Dual-Energy Computed Tomography for the Diagnosis of Mediastinal Lymph Node Metastasis in Lung Cancer Patients: A Preliminary Study.

OBJECTIVE: This study explored the feasibility of dual-energy computed tomography (DECT) for the diagnosis of mediastinal lymph node (LN) metastasis in patients with lung cancer. METHODS: Forty-two consecutive patients with lung cancer, who underwent DECT, were included in this retrospective study. The attenuation value (Hounsfield unit) in virtual monochromatic images and the iodine concentration in the iodine map were measured at mediastinal LNs. The slope of the spectral attenuation curve (K) and normalized iodine concentration (in thoracic aorta) were calculated. The measurement results were statistically compared using 2 independent samples t test. Receiver operating characteristic curve analysis, net reclassification improvement, and integrated discrimination improvement were used to evaluate the diagnostic performance of DECT for mediastinal LN metastasis. RESULTS: A total of 74 mediastinal LNs were obtained, including 33 metastatic LNs and 41 nonmetastatic LNs. The attenuation value at the lower energy levels of virtual monochromatic images (40-90 keV), K, and normalized iodine concentration demonstrated a significant difference between metastatic LNs and nonmetastatic LNs. The attenuation value at 40 keV was the most favorable biomarker for the diagnosis of mediastinal LN metastasis (area under curve, 0.91; sensitivity, 0.94; specificity, 0.81), which showed a much better performance than the LN diameter-based evaluation method (area under curve, 0.72; sensitivity, 0.66; specificity, 0.82; net reclassification improvement, 0.359; integrated discrimination improvement, 0.330). CONCLUSIONS: Dual-energy computed tomography is a promising diagnostic approach for the diagnosis of mediastinal LN metastasis in patients with lung cancer, which may help clinicians implement personalized treatment strategies.

A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age.

OBJECTIVE: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. METHODS: Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, laboratory examination and chest CT imaging features were analyzed, and a stratification analysis was performed according to gender and age [younger group: <50 years old, elderly group ≥50 years old]. RESULTS: Most patients had a history of epidemic exposure within 2 weeks (96%). The main clinical complaints are fever (54%) and cough (46%). In chest CT images, ground-glass opacity (GGO) is the most common feature (37/38, 97%) in abnormal CT findings, with the remaining 12 patients (12/50, 24%) presenting normal CT images. Other concomitant abnormalities include dilatation of vessels in lesion (76%), interlobular thickening (47%), adjacent pleural thickening (37%), focal consolidation (26%), nodules (16%) and honeycomb pattern (13%). The lesions were distributed in the periphery (50%) or mixed (50%). Subgroup analysis showed that there was no difference in the gender distribution of all the clinical and imaging features. Laboratory findings, interlobular thickening, honeycomb pattern and nodules demonstrated remarkable difference between younger group and elderly group. The average CT score for pulmonary involvement degree was 5.0±4.7. Correlation analysis revealed that CT score was significantly correlated with age, body temperature and days from illness onset (p < 0.05). CONCLUSIONS: COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention.

Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma.

PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. -1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.

[Application of CT-based radiomics in differentiating primary gastric lymphoma from Borrmann type IV gastric cancer].

OBJECTIVE: To explore the feasibility of CT-based image radiomics signature in identification of primary gastric lymphoma and Borrmann type IV gastric cancer.
 Methods: A retrospective analysis of 71 patients with primary gastric lymphoma or Borrmann type IV gastric cancer confirmed by pathology in our Hospital from January 2009 to April 2017 was performed. There were 28 patients with primary gastric lymphoma and 43 patients with Borrmann type IV gastric cancer. The feature extraction algorithm based on Matlab 2017a software was used to extract the features of image, and the logistic regression model was used to screen the features to establish radiomics signature. The CT sign diagnosis model was established, which included the periplasmic fat infiltration, softness of the stomach wall, abdominal lymph node and peripheral organ metastasis, ascites, mucosal white line sign and lesion thickness. The classification of the two models was evaluated by receiver operating characteristic curve.
 Results: A total of 32 3D features were extracted from CT image for each patients. Two features were found to be the most important differential diagnosis factors, and the radiomics signature was established. The CT sign diagnosis model consisted of ascites, periplasmic fat infiltration, stomach wall softness and mucosal white line. For the radiomics signature and the CT subjective finding model, the AUCs were 0.964 and 0.867 with the accuracy at 94.4% and 80.2%, the sensitivity at 93.0% and 74.4%, the specificity at 96.4% and 89.3%, respectively. After Delong test, the diagnostic efficacy of the radiomics signature was higher than the CT sign diagnosis model (P<0.001).
 Conclusion: CT-based image radiomics signature can accurately identify primary gastric lymphoma and Borrmann type IV gastric cancer, and can potentially provide important assistance in clinical diagnosis for the two diseases.

