CT Features Research Articles

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography

PurposeThis study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set.ResultsSUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC.ConclusionOur nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

Clinical features and temporal CT findings in patients with Branchio-Oto-Renal or Branchio-Oto Syndrome

Objective: To assess the clinical features and CT diagnostic characteristics of Branchio-Oto-Renal or Branchio-Oto Syndrome. Methods: The temporal CT findings and clinical features observations of 13 patients with Branchio-Oto-Renal Syndrome (BORS) or Branchio-Oto Syndrome(BOS) confirmed by genetic testing were retrospectively analyzed. There were 8 males and 5 females, aged from 1 to 39 years, with a median age of 9 years, in which 3 pairs (6 cases) were parent-child relationship. Results: All of 13 cases had hearing loss and preauricular fistula, 11 cases accompanied by 2nd branchial fistulas. There were 20 ears of mixed hearing loss, 3 ears of sensorineural hearing loss, and 2 ears of conductive hearing loss. The mutation point of gene testing was located in EYA1 in 12 cases and SIX1 in 1 case. Twenty ears showed gradually narrowing of the diameter of basal turn, with hypoplasia in the second turn and aplasia in apical turn. There were irregular wall of vestibule and horizontal semicircular canal in 10 ears,widened vestibular in 7 ears, and vestibular fusion with horizontal semicircular canal in 3 ears. Three ears had an enlarged vestibular aqueduct, 8 ears showed enlargement of internal auditory canal. Seventeen ears had adhesion of malleolus to tympanic cavity. Six ears could not measured the incudostapedial joint angle by reason of tympanic inflammatory cover, 3 ears could not show incudostapedial joint, and 8 ears showed the incudostapedial joint angle more than 122°. Six ears showed poor oval window, and 1 ear had poor round window. Eighteen ears showed distended eustachian tube, and accompanied by tympanic or mastoiditis in 11 ears. Anterolateral shift of tympanum was found in 22 ears, 17 ears had low middle cranial fossa, and 3 ears had stenotic external auditory canal. Conclusions: Cochlear dysplasia, ossicular chain malformation and distended eustachian tube comprise the characteristic CT signs of BOS/BORS, which possesses versatile and complex CT findings. Temporal CT can accurately assess the important structures such as cochlea, ossicles, vestibule, semicircular canal, vestibular aqueduct and internal auditory canal. Combing with the clinical characteristics of bilateral, mixed hearing loss, preauricular fistula and branchial fistula can provide valuable information for early diagnosis and treatment.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.

Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models. A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots. A total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models. The GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

CT Imaging Patterns in Major Histological Types of Lung Cancer.

Lung cancer ranks as the second most prevalent cancer globally and is the primary contributor to neoplastic-related deaths. The approach to its treatment relies on both tumour staging and histological type determination. Data indicate that the prognosis of lung cancer is strongly linked to its clinical stage, underscoring the importance of early diagnosis in enhancing patient outcomes. Consequently, the choice of an appropriate diagnostic method holds significant importance in elevating both the early detection rate and prognosis of lung cancer. This paper aims to assess computer tomography features specific to the most common lung cancer types (adenocarcinoma, squamous cell carcinomas and small cell lung cancer). Data were collected retrospectively from CT scans of 58 patients pathologically diagnosed with lung cancer. The following CT features were evaluated and recorded for each case: location, margins, structure, lymph node involvement, cavitation, vascular bundle-thickening, bronchial obstruction, and pleural involvement. Squamous cell carcinoma (SQCC) and small cell lung cancer (SCLC) showed a higher incidence of central location, while adenocarcinoma (ADC) showed a significant predilection for a peripheral location. Internal cavitation was mostly observed in SQCC, and a solid structure was observed in almost all cases of ADC. These features can provide information about the prognosis of the patient, considering that NSCLCs are more frequent but tend to demonstrate positive results for targetable driver mutations, such as EGFR, thereby increasing the overall survival. In addition, SCLC presents with early distant spreads, which limits the opportunity to investigate the evolution of tumorigenesis and gene alterations at early stages but can have a rapidly positively response to chemotherapy. The location of the lung cancer exhibits distinct forecasts, with several studies suggesting that peripheral lung tumours offer a more favourable prognosis. Cavity formation appears correlate with a poorer prognosis. Histopathological analysis is the gold standard for diagnosing the type of lung cancer; however, using CT scanning for the purpose of a rough, but fast, preliminary diagnosis has the potential to shorten the waiting time for treatment by helping clinicians and patients to know more about the diagnosis and prognosis.

