Computed Tomography Features Research Articles

Whole-body low-dose CT can be of value in prostate cancer decision-making: a retrospective study on 601 patients

ObjectivesTo evaluate the diagnostic value of whole-body low-dose computed tomography (CT) to detect bone metastasis in prostate cancer (PCa) patients and its possible utility in therapeutic decision-making. Also, to determine the valuable CT features for lesion characterisation.MethodsThis IRB-approved retrospective study reviewed PCa patients who underwent 68Ga-PSMA PET/CT in our centre from March 2017 to August 2022. Two board-certified radiologists and one nuclear medicine specialist reported all whole-body low-dose CT scans separately, unaware of the 68Ga-PSMA-PET results. The per-lesion and per-patient diagnostic performances were calculated. Also, the significance of CT features was evaluated. Moreover, the inter-observer agreement was analysed. A two-tailed p value < 0.05 was considered significant.ResultsFrom 727 reviewed PCa patients, 601 (mean age = 68.7 ± 8.1) were found to be eligible, including 211 (35.1%) referrals for initial staging and 390 (64.9%) for evaluating the extent of the disease after biochemical recurrence. Per-patient diagnostic analysis for three reviewers showed 81.0–89.4% sensitivity and 96.6–98.5% specificity in detecting osteo-metastasis. It was able to correctly detect high-burden disease based on both CHAARTED and LATITUDE criteria. Regarding the value of underlying CT features, size > 1 cm, ill-defined borders, presence of soft-tissue component, and cortical destruction were statistically in favour of metastasis. Also, Hu > 900 was in favour of benign entities with 93% specificity.ConclusionsAlthough not as accurate as 68Ga-PSMA PET/CT, whole-body low-dose CT might precisely classify PCa patients considering therapeutic decision-making. Additionally, we proposed diagnostic CT features that could help radiologists with better characterisation of the detected lesions.Critical relevance statementThe whole-body low-dose CT can be considered valuable in the clinical decision-making of prostate cancer patients. This modality may obviate performing multiple imaging sessions and high-cost scans in patients diagnosed with the high-burden disease.Graphical abstract

Can Perfusion CT Differentiate GIST from Other Benign Subepithelial Tumors in the Stomach?

Purpose: To evaluate the diagnostic performance of perfusion computed tomography (PCT) parameters for differentiating gastrointestinal stromal tumors (GISTs) from other benign subepithelial tumors (SETs) in the stomach.Materials and Methods: Thirty-one patients with gastric SETs subsequently confirmed via surgery underwent PCT using a multidetector computed tomography (CT) scanner at 80 kVp. Two radiologists analyzed key CT features including homogeneity and degree of enhancement. PCT parameters including blood flow, blood volume, mean transit time, and permeability surface value (PS) were independently calculated by two other radiologists. Comparative analysis of the CT features and perfusion parameters of GISTs and other benign SETs was then performed. Diagnostic performances of the perfusion parameters were also evaluated using receiver operating characteristic (ROC) analysis.Results: Twenty-four patients were proven to have GISTs via histologic examination, while the remaining non-GISTs included 3 leiomyomas, 3 schwannomas, and 1 totally necrotic nodule. Among the conventional CT features, lymphadenopathy was more frequent in non-GISTs (2/7, 28.6%) than in GISTs (0/24, 0%) (p = 0.045). Among the PCT parameters, the mean PS in the areas of strongest enhancement of GISTs (25.3 ± 23.9 mL/100 g/min) was significantly higher than that of other SETs (8.8 ± 8.8 mL/100 g/min) (p = 0.029). In ROC analysis, an area under the curve of 0.774 (p = 0.03), sensitivity of 91.7%, and specificity of 57.1% were achieved when the PS cut-off was set at 7.17 mL/100 g/min.Conclusions: Perfusion parameters were helpful for differentiating GISTs from other benign SETs, as GISTs exhibited significantly higher PS than non-GISTs.

Post-tuberculosis lung disease and chronic obstructive pulmonary disease.

The burden of chronic airway diseases, including chronic obstructive pulmonary disease (COPD), continues to increase, especially in low- and middle-income countries. Post-tuberculosis lung disease (PTLD) is characterized by chronic lung changes after the "cure" of pulmonary tuberculosis (TB), which may be associated with the pathogenesis of COPD. However, data on its prevalence, clinical manifestations, computed tomography features, patterns of lung function impairment, and influencing factors are limited. The pathogenic mechanisms underlying PTLD remain to be elucidated. This review summarizes the recent advances in PTLD and TB-associated COPD. Research is urgently needed both for the prevention and management of PTLD.