Radiomics in predicting tumor molecular marker P63 for non-small cell lung cancer.

OBJECTIVE: To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63.
 Methods: A total of 245 NSCLC patients who underwent CT scans were retrospectively included. All patients were confirmed by histopathological examinations and P63 expression were examined within 2 weeks after CT examination. Radiomics features were extracted by MaZda software and subjective image features were defined from original non-enhanced CT images. The Lasso-logistic regression model was used to select features and develop radiomics signature, subjective image features model, and combined diagnostic model. The predictive performance of each model was evaluated by the receiver operating characteristic (ROC) curve, and compared with Delong test.
 Results: Of the 245 patients, 96 were P63 positive and 149 were P63 negative. The subjective image feature model consisted of 6 image features. Through feature selection, the radiomics signature consisted of 8 radiomics features. The area under the ROC curves of the subjective image feature model and the radiomics signature in predicting P63 expression statue were 0.700 and 0.755, respectively, without a significant difference (P>0.05). The combined diagnostic model showed the best predictive power (AUC=0.817, P<0.01).
 Conclusion: The radiomics-based CT scan images can predict the expression status of NSCLC molecular marker P63. The combination of the radiomics features and subjective image features can significantly improve the predictive performance of the predictive model, which may be helpful to provide a non-invasive way for understanding the molecular information for lung cancer cells.

[Development of a radiomics signature to predict Ki-67 expression level in non-small cell lung cancer].

OBJECTIVE: To develop a radiomics signature based on CT image features to estimate the expression level of Ki-67 in non-small cell lung cancer (NSCLC).
 Methods: A total of 108 NSCLC patients, who underwent non-enhanced and contrast-enhanced CT scan in our hospital from January 2014 to November 2017, were retrospectively analyzed. They were confirmed by histopathological examination and undergone Ki-67 expression level test within 2 weeks after CT examination. The non-enhanced and contrast-enhanced CT three-dimensional structural images of the lesions were manually delineated by MaZda software, and the texture features of the region of interest were extracted. Combination of feature selection and classification methods were used to build radiomics signatures, and the classification were assessed using misclassification rates. The MaZda software provides texture feature selection methods including mutual information (MI), Fisher coefficients (Fisher), classification error probability combined with average correlation coefficients (POE+ACC), and Fisher+POE+ACC+MI (FPM), and texture feature analysis including raw data analysis (RDA), principal component analysis (PCA), linear classification analysis (LDA) and nonlinear classification analysis (NDA).
 Results: Among the 108 patients, 50 cases were at high levels of Ki-67 expression and 58 cases were at low levels of Ki-67 expression, respectively. The differences of gender, age and pathological type between the two groups were statistically significant (P<0.05). The radiomics signature built by FPM feature selection combined with NDA feature analysis based on non-enhanced CT images achieved the best performance for predicting the level of Ki-67 with a misclassification rate of 14.81%. However, radiomics signature based on contrast-enhanced CT images did not reduce the misclassification rate.
 Conclusion: The radiomics signature based on conventional CT image texture features is helpful to predict the expression of Ki-67 in NSCLC lesions, which can provide a non-invasive technique for assessing the invasiveness and prognosis for NSCLC.

Inflammatory myofibroblastic tumor of the pancreatic neck misdiagnosed as neuroendocrine tumor: A case report.