Diagnostic yield of cone beam CT based navigation bronchoscopy in patients with metastatic lesions: A propensity score matched case-control study

BackgroundCone beam CT based Navigation Bronchoscopy (CBCT-NB) has predominantly been investigated as a diagnostic tool in (suspected) primary lung cancers. Small metastatic lesions are clinically considered more challenging to diagnose, but no study has explored the yield of navigation bronchoscopy in patients with pulmonary metastatic lesions (ML) compared to primary lung cancers (PL), correcting for known lesion characteristics affecting diagnostic yield. Materials and methodsThis is a single-center, retrospective, propensity score-matched case-control study. We matched a subset of patients who underwent CBCT-NB and received a final diagnosis of pulmonary metastases of solid tumors between December 2017 and 2021 against confirmed primary lung cancer lesions subjected to CBCT-NB in the same time period. The lesions were propensity score matched based on known characteristics affecting yield, including location (upper lobe, lower lobe), size, bronchus sign, and lesion solidity. ResultsFifty-six metastatic pulmonary lesions (mean size 14.7 mm) were individually case-matched to a selection of 297 available primary lung cancer lesions. Case-matching revealed non-significant differences in navigation success rate (PL: 89.3 % vs. ML: 82.1 %, 95%CI on differences: −21.8 to +7.5) and yield (PL: 60.7 % vs. ML: 55.4 %, 95%CI on differences: −25.4 to +14.7). The overall complication rate was comparable (5.4 % in PL vs. 5,4 % in ML). ConclusionAfter matching primary and metastatic lesions based on CT assessable lesions characteristics, CBCT-NB showed no clinically relevant or significantly different navigation success or yield in either group. We recommend a careful assessment of CT characteristics to determine procedural difficulty rather than selecting based on the suspicion of lesion origin.

Diagnostic Value of Combined High-resolution Computed Tomography and Artificial Intelligence-Aided Diagnosis System in Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules

Background: The widespread use of high-resolution computed tomography (HRCT) in lung cancer screening has allowed for an increased detection rate of ground-glass nodules (GGNs) in the lung. Hence, obtaining the correct clinical diagnosis of benign and malignant GGNs has become crucial. Objectives: Most artificial intelligence and computer-aided diagnosis (AI-CAD) systems for the classification of pulmonary GGNs fail to extract CT features. This study used HRCT and AI to analyze the CT features of GGNs to improve the prediction of benign and malignant pulmonary GGNs. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. A total of 204 patients with GGNs were recruited and divided into 2 groups according to their pathological results. Group A consisted of 69 cases with precursor glandular lesions (atypical adenomatous hyperplasia [AAH] and adenocarcinoma in situ [AIS]), inflammatory nodules, and benign nodules. Group B consisted of 135 cases with invasive lesions (minimally invasive adenocarcinoma [MIA], invasive adenocarcinoma [IAC], and other malignant lesions). Various CT features were compared between the 2 groups. The diagnostic efficacy of an AI-CAD system and radiologists’ reports for benign and malignant nodules were analyzed. A multivariate logistic regression analysis was performed to determine independent predictors of malignant GGN. A model that combined the AI system and manual extraction of radiological features was constructed. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the model. Results: Significant differences were found between malignant and benign groups according to the following 7 CT features: The GGN size (long and short diameters), vacuole sign, air bronchogram sign, vascular convergence sign, vascular perforator sign, interlobular septal obstruction sign, and spiculation (P < 0.05). The volume and mean CT values of precursor glandular lesions of the lungs were significantly different from those of invasive lesions (P < 0.05). The logistic regression model showed that the sensitivity and specificity of the AI system in diagnosing malignant groups were 0.756 and 0.696, respectively. The sensitivity and specificity of radiologists’ reports in diagnosing the malignant groups were 0.726 and 0.783, respectively. The combination of the 2 had a sensitivity of 0.768 and a specificity of 0.793. Conclusion: Prediction of the nature of GGNs based on CT features, including the vacuole sign, vascular perforator sign, and interlobular septal obstruction sign, were relatively accurate for a preliminary diagnosis. The AI system had a poorer diagnostic accuracy for GGNs than radiologists’ reports of CT images. The combination of AI and radiologists’ reports showed the highest diagnostic efficacy.

