Computed Tomography Features Research Articles

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.

A CT-based radiomics nomogram for distinguishing between malignant and benign Bosniak IIF masses: a two-centre study

To establish and assess a computed tomography (CT)-based radiomics nomogram for identifying malignant and benign Bosniak IIF masses. In total, 150 patients with Bosniak IIF masses were separated into a training set (n=106) and a test set (n=44) in a ratio of 7:3. A radiomics signature was calculated based on extracted features from the three phases of CT images. A clinical model was constructed based on clinical characteristics and CT features, and a nomogram incorporating the radiomics signature and independent clinical variables was established. The calibration ability, discrimination accuracy, and clinical value of the nomogram model were assessed. Twelve features derived from CT images were applied to establish the radiomics signature. The performance levels of three machine-learning models were improved by adding the synthetic minority oversampling technique algorithm. The optimised machine learning model was a combination of the minimum redundancy maximum relevance-least absolute shrinkage and selection operator feature screening method+logistic regression classifier+synthetic minority oversampling technique algorithm, which demonstrated excellent identification ability on the test set (area under the curve [AUC], 0.970; 95% confidence interval [CI], 0.940-1.000). The nomogram model displayed outstanding discrimination ability on the test set (AUC, 0.972; 95% CI, 0.942-1.000). The CT-based radiomics nomogram was useful for discriminating between malignant and benign Bosniak IIF masses, which improved the precision of preoperative diagnosis.

Misdiagnosis of primary dumbbell chordoma of the cervical spine.

This study aimed to analyze the clinical characteristics and outcomes of patients with primary dumbbell chordoma of the cervical spine and to summarize the causes of misdiagnosis. The clinical data of patients were retrospectively collected. The diagnostic process, surgical procedures, and outcomes were analyzed, then the difference was compared between dumbbell and non-dumbbell chordomas of the cervical spine. This study included six patients with primary dumbbell chordoma (one male and five females) with a mean age of 32.2±24.5years (range: 5-61years). Five cases with no computed tomography (CT) examination before the first operation were misdiagnosed, and on magnetic resonance imaging (MRI), primary dumbbell chordoma showed the following specific features: extensive invasion of the surrounding soft tissues with an obscure boundary (≥5cm), intervertebral disc sparing, and hemorrhagic necrosis, furthermore, the CT features included atypical destructive vertebral lesions, minimal intralesional calcification, and neural foraminal enlargement. After comparison with non-dumbbell chordomas, it show statistical difference (p<0.05) in terms of calcification, foramen enlargement, FNA, misdiagnosis rate but with different recurrent rate. Primary dumbbell chordomas of the cervical spine can easily be misdiagnosed as neurogenic tumors. Preoperative CT-guided fine-needle aspiration puncture biopsy helps make an accurate diagnosis. Gross total excision with postoperative radiotherapy has been proven effective in reducing the recurrence rate.

Lesser omental hernia through a defect in the posterior layer of the lesser omentum

BackgroundIn previously reported cases of lesser omental hernia, a rare clinical presentation, the herniated intestinal tract was passing through both peritoneal layers of the lesser omentum to herniate into the peritoneal cavity or bursa omentalis. Here we present a very rare case of lesser omentum hernia, where the transverse colon entered through only the posterior layer of the lesser omentum to form a hernia between the anterior and posterior layers.Case presentationA 43-year-old man was admitted to the emergency department with acute abdominal pain. Plain abdominal computed tomography (CT) revealed a change in the caliber of the transverse colon between the stomach and pancreas, forming a closed loop on the cephaloventral side of the stomach. On contrast-enhanced CT images, vessels were observed in the contrast-enhanced lesser omentum surrounding the herniated intestine. The patient was diagnosed with a lesser omental hernia and underwent laparoscopic surgery. Intraoperatively, the transverse colon was covered by the anterior layer of the lesser omentum, and a defect was found in the posterior layer of the lesser omentum on the dorsal side of the stomach. A 2-cm incision was made in the posterior layer of the lesser omentum to widen the small defect. The herniated intestinal section was removed from the hernia sac, and the transverse colon was retained unresected. The postoperative course was uneventful.ConclusionsAs illustrated in this first case of a lesser omental hernia forming between the anterior and posterior layers, characteristic CT findings may play an active role in the diagnosis of this rare presentation.

