imaging strongly suggest the presence of poor prognostic factors including a high Ki-67 index and/or high-risk category.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.

Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer

BackgroundOccult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients.Patients and methodsA total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients’ PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability.ResultsRS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923–0.993], 0.941 (95% CI 0.904–0.977), 0.928 (95% CI 0.886–0.971), and 0.920 (95% CI 0.862–0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability.ConclusionCT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.

Identification of Chronic Obstructive Pulmonary Disease Axes That Predict All-Cause Mortality

Chronic obstructive pulmonary disease (COPD) is a syndrome caused by damage to the lungs that results in decreased pulmonary function and reduced structural integrity. Pulmonary function testing (PFT) is used to diagnose and stratify COPD into severity groups, and computed tomography (CT) imaging of the chest is often used to assess structural changes in the lungs. We hypothesized that the combination of PFT and CT phenotypes would provide a more powerful tool for assessing underlying morphologic differences associated with pulmonary function in COPD than does PFT alone. We used factor analysis of 26 variables to classify 8,157 participants recruited into the COPDGene cohort between January 2008 and June 2011 from 21 clinical centers across the United States. These factors were used as predictors of all-cause mortality using Cox proportional hazards modeling. Five factors explained 80% of the covariance and represented the following domains: factor 1, increased emphysema and decreased pulmonary function; factor 2, airway disease and decreased pulmonary function; factor 3, gas trapping; factor 4, CT variability; and factor 5, hyperinflation. After more than 46,079 person-years of follow-up, factors 1 through 4 were associated with mortality and there was a significant synergistic interaction between factors 1 and 2 on death. Considering CT measures along with PFT in the assessment of COPD can identify patients at particularly high risk for death.

Present and future utility of CT phenotypes in the assessment and management of COPD

Chronic obstructive pulmonary disease (COPD) is currently defined on the basis of spirometric evidence of airway obstruction in the setting of well-recognised risk factors. The Global Initiative for Obstructive Lung Disease (GOLD) system has been widely used to identify and classify the severity and risk predisposition of individual patients with COPD. However, individuals with identical GOLD stages have different morphologic appearances at computed tomography (CT) which may reflect important differences in the underlying pathophysiology and genomic profile of COPD.

Left Atrial Volume in Chronic Obstructive Pulmonary Disease

The aim of the study was to evaluate left atrial (LA) volume in smokers according to the severity of emphysema, with the objective of providing indirect evidence of reduced pulmonary venous return due to capillary destruction. A total of 121 smokers underwent a high-pitch and high-temporal resolution computed tomography (CT) angiographic examination, enabling quantification of emphysema, total lung volume, and LA volume measurements normalized to body surface area. The CT phenotypes were as follows: emphysema predominant (group 1; n=57); airway predominant (group 2; n=30); a mixed pattern of emphysema and airway disease (group 3; n=15); and absence of bronchopulmonary CT abnormalities (group 4; n=19). A negative correlation was found between the indexed LA volume and the percentage of emphysema: (a) in the overall study group (P=0.032; r=-0.19); (b) in group 1 (P=0.0163; r=-0.32); and (c) in groups 1 and 3 when analyzed together (P=0.0492; r=-0.23). A negative correlation was found between the indexed LA volume and the total lung volume in the overall study group (P=0.039; r=-0.19) and in group 1 (P=0.048; r=-0.26), whereas no correlations were found in group 2 (P=0.44; r=-0.15), group 3 (P=0.52; r=-0.17), and groups 1 and 3 analyzed as a whole (P=0.14; r=-0.17). The indexed LA volume, impacting left ventricular preload, is correlated to the severity of emphysema.

Chronic Obstructive Pulmonary Disease: More Imaging, More Phenotyping...Better Care?

Chronic Obstructive Pulmonary Disease: More Imaging, More Phenotyping...Better Care?