CT characteristics of recurrent acute pancreatitis and acute pancreatitis in different stages-a retrospective cross-sectional study.

Acute pancreatitis (AP), recurrent acute pancreatitis (RAP), and chronic pancreatitis (CP) are a continuum of the same disease. The course of RAP and AP is a dynamic process. Previous studies are contradictory regarding the severity of RAP and AP. We conducted this study to investigate the computed tomography (CT) characteristics of RAP and AP in the early and late stages; respectively. Patients who underwent contrast-enhanced computed tomography for symptoms during RAP or AP episodes were retrospectively collected from three tertiary hospitals in Sichuan Province, China from January 2015 to December 2019. The patients were categorized into RAP and AP groups based on recurrence and initial events. Both the RAP and AP groups were divided into early (first week) and late stages (after the first week) based on the 2012 revised Atlanta classification (RAC). Patient demographic data, RAC, CT findings, CT severity index (CTSI) scores, and extrapancreatic inflammation on CT scores in the early and late phases were analyzed between the two groups. The Wilcoxon signed-rank test, χ2 test, and Fisher's exact test were used to compare continuous and categorical variables between the two groups respectively. In 683 RAP and 1,829 AP patients, the most common etiologies were hypertriglyceridemia and cholelithiasis, respectively. The RAP group had lower extrapancreatic inflammation on CT scores and Acute Physiology and Chronic Health Evaluation II scores than the AP group in the early stage (both P<0.001). The RAP group had higher CTSI scores than the AP group in the late stage (P=0.022). Compared with AP patients, the most common cause of RAP patients was hypertriglyceridemia in China, and the severity of RAP was lower than that of initial AP in the early stage and higher than that of initial AP in the late stage.

Rectal stump leakage: A neglected complication after Hartmann’s procedure for colorectal cancer

BackgroundHartmann’s procedure is widely used in large bowel obstruction caused by colorectal cancer. However, rectal stump leakage, one of its serious complications, has not been well investigated in the literature. MethodsPatients with colorectal cancer who underwent Hartmann’s procedure between January 2015 and January 2022 were retrospectively assessed. Rectal stump leakage was diagnosed based on the clinical symptoms, nature of drainage fluid, and computed tomography characteristics. The patients were categorized into the following 2 groups: non-rectal stump leakage group and rectal stump leakage group. A multivariate logistic regression model was used to identify independent risk factors for rectal stump leakage. ResultsThe incidence rate of postoperative rectal stump leakage was 11.6% in our patients. Univariate analysis revealed that male sex, body mass index (underweight), and tumor location (below the peritoneal reflection) were risk factors for rectal stump leakage (P < .05). Multivariate regression analysis confirmed these 3 factors were independent risk factors for rectal stump leakage (P < .05). Computed tomography imaging characteristics of patients with rectal stump leakage usually included inflammatory exudate and edema of the rectal stump, fluid, or gas-containing abscess around the rectal stump. The computed tomography imaging characteristics of a gas-containing abscess around the rectal stump and an abdominal drainage tube advanced into the rectum via the rectal stump could confirm the diagnosis of rectal stump leakage. The incidence rate of small bowel obstruction in group 2 (69.2%) was significantly higher than that in group 1 (15.7%) (P = .000). ConclusionMale sex, body mass index (underweight), and tumor location (below the peritoneal reflection) were independent risk factors for rectal stump leakage after Hartmann’s procedure. We suggested that rectal stump leakage be classified into inflammatory exudation and abscess stages on computed tomography imaging. Unexplained small bowel obstruction after Hartmann’s procedure may be an important clue to the early diagnosis of rectal stump leakage.

Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children

Abusive head trauma (AHT) in children is often missed in medical encounters, and retinal hemorrhage (RH) is considered strong evidence for AHT. Although head computed tomography (CT) is obtained routinely, all but exceptionally large RHs are undetectable on CT images in children. To examine whether deep learning-based image analysis can detect RH on pediatric head CT. This diagnostic study included 301 patients diagnosed with AHT who underwent head CT and dilated fundoscopic examinations at a quaternary care children's hospital. The study assessed a deep learning model using axial slices from 218 segmented globes with RH and 384 globes without RH between May 1, 2007, and March 31, 2021. Two additional light gradient boosting machine (GBM) models were assessed: one that used demographic characteristics and common brain findings in AHT and another that combined the deep learning model's risk prediction plus the same demographic characteristics and brain findings. Sensitivity (recall), specificity, precision, accuracy, F1 score, and area under the curve (AUC) for each model predicting the presence or absence of RH in globes were assessed. Globe regions that influenced the deep learning model predictions were visualized in saliency maps. The contributions of demographic and standard CT features were assessed by Shapley additive explanation. The final study population included 301 patients (187 [62.1%] male; median [range] age, 4.6 [0.1-35.8] months). A total of 120 patients (39.9%) had RH on fundoscopic examinations. The deep learning model performed as follows: sensitivity, 79.6%; specificity, 79.2%; positive predictive value (precision), 68.6%; negative predictive value, 87.1%; accuracy, 79.3%; F1 score, 73.7%; and AUC, 0.83 (95% CI, 0.75-0.91). The AUCs were 0.80 (95% CI, 0.69-0.91) for the general light GBM model and 0.86 (95% CI, 0.79-0.93) for the combined light GBM model. Sensitivities of all models were similar, whereas the specificities of the deep learning and combined light GBM models were higher than those of the light GBM model. The findings of this diagnostic study indicate that a deep learning-based image analysis of globes on pediatric head CTs can predict the presence of RH. After prospective external validation, a deep learning model incorporated into CT image analysis software could calibrate clinical suspicion for AHT and provide decision support for which patients urgently need fundoscopic examinations.

Lobulated tumor contour as a predictor of preoperative tumor invasion of the lung or pericardium in thymoma patients.

Preoperative investigations to detect invasion to neighboring organs are important for deciding on the most appropriate surgical approach for thymoma. We evaluated preoperative computed tomography (CT) findings in thymoma patients to identify the CT features associated with tumor invasion. Clinicopathologic information on 193 patients who underwent surgical resection for thymoma at Chiba University Hospital between 2002 and 2016 was collected retrospectively. The surgical pathology identified invasion of thymoma in 35 patients: in the lung (n = 18), pericardium (n = 11), or both (n = 6). Contact lengths between the tumor contour and lung (CLTL) or pericardium (CLTP) were measured at the maximum section of the tumor on axial CT. Univariate and multivariate analyses were performed to analyze the relationship between pathological invasion of the lung or pericardium and the clinicopathologic features. The mean CLTL and CLTP were significantly longer in patients with invasion of the neighboring organs than in those without invasion. A lobulated tumor contour was identified in 95.6% of the patients with invasion of the neighboring organs. A multivariate analysis revealed that a lobulated tumor contour was significantly associated with both lung and pericardial invasion. A lobulated tumor contour was significantly associated with lung and/or pericardial invasion in thymoma patients.

Predictors of acute incisional hernia incarceration at initial hernia diagnosis on computed tomography.

Acute incisional hernia incarceration is associated with high morbidity and mortality yet there is little evidence to guide which patients will benefit most from prophylactic repair. We explored baseline computed tomography (CT) characteristics associated with incarceration. A case-control study design was utilized to explore adults (≥18 years) diagnosed with an incisional hernia between 2010 and 2017 at a single institution with a 1-year minimum follow-up. Computed tomography imaging at the time of initial hernia diagnosis was examined. Following propensity score matching for baseline characteristics, multivariable logistic regression was performed to identify independent predictors associated with acute incarceration. A total of 532 patients (27.26% male, mean 61.55 years) were examined, of whom 238 experienced an acute incarceration. Between two well-matched cohorts with and without incarceration, the presence of small bowel in the hernia sac (odds ratio [OR], 7.50; 95% confidence interval [CI], 3.35-16.38), increasing sac height (OR, 1.34; 95% CI, 1.10-1.64), more acute hernia angle (OR, 0.98 per degree; 95% CI, 0.97-0.99), decreased fascial defect width (OR, 0.68; 95% CI, 0.58-0.81), and greater outer abdominal fat (OR, 1.28; 95% CI, 1.02-1.60) were associated with acute incarceration. Using threshold analysis, a hernia angle of <91 degrees and a sac height of >3.25 cm were associated with increased incarceration risk. Computed tomography features present at the time of hernia diagnosis provide insight into later acute incarceration risk. Improved understanding of acute incisional hernia incarceration can guide selection for prophylactic repair and thereby may mitigate the excess morbidity associated with incarceration. Prognostic and Epidemiological; Level III.

