CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis.

PURPOSE: To construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM). METHODS AND METHODS: 293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score. RESULTS: AUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor. CONCLUSIONS: Radiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma.

BACKGROUND: The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain. OBJECTIVE: To investigate the prediction performance of MRI radiomics for MVI in HCC. METHODS: Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses. RESULTS: 13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 - 0.86), specificity of 0.79 (95%CI: 0.76 - 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 - 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05). CONCLUSION: MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability.

PURPOSE: To develop CT radiomics models of resectable esophageal squamous cell carcinoma (ESCC) and lymph node (LN) to preoperatively identify LN+. MATERIALS AND METHODS: 299 consecutive patients with ESCC were enrolled in the study, 140 of whom were LN+ and 159 were LN-. Of the 299 patients, 249 (from the same hospital) were randomly divided into a training cohort (n = 174) and a test cohort (n = 75). The remaining 50 patients, from a second hospital, were assigned to an external validation cohort. In the training cohort, preoperative contrast-enhanced CT radiomics features of ESCC and LN were extracted, then integrated with clinical features to develop three models: ESCC, LN and combined. The performance of these models was assessed using area under receiver operating characteristic curve (AUC), and F-1 score, which were validated in both the test cohort and external validation cohort. RESULTS: An ESCC model was developed for the training cohort utilizing the 8 tumor radiomics features, and an LN model was constructed using 9 nodal radiomics features. A combined model was constructed using both ESCC and LN extracted features, in addition to cT stage and LN+ distribution. This combined model had the highest predictive ability among the three models in the training cohort (AUC = 0.948, F1-score = 0.878). The predictive ability was validated in both the test and external validation cohorts (AUC = 0.885 and 0.867, F1-score = 0.816 and 0.773, respectively). CONCLUSION: To preoperatively determine LN+, the combined model is superior to models of ESCC and LN alone.

CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis.

The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC: 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.

CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis

Abstract The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC: 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.

A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis.

PURPOSE: To build a radiomics model of liver contrast-enhanced computed tomography (CT) to predict hepatic encephalopathy secondary to Hepatitis B related cirrhosis. MATERIALS AND METHODS: This study consisted of 304 consecutive patients with first-diagnosed hepatitis B related cirrhosis. 212 and 92 patients were randomly computer-generated into training and testing cohorts, among which 38 and 21 patients endured HE, respectively. 356 radiomics features of liver were extracted from portal venous-phase CT data, and 3 clinical features were collected from medical record. After data were standardized by Z-score, we used least absolute shrinkage and selection operator to choose useful radiomics features. Ultimately, three predictive models including a radiomics model, a clinical model and an integrated model of radiomics and clinical features were built by analysis of R-software. Predictive performance was tested by multivariable logistic regression, and evaluated by area under receiver-operating characteristic curve (AUC), and accuracy. RESULTS: 19 radiomics features of liver CT were selected. The selected radiomics features and 3 relevant clinical features were applied to develop a radiomics model, a clinical model, and an integrated model of both radiomics and clinical features. The integrated model showed better performance than the radiomics model or clinical model to predict HE (AUC = 0.94 vs. 0.91 or 0.76, and 0.87 vs. 0.86 or 0.73; accuracy = 0.93 vs. 0.89 or 0.83, and 0.83 vs. 0.84 or 0.77) in the training and testing cohorts, respectively. CONCLUSION: The integrated model of radiomics and clinical features could well predict HE secondary to hepatitis B related cirrhosis.

Concordance of Chest CT and Nucleic Acid Testing in Diagnosing Coronavirus Disease Outside its District of Origin (Wuhan, China).

