A novel model based on liver/spleen volumes and portal vein diameter on MRI to predict variceal bleeding in HBV cirrhosis.

OBJECTIVE: To develop a novel logistic regression model based on liver/spleen volumes and portal vein diameter measured on magnetic resonance imaging (MRI) for predicting oesophagogastric variceal bleeding (OVB) secondary to HBV cirrhosis. METHODS: One hundred eighty-five consecutive cirrhotic patients with hepatitis B undergoing abdominal contrast-enhanced MRI were randomly divided into training cohort (n = 130) and validation cohort (n = 55). Spleen volume, total liver volume, four liver lobe volumes, and diameters of portal venous system were measured on MRI. Ratios of spleen volume to total liver and to individual liver lobe volumes were calculated. In training cohort, univariate analyses and binary logistic regression analyses were to determine independent predictors. Performance of the model for predicting OVB constructed based on independent predictors from training cohort was evaluated by receiver operating characteristic (ROC) analysis, and was validated by Kappa test in validation cohort. RESULTS: OVB occurred in 42 and 18 individuals in training and validation cohorts during the 2 years' follow-up, respectively. An OVB prediction model was constructed based on the independent predictors including right liver lobe volume (RV), left gastric vein diameter (LGVD) and portal vein diameter (PVD) (odds ratio = 0.993, 2.202 and 1.613, respectively; p-values < 0.001 for all). The logistic regression model equation (-0.007 × RV + 0.79 × LGVD + 0.478 × PVD-6.73) for predicting OVB obtained excellent performance with an area under ROC curve of 0.907. The excellent performance was confirmed by Kappa test with K-value of 0.802 in validation cohort. CONCLUSION: The novel logistic regression model can be reliable for predicting OVB. KEY POINTS: • Patients with oesophagogastric variceal bleeding are mainly characterized by decreased right lobe volume, and increased spleen volume and diameters of portal vein system. • The right liver lobe volume, left gastric vein diameter and portal vein diameter are the independent predictors of oesophagogastric variceal bleeding. • The novel model developed based on the independent predictors performed well in predicting oesophagogastric variceal bleeding with an area under the receiver operating characteristic curve of 0.907.

Forensic age prediction and age classification for critical age thresholds via 3.0T magnetic resonance imaging of the knee in the Chinese Han population.

OBJECTIVES: To assess the performance of knee MRI for forensic age prediction and classification for 12-, 14-, 16-, and 18-year thresholds. METHODS: The ossification stages of distal femoral epiphyses and proximal tibial epiphyses were assessed using an integrated staging system by Schmeling et al. and Kellinghaus et al. for knee 3.0T MRI with T1-weighted turbo spin-echo (T1-TSE) in sagittal orientation among 852 Chinese Han individuals (483 males and 369 females) aged 7-30 years. Regression models for age prediction were constructed and their performances were evaluated based on mean absolute deviation (MAD) values. In addition, the performances of age classification were assessed using receiver operating characteristic (ROC) analyses. RESULTS: The intra- and inter-observer agreement levels were very good (κ > 0.80). The complete fusion of those two types of epiphyses took place before 18.0 years in our study participants. The minimum MAD values were 2.51 years (distal femur) and 2.69 years (proximal tibia) in males, and 2.75 years (distal femur) and 2.87 years (proximal tibia) in females. The specificity values of constructed prediction models were all above 90% for the 12-, 14-, and 16-year thresholds, compared to the 74.8-84.6% for the 18-year threshold. Better performances of age prediction and classification were observed in males by distal femoral epiphyses. CONCLUSIONS: Ossification stages via 3.0T MRI of the knee with T1-TSE sequence using an integrated staging system could be a reliable noninvasive method for age prediction or for age classification for 12-, 14-, and 16-year thresholds, especially in males by distal femoral epiphyses. However, assessments based on the full bony fusion of the distal femoral epiphysis and proximal tibial epiphysis seemed not reliable for age classification for the 18-year threshold in the Chinese Han population.

Radiomics model of contrast-enhanced MRI for early prediction of acute pancreatitis severity.

BACKGROUND: Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity. PURPOSE: To develop a contrast-enhanced (CE) MRI-based radiomics model for the early prediction of AP severity. STUDY TYPE: Retrospective. SUBJECTS: A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe). FIELD STRENGTH/SEQUENCE: 3.0T, T1 -weighted CE-MRI. ASSESSMENT: Radiomics features were extracted from the portal venous-phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP. STATISTICAL TESTS: Independent t-test, Mann-Whitney U-test, chi-square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test. RESULTS: Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI). DATA CONCLUSION: The radiomics model had good performance in the early prediction of AP severity. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:397-406.

Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis.