Preoperative CT Radiomics Nomogram for Predicting Microvascular Invasion in Stage I Non-Small Cell Lung Cancer

This retrospective study analyzed 188 cases of stage I NSCLC (63 MVI positives and 125 negatives), which were randomly assigned to training (n=133) and validation cohorts (n=55) at a ratio of 7:3. Preoperative non-contrast and contrast-enhanced CT (CECT) images were used to analyze computed tomography (CT) features and extract radiomics features. The student's t-test, the Mann-Whitney-U test, the Pearson correlation, the least absolute shrinkage and selection operator, and multivariable logistic analysis were used to select the significant CT and radiomics features. Multivariable logistic regression analysis was performed to build the clinical-CT, radiomics, and integrated models. The predictive performances were evaluated through the receiver operating characteristic curve and compared with the DeLong test. The integrated nomogram was analyzed regarding discrimination, calibration, and clinical significance. The rad-score was developed with one shape and four textural features. The integrated nomogram incorporating radiomics score, spiculation, and the number of tumor-related vessels (TVN) demonstrated better predictive efficacy than the radiomics and clinical-CT models in the training cohort (area under the curve [AUC], 0.893 vs 0.853 and 0.828, andp=0.043 and 0.027, respectively) and validation cohort (AUC, 0.887 vs 0.878 and 0.786, andp=0.761 and 0.043, respectively). The nomogram also demonstrated good calibration and clinical usefulness. The radiomics nomogram integrating the radiomics with clinical-CT features demonstrated good performance in predicting MVI status in stage I NSCLC. The nomogram may be a useful tool for physicians in improving personalized management of stage I NSCLC.

Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors.

Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs). To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs. The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification. There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively. CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.

Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics

The purpose of this study was to differentiate the retroperitoneal paragangliomas and schwannomas using computed tomography (CT) radiomics. This study included 112 patients from two centers who pathologically confirmed retroperitoneal pheochromocytomas and schwannomas and underwent preoperative CT examinations. Radiomics features of the entire primary tumor were extracted from non-contrast enhancement (NC), arterial phase (AP) and venous phase (VP) CT images. The least absolute shrinkage and selection operator method was used to screen out key radiomics signatures. Radiomics, clinical and clinical-radiomics combined models were built to differentiate the retroperitoneal paragangliomas and schwannomas. Model performance and clinical usefulness were evaluated by receiver operating characteristic curve, calibration curve and decision curve. In addition, we compared the diagnostic accuracy of radiomics, clinical and clinical-radiomics combined models with radiologists for pheochromocytomas and schwannomas in the same set of data. Three NC, 4 AP, and 3 VP radiomics features were retained as the final radiomics signatures for differentiating the paragangliomas and schwannomas. The CT characteristics CT attenuation value of NC and the enhancement magnitude at AP and VP were found to be significantly different statistically (P < 0.05). The NC, AP, VP, Radiomics and clinical models had encouraging discriminative performance. The clinical-radiomics combined model that combined radiomics signatures and clinical characteristics showed excellent performance, with an area under curve (AUC) values were 0.984 (95% CI 0.952–1.000) in the training cohort, 0.955 (95% CI 0.864–1.000) in the internal validation cohort and 0.871 (95% CI 0.710–1.000) in the external validation cohort. The accuracy, sensitivity and specificity were 0.984, 0.970 and 1.000 in the training cohort, 0.960, 1.000 and 0.917 in the internal validation cohort and 0.917, 0.923 and 0.818 in the external validation cohort, respectively. Additionally, AP, VP, Radiomics, clinical and clinical-radiomics combined models had a higher diagnostic accuracy for pheochromocytomas and schwannomas than the two radiologists. Our study demonstrated the CT-based radiomics models has promising performance in differentiating the paragangliomas and schwannomas.

Comparison between original SARS-CoV-2 strain and omicron variant on thin-section chest CT imaging of COVID-19 pneumonia.