OBJECTIVES: This study aimed to determine the concordance between CT and nucleic acid testing in diagnosing coronavirus disease (COVID-19) outside its district of origin (Wuhan, China). METHODS: Twenty-three consecutive patients with COVID-19, confirmed by nucleic acid testing, were enrolled from two designated hospitals outside the district of disease origin. We collected clinical, laboratory, and CT data and assessed the concordance between CT manifestations and nucleic acid test results by comparing the percentage of patients with and without abnormal CT findings. Furthermore, using Chi-square tests, we analyzed the differences in CT manifestations between patients with and without an exposure history or symptoms. RESULTS: Multiple ground-glass opacities (GGOs), with or without consolidation, were observed on the initial CT scans of 19 patients (82.6%), whereas the remaining 4 (17.4%) showed no CT abnormalities, indicating that the initial chest CT findings were not entirely concordant with the nucleic acid test results in diagnosing COVID-19. Among the latter 4 patients, we observed multiple GGOs with and without consolidation in 2 patients on the follow-up chest CT scans taken on days 7 and 14 after admission, respectively. The remaining 2 patients showed no abnormalities on the follow-up CT scans. Furthermore, abnormal CT findings were found more frequently in patients who had been exposed to COVID-19 in its district of origin than in those who had not been exposed and in symptomatic patients than in asymptomatic patients (all p<0.05). CONCLUSIONS: Patients with positive results on nucleic acid testing may or may not have the abnormal CT manifestations that are frequently found in symptomatic patients with a history of exposure to the district of COVID-19 origin.

CT manifestations of the coronavirus disease 2019 of imported infection versus second-generation infection in patients outside the original district (Wuhan, China) of this disease: An observational study.

To explore the discrepancy in computed tomography (CT) manifestations of the coronavirus disease 2019 (COVID-19) in patients outside the original district (Wuhan, China) between cases with imported infection and second-generation infection, 22 patients with COVID-19 from 2 hospitals in Nanchong, China, 938 km away from the original district (Wuhan, China) of this disease were enrolled. All patients underwent initial and follow-up CT after admission during the treatment, and were divided into 2 groups. Group A and B were composed of 15 patients with a history of exposure to the original district (Wuhan, China) in short-term (i.e., imported infection), and 7 with a close contact with the patients with confirmed COVID-19 or with the healthy individuals from the original district (i.e., second-generation infection), respectively. Initial CT features including extent score and density score between groups were statistically compared. We found that all patients in group A and 3 of 7 patients in group B had abnormal CT findings while 4 of 7 patients in group B had not. Patients with abnormal CT findings were more frequent in group A than in group B (P < .05). On initial CT, pure ground glass opacity (GGO), and GGO with consolidation and/or other abnormalities were found in 20% (3/15) and 80% (12/15) patients in group A, respectively, while 1 (14.3%), 2 (28.6%), and 4 (57.1%) had pure GGOs, GGO with focal consolidation, and normal CT appearances in Group B, respectively. Patients with extent and density scores of ≥5 were more frequent in group A than in group B (all P-values < .01). Additionally, 3 of 4 (75%) patients with normal initial CT findings had focal pure GGO lesions on follow-up. In conclusion, COVID-19 in patients with a history of exposure to the original district can be severer than with the second-generation infection on CT.

Liver lobe based intravoxel incoherent motion diffusion weighted imaging in hepatitis B related cirrhosis: Association with child-pugh class and esophageal and gastric fundic varices.

Liver cirrhosis is a common chronic progressive liver disease in clinical practice, and intravoxel incoherent motion (IVIM) is a promising magnetic resonance method to assess liver cirrhosis, so our purpose was to investigate association of liver-lobe-based IVIM-derived parameters with hepatitis-B-related cirrhosis and its severity, and esophageal and gastric fundic varices. Seventy-four patients with hepatitis-B-related cirrhotic and 25 healthy volunteers were enrolled and underwent upper abdominal IVIM diffusion-weighted imaging with b-values of 0, 20, 50, 80, 100, 200, 400, 600, and 800 s/mm. IVIM-derived parameters (D, pure molecular diffusion; D, pseudo diffusion; and f, perfusion fraction) of left lateral lobe (LLL), left medial lobe (LML), right lobe (RL), and caudate lobe (CL) were assessed statistically to show their associations with cirrhosis and its severity, and esophageal and gastric fundic varices. In this research, we found that D, D, and f values of LLL, LML, RL, and CL were lower in cirrhotic liver than in normal liver (all P-values <.05). D, D, and f values of LLL, LML, RL, and CL were inversely correlated with Child-Pugh class of cirrhosis (r = -0.236 to -0.606, all P-values <.05). D of each liver lobe, D of LLL and CL, and f of LLL, LML, and CL in patients with esophageal and gastric fundic varices were lower than without the varices (all P-values <.05). D values of RL and CL could best identify cirrhosis, and identify esophageal and gastric fundic varices with areas under receiver-operating characteristic curve of 0.857 and 0.746, respectively. We concluded that liver-lobe-based IVIM-derived parameters can be associated with cirrhosis, and esophageal and gastric fundic varices.