OBJECTIVES: To predict the recurrence of acute pancreatitis (AP) by constructing a radiomics model of contrast-enhanced computed tomography (CECT) at AP first attack. METHODS: We retrospectively enrolled 389 first-attack AP patients (271 in the primary cohort and 118 in the validation cohort) from three tertiary referral centers; 126 and 55 patients endured recurrent attacks in each cohort. Four hundred twelve radiomics features were extracted from arterial and venous phase CECT images, and clinical characteristics were gathered to develop a clinical model. An optimal radiomics signature was chosen using a multivariable logistic regression or support vector machine. The radiomics model was developed and validated by incorporating the optimal radiomics signature and clinical characteristics. The performance of the radiomics model was assessed based on its calibration and classification metrics. RESULTS: The optimal radiomics signature was developed based on a multivariable logistic regression with 10 radiomics features. The classification accuracy of the radiomics model well predicted the recurrence of AP for both the primary and validation cohorts (87.1% and 89.0%, respectively). The area under the receiver operating characteristic curve (AUC) of the radiomics model was significantly better than that of the clinical model for both the primary (0.941 vs. 0.712, p = 0.000) and validation (0.929 vs. 0.671, p = 0.000) cohorts. Good calibration was observed for all the models (p > 0.05). CONCLUSIONS: The radiomics model based on CECT performed well in predicting AP recurrence. As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to potential precautions. KEY POINTS: • The incidence of recurrence after an initial episode of acute pancreatitis is high, and quantitative methods for predicting recurrence are lacking. • The radiomics model based on contrast-enhanced computed tomography performed well in predicting the recurrence of acute pancreatitis. • As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to the potential need to take precautions.

MR imaging of hemorrhage associated with acute pancreatitis.

PURPOSE: To study MRI findings of hemorrhage in acute pancreatitis (AP) and correlate the presence and extent of hemorrhage with the MR severity index (MRSI), Acute Physiology And Chronic Health Evaluation (APACHE) II scores, and clinical outcome. MATERIALS AND METHODS: This retrospective study included 539 patients with AP. Hemorrhage was defined as areas of hyperintensity in or outside the pancreas on liver imaging with volume acceleration flexible (LAVA-Flex). The presence of hemorrhage was classified into three areas: within the pancreatic parenchyma, retroperitoneal space, and sub-or intraperitoneal space. Involvement of each area was awarded 1 point resulting in the hemorrhage severity index (HSI) score. The predicted severity of AP was graded by MRSI and APACHE II score. The association between HSI, MRSI, and APACHE II scores was analyzed. The length of hospital stay and organ dysfunction was used as clinical outcome parameters. RESULTS: Among 539 AP patients, 62 (11.5%) had hemorrhage. The prevalence of hemorrhage was 1.1% (2/186), 13.9% (43/310), and 39.5% (17/43) in predicted mild, moderate, and severe AP, respectively, based on MRSI (χ2 = 55.3, p = 0.00); and 7.7% (21/273) and 19.2% (18/94) in predicted mild and severe AP, respectively, based on APACHE II (χ2 = 21.2, p = 0.00). HSI score significantly correlated with MRSI (r = 0.36, p < 0.001) and APACHE II scores (r = 0.21, p = 0.00). The prevalence of organ dysfunction was higher and length of hospital stay was longer in patients with hemorrhage than in those without hemorrhage (p < 0.01). CONCLUSIONS: Hemorrhage in AP is common. The presence of hemorrhage, rather than its extent, correlates with poor clinical outcome.

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.

Thickness of soft tissue of lower extremities measured with magnetic resonance imaging as a new indicator for staging unilateral secondary lower extremity lymphedema.

BACKGROUND: Chronic progressive swelling of the lower extremity due to secondary lower extremity lymphedema (LEL) can affect a patient's quality of life, both physically and psychologically. A feasible and reproducible method for detecting and staging LEL will facilitate decision-making about appropriate management strategies. PURPOSE: To determine whether the thickness of the soft tissues of the lower extremities, measured with magnetic resonance imaging (MRI), could stage unilateral secondary LEL. MATERIAL AND METHODS: Seventy-two women with unilateral LEL and 22 participants without LEL underwent lower extremity MRI after treatment of uterine malignancies. LEL was classified clinically as stage 0, 1, 2, or 3. On fat-suppressed T2-weighted mid-axial images of calves and thighs, the total thickness of the soft tissue (TT), muscle thickness (MT), subcutaneous tissue thickness (STT), and the differences in TT (DTT), MT (DMT), and STT (DSTT) values and corresponding measurements in the contralateral lower extremity, were obtained and analyzed statistically for staging LEL. RESULTS: There was a trend for the TT and STT of the affected calf and thigh to increase with increasing LEL stage. These parameters were strongly and moderately correlated with LEL stage, respectively (P < 0.001). Both the DTT and DSTT of the calves or thighs were strongly correlated with LEL stage (P < 0.001). Among the parameters, the DSTT of the calves could best stage LEL, with an area under the receiver operating curve of more than 0.89. CONCLUSION: The DSTT of the calves could be recommended as an informative indicator for staging LEL.