We investigated different computed tomography (CT) features between Omicron-variant and original-strain SARS-CoV‑2 pneumonia to facilitate the clinical management. Medical records were retrospectively reviewed to select patients with original-strain SARS-CoV‑2 pneumonia from February22 to April22, 2020, or Omicron-variant SARS-CoV‑2 pneumonia from March26 to May31, 2022. Data on the demographics, comorbidities, symptoms, clinical types, and CT features were compared between the two groups. There were 62 and 78patients with original-strain or Omicron-variant SARS-CoV‑2 pneumonia, respectively. There were no differences between the two groups in terms of age, sex, clinical types, symptoms, and comorbidities. The main CT features differed between the two groups (p = 0.003). There were37 (59.7%) and 20 (25.6%) patients with ground-glass opacities (GGO) in the original-strain and Omicron-variant pneumonia, respectively. Aconsolidation pattern was more frequently observed in the Omicron-variant than original-strain pneumonia (62.8% vs. 24.2%). There was no difference in crazy-paving pattern between the original-strain and Omicron-variant pneumonia (16.1% vs. 11.6%). Pleural effusion was observed more often in Omicron-variant pneumonia, while subpleural lesions were more common in the original-strain pneumonia. The CT score in the Omicron-variant group was higher than that in the original-strain group for critical-type (17.00, 16.00-18.00 vs. 16.00, 14.00-17.00, p = 0.031) and for severe-type (13.00, 12.00-14.00 vs 12.00, 10.75-13.00, p = 0.027) pneumonia. The main CT finding of the Omicron-variant SARS-CoV‑2 pneumonia included consolidations and pleural effusion. By contrast, CT findings of original-strain SARS-CoV‑2 pneumonia showed frequent GGO and subpleural lesions, but without pleural effusion. The CT scores were also higher in the critical and severe types of Omicron-variant than original-strain pneumonia.

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics

PurposeDifferentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs). MethodsWe enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features. ResultsFor the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak. ConclusionsThe Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.

Recognition of liver tumors by predicted hyperspectral features based on patient's computed tomography radiomics features

BackgroundPrimary liver tumors can be a serious threat to life and health. Early diagnosis may be life saving. Therefore, enhancing the accuracy of non-invasive early detection of liver tumors is imperative. MethodsFirstly, image enhancement was applied to augment the dataset, resulting in a total of 464 samples after employing seven data augmentation methods. Subsequently, the XGBoost model was utilized to construct and learn the mapping relationship between Computed Tomography (CT) and corresponding hyperspectral imaging (HSI) data. This model enables the prediction of HSI features corresponding to CT features, thereby enriching CT with more comprehensive hyperspectral information. ResultsFour classifiers were employed to discern the presence of tumors in patients. The results demonstrated exceptional performance, with a classification accuracy exceeding 90%. ConclusionsThis study proposes an artificial intelligence-based methodology that utilizes early CT radiomics features to predict HSI features. Subsequently, the results are utilized for non-invasive tumor prediction and early screening, thereby enhancing the accuracy of non-invasive liver tumor detection.

Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography-Based Radiomics Models Covering Tumor Per Se and Peritumoral Signatures.

We aimed to develop and validate a computed tomography (CT)-based radiomics model for early relapse prediction in patients with Ewing sarcoma (ES). We recruited 104 patients in this study. Tumor areas and areas with a tumor expansion of 3 mm were used as regions of interest for radiomics analysis. Six different models were constructed: Pre-CT, CT enhancement (CTE), Pre-CT +3 mm , CTE +3 mm , Pre-CT and CTE combined (ComB), and Pre-CT +3 mm and CTE +3 mm combined (ComB +3 mm ). All 3 classifiers used a grid search with 5-fold cross-validation to identify their optimal parameters, followed by repeat 5-fold cross-validation to evaluate the model performance based on these parameters. The average performance of the 5-fold cross-validation and the best one-fold performance of each model were evaluated. The AUC (area under the receiver operating characteristic curve) and accuracy were calculated to evaluate the models. The 6 radiomics models performed well in predicting relapse in patients with ES using the 3 classifiers; the ComB and ComB +3 mm models performed better than the other models (AUC -best : 0.820-0.922/0.823-0.833 and 0.799-0.873/0.759-0.880 in the training and validation cohorts, respectively). Although the Pre-CT +3 mm , CTE +3 mm, and ComB +3 mm models covering tumor per se and peritumoral CT features preoperatively forecasted ES relapse, the model was not significantly improved. The radiomics model performed well for early recurrence prediction in patients with ES, and the ComB and ComB +3 mm models may be superior to the other models.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

PurposePersistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population.Material and methodsWe retrospectively selected 60 NSNs with an axial diameter of 6–30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses.ResultsIn multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth.ConclusionAccording to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Non-contrast computed tomography features predict intraventricular hemorrhage growth

ObjectivesNon-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth.MethodsPatients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models.ResultsA total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16–2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48–2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91–3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion.ConclusionsNCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies.Clinical relevance statementNon-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies.Key Points• NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences.• The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion.• Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.

Radiomics nomogram for the prediction of Ki-67 index in advanced non-small cell lung cancer based on dual-phase enhanced computed tomography.