BACKGROUND: Inflammatory myofibroblastic tumor (IMT) is a relatively rare tumor. The global incidence of IMT is less than 1%. There is no specific clinical manifestation. It usually occurs in the lungs, but the pancreas is not the predilection site. CASE SUMMARY: We present a case of a male patient, 51 years old, who was diagnosed with a pancreatic neck small mass on ultrasound one year ago during a physical examination. As he had no clinical symptoms and the mass was relatively small, he did not undergo treatment. However, the mass was found to be larger on review, and he was referred to our hospital. Since the primal clinical diagnosis was pancreatic neuroendocrine tumor, the patient underwent surgical treatment. However, the case was confirmed as pancreatic IMT by postoperative pathology. CONCLUSION: Pancreatic IMT is relatively rare and easily misdiagnosed. We can better under-stand and correctly diagnose this disease by this case report.

CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma.

BACKGROUND: To explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC). METHODS: We retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient's body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method. RESULTS: A total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively. CONCLUSIONS: CT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.

Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics.

BACKGROUND: Liver cancer is one of the most common malignant tumors, and ranks as the fourth leading cause of cancer death worldwide. Microvascular invasion (MVI) is considered one of the most important factors for recurrence and poor prognosis of liver cancer. Thus, accurately identifying MVI before surgery is of great importance in making treatment strategies and predicting the prognosis of patients with hepatocellular carcinoma (HCC). Radiomics as an emerging field, aims to utilize artificial intelligence software to develop methods that may contribute to cancer diagnosis, treatment improvement and evaluation, and better prediction. AIM: To investigate the predictive value of computed tomography radiomics for MVI in solitary HCC ≤ 5 cm. METHODS: A total of 185 HCC patients, including 122 MVI negative and 63 MVI positive patients, were retrospectively analyzed. All patients were randomly assigned to the training group (n = 124) and validation group (n = 61). A total of 1351 radiomic features were extracted based on three-dimensional images. The diagnostic performance of the radiomics model was verified in the validation group, and the Delong test was applied to compare the radiomics and MVI-related imaging features (two-trait predictor of venous invasion and radiogenomic invasion). RESULTS: A total of ten radiomics features were finally obtained after screening 1531 features. According to the weighting coefficient that corresponded to the features, the radiomics score (RS) calculation formula was obtained, and the RS score of each patient was calculated. The radiomics model exhibited a better correction and identification ability in the training and validation groups [area under the curve: 0.72 (95% confidence interval: 0.58-0.86) and 0.74 (95% confidence interval: 0.66-0.83), respectively]. Its prediction performance was significantly higher than that of the image features (P < 0.05). CONCLUSION: Computed tomography radiomics has certain predictive value for MVI in solitary HCC ≤ 5 cm, and the predictive ability is higher than that of image features.

Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.

PURPOSE: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC). METHODS: 245 histopathological confirmed NSCLC patients who underwent CT scans were retrospectively included. The Ki-67 proliferation index (Ki-67 PI) were measured within 2 weeks after CT scans. A lesion volume of interest (VOI) was manually delineated and radiomics features were extracted by MaZda software from CT images. A random forest feature selection algorithm (RFFS) was used to reduce features. Six kinds of machine learning methods were used to establish radiomics classifiers, subjective imaging feature classifiers and combined classifiers, respectively. The performance of these classifiers was evaluated by the receiver operating characteristic curve (ROC) and compared with Delong test. RESULTS: 103 radiomics features were extracted and 20 optimal features were selected using RFFS. Among the radiomics classifiers established by six machine learning methods, random forest-based radiomics classifier achieved the best performance (AUC = 0.776) in predicting the Ki-67 expression level with sensitivity and specificity of 0.726 and 0.661, which was better than that of subjective imaging classifiers (AUC = 0.625, P < 0.05). However, the combined classifiers did not improve the predictive performance (AUC = 0.780, P > 0.05), with sensitivity and specificity of 0.752 and 0.633. CONCLUSIONS: The machine learning-based CT radiomics classifier in NSCLC can facilitate the prediction of the expression level of Ki-67 and provide a novel non-invasive strategy for assessing the cell proliferation.