CT features of pulmonary embolism in patients with COVID-19 pneumonia.

The aim of this study was to describe the Computed Tomography (CT) features of pulmonary embolism in patients hospitalized for acute COVID-19 pneumonia and to evaluate the prognostic significance of these features. This retrospective study included 110 consecutive patients who were hospitalized for acute COVID-19 pneumonia and underwent pulmonary computed tomography angiography (BTPA) on the basis of clinical suspicion. The diagnosis of COVID-19 infection was determined by CT findings typical of COVID-19 pneumonia and/or a positive result of a reverse transcriptase-polymerase chain reaction test. Of the 110 patients, 30 (27.3%) had acute pulmonary embolism and 71 (64.5%) had CT features of chronic pulmonary embolism. Of the 14 (12.7%) patients who died despite receiving therapeutic doses of heparin, 13 (92.9%) had CT features of chronic pulmonary embolism and 1 (7.1%) of acute pulmonary embolism. CT features of chronic pulmonary embolism were more common in deceased patients than in surviving patients (92.9% vs. 60.4%, p=0.01, respectively). Low oxygen saturation and high urine microalbumin creatinine ratio at admission in COVID-19 patients are important determinants of mortality after adjusting for sex and age in logistic procedures. CT features of chronic pulmonary embolism are common in COVID-19 patients undergoing Computed Tomography Pulmonary Angiography (CTPA) in the hospital. The coexistence of albuminuria, low oxygen saturation and CT features of chronic pulmonary embolism at admission in COVID-19 patients may herald fatal outcomes.

Ultrasound-guided lung biopsy for small (≤2 cm) subpleural lung lesions: comparison of diagnostic yield and safety with larger lesions.

Ultrasound (US)-guided percutaneous core needle biopsy (PCNB) has been used to diagnose subpleural lung lesions with high diagnostic performance and acceptable complication rates. However, with regard to the role of US-guided needle biopsy for the diagnosis of small (≤2 cm) subpleural lesions, limited information is available. From April 2011 to October 2021, a total of 572 US-guided PCNBs in 572 patients were retrospectively reviewed. The lesion size, pleural contact length (PCL), lesion location, and operator's experience were analyzed. Computed tomography features including peri-lesional emphysema, air-bronchogram, and cavitary change were also included in image analysis. The patients were divided into three groups according to lesion size (lesions ≤2 cm vs. 2 cm< lesions ≤5 cm vs. lesions >5 cm). The sample adequacy, diagnostic success rate, diagnostic accuracy, and complication rate was calculated. For statistical analysis, one-way ANOVA, Kruskal-Wallis test, or the chi-square test were used. The overall sample adequacy, diagnostic success rate, and diagnostic accuracy were 96.2%, 82.9%, and 90.4%, respectively. In the subgroup analysis, sample adequacy (93.1% vs. 96.1% vs. 96.9%, P=0.307), diagnostic success rate (75.0% vs. 81.6% vs. 85.7%, P=0.079), and diagnostic accuracy (84.7% vs. 90.8% vs. 90.5%, P=0.301) were not significantly different. Operator's experience (OR, 0.64; 95% CI: 0.49-0.80; P<0.001), lesion size (OR, 0.68; 95% CI: 0.54-0.83; P<0.001), PCL (OR, 0.68; 95% CI: 0.52-0.84; P=0.001), and presence of air-bronchogram (OR, 14.36; 95% CI: 4.18-48.53; P<0.001) were independently associated with complication rate. US-guided PCNB performed by an experienced radiologist could be an effective and safe diagnostic approach for subpleural lesions, even in small lesions.