To develop a radiomics nomogram based on dual-phase enhanced computed tomography (CT) for predicting the Ki-67 index status in patients with advanced non-small cell lung cancer (NSCLC). 137 patients with NSCLC who had undergone dual-phase enhanced CT scans and Ki-67 examination within 2weeks were retrospectively enrolled between January 2020 and December 2022. Clinical and laboratory data were collected, and the patients were categorized based on low or high expression of Ki-67 index, with a cut-off value of 40%. The cohort was randomly divided into a training group (n = 95) and a testing group (n = 42) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select the most valuable radiomics features from the dual-phase enhanced CT images. Subsequently, a nomogram that incorporated the radiomics score and clinical factors associated with Ki-67 index status was established through univariate and multivariate logistic regression analyses. The predictive performance of the nomogram was evaluated using the area under the curve (AUC). The AUC values of the radiomics features of artery phase and vein phase CT in the testing group were 0.748 and 0.758, respectively. The AUC of the dual-phase enhanced CT was 0.785, and the AUC of the developed nomogram was 0.859, which was higher than those of the radiomics (AUC, 0.785) and clinical models (AUC, 0.736). The radiomics nomogram based on dual-phase enhanced CT images provides a promising method for predicting the Ki-67 index status in patients with advanced NSCLC.

Peritumoral Adipose Tissue Density Predicts the Malignancy in cT1 Renal Masses.

Not only the frequency of surgery for small renal masses has increased secondary to the improvements and frequent use of imaging techniques but also the frequency of detection of benign lesions in nephrectomy specimens has increased as well. We aimed to assess the predictive value of computed tomography density of perirenal adipose tissue and peritumoral adipose tissue in distinguishing between benign and malignant renal masses. The current study included 116 patients who underwent nephrectomy for renal masses between January 2015 and December 2020. Clinicodemographic and preoperative computed tomography features and final pathological findings of the patients were recorded. According to the final pathological results, the patients were divided into 2 groups benign (n = 32) and malignant (n = 84). Groups were compared statistically in terms of perirenal adipose tissue and peritumoral adipose tissue density. The median tumor size was 5.00 cm. The rate of benign tumors was higher in female patients (P = .005). The median peritumoral adipose tissue density among cT1 and cT1a tumors was higher in the malignant group (P < .001, for each). At a cutoff value of 97.50 Hounsfield Units, the peritumoral adipose tissue density had a sensitivity of 83.0% and a specificity of 79.2% for predicting the presence of malignant tumors in ≤7 cm renal masses. Using a cutoff value of -97.50 Hounsfield Units, the peritumoral adipose tissue density had a sensitivity of 88.9% and a specificity of 83.3% for predicting the presence of malignant tumors in ≤4 cm renal masses. The peritumoral adipose tissue density in the preoperative computed tomography images predicts the malignancy in cT1 renal masses.

Surgical intervention on the wound channels of gunshot non-penetrating craniocerebral injuries

Objective – to optimize of surgical approaches to damaged tissues of the head and methods of primary surgical treatment for gunshot non-penetrating gunshot craniocerebral injuries in accordance with the principles of comprehensive and single-stage surgical treatment accepted in military field surgery. Materials and methods. An analysis of the surgical treatment of 155 wounded people with non-penetrating gunshot wounds of the skull vault and brain for the period from 2014 to 2020 was carried out. Anatomical and morphological features of wound canals were studied depending on the traumatic agent. Variants of surgical approaches for the primary surgical treatment of non-penetrable gunshot wounds of the skull and brainwere developed based on the clinical X-ray and computed tomography characteristics of the affected head tissues. Results. The choice of surgical approaches depends on the nature of the wound canals (single fragment blind, multiple fragment blind, ball and fragment tangent) and the presence of concomitant epidural and subdural hematomas. Primary plasty of the skull defect with a titanium plate during primary surgical treatment is indicated for gunshot impenetrable craniocerebral injuries with a single fragmental blind wound channel. Such plastic surgery was performed in 33.5% of cases with a favorable postoperative course. Peculiarities of surgical approaches to damaged tissues of the head in case of single fragmentary, blind wound canal; with multiple blind fragmental wound canals, the adjacent wound canal and the sequence of their surgical treatment were established. Conclusions. Planning of surgical approaches to damaged tissues of the head in case of gunshot impenetrable craniocerebral wounds in modern local wars should be carried out taking into account clinical and computed tomography data. The high proportion of comprehensive and one-time early primary surgical treatments (92.9%) performed on the first day after the injury testifies to the high efficiency of the system of providing specialized medical care during the local war.