Association of lower extremity peripheral arterial disease with quantitative muscle features from computed tomography angiography

ObjectivesTo explore the association between lower extremity muscle features from CTA and peripheral arterial disease (PAD) severity using digital subtraction angiography (DSA) as reference standard.MethodsInformed consent was waived for this Institutional Review Board approved retrospective study. PAD patients were recruited from July 2016 to September 2020. Two radiologists evaluated PAD severity on DSA and CTA using runoff score. The patients were divided into two groups: mild PAD (DSA score ≤ 7) vs. severe PAD (DSA score > 7). After segmenting lower extremity muscles from CTA, 95 features were extracted for univariable analysis, logistic regression model (LRM) analysis, and sub-dataset analysis (PAD prediction based on only part of the images). AUC of CTA score and LRMs for PAD prediction were calculated. Features were analyzed using Student’s t test and chi-squared test. p < 0.05 was considered statistically significant.ResultsA total of 56 patients (69 ± 11 years; 38 men) with 56 lower legs were enrolled in this study. The lower leg muscles of mild PAD group (36 patients) showed higher CT values (44.6 vs. 39.5, p < 0.001) with smaller dispersion (35.6 vs. 41.0, p < 0.001) than the severe group (20 patients). The AUC of CTA score, LRM-I (constructed with muscle features), and LRM-II (constructed with muscle features and CTA score) for PAD severity prediction were 0.81, 0.84, and 0.89, respectively. The highest predictive performance was observed in the image subset of the middle and inferior segments of lower extremity (LRM-I, 0.83; LRM-II, 0.90).ConclusionsLower extremity muscle features are associated with PAD severity and can be used for PAD prediction.Critical relevance statementQuantitative image features of lower extremity muscles are associated with the degree of lower leg arterial stenosis/occlusion and can be a beneficial supplement to the current imaging methods of vascular stenosis evaluation for the prediction of peripheral arterial disease severity.Key points• Compared with severe PAD, lower leg muscles of mild PAD showed higher CT values (39.5 vs. 44.6, p < 0.001).• Models developed with muscle CT features had AUC = 0.89 for predicting PAD.• PAD severity prediction can be realized through the middle and inferior segment of images (AUC = 0.90).Graphical

Validation of the 2018 FIGO staging system for stage IIIC cervical cancer by determining the metabolic and radiomic heterogeneity of primary tumors based on 18F-FDG PET/CT.

