A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development.
Ontology highlight
ABSTRACT:
SUBMITTER: Cui S
PROVIDER: S-EPMC9513435 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development.
Cui Sijia S Shu Zhenyu Z Ma Yanqing Y Lin Yi Y Wang Haochu H Cao Hanbo H Liu Jing J Gong Xiangyang X
Frontiers in medicine 20220913
The common respiratory abnormality, small airway dysfunction (fSAD), is easily neglected. Its prognostic factors, prevalence, and risk factors are unclear. This study aimed to explore the early detection of fSAD using radiomic analysis of computed tomography (CT) images to predict fSAD progress. The patients were divided into fSAD and non-fSAD groups and divided randomly into a training group (<i>n</i> = 190) and a validation group (<i>n</i> = 82) at a 7:3 ratio. Lung kit software was used for a ...[more]
Similar Datasets
Project description:Objectives: Determining the presence of extrathyroidal extension (ETE) is important for patients with papillary thyroid carcinoma (PTC) in selecting the proper surgical approaches. This study aimed to explore a radiomic model for preoperative prediction of ETE in patients with PTC. Methods: The study included 624 PTC patients (without ETE, n = 448; with minimal ETE, n = 52; with gross ETE, n = 124) whom were divided randomly into training (n = 437) and validation (n = 187) cohorts; all data were gathered between January 2016 and November 2017. Radiomic features were extracted from computed tomography (CT) images of PTCs. Key radiomic features were identified and incorporated into a radiomic signature. Combining the radiomic signature with clinical risk factors, a radiomic nomogram was constructed using multivariable logistic regression. Delong test was used to compare different receiver operating characteristic curves. Results: Five key radiomic features were incorporated into the radiomic signature, which were significantly associated with ETE (p < 0.001 for both cohorts) and slightly better than clinical model integrating significant clinical risk factors in the training cohort (area under the receiver operating characteristic curve (AUC), 0.791 vs. 0.778; F1 score, 0.729 vs. 0.714) and validation cohort (AUC, 0.772 vs. 0.756; F1 score, 0.710 vs. 0.692). The radiomic nomogram significantly improved predictive value in the training cohort (AUC, 0.837, p < 0.001; F1 score, 0.766) and validation cohort (AUC, 0.812, p = 0.024; F1 score, 0.732). Conclusions: The radiomic nomogram significantly improved the preoperative prediction of ETE in PTC patients. It indicated that radiomics could be a valuable method in PTC research.
| S-EPMC6736997 | biostudies-literature
Project description:PurposeTo develop and validate a clinical-radiomic nomogram for the preoperative prediction of the aldosterone-producing adenoma (APA) risk in patients with unilateral adrenal adenoma.Patients and methodsNinety consecutive primary aldosteronism (PA) patients with unilateral adrenal adenoma who underwent adrenal venous sampling (AVS) were randomly separated into training (n = 62) and validation cohorts (n = 28) (7:3 ratio) by a computer algorithm. Data were collected from October 2017 to June 2020. The prediction model was developed in the training cohort. Radiomic features were extracted from unenhanced computed tomography (CT) images of unilateral adrenal adenoma. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensions, select features, and establish a radiomic signature. Multivariable logistic regression analysis was used for the predictive model development, the radiomic signature and clinical risk factors integration, and the model was displayed as a clinical-radiomic nomogram. The nomogram performance was evaluated by its calibration, discrimination, and clinical practicability. Internal validation was performed.ResultsSix potential predictors were selected from 358 texture features by using the LASSO regression model. These features were included in the Radscore. The predictors included in the individualized prediction nomogram were the Radscore, age, sex, serum potassium level, and aldosterone-to-renin ratio (ARR). The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.900 [95% confidence interval (CI), 0.807 to 0.993], and good calibration. The nomogram still showed good discrimination [AUC, 0.912 (95% CI, 0.761 to 1.000)] and good calibration in the validation cohort. Decision curve analysis presented that the nomogram was useful in clinical practice.ConclusionsA clinical-radiomic nomogram was constructed by integrating a radiomic signature and clinical factors. The nomogram facilitated accurate prediction of the probability of APA in patients with unilateral adrenal nodules and could be helpful for clinical decision making.
| S-EPMC8300014 | biostudies-literature
Project description:Thorough assessment of dynamic upper airway obstruction (UAO) in Robin sequence (RS) is critical, but traditional evaluation modalities have significant limitations. Four-dimensional computed tomography (4D-CT) is promising in that it enables objective and quantitative evaluation throughout all phases of respiration. However, there exist few protocols or analysis tools to assist in obtaining and interpreting the vast amounts of obtained data. A protocol and set of data analysis tools were developed to enable quantification and visualization of dynamic 4D-CT data. This methodology was applied to a sample case at 2 time points. In the patient with RS, overall increases in normalized airway caliber were observed from 5 weeks to 1 year. There was, however, continued dynamic obstruction at all airway levels, though objective measures of UAO did improve at the nasopharynx and oropharynx. Use of 4D-CT and novel analyses provide additional quantitative information to evaluate UAO in patients with RS.
