Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study.

PURPOSE: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. METHOD: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. RESULTS: Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. CONCLUSION: The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.

Pulmonary CT scans of white rabbits using the selective photon shield technique of the third-generation dual-source CT.

This study aims to optimise the protocol for the low-dose pulmonary computed tomography (CT) scanning of infants by studying the effects of the selective photon shield (SPS) technique of the third-generation dual-source CT (DSCT) on the image quality and radiation dose of a chest CT in white rabbits under different tube currents. Twelve white rabbits of a similar weight to an infant were selected and randomly divided into an experimental group and a control group. The experimental groups (A1-A5) were scanned at low dose by the third-generation DSCT using SPS under different tube current × time (60, 50, 40, 30, and 20 mAs). The control group (B) was scanned under a conventional tube voltage (100 kV) and current × time (20 mAs). Advanced model iterative reconstruction at strength three was used for the objective and subjective evaluation of the image quality and radiation dose of the lung and mediastinal windows. With the standard deviation of the air in the trachea as image noise, the signal-to-noise ratio (SNR), contrast-to-noise ratio, and CT values of each site were evaluated. Radiation doses were compared using the volume CT dose index, dose length product, and effective dose. The differences in subjective image quality between groups A2 and B were not statistically significant (P= 0.34). The differences in the SNRs of the lung and mediastinal windows between groups A2 and B were not statistically significant (P> 0.05). The radiation dose of group A2 was 83.2% lower than that of group B. The SPS of the third-generation DSCT under 50 mAs might be applied in the pulmonary CT examination of infants.

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.

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.

Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma.

OBJECTIVES: To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients. METHODS: Data of 230 consecutive patients were retrospectively analyzed (154 in the training set and 76 in the test set). A total of 1576 radiomics features were extracted from arterial-phase CT images of the whole primary tumor. LASSO logistic regression was performed to choose the key features and construct a radiomics signature. A radiomics nomogram incorporating this signature was developed on the basis of multivariable analysis in the training set. Nomogram performance was determined and validated with respect to its discrimination, calibration and reclassification. Clinical usefulness was estimated by decision curve analysis. RESULTS: The radiomics signature including five features was significantly associated with LN metastasis. The radiomics nomogram, which incorporated the signature and CT-reported LN status (i.e. size criteria), distinguished LN metastasis with an area under curve (AUC) of 0.758 in the training set, and performance was similar in the test set (AUC 0.773). Discrimination of the radiomics nomogram exceeded that of size criteria alone in both the training set (p <0.001) and the test set (p=0.005). Integrated discrimination improvement (IDI) and categorical net reclassification improvement (NRI) showed significant improvement in prognostic value when the radiomics signature was added to size criteria in the test set (IDI 17.3%; p<0.001; categorical NRI 52.3%; p<0.001). Decision curve analysis supported that the radiomics nomogram is superior to size criteria. CONCLUSIONS: The radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria. KEY POINTS: • A radiomics nomogram was built and validated to predict LN metastasis in resectable ESCC. • The radiomics nomogram outperformed size criteria. • Radiomics helps to unravel intratumor heterogeneity and can serve as a novel biomarker for determination of LN status in resectable ESCC.

[Preoperative prediction for lymph node metastasis of rectal nonmucinous adenocarcinoma based on radiomics classifier].

OBJECTIVE: To determine the value of radiomics in identifying lymph node (LN) metastasis in patients with rectal nonmucinous adenocarcinoma.
 Methods: Imaging data of 91 patients were retrospectively analyzed (61 in the training set and 30 in the test set). A total of 1 301 radiomics features were extracted from high-resolution T2-weighted images of the whole primary tumor. The least absolute shrinkage and selection operator (LASSO) logistic regression was performed to choose the optimal features and construct a radiomics classifier in the training set. Its discrimination performance was compared with that of morphological criteria by receiver operating characteristic (ROC) curve analysis, which was validated in the test set.
 Results: The radiomics classifier combined with five key features was significantly associated with LN metastasis, which distinguished LN metastasis with an area under curve (AUC) at 0.874 (95% CI 0.787 to 0.960) in the training set, and the performance was similar in the test set (AUC 0.878, 95% CI 0.727 to 1.000). The AUCs according to the morphological criteria in the training set and test set were 0.619 (95% CI 0.487 to 0.752) and 0.556 (95% CI 0.355 to 0.756), respectively. Discrimination of the radiomics classifier was superior to that of morphological criteria in both the two datasets (both P <0.05).
 Conclusion: The radiomics classifier provides individualized risk estimation for LN metastasis in rectal nonmucinous adenocarcinoma patients and it has the advantage over the morphological criteria.

Radiomics approach for preoperative identification of stages I-II and III-IV of esophageal cancer.

OBJECTIVE: To predict preoperative staging using a radiomics approach based on computed tomography (CT) images of patients with esophageal squamous cell carcinoma (ESCC). METHODS: This retrospective study included 154 patients (primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator (Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages I-II and III-IV was evaluated and compared using the receiver operating characteristics (ROC) curves and net reclassification improvement (NRI). RESULTS: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging (P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve (AUC): 0.795vs. 0.694, P=0.003; NRI=0.424)] and validation cohorts (AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834). CONCLUSIONS: The quantitative approach has the potential to identify stage I-II and III-IV ESCC before treatment.