This study aimed to validate the 2018 FIGO staging system of cervical cancer (CC) by determining the metabolic and radiomic heterogeneity of primary tumors between stage IIIC1 and IIIC2. 168 patients with squamous cell CC underwent pre-treatment fluorine-18 fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) and were randomly allocated to training and testing cohorts with a 7:3 ratio. Radiomics features were extracted from the primary tumors based on CT and PET data. Ten metabolic parameters of the primary tumors were also assessed. After feature selection, three logistic regression radiomics models, involving (1) 2 CT features, (2) 3 PET features, and (3) 2 CT features + 3 PET features, respectively, and one random forest model were established. Finally, area under the curve (AUC) values and calibration curves were used to evaluate the 4 models. The IIIC1 and IIIC2 groups did not differ significantly in age, weight, height, or the 10 major metabolic parameters (P > 0.05). The AUCs of the 4 models were 0.577, 0.639, 0.763, and 0.506, respectively, in the training cohort, and 0.789, 0.699, 0.761, and 0.538, respectively, in the testing cohort. The model fit of the logistic regression model based on CT + PET data was good in both the training and testing cohorts. Our study offers additional diagnostic options for PALN metastasis, which could impact treatment decisions. Our results indirectly support the conclusions of previous studies recommending that primary tumors should be considered during IIIC staging.

Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma

BackgroundPersonalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival.MethodsFollowing ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables (‘Clinical’ model: age, clinical T-stage, clinical N-stage; ‘ClinVol’ model: clinical features + CT tumour volume; ‘ClinRad’ model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor (‘Stage’). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality.ResultsA total of 162 participants were included (144 male; median 67 years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998 days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p = .04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p > .05). Test sensitivity of 90% was achieved by ClinRad and Stage models only.ConclusionsCompared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival.Clinical relevance statementPreviously identified radiomic features are prognostic but may not substantially improve risk stratification on their own.Key Points• Better risk stratification is needed in primary oesophageal cancer to personalise management.• Previously identified CT features—GLCM_Correlation and GLCM_Contrast—contain incremental prognostic information to age and clinical stage.• Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival.

Parallels between computed tomography and clinical observations in diagnosing obstructive pancreatic masse

Aim. To identify the underlying causes of complications in distinguishing the origin of head masses through a comparative analysis of CT findings and clinical observations.Materials and methods. A retrospective analysis of radiological imaging for pancreatic cancer (36 cases) and chronic pancreatitis (24 cases) was conducted. Pancreaticoduodenectomy was performed in 23 cases (14 for cancer and 9 for chronic pancreatitis), while drainage interventions were carried out in 35 cases.Results. On pre-contrast images, pancreatic cancer appeared isodense in 97 % of cases, while in the arterial phase, it was hypodense in 94 %. For chronic pancreatitis, these percentages were 67 % and 46 %, respectively. The density curves of the pancreatic head and normal parenchyma were nearly identical across all phases, but the density gradient difference was more pronounced in pancreatic cancer (27 HU compared to 15 HU). Bile duct strictures with interruption were observed more frequently in cancer (44 %), along with arterial vessel invasion (25 %). CT signs indicative of chronic pancreatitis included wirsugolithiasis (21 %) and portal hypertension (42 %). Both diseases exhibited similar CT features, such as cystic lesions, peripancreatic tissue infiltration and peripancreatic lymphadenopathy. In six cases of pancreatic head cancer and seven of chronic pancreatitis, identical pathological changes were identified during intraoperative revision: pseudocysts (2 in cancer and 4 in chronic pancreatitis), infected necrosis and abscesses (3 and 2, respectively) and similar views of the head mass cut plane (1 each).Conclusion. The overlap in CT features due to similar pathomorphological changes presents an objective challenge in differentiating pancreatic head masses

Clinical presentation and radiologic imaging findings of phyllodes tumors: benign and borderline/malignant phyllodes tumors