| S-EPMC8426414 | biostudies-literature
Project description:BackgroundIn pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required.PurposeTo develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice.Study typeRetrospective.PopulationWe enrolled 303 patients from two medical centers. Patients with a disease-free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126).Field strength/sequence3.0T axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w).AssessmentER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated.Statistical testsThe area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit.ResultsThe AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort).Data conclusionThe radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer.Level of evidence4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.
| S-EPMC7317738 | biostudies-literature
Project description:Variability in the x-ray tube current used in computed tomography may affect quantitative features extracted from the images. To investigate these effects, we scanned the Credence Cartridge Radiomics phantom 12 times, varying the tube current from 25 to 300 mA∙s while keeping the other acquisition parameters constant. For each of the scans, we extracted 48 radiomic features from the categories of intensity histogram (n = 10), gray-level run length matrix (n = 11), gray-level co-occurrence matrix (n = 22), and neighborhood gray tone difference matrix (n = 5). To gauge the size of the tube current effects, we scaled the features by the coefficient of variation of the corresponding features extracted from images of non-small cell lung cancer tumors. Variations in the tube current had more effect on features extracted from homogeneous materials (acrylic, sycamore wood) than from materials with more tissue-like textures (cork, rubber particles). Thirty-eight of the 48 features extracted from acrylic were affected by current reductions compared with only 2 of the 48 features extracted from rubber particles. These results indicate that variable x-ray tube current is unlikely to have a large effect on radiomic features extracted from computed tomography images of textured objects such as tumors.
| S-EPMC5799381 | biostudies-literature
Project description:BackgroundStudies on consistency among spirometry, impulse oscillometry (IOS), and histology for detecting small airway dysfunction (SAD) remain scarce. Considering invasiveness of lung histopathology, we aimed to compare spirometry and IOS with chest computed tomography (CT) for SAD detection, and evaluate clinical characteristics of subjects with SAD assessed by these three techniques.MethodsWe collected baseline data from the Early COPD (ECOPD) study. CT-defined SAD was defined as parametric response mapping quantifying SAD (PRMfSAD) ≥ 15%. Spirometry-defined SAD was defined as at least two of maximal mid-expiratory flow (MMEF), forced expiratory flow 50% (FEF50), and forced expiratory flow 75% (FEF75) less than 65% of predicted. IOS-defined SAD was defined as peripheral airway resistance R5 - R20 > 0.07 kPa/L/s. The consistency of spirometry, IOS and CT for diagnosing SAD was assessed using Kappa coefficient. Correlations among the three techniques-measured small airway function parameters were assessed by Spearman correlation analysis.Results2055 subjects were included in the final analysis. There was low agreement in SAD assessment between spirometry and CT (Kappa = 0.126, 95% confidence interval [CI]: 0.106 to 0.146, p < 0.001), between IOS and CT (Kappa = 0.266, 95% CI: 0.219 to 0.313, p < 0.001), as well as among spirometry, IOS, and CT (Kappa = 0.056, 95% CI: 0.029 to 0.082, p < 0.001). The correlation was moderate (|r|: 0.5 to 0.7, p < 0.05) between spirometry and CT-measured small airway function parameters, and weak (|r|< 0.4, p < 0.05) between IOS and CT-measured small airway function parameters. Only spirometry-defined SAD group had more lower lung function (FEV1/FVC: adjusted difference=-10.7%, 95% CI: -13.5% to -7.8%, p < 0.001) and increased airway wall thickness (Pi 10: adjusted difference = 0.3 mm, 95% CI: 0 to 0.6 mm, p = 0.046) than only CT-defined SAD group. Only IOS-defined SAD group had better lung function (FEV1/FVC: adjusted difference = 3.9%, 95% CI: 1.9 to 5.8%, p < 0.001), less emphysema (inspiratory LAA- 950: adjusted difference=-2.1%, 95% CI:-3.1% to -1.1%, P < 0.001; PRMEmph: adjusted difference=-2.3%, 95% CI: -3.2% to -1.4%, p < 0.001), and thicker airway wall (Pi 10: adjusted difference = 0.2 mm, 95% CI: 0.1 mm to 0.4 mm, p = 0.005) than only CT-defined SAD group.ConclusionsThere was low consistency in the assessment of SAD between spirometry and CT, between IOS and CT, as well as among spirometry, IOS, and CT.Clinical trial numberNot applicable.