Computed tomography features of gastric leiomyoma versus gastric stromal tumor: a case–control study with propensity score matching

To differentiate gastric leiomyomas (GLs) and gastric stromal tumors (GSTs) based on preoperative enhanced computed tomography characteristics. Twenty-six pathologically confirmed GLs were propensity score-matched to 26 GSTs in a 1:1 ratio based on sex, age, tumor site, and tumor size. Tumor shape and contour, mucosal ulceration, growth pattern, enhancement pattern and degree, longest diameter, and longest diameter/vertical diameter ratio were compared between the groups. Hemorrhage, calcification, peripheral invasion, and distant metastasis were also included in the regression analysis for differentiation of the two tumors. Mucosal ulceration was significantly more frequent in GSTs than GLs. The enhancement degree of GSTs was significantly higher than that of GLs in the arterial and portal venous phases. Using enhancement degrees of 18 HU and 23 HU in the arterial phase and venous phase as cutoff values, respectively, we found that an enhancement degree of <18 HU in the arterial phase was an independent influential factor for diagnosis of GLs. No significant differences were found in other morphological characteristics. GLs did not metastasize or invade adjacent tissues. A low enhancement degree in GLs is the most valuable quantitative feature for differentiating these two similar tumors.

A quantitative analysis of spectral computed tomography characteristics of osseous metastases.

Spectral detector computed tomography (SDCT) is a recent advancement that enables elemental material decomposition which could improve the detection of osseous metastases in the oncologic patient. Sixteen patients who underwent oncologic staging SDCT as well as WBBS with Technetium-99 m hydroxydiphosphonate (Tc99m HDP) were included in this retrospective study. A total of 50 osseous metastases were identified and confirmed on bone scintigraphy. Quantitative ROI-based measurements of each lesion and a similar region of normal bone were performed, and the acquired spectral data were used for comparison. These parameters included effective atomic number (Zeff ), electron density (%EDW) and calcium suppression (HU). Receiver operating characteristic (ROC) analysis was performed. In comparison to normal bone, osseous metastases showed statistically significantly elevated values in effective atomic number, electron density and calcium suppression. ROC analysis demonstrated outstanding discrimination with area under the curve (AUC) values of 0.934 and 0.915 for effective atomic number and electron density, and excellent discrimination with an AUC value of 0.884 for calcium suppression. Threshold values of effective atomic number (Zeff ) >9.7, electron density >115% EDW and calcium suppression values >0 HU were demonstrated to be able to differentiate an osseous lesion from normal bone with a sensitivity of 82%, 82% and 84% and a specificity of 86%, 92% and 88% respectively. Spectral analysis of osseous metastases demonstrated significantly elevated values in effective atomic number, electron density and calcium suppression as compared to normal bone which would be useful adjunct quantitative parameters in CT imaging to increase diagnostic confidence.

Application of Spectral Computed Tomography (CT) Combined with Conventional CT Features in Differentiating Malignant Pulmonary Mass-Like Lesions