Background Phyllodes tumor is a rare fibroepithelial neoplasm of the breast, which is classified histologically as benign, borderline, or malignant. Accurate preoperative diagnosis allows the correct surgical planning and reoperation avoidance. Objective To describe the clinical presentation and radiologic features of phyllodes tumors and differentiate between benign and non-benign (borderline and malignant) groups. Methods A retrospective study of 57 patients with a diagnosis of phyllodes tumor who had preoperative imaging (mammography, ultrasound, or CT chest) and histological confirmation. The data was collected from 1 June 2011 to 30 September 2021. The imaging features of the phyllodes tumors were described according to the 5th edition of the ACR BI-RADS lexicon. For comparing between two groups, the student t-test, Wilcoxon rank sum test, Chi-square test, and Fisher’s exact test were used for statistical analyses. The logistic regression analysis was calculated for non-benign phyllodes tumor prediction. Results From 57 patients, the pathologic results were benign for 43 cases and non-benign phyllodes tumors for 14 cases. There was no differentiation of mammographic and CT features between benign and non-benign groups. Non-benign phyllodes tumors had the statistical significance of menopausal status, entire breast involvement, tumor size larger than 10 cm, and heterogeneous echo on univariable analysis. After multivariable analysis, menopausal status (odd ratios=13.79, p=0.04) and presence of vessels in the rim (odd ratios=16.51, p=0.019) or absent vascularity (odd ratios=8.45, p=0.047) on doppler ultrasound were significantly increased possibility of non-benign phyllodes tumor. Conclusions Menopausal status and presence of vessels in the rim or absent vascularity on Doppler ultrasound were important predictors for the diagnosis of non-benign phyllodes tumor.

Massive spinal epidural infantile hemangioma, image findings, and treatment: a case report and review of literature

Spinal involvement of infantile hemangiomas is rare with the predilection to involve the epidural space. A proper diagnosis might be challenging due to the atypical location and variable/inconsistent use of the International Society for the Study of Vascular Anomalies (ISSVA) classification by radiologists, pathologists, and clinicians. A proper diagnosis of epidural infantile hemangioma is key due to the different aggressiveness of the treatment options with inconstant literature regarding the best available treatment. Herein, we present a case of a massive epidural infantile hemangioma successfully treated with only beta-blocker. We discuss the clinical, MRI, CT, ultrasound, and histological features of this lesion as we review the literature with the objective of addressing some of the confusion surrounding the subject.

Imaging Features and Misdiagnosis of Giant Cerebral Cavernous Malformations.

While cerebral cavernous malformations (CCMs) have been extensively described, few reports have described the imaging appearance of giant CCMs (GCCMs). To describe the imaging characteristics of GCCMs and study the reasons for preoperative misdiagnosis. We retrospectively analyzed the data of 12 patients (5 men, 7 women; mean age, 35.23 ± 12.64 years) with histopathologically confirmed GCCMs. Two radiologists analyzed the CT (n = 12) and MRI (n = 10) features: location, number, size, shape, boundary, signal intensity, and enhancement. The sellar region, cerebral hemisphere, skull bone, and ventricle were involved in 5, 4, 2, and 1 patients, respectively. Three tumors were irregularly shaped, while nine were oval. Eleven lesions showed slightly high- and/or high-density on CT; 1 lesion appeared as a low-density cyst. Calcifications were found in 11 lesions. Four tumors showed uniform hypointensity on T1-weighted imaging (T1WI) and hyperintense signals on T2-weighted imaging (T2WI). Six tumors showed mixed low-, equal-, and high-intensity signals on T1WI and T2WI. Noticeable contrast enhancement and gradual strengthening were noted on T1WI. Ten lesions showed hemorrhage and hemosiderin deposition. The GCCMs were wrongly diagnosed as cartilage-derived tumors/ meningioma (3 patients); tumor and hematoma (2 patients each); and pituitary tumor/ meningioma, chondroma, chordoma, ependymoma, and macroadenoma (1 patient each). GCCMs present as an oval mass with slightly high- and/or high-density calcifications on CT and show hemorrhage and hemosiderin accumulation on MRI. Therefore, slightly high- and/or high-density calcification and hemosiderin accumulation are critical clinical characteristics of GCCMs.

Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma

ObjectiveTo investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models.BackgroundPrimary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored.MethodsThis retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model and calculate the accuracy rate. Model training and efficacy test were performed as before.ResultsStatistical analysis showed that some clinical data (gender and concomitant cirrhosis) and imaging features (presence of envelope, marked enhancement in the arterial phase, rapid contouring in the portal phase, uniform density/signal and concomitant steatosis) were statistical differences (P < 0.001). The results of machine learning models showed that KNN had the best diagnostic efficacy. The results of the combined model showed that SVM had the best diagnostic efficacy, indicating that the combined model (accuracy 0.824) had better diagnostic efficacy than the radiomics-only model.ConclusionsOur results demonstrate that the radiomic features of CT and MRI combined with machine learning models enable differential diagnosis of HCC and non-HCC (malignant, benign). The diagnostic model with dual radiomic had better diagnostic efficacy. The combined model was superior to the radiomic model alone.

Pulmonary Sclerosing Pneumocytoma - A Comprehensive Exploration of Clinical, Radiological, and Surgical Dimensions By IJISRT

Objectives: The study findings have important Clinical implications, especially in the preoperative evaluation of Lung Nodules and aid in the radiological diagnosis based on CT features. Pulmonary Sclerosing pneumocytoma is a Benign, rare tumor of the lung that represents a diagnostic challenge due to the Non-specific CT findings. The Aim of this study was to present a 10-year experience with sclerosing pneumocytoma of a large center of CHINA for the diagnosis and treatment of pulmonary diseases, and to emphasize differential diagnostic dilemmas as a potential source of errors.  Material and Methods: This represents a retrospective study of 31 patients diagnosed and treated with sclerosing pneumocytoma in the 10-year period. The study analyzed various Variables, including Gender, Age, Smoking history, Reason for CT, Nodule location, Nodule shape, Clinical symptoms, Calcifications, and Surgical Resections.  Results: Sclerosing pneumocytoma was more frequently diagnosed in females (93.5%). The patients ranged in age from 28 to 68. Most of the patients (77.4%) were asymptomatic. 30 patients had no history of smoking (96.8%). Mean Nodule size was 14.4mm. Most nodules have Round, oval and smooth margin, with majority of Nodules have location in the lower lobes of both lungs. Most nodules were peripherally situated (54.83%). VATS with lobectomy performed in 25 (80.64%) patients while VATS with wedge resection performed in 6(19.64%) patients, without post-surgical complications and Normal follow up.

Diagnostic challenge and survival analysis of pulmonary oligometastases and primary lung cancer in breast cancer patients.

The aim of this study was to compare breast cancer patients with pulmonary oligometastases (POM) and primary lung cancer (PLC) and to assess whether there were differences in clinical features, CT features, and survival outcomes between the two groups. From January 2010 to December 2021, the clinical records of 437 with malignant pulmonary nodules who had breast cancer patients were reviewed. POM was identified in 45 patients and PLC in 43 patients after the initial detection of pulmonary nodules. The clinicopathological characteristics, CT appearance of pulmonary nodules, and survival of the two groups were compared. Stage II to IV breast tumors (p < 0.001), high pathological grade of breast cancer (p = 0.001), low proportion of luminal-type breast cancer (p = 0.003), and the higher serum CYFRA 21-1 level (p = 0.046) were the clinical characteristics of pulmonary nodules suggestive of POM rather than PLC. The CT features of lung nodules indicative of PLC rather than POM were the subsolid component (p < 0.001), lobulation (p = 0.010), air bronchogram (p < 0.001) and pleural indentation (p = 0.004). Ten-year survival rate for PLC was 93.2%, which was higher compared with 57.8% in those with POM (p = 0.001). Elevated serum CYFRA 21-1 levels and late-stage breast cancer may be beneficial for the diagnosis of POM. CT imaging appearances of the subsolid component, lobulation, air bronchogram, and pleural indentation increase the likelihood of PLC. Breast cancer patients with PLC presented better survival with attentive monitoring than those with POM.

Clinical and Temporal Radiological Findings from Critical Patients with COVID-19 Pneumonia: A Descriptive Study.