| S-EPMC11773755 | biostudies-literature
Project description:IntroductionComputed tomography (CT) is routinely employed on the evaluation of dyspnea, yet limited data exist on its assessment of diaphragmatic muscle. This study aimed to determine the capability of CT in identifying structural changes in the diaphragm among patients with ultrasound-confirmed diaphragmatic dysfunction.MethodsDiaphragmatic ultrasounds conducted between 2018 and 2021 at our center in Marseille, France, were retrospectively collected. Diaphragmatic pillars were measured on CT scans at the L1 level and the celiac artery. Additionally, the difference in height between the two diaphragmatic domes in both diaphragmatic dysfunction cases and controls was measured and compared.ResultsA total of 65 patients were included, comprising 24 with diaphragmatic paralysis, 13 with diaphragmatic weakness, and 28 controls. In the case group (paralysis and weakness) with left dysfunctions (n = 24), the CT thickness of the pillars at the level of L1 and the celiac artery was significantly thinner compared with controls (2.0 mm vs. 7.4 mm and 1.8 mm vs. 3.1 mm, p < 0.001 respectively). Significantly different values were observed for paralysis (but not weakness) in the right dysfunction subgroup (n = 15) (2.6 mm vs. 7.4 mm and 2.2 mm vs. 3.8 mm, p < 0.001 respectively, for paralysis vs. controls). Regardless of the side of dysfunction, a significant difference in diaphragmatic height was observed between cases and controls (7.70 cm vs. 1.16 cm and 5.51 cm vs. 1.16 cm, p < 0.001 for right and left dysfunctions, respectively). Threshold values determined through ROC curve analyses for height differences between the two diaphragmatic domes, indicative of paralysis or weakness in the right dysfunctions, were 4.44 cm and 3.51 cm, respectively. Similarly for left dysfunctions, the thresholds were 2.70 cm and 2.48 cm, respectively, demonstrating good performance (aera under the curve of 1.00, 1.00, 0.98, and 0.79, respectively).ConclusionIn cases of left diaphragmatic dysfunction, as well as in paralysis associated with right diaphragmatic dysfunction, CT revealed thinner pillars. Additionally, a notable increase in the difference in diaphragmatic height demonstrated a strong potential to identify diaphragmatic dysfunction, with specific threshold values.
| S-EPMC10956268 | biostudies-literature
Project description:As we arrive at the 50th anniversary of the first computed tomography (CT) scan of a live patient, we take this opportunity to revisit the history of early CT development. It is not an exaggeration to say that the invention of CT may represent the greatest revolution in medical imaging since the discovery of x-rays. We cover events over a period of about two decades that started with the realization that accurate cross-sectional soft-tissue detail is possible and could be a significant advance. We describe in some detail the development of the first CT system and then the rapid technical advances during the following years that included the entry of many companies into the field and the circumstances that led many of those entrants to exit the field. Rather than focusing on the specific technical details (which can be found elsewhere), we include stories and events in the hope that broader lessons can be learned. As the first x-ray-based digital imaging modality, CT brought into common use an exceptional tool that benefits countless patients every day. It also introduced dramatic changes to biomedical imaging as a field that continues to influence progress to this day.
| S-EPMC8555965 | biostudies-literature
Project description:BackgroundImmunotherapies, such as programmed death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies have been shown to improve overall and progression-free survival (PFS) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). However, not all patients derive a meaningful clinical benefit. Additionally, patients receiving anti-PD-1/PD-L1 therapy can experience immune-related adverse events (irAEs). Clinically significant irAEs may require temporary pause or discontinuation of treatment. Having a tool to identify patients who may not benefit and/or are at risk for developing severe irAEs from immunotherapy will aid in an informed decision-making process for the patients and their physicians.MethodsComputed tomography (CT) scans and clinical data were retrospectively collected for this study to develop three prediction models using (I) radiomic features, (II) clinical features, and (III) radiomic and clinical features combined. Each subject had 6 clinical features and 849 radiomic features extracted. Selected features were run through an artificial neural network (NN) trained on 70% of the cohort, maintaining the case and control ratio. The NN was assessed by calculating the area-under-the-receiver-operating-characteristic curve (AUC-ROC), area-under-the-precision-recall curve (AUC-PR), sensitivity, and specificity.ResultsA cohort of 132 subjects, of which 43 (33%) had a PFS ≤90 days and 89 (67%) of which had a PFS >90 days was used to develop the prediction models. The radiomic model was able to predict progression-free survival with a training AUC-ROC of 87% and testing AUC-ROC, sensitivity, and specificity of 83%, 75%, and 81%, respectively. In this cohort, the clinical and radiomic combined features did add a slight increase in the specificity (85%) but with a decrease in sensitivity (75%) and AUC-ROC (81%).ConclusionsWhole lung segmentation and feature extraction can identify those that would see a benefit from anti-PD-1/PD-L1 therapy.
| S-EPMC10261870 | biostudies-literature
Project description:ObjectivesThe purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.MethodsAfter rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.ResultsSix radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.ConclusionsOur radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.
| S-EPMC10189057 | biostudies-literature