Background: Pulmonary mass-like lesions are one of the common manifestations of respiratory disorders. Differential diagnosis of these lesions is a major challenge in imaging studies. Objectives: This study aimed to explore the efficacy of spectral computed tomography (CT) features combined with conventional CT features in the differential diagnosis of pulmonary mass-like lesions. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. The imaging characteristics and spectral CT parameters were evaluated in 77 patients who met the inclusion criteria. A multivariate logistic regression analysis was performed to determine independent predictors of malignant pulmonary lump-like lesions. Three models were established, including a radiomic feature model, a spectral CT model, and a combined model. A receiver operating characteristic (ROC) curve was also plotted to evaluate the diagnostic efficiency of the models. Results: Some CT features were significantly different between the malignant and benign groups, including the long-axis diameter (44.86 ± 18.42 in the malignant group vs. 55.59 ± 22.57 in the benign group; P = 0.07), mediastinal lymphadenopathy (25.00% in the benign group vs. 62.26% in the malignant group; P = 0.02), and mediastinal lymph node confluence (4.17% in the benign group vs. 41.51% in the malignant group; P = 0.01). The CT values at 40 keV (157.25 ± 79.23 vs. 148.46 ± 25.36, P = 0.047) and K40 - 70 keV (2.76 ± 2.05 vs. 2.52 ± 0.60, P = 0.04) were significantly higher in the benign group compared to the malignant group in the arterial phase (AP). Besides, the iodine concentration (IC) (14.73 ± 10.65 vs. 13.44 ± 3.24, P = 0.039; 17.52 ± 5.29 vs. 13.87 ± 5.81, P = 0.035), normalized iodine concentration (NIC) (0.15 ± 0.06 vs. 0.11 ± 0.05, P = 0.015; 0.41 ± 0.11 vs. 0.35 ± 0.10, P = 0.017), and Zeff value (8.46 ± 0.63 vs. 8.43 ± 0.28, P = 0.034; 8.60 ± 0.29 vs. 8.39 ± 0.33, P = 0.035) were significantly higher in the benign group compared to the malignant group, both in the AP and venous phase (VP). The logistic regression model, integrating CT features and spectral CT parameters, showed the highest diagnostic efficacy (area under the curve [AUC], 0.956; sensitivity, 87.5%; specificity, 90.6%). Conclusion: The quantitative spectral CT parameters, combined with conventional CT features, could help distinguish benign and malignant pulmonary mass-like lesions, providing an essential basis for developing treatment plans.

Application of Spectral Computed Tomography (CT) Combined with Conventional CT Features in Differentiating Malignant Pulmonary Mass-Like Lesions

Background: Pulmonary mass-like lesions are one of the common manifestations of respiratory disorders. Differential diagnosis of these lesions is a major challenge in imaging studies. Objectives: This study aimed to explore the efficacy of spectral computed tomography (CT) features combined with conventional CT features in the differential diagnosis of pulmonary mass-like lesions. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. The imaging characteristics and spectral CT parameters were evaluated in 77 patients who met the inclusion criteria. A multivariate logistic regression analysis was performed to determine independent predictors of malignant pulmonary lump-like lesions. Three models were established, including a radiomic feature model, a spectral CT model, and a combined model. A receiver operating characteristic (ROC) curve was also plotted to evaluate the diagnostic efficiency of the models. Results: Some CT features were significantly different between the malignant and benign groups, including the long-axis diameter (44.86 ± 18.42 in the malignant group vs. 55.59 ± 22.57 in the benign group; P = 0.07), mediastinal lymphadenopathy (25.00% in the benign group vs. 62.26% in the malignant group; P = 0.02), and mediastinal lymph node confluence (4.17% in the benign group vs. 41.51% in the malignant group; P = 0.01). The CT values at 40 keV (157.25 ± 79.23 vs. 148.46 ± 25.36, P = 0.047) and K40 - 70 keV (2.76 ± 2.05 vs. 2.52 ± 0.60, P = 0.04) were significantly higher in the benign group compared to the malignant group in the arterial phase (AP). Besides, the iodine concentration (IC) (14.73 ± 10.65 vs. 13.44 ± 3.24, P = 0.039; 17.52 ± 5.29 vs. 13.87 ± 5.81, P = 0.035), normalized iodine concentration (NIC) (0.15 ± 0.06 vs. 0.11 ± 0.05, P = 0.015; 0.41 ± 0.11 vs. 0.35 ± 0.10, P = 0.017), and Zeff value (8.46 ± 0.63 vs. 8.43 ± 0.28, P = 0.034; 8.60 ± 0.29 vs. 8.39 ± 0.33, P = 0.035) were significantly higher in the benign group compared to the malignant group, both in the AP and venous phase (VP). The logistic regression model, integrating CT features and spectral CT parameters, showed the highest diagnostic efficacy (area under the curve [AUC], 0.956; sensitivity, 87.5%; specificity, 90.6%). Conclusion: The quantitative spectral CT parameters, combined with conventional CT features, could help distinguish benign and malignant pulmonary mass-like lesions, providing an essential basis for developing treatment plans.

Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors.

preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST's prognosis as determined by the Miettinen classification. patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.