CT Features Research Articles

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease.

It is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system. Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis. A total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827-0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816-0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720-0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness. The CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

Synovial Sarcoma in the Extremity: Diversity of Imaging Features for Diagnosis and Prognosis.

Synovial sarcomas are rare and highly aggressive soft-tissue sarcomas, primarily affecting adolescents and young adults aged 15-40 years. These tumors typically arise in the deep soft tissues, often near the large joints of the extremities. While the radiological features of these tumors are not definitely indicative, the presence of calcification in a soft-tissue mass (occurring in 30% of cases), adjacent to a joint, strongly suggests the diagnosis. Cross-sectional imaging characteristics play a crucial role in diagnosing synovial sarcomas. They often reveal significant characteristics such as multilobulation and pronounced heterogeneity (forming the "triple sign"), in addition to features like hemorrhage and fluid-fluid levels with septa (resulting in the "bowl of grapes" appearance). Nevertheless, the existence of non-aggressive features, such as gradual growth (with an average time to diagnosis of 2-4 years) and small size (initially measuring < 5 cm) with well-defined margins, can lead to an initial misclassification as a benign lesion. Larger size, older age, and higher tumor grade have been established as adverse predictive indicators for both local disease recurrence and the occurrence of metastasis. Recently, the prognostic importance of CT and MRI characteristics for synovial sarcomas was elucidated. These include factors like the absence of calcification, the presence of cystic components, hemorrhage, the bowl of grape sign, the triple sign, and intercompartmental extension. Wide surgical excision remains the established approach for definitive treatment. Gaining insight into and identifying the diverse range of presentations of synovial sarcomas, which correlate with the prognosis, might be helpful in achieving the optimal patient management.

Cavitary pulmonary lesion wall thickness, presence of additional nodules, and intralesional contrast enhancement are associated with malignancy in dogs and cats.

To investigate the CT features of cavitary pulmonary lesions and determine their utility to differentiate malignant from benign lesions. This retrospective study included cases from 5 veterinary medical centers between January 1 2010, and December 31, 2020. Inclusion criteria included having a gas-filled cavitary pulmonary lesion on thoracic CT and definitive diagnosis by either cytology or histopathology. Forty-two animals (27 dogs and 15 cats) were included in this study. Medical records systems/imaging databases were searched, and cases meeting inclusion criteria were selected. The CT studies were interpreted by a third-year radiology resident, and findings were reviewed by a board-certified veterinary radiologist. 7 of the 13 lesion characteristics investigated were not statistically associated with the final diagnosis of the lesion, whereas 6 were statistically associated. Those that were associated included the presence of intralesional contrast enhancement, type of intralesional contrast enhancement (heterogenous and homogenous analyzed separately), presence of additional nodules, wall thickness of the lesion at its thickest point, and wall thickness at the thinnest point. Results from the present study showed that thoracic CT imaging of cavitary pulmonary lesions can be used to further refine the list of differential diagnoses. Based on this data set, in lesions that have heterogenous contrast enhancement, additional pulmonary nodules, and wall thickness > 40 mm at their thickest point, it would be reasonable to consider malignant neoplastic disease higher on the list of differentials than other causes.

Combined Modeling of CT Radiomics and Clinical Parameters for Risk of Cachexia in Extensive-Stage Small Cell Lung Cancer Patients Treated with First-Line Immunotherapy