In late December 2019, Wuhan, the capital of Hubei Province, China, became the center of an outbreak of pneumonia caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The radiological changes in the lungs of critical people with coronavirus disease 2019 (COVID-19) pneumonia at different times have not been fully characterized. We aim to describe the computed tomography findings of patients with critical COVID-19 pneumonia at different disease stages. Clinical and laboratory features of critical patients were assessed. CT scans were assigned to groups 1, 2, 3, or 4 based on the interval from symptom onset (within 2 weeks; ≥ 2-4 weeks; ≥ 4-6 weeks; or ≥ 6 weeks, respectively). Imaging features were analyzed and compared across the four groups. Total CT scores, corresponding periods of laboratory findings, and glucocorticoid dosages during the imaging intervals were longitudinally observed in five patients with complete data. All 11 critical patients (median age: 60 years [42-69]) underwent a total of 40 CT examinations, and the acquisition times ranged from 1-59 days after symptom onset. Median total CT scores were 18 (9-25.25); 44.5 (42.88-47.62); 43.75 (38.62-49.38); and 42 (32.25-53.25) in groups 1, 2, 3, and 4, respectively. The observed lesions were mainly bilateral (37 [92.5%]). The median values of involved lung segments were 10.5 (4.5-13.5); 17 (16-18.5); 18 (18-19.5); and 18 (18-19) in groups 1-4, respectively. The predominant patterns of observed abnormalities were ground-glass opacities (GGO) (9 [90%]); GGO with reticulation and mixed patterns (3 [37.5%] for both); GGO with consolidation (3 [30%]); and GGO with reticulation (8 [66.7%]) in groups 1-4, respectively. Patients developed fibrotic manifestations at later stages. Critical patients with COVID-19 infection generally presented with temporally changing abnormal CT features from focal unilateral to diffuse bilateral GGO and consolidation that progressed to or coexisted with reticulation in the long term after symptom onset. Low-dose glucocorticoids may be effective in patients with interstitial changes on CT findings.

Possibilities of Computer Tomography in Preoperative Diagnosis and Surgical Treatment Planning of Juvenile Angiofibroma of the Nasopharynx and Skull Base

The purpose of work was to determine CECT characteristics of primary juvenile angiofibroma of the nasopharynx and skull base (JNA), as well as determination of the paths of spread for the tumor, search for those of them, which are statistically significantly associated with surgical blood loss of subsequent treatment and search for ways to model possible blood loss on their basis.Materials and methods. The data of 60 patients aged from 8 to 17 years were analyzed retrospectively. All patients were boys. The median age was 14 years. The median duration of the disease at the time of surgery was 7 months. The correlation between each of 45 evaluation criteria and intraoperative blood loss was determined for patients in the study group.Results. CT characteristics of JNA were established. The factors, detected by CECT, statistically significantly influencing intraoperative blood loss in angiofibroma treatment were established. A method for modeling intraoperative blood loss based on imaging data was presented.Conclusion. CT with contrast enhancement allows determining of characteristic radiological signs of JNA as well as its characteristics influencing blood loss during surgical treatment. Based on CT data it is possible to model blood loss potential during the JNA treatment.

Evaluation of the efficacy of pretreatment chest CT markers in predicting response to neoadjuvant chemoradiotherapy in locally advanced non-small cell lung cancer (NSCLC)

Aim: To investigate baseline enhanced chest CT findings that may predict progression or response to neoadjuvant chemoradiotherapy.&#x0D; Materials and methods: Multiple parameters to be obtained from baseline enhanced chest CT scans of 140 patients with NSCLC who had baseline enhanced chest CT scans before neoadjuvant chemoradiotherapy were noted. In addition to CT features of tumour tissues, age, gender, tumour cell types, lymph node TNM stages, distant metastases on baseline enhanced chest CT, bronchial and vascular invasion were also evaluated. Chest CT findings and changes in tumour tissue at 3 and 6 months during neoadjuvant treatment were noted. Patients were operated after the end of neoadjuvant treatment. It was investigated which parameters could predict response to neoadjuvant treatment and which findings could predict progression.&#x0D; Results: Progression and mortality rates were found to be low in patients with remission (p