Concordance of chest ct and nucleic acid testing in diagnosing coronavirus disease outside its district of origin (Wuhan, China)

OBJECTIVES: This study aimed to determine the concordance between CT and nucleic acid testing in diagnosing coronavirus disease (COVID-19) outside its district of origin (Wuhan, China). METHODS: Twenty-three consecutive patients with COVID-19, confirmed by nucleic acid testing, were enrolled from two designated hospitals outside the district of disease origin. We collected clinical, laboratory, and CT data and assessed the concordance between CT manifestations and nucleic acid test results by comparing the percentage of patients with and without abnormal CT findings. Furthermore, using Chi-square tests, we analyzed the differences in CT manifestations between patients with and without an exposure history or symptoms. RESULTS: Multiple ground-glass opacities (GGOs), with or without consolidation, were observed on the initial CT scans of 19 patients (82.6%), whereas the remaining 4 (17.4%) showed no CT abnormalities, indicating that the initial chest CT findings were not entirely concordant with the nucleic acid test results in diagnosing COVID-19. Among the latter 4 patients, we observed multiple GGOs with and without consolidation in 2 patients on the follow-up chest CT scans taken on days 7 and 14 after admission, respectively. The remaining 2 patients showed no abnormalities on the follow-up CT scans. Furthermore, abnormal CT findings were found more frequently in patients who had been exposed to COVID-19 in its district of origin than in those who had not been exposed and in symptomatic patients than in asymptomatic patients (all p<0.05). CONCLUSIONS: Patients with positive results on nucleic acid testing may or may not have the abnormal CT manifestations that are frequently found in symptomatic patients with a history of exposure to the district of COVID-19 origin.

CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study.

BACKGROUND: Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. METHODS: Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. RESULTS: Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values < 0.01 for both cohorts). Multivariable logistic regression model showed the best performance (AUC = 0.92 ± 0.04 and 0.87 ± 0.02, accuracy = 0.87 and 0.86, and F-1score = 0.93 and 0.86 in training and validation cohorts, respectively) in comparison with any other model (P-value < 0.001). Good calibration was observed for multivariable logistic regression model. CONCLUSION: CT radiomic models could help predict resectability of oesophageal SCC, and multivariable logistic regression model is the most predictive model.

Is there association of gross tumor volume of adenocarcinoma of oesophagogastric junction measured on magnetic resonance imaging with N stage?

PURPOSE: To determine whether gross tumour volume (GTV) of adenocarcinoma of oesophagogastric junction (AOG) measured on fat-suppression T2-weighted imaging (FS-T2WI) and diffusion-weighted imaging (DWI) correlates with regional lymph node metastasis and N stage. MATERIALS AND METHODS: The study was approved by the institutional ethics committee, and written informed consent was obtained. Forty-six patients with AOG underwent preoperative magnetic resonance scans including FS-T2WI and DWI with b-values of 500 and 800 s/mm2. GTV was measured on FS-T2WI and DWI. Statistical analyses were performed to determine association of GTV with N stage. RESULTS: Univariate analysis showed GTV measured on FS-T2WI and DWI with b-values of 500 and 800 s/mm2 were correlated with lymph node metastasis (all Ps < 0.05). Spearman rank correlation tests demonstrated a trend toward an increase in GTV obtained on previous sequences with increasing N stage (r = 0.578 to 0.591, all Ps < 0.001). Mann-Whitney U tests showed GTV obtained on previous sequences could distinguish grouped N stages (all Ps < 0.05). Receiver operating curve analyses demonstrated that GTV obtained on FS-T2WI and DWI with b-value of 500 s/mm2 and DWI with b-value of 800 s/mm2 might differentiate stage N0 from stages N1-3 (cutoff, 19.70 cm3, 16.70 cm3 and 12.24 cm3, respectively), stages N0-1 from N2-3 (cutoff: 22.16 cm3, 17.54 cm3 and 14.17 cm3, respectively), stages N0-2 from N3 (cutoff: 25.57 cm3, 29.27 cm3 and 22.73 cm3, respectively). CONCLUSION: There is a trend toward an increase in GTV obtained on FS-T2WI and DWI sequences with increasing N stage.