Cachexia occurs in about 60% of patients with extensive-stage small cell lung cancer (ES-SCLC), which may contribute to resistance to immunotherapy. This study integrating CT radiomics and clinical parameters aimed to identify patients at risk of developing cachexia that may be associated with prognosis in extensive-stage small cell lung cancer patients treated with first-line immunotherapy. This study retrospectively included 200 ES-SCLC patients treated with first-line immunotherapy. All patients were randomly divided into training cohort and validation cohort according to a ratio of 7:3. The clinical characteristics and CT images before treatment were collected. Univariable and multivariable logistic regression analyses were conducted to evaluate the predictive role clinical characteristics. 3D slicer was used to retrieve radiomics features from CT images at the level of the third lumbar vertebra (L3). The radiomics signature was conducted by using the least absolute shrinkage and selection operator (LASSO). The receiver operating characteristic (ROC) curve analysis and Delong test were performed to determine and compare the predictive performance of radiomics, clinical and combined models. All patients were divided into training (n = 140) and validation (n = 60) cohorts. Survival analysis indicated the inferior PFS (7.3 vs 8.7 months, p = 0.0053) and OS (7.9 vs 9.3 months, p = 0.00099) for patients with cachexia. The multivariate analysis indicated that pretherapeutic lactate dehydrogenase (LDH) and post-treatment advanced lung cancer inflammation index (pretreatment BMI×serum albumin/NL) were independent risk factors for cachexia. A total of 883 radiomics features were obtained from each patient's CT scan, and four CT features were selected to build radiomics signature. The radiomics signature achieved area under curves (AUCs) of 0.706 (95% CI 0.624-0.780) in the training cohort, and 0.610 (95% CI 0.475-0.733) in the validation cohort. The combined model achieved AUCs of 0.769 and 0.674 in the training and validation cohorts, respectively, which performed better than clinical model or radiomics model only. Kaplan-Meier survival analysis showed that for the training cohort, the PFS (p = 0.0012) and OS (p = 0.0007) were significantly longer among patients with low RS compared to patients with high RS. For the validation cohort, similar results could also be observed in training cohort for PFS (p = 0.029) and OS (p = 0.091) estimation, respectively. The combined model based on CT and clinical parameters improved the performance for predicting cachexia in ES-SCLC patients receiving first-line immunotherapy. It provided a novel strategy for clinicians to identify ES-SCLC patients who may suffer from the cachexia and early interventions would improve prognosis of response to immunotherapy.

Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities.

SARS-CoV-2 PCR testing data has been widely used for COVID-19 surveillance. Existing COVID-19 forecasting models mainly rely on case counts obtained from qPCR results, even though the binary PCR results provide a limited picture of the pandemic trajectory. Most forecasting models have failed to accurately predict the COVID-19 waves before they occur. Recently a model utilizing cross-sectional population cycle threshold (Ct-the number of cycles required for the fluorescent signal to cross the background threshold) values obtained from PCR tests (Ct-based model) was developed to overcome the limitations of using only binary PCR results. In this study, we aimed to improve on COVID-19 forecasting models using features derived from the Ct-based model, to detect epidemic waves earlier than case-based trajectories. PCR data was collected weekly at Northeastern University (NU) between August 2020 and January 2022. Campus and county epidemic trajectories were generated from case counts. A novel forecasting approach was developed by enhancing a recent deep learning model with Ct-based features and applied in Suffolk County and NU campus. For this, cross-sectional Ct values from PCR data were used to generate Ct-based epidemic trajectories, including effective reproductive rate (Rt) and incidence. The improvement in forecasting performance was compared using absolute errors and residual squared errors with respect to actual observed cases at the 7-day and 14-day forecasting horizons. The model was also tested prospectively over the period January 2022 to April 2022. Rt curves estimated from the Ct-based model indicated epidemic waves 12 to 14 days earlier than Rt curves from NU campus and Suffolk County cases, with a correlation of 0.57. Enhancing the forecasting models with Ct-based information significantly decreased absolute error (decrease of 49.4 and 221.5 for the 7 and 14-day forecasting horizons) and residual squared error (40.6 and 217.1 for the 7 and 14-day forecasting horizons) compared to the original model without Ct features. Ct-based epidemic trajectories can herald an earlier signal for impending epidemic waves in the community and forecast transmission peaks. Moreover, COVID-19 forecasting models can be enhanced using these Ct features to improve their forecasting accuracy. In this study, we make the case that public health agencies should publish Ct values along with the binary positive/negative PCR results. Early and accurate forecasting of epidemic waves can inform public health policies and countermeasures which can mitigate spread.

Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification

PALACE-1 trial has confirm that the addition of pembrolizumab to neoadjuvant chemoradiotherapy (NCRT) improves the pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC), which might be a novel treatment strategy for ESCC. In the present study, we aim to establish a machine learning model to predict the local response to NCRT+ pembrolizumab for ESCC by using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) and contrast-enhanced plan CT images. A total of 65 cases treated with NCRT+ pembrolizumab followed by surgery were prospectively enrolled for analysis from 2019-2022. Each patient contains a contrast-enhanced plan CT and FDG PET images. 52 patients were randomly divided into training set and 13 patients were used as test set. The Extraction of radiomics features was performed using an open-source Python library PyRadiomics automatically. Features were computed according to the radiologist-drawn ROIs on both CT and PET images. In the feature selection stage least absolute shrinkage and selection operator (LASSO) was utilized on CT features and PET features separately. Four different machine learning models were implemented: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and XGBoost (XGB). The features selected by LASSO regression were used as model input and the output of the model is "pCR" or "non-pCR". To find the optimal parameter, the 5-fold cross-validation method was used in the training stage. In this study, we use accuracy, sensitivity and specificity as the metrics to evaluate the performance of the model on the testing cohort. The predictive performance of the model was assessed using the area under curve (AUC) of the receiver operating characteristics curve (ROC). Of the 65 cases treated with NCRT+pembrolizumab, 35 patients archived pCR (53.8%), and 30 archived non-pCR. 1684 radiomics features were extracted from each case, and half of them (842 features) were from CT and others were from PET. Among the machine learning models mentioned above SVM achieves the most promising performance on the evaluation metrics. Accuracy, sensitivity, specificity and AUC score on test set were 0.692, 0.833, 0.571 and 0.786 for CT features and 0.615, 0.667, 0.571 and 0.762 for PET features, respectively. For CT+FDG PET fused features accuracy, sensitivity, specificity and AUC score on test set were 0.769, 0.667, 0.857 and 0.833. In this study, we performed several different machine learning models to predict the response to NCRT+ pembrolizumab among ESCC based on the extracted radiomics features from CT and FDG PET images. The best-performing model based on radiomics features of CT and PET images could identify non-pCR to NCRT + pembrolizumab in EC patients.

Computed tomography and magnetic resonance imaging are potential noninvasive methods for evaluating the cisterna chyli in cats.

There is limited information on the normal appearance of the cisterna chyli (CC) in cats on CT and MRI. The aim of this retrospective study was to describe the CT and MRI characteristics of the CC in a group of cats without lymphatic system pathology. A total of 31 CT and 63 MRI images were obtained of client-owned cats between January 2017 and March 2022. The presence, location, shape, maximum width, MRI-signal intensity, mean attenuation, and contrast enhancement of the CC were recorded from CT and MRI scans. The CC was identified in all the CT scans and in 60 MRI studies. The CC was located level with the cranial mesenteric artery in 56 of 91 cases. It was crescent shaped in 34 of 54 cases. On precontrast CT images, the mean attenuation of the CC was 17 HU, and the mean postcontrast attenuation was 28 HU. On T2-weighted sequences, the CC was isointense to CSF and hyperintense to the muscles, while on T1-weighted images, it was isointense to the muscles. Contrast enhancement was variable in both techniques. CT and MRI have the potential for noninvasive evaluation of CC in cats.