Magnetic Resonance Diffusion Kurtosis Imaging for Evaluating Stage of Liver Fibrosis in a Rabbit Model.

RATIONALE AND OBJECTIVES: As an extension of the conventional diffusion weighted imaging, diffusion kurtosis imaging (DKI) is based on the non-Gaussian diffusion model that can inherently account for restricted water diffusion within the complex microstructure of most tissues. This study aimed to investigate association of liver DKI derived parameter with stage of liver fibrosis. MATERIALS AND METHODS: Fifty-six healthy New Zealand white rabbits were enrolled into this study, among which 48 rabbits were randomly given carbon tetrachloride to model liver fibrosis, and 8 rabbits treated with normal saline served as control subjects. All rabbits underwent liver DKI followed by biopsy to stage fibrosis (stages F0-F4) on 6th, 8th, 10th, and 12th weekends after initiation of modeling fibrosis. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusion (MD) were derived from DKI data. Statistical analysis was to evaluate association of DKI derived parameter with stage of fibrosis. RESULTS: FA (r = 0.512) and MK (r = 0.567) increased, and MD (r = -0.574) decreased with increasing stage of fibrosis from F0 to F4 (all p values < 0.05). Significant differences were found in all parameters between F0 and F3 or F4, F1 and F4, F0 and F1-4, and F0-1 and F2-4 (all p values < 0.05). FA and MD could distinguish between F0 from F2, MD, and MK could distinguish F1 from F3, F0-2 from F3-4, and F1-2 from F3-4, and MK and FA could distinguish F2 from F4, and F0-3 from F4 (all p values < 0.05). According to receiver operating characteristic analysis, MK could best predict stage ≥F1, ≥F2, ≥F3, and F4, and discriminate F1-2 from F3-4 with areas under receiver operating characteristic curve of 0.766-0.930. CONCLUSION: DKI derived parameters can help stage fibrosis.

Tumour volume of resectable oesophageal squamous cell carcinoma measured with MRI correlates well with T category and lymphatic metastasis.

OBJECTIVES: To determine association of gross tumour volume (GTV) of resectable oesophageal squamous cell carcinoma (SCC) measured on T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI) and diffusion-weighted imaging (DWI) with T category and lymphatic metastasis (LM). METHODS: Sixty oesophageal SCC patients underwent fat-suppressed T2WI, CE-T1WI and DWI with b values of 0, 500 and 800 s/mm2. GTV was measured on three sequences. Statistical analyses were performed to determine association of GTV with T category and LM. RESULTS: Spearman's rank correlation analysis showed positive association of GTV with T category and LM (all p values < 0.01). Differences in GTV were found between T1 and T2 or T3 categories shown by Kruskal-Wallis H and one-way ANOVA tests, and between T1/T2 and T3 and between tumours with and without LM by Mann-Whitney U tests (all p values < 0.05). Receiver operating characteristic analyses showed cut-off GTVs of 5.795, 5.276 and 10.11 cm3 on CE-T1WI could better differentiate T1 from T2 categories, T1 from T3, and T1-2 from T3 than those of 7.066, 7.045 and 8.504 cm3 on T2WI, of 5.793, 6.609 and 6.989 cm3 on DWI with b value of 500 s/mm2, and of 4.156, 4.519 and 4.985 cm3 with b value of 800 s/mm2, respectively. Cut-off of 10.462 cm3 on DWI with b value of 500 s/mm2 could better identify LM than of 12.38, 8.793 and 9.600 cm3 on T2WI, CE-T1WI and DWI with b value of 800 s/mm2, respectively. CONCLUSIONS: GTVs on T2WI, CE-T1WI and DWI are associated with T category of and LM of oesophageal SCC. KEY POINTS: • GTV is associated with T category and lymphatic metastasis of oesophageal SCC • GTV measured on contrast-enhanced T 1 -weighted imaging better identifies T category • GTV measured on DWI with b value of 500 s/mm 2 better identifies lymphatic metastasis.