Correlations between iodine uptake, invasive CT features and pleural invasion in adenocarcinomas with pleural contact

Pleural contact in lung cancers does not always imply pleural invasion (PI). This study was designed to determine whether specific invasive CT characteristics or iodine uptake can aid in the prediction of PI. The sample population comprised patients with resected solid lung adenocarcinomas between April 2019 and May 2022. All participants underwent a contrast enhanced spectral CT scan. Two proficient radiologists independently evaluated the CT features and iodine uptake. Logistic regression analyses were employed to identify predictors for PI, via CT features and iodine uptake. To validate the improved diagnostic efficiency, accuracy analysis and ROC curves were subsequently used. A two-tailed P value of less than 0.05 was considered statistically significant. We enrolled 97 consecutive patients (mean age, 61.8 years ± 10; 48 females) in our study. The binomial logistic regression model revealed that a contact length > 10 mm (OR 4.80, 95% CI 1.92, 11.99, p = 0.001), and spiculation sign (OR 2.71, 95% CI 1.08, 6.79, p = 0.033) were independent predictors of PI, while iodine uptake was not. Enhanced sensitivity (90%) and a greater area under the curve (0.73) were achieved by integrating the two aforementioned CT features in predicting PI. We concluded that the combination of contact length > 10 mm and spiculation sign can enhance the diagnostic performance of PI.

Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study

PurposeTo establish and validate a multiparameter prediction model for early recurrence after radical resection in patients diagnosed with combined hepatocellular-cholangiocarcinoma (cHCC-CC).Materials and methodsThis study reviewed the clinical characteristics and preoperative CT images of 143 cHCC-CC patients who underwent radical resection from three institutions. A total of 110 patients from institution 1 were randomly divided into training set (n = 78) and testing set (n = 32) in the ratio of 7–3. Univariate and multivariate logistic regression analysis were used to construct a nomogram prediction model in the training set, which was internally and externally validated in the testing set and the validation set (n = 33) from institutions 2 and 3. The area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration analysis were used to evaluate the model’s performance.ResultsThe combined model demonstrated superior predictive performance compared to the clinical model, the CT model, the pathological model and the clinic-CT model in predicting the early postoperative recurrence. The nomogram based on the combined model included AST, ALP, tumor size, tumor margin, arterial phase peritumoral enhancement, and MVI (Microvascular invasion). The model had AUCs of 0.89 (95% CI 0.81–0.96), 0.85 (95% CI 0.70–0.99), and 0.86 (95% CI 0.72–1.00) in the training, testing, and validation sets, respectively, indicating high predictive power. DCA showed that the combined model had good clinical value and correction effect.ConclusionA nomogram incorporating clinical characteristics and preoperative CT features can be utilized to effectively predict the early postoperative recurrence in patients with cHCC-CC.

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

ObjectiveTo construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.MethodsA total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan–Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.ResultsIn this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).ConclusionThe combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.Critical relevance statementMSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.Key points• Tumor location and CT_N staging were independent predictors for MSI-H in GC.• Quantitative DLCT parameters showed potential in predicting MSI status in GC.• The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance.• The prediction results could stratify the risk of tumor recurrence after surgery.Graphical

A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model

ObjectiveTo identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST).Materials and methodsThis retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models.ResultsThe nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p > 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness.ConclusionTwo diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer.

The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data. A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively. The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly. The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT

BackgroundPulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs.ResultsFrom January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients’ age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037–3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235–8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161–4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567–17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847–12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526–16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001).ConclusionIn patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant.Critical relevance statementThe benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection.Key points• The solid pleura-attached nodules (SPANs) are closely related to the pleura.• Relationship between nodule and pleura and pleural changes are important for differentiating SPANs.• Benign SPANs frequently have broad pleural thickening or embed in thickened pleura.• Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs.• Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.Graphical

A Method to Evaluate the Reduction Difficulty of Atlantoaxial Dislocation Based on Computed Tomography Quantitative Analysis

To evaluate the difficulty of reduction of congenital atlantoaxial dislocation with or without os odontoideum or basilar invagination based on computed tomography (CT) quantitative analysis. From March 2018 to December 2022, the CT features of 108 patients with atlantoaxial dislocation with or without os odontoideum or basilar invagination were analyzed. Quantitative scores were defined according to imaging features, including sloping of the lateral mass; osteophyte between the lateral mass joint; ball-and-socket deformity of the lateral mass joint; vertical interlocking of the lateral mass joint; callus between the lateral mass joint; and atlanto-odontoid joint hyperplasia, blocking, or fusion. Grades were calculated according to the sum of points of the atlanto-odontoid joint and lateral mass joints, as follows: I, 0-1 points; Ⅱ, 2-3 points; Ⅲ, 4-6 points; IV, 7-10 points. After 1 week of bidirectional cervical traction, CT scans were performed, and atlantodens interval and vertical distance from dens to Chamberlain line were measured. The vertical reduction rate, horizontal reduction rate, and overall reduction rate of atlantoaxial dislocation were calculated. The vertical distance from dens to Chamberlain line values after traction were significantly reduced compared with before traction, including grades I, II, III, and IV. The overall reduction rates were 85.1% ± 11.8%, 65.8% ± 8.3%, 45.0% ± 8.5%, and 38.4% ± 13.0% respectively, after 1 week of bidirectional cervical traction. The CT quantitative score system is an effective noninvasive evaluation to judge the reduction difficulty of atlantoaxial dislocation with or without os odontoideum or basilar invagination.

Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy: development and external validation.

Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula. Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020-2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score. Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79). The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIR-Consortium/POPF-predictor.

Cone-beam CT imaging features of common cystic lesions associated with the impacted mandibular third molar

Objective: To analyze the imaging features of cone-beam CT (CBCT) of ameloblastoma (AB), odontogenic keratocyst (OKC) and dentigerous cysts (DC) associated with the mandibular impacted third molars,so as to provide useful information for differential diagnosis of these lesions. Methods: The patients who were with complete clinical data, pathological diagnosis and CBCT images from AB, OKC and DC around the mandibular impacted third molars were collected in Peking University Hospital of Stomatology from August 2016 to December 2021. A total of 109 patients (14 were diagnosed as AB, 23 were diagnosed as OKC and the others were diagnosed as dental cysts) were collected, including 73 males and 36 females. The age ranged from 11 to 70. The analyzed imaging features included location and internal density of the lesions, bone expansion, root resorption of adjacent teeth and types of the impacted teeth. The Chi square test was used to compare the gender of different lesions, and the Fisher's exact test was used to compare imaging features of lesions. When P<0.05, there was a significant difference among the three. Logistic regression analysis was performed to determine the imaging features that significantly contribute to correct imaging diagnosis. Corresponding P-values were calculated for all factors from multivariate models. Results: In the 23 cases of OKC, no special location was observed for the center of the lesion, heterogeneous high-density were seen in 21.7% of the cases, 56.5% of the cases had no significant bone expansion and the impacted teeth were not specially oriented. Among the 14 AB, 7 cases (7/14) were mainly located in the ramus of the mandible, and all cases (14/14) had buccal/lingual expansion of the jaw, 8 cases (8/14) presented root resorption of the adjacent teeth, and mesial impacted mandibular third molar were seen in 6 cases (6/14). Among the 72 DC, 88.9% (64/72) of the cases were mainly limited to the crown of the impacted third molar, 72.2% (52/72) of the cases had no obvious bone expansion, inverted impacted teeth were shown in 56.9% (41/72) of the cases. There was a significant difference among the three groups (χ2=7.30, P=0.026) in gender. AB and odontogenic cyst were more common in men than in women, while the incidence of OKC was roughly equal between men and women.There were significant differences in the location (P<0.001), internal density (P=0.001) of the lesions, bone expansion (P<0.001) and types of the impacted teeth (P<0.001), while no statistical difference was found for root resorption of adjacent teeth (P=0.153). Logistics regression analysis showed that the location of the lesion, internal density, bone expansion, root resorption of adjacent teeth and the types of impacted teeth had significant effects on the accurate diagnosis of the three kinds of lesions. Conclusions: Location, internal density, bone expansion and types of the impacted teeth played an important role in the correct imaging diagnosis. Further analysis indicates that when the classification of impacted teeth and the location of lesions are considered synchronously, DC can be differentiated from AB and OKC.

Unenhanced computed tomography radiomics help detect endoleaks after endovascular repair of abdominal aortic aneurysm

ObjectivesTo explore the feasibility of unenhanced CT images for endoleak detection of abdominal aortic aneurysm (AAA) after endovascular repair (EVAR).MethodsPatients who visited our hospital after EVAR from July 2014 to September 2021 were retrospectively collected. Two radiologists evaluated the presence or absence of endoleaks using the combination of contrast-enhanced and unenhanced CT as the referenced standard. After segmenting the aneurysm sac of the unenhanced CT, the radiomic features were automatically extracted from the region of interest. Histogram features of patients with and without endoleak were statistically analyzed to explore the differences between the two groups. Twelve common machine learning (ML) models based on radiomic features were constructed to evaluate the performance of endoleak detection with unenhanced CT images.ResultsThe study included 216 patients (69 ± 8 years; 191 men) with AAA, including 64 patients with endoleaks. A total of 1955 radiomic features of unenhanced CT were extracted. Compared with patients without endoleak, the aneurysm sac outside the stent of patients with endoleak had higher CT attenuation (41.7 vs. 33.6, p < 0.001) with smaller dispersion (51.5 vs. 58.8, p < 0.001). The average area under the curve (AUC) of the ML models constructed with unenhanced CT radiomics was 0.86 ± 0.05, the accuracy was 81% ± 4, the sensitivity was 88% ± 10, and the specificity was 78% ± 5. When fixing the sensitivity to > 90% (92% ± 2), the models retained specificity at 72% ± 10.ConclusionsUnenhanced CT features exhibit significant differences between patients with and without endoleak and can help detect endoleaks in AAA after EVAR with high sensitivity.Clinical relevance statementUnenhanced CT radiomics can help provide an alternative method of endoleak detection in patients who have adverse reactions to contrast media. This study further exploits the value of unenhanced CT examinations in the clinical management and surveillance of postoperative abdominal aortic aneurysm.Key Points• Unenhanced CT features of the aneurysm sac outside the stent exhibit significant differences between patients with and without endoleak. The endoleak group showed higher unenhanced CT attenuation (41.7 vs 33.6, p < .001) with smaller dispersion (51.5 vs 58.8, p < .001) than the nonendoleak group.• Unenhanced CT radiomics can help detect endoleaks after intervention. The average area under the curve (AUC) of twelve common machine learning models constructed with unenhanced CT radiomics was 0.86 ± 0.05, the average accuracy was 81% ± 4.• When fixing the sensitivity to > 90% (92% ± 2), the machine learning models retained average specificity at 72% ± 10.

Short-term mortality prediction using a combination of clinical and CT features: Refining the prognosis of critically ill patients in shock

PurposeTo assess the predictive value of combining CT and clinical findings for predicting 10-day mortality in critically ill patients in shock. Materials and methodsFrom January 1, 2018, to December 31, 2021, 289 consecutives critically ill patients in shock who underwent a contrast enhanced CT were included. Variables at the time of the CT were retrospectively extracted from medical charts. CT examinations were blindly analyzed by two independent radiologists. Multivariable analysis was performed, combining clinical and CT features. A simple survival score for 10-day mortality prediction was built and validated in a further independent external cohort of 70 patients. Results10-day mortality rate was 135/289 (47%) in the study sample. At multivariate analysis, catecholamine infusion (OR = 2.11; 95%CI [1.21–4.18], P = 0.011), lactates level > 5 mmol/l (OR = 3.54; 95%CI [1.94–6.54], P < 0.001); total bilirubin > 50 mg/l (OR = 1.79 CI 95% [1.03–3.13], P = 0.039); small bowel dilation (OR = 1.82; 95%CI [1.01–3.32], P = 0.047); diffuse kidney infarction (OR = 2.76; 95%CI [1.26–6.37], P = 0.013) and superior mesentery artery < 5 mm (OR = 1.96; 95%CI [1.10–3.49], P = 0.021) were associated with 10-days mortality. The AUC of the combined model was 0.79; 95%CI [0.74–0.85] in the study sample and 0.87; 95%CI [0.71–0.91] in the validation cohort. ConclusionThe combination of CT imaging features and clinical data should emerge as a novel approach to predict short-term mortality in critically ill patients in shock.

Automatic statistical diagnosis of COVID-19 based on multi-modal CT feature extraction

Background and purposeComputed tomography (CT) is highly sensitive to lung-related abnormalities as a non-invasive method and has become an essential tool for screening and diagnosing Coronavirus disease 2019 (COVID-19). To reduce the stress of work for physicians and speed up diagnosis, we propose a novel automatic diagnosis pipeline for COVID-19 based on high-dimensional radiomic features extracted from multimodal CT scans (multi-geometric and multiscale). Materials and methodsThere are 746 CT scans involved in this study, where 349 CT scans are COVID-19 positive and 397 CT scans are COVID-19 negative. All of them are from the public dataset. We first construct a transfer learning-based auto-segmentation model with a morphological post-processing block to improve the lung region segmentation. Then the radiomics feature extraction is guided by the proposed multi-modal CT scans strategy. In addition, our automatic diagnosis pipeline is driven by a well-designed loss function. We also explain the diagnosis capability from the related theory of linear subspace spanned by multi-modal radiomics features. ResultsUnder the 10-fold cross-validation strategy, our approach can achieve an improvement in diagnostic performance of 5. 77%, 7. 78%, 7. 74%, 7. 78%, 7. 45% compared to the radiomic features extracted from the original CT scans, and diagnosis performance is promoted to 91.53%, 86.46%, 86.47%, 86.46%, 86.95% in terms of AUC, Acc, F1, Recall and Precision in public datasets. ConclusionsWe demonstrate a statistically significant improvement of the proposed statistical learning method compared to the state-of-the-art machine learning-based diagnosis approaches. Thanks to theoretical support and excellent diagnostic performance, our method can be deployed in clinical auxiliary diagnosis, releasing the overstretched medical resources.

Differentiation between Chondrosarcoma and Synovial Chondromatosis of the Temporomandibular Joint Using CT and MR Imaging.

Chondrosarcoma and synovial chondromatosis of the temporomandibular joint share overlapping clinical and histopathologic features. We aimed to identify CT and MR imaging features to differentiate chondrosarcoma from synovial chondromatosis of the temporomandibular joint. The CT and MR images of 12 and 35 patients with histopathologically confirmed chondrosarcoma and synovial chondromatosis of the temporomandibular joint, respectively, were retrospectively reviewed. Imaging features including lesion size, center, enhancement, destruction/sclerosis of surrounding bone, infiltration into the tendon of the lateral pterygoid muscle, calcification, periosteal reaction, and osteophyte formation were assessed. A comparison between chondrosarcoma and synovial chondromatosis was performed with a Student t test for quantitative variables and the Fisher exact test or linear-by-linear association test for qualitative variables. Receiver operating characteristic analysis was performed to determine the diagnostic performance for differentiation of chondrosarcoma and synovial chondromatosis based on a composite score obtained by assigning 1 point for each of 9 imaging features. High-risk imaging features for chondrosarcoma were the following: lesion centered on the mandibular condyle, destruction of the mandibular condyle, no destruction/sclerosis of the articular eminence/glenoid fossa, infiltration into the tendon of the lateral pterygoid muscle, absent or stippled calcification, periosteal reaction, internal enhancement, and size of ≥30.5 mm. The best cutoff value to discriminate chondrosarcoma from synovial chondromatosis was the presence of any 4 of these high-risk imaging features, with an area under the curve of 0.986 and an accuracy of 95.8%. CT and MR imaging features can distinguish chondrosarcoma from synovial chondromatosis of the temporomandibular joint with improved diagnostic performance when a subcombination of 9 imaging features is used.