Abdominal MRI at 3.0 T: LAVA-Flex compared with conventional fat suppression T1-weighted images.

PURPOSE: To study liver imaging with volume acceleration-flexible (LAVA-Flex) for abdominal magnetic resonance imaging (MRI) at 3.0 T and compare the image quality of abdominal organs between LAVA-Flex and fast spoiled gradient-recalled (FSPGR) T1-weighted imaging. MATERIALS AND METHODS: Our Institutional Review Board approval was obtained in this retrospective study. Sixty-nine subjects had both FSPGR and LAVA-Flex sequences. Two radiologists independently scored the acquisitions for image quality, fat suppression quality, and artifacts and the values obtained were compared with the Wilcoxon signed rank test. According to the signal intensity (SI) measurements, the uniformity of fat suppression, the contrast between muscle and fat and normal liver and liver lesions were compared by the paired t-test. The liver and spleen SI on the fat-only phase were analyzed in the fatty liver patients. RESULTS: Compared with FSPGR imaging, LAVA-Flex images had better and more homogenous fat suppression and lower susceptibility artifact (qualitative scores: 4.70 vs. 4.00, 4.86% vs. 7.14%, 4.60 and 4.10, respectively). The contrast between muscle and fat and between the liver and pathologic lesions was significantly improved on the LAVA-Flex sequence. The contrast value of the fatty liver and spleen was higher than that of the liver and spleen. CONCLUSION: The LAVA-Flex sequence offers superior and more homogenous fat suppression of the abdomen than does the FSPGR sequence. The fat-only phase can be a simple and effective method of assessing fatty liver.

Liver dynamic contrast-enhanced MRI for staging liver fibrosis in a piglet model.

PURPOSE: To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could monitor progression of liver fibrosis in a piglet model, and which DCE-MRI parameter is most accurate for staging this disease. MATERIALS AND METHODS: Sixteen piglets were prospectively used to model liver fibrosis and underwent liver DCE-MRI followed by biopsy on the 0, 5th, 9th, 16th, and 21st weekends after modeling of fibrosis. Time of peak (TOP), time to peak (TTP), positive enhancement integral (PEI), maximum slope of increase (MSI), and maximum slope of decrease (MSD) were measured and statistically analyzed for the monitoring and staging. RESULTS: As fibrosis progresses, TOP and TTP tended to increase, whereas MSI, MSD, and PEI tended to decrease (all P < 0.05). TOP, TTP, and MSI could discriminate fibrosis stage 0 from 1-4, 0-1 from 2-4, 0-2 from 3-4, and 0-3 from 4; PEI could distinguish the above-mentioned stages except 0-3 from 4; and MSD could distinguish stage 0-3 from 4, and 0 from 1-4 (all P < 0.05). For predicting stage ≥1, ≥2, and ≥3, the area under receiver operating characteristic curve (AUC) of MSI was largest among all parameters; for stage 4 AUC of TTP was largest. CONCLUSION: DCE-MRI has the potential to dynamically stage progression of liver fibrosis.

Diameters of left gastric vein and its originating vein on magnetic resonance imaging in liver cirrhosis patients with hepatitis B: Association with endoscopic grades of esophageal varices.

AIM: To determine whether diameters of the left gastric vein (LGV) and its originating vein are associated with endoscopic grades of esophageal varices. METHODS: Ninety-eight liver cirrhotic patients with hepatitis B undergoing magnetic resonance (MR) portography, and upper gastrointestinal endoscopy for grading esophageal varices were enrolled. Diameters of the LGV and its originating vein - the splenic vein (SV) or portal vein (PV) - were measured on MR imaging. Statistical analyses were performed to identify the association of the diameters with the endoscopic grades. RESULTS: Univariate analysis showed that the SV was predominantly the originating vein of the LGV, and diameters of the LGV and SV were associated with grades of esophageal varices. Diameters of the LGV (P = 0.023, odds ratio [OR] = 1.583) and SV (P = 0.012, OR = 2.126) were independent risk factors of presence of the varices. Cut-off LGV diameters of 5.1 mm, 5.9 mm, 6.6 mm, 7.1 mm, 7.8 mm and 5.8 mm; or cut-off SV diameters of 7.3 mm, 7.9 mm, 8.4 mm, 9.5 mm, 10.7 mm and 8.3 mm, could discriminate grades 0 from 1, 0 from 2, 0 from 3, 1 from 3, 2 from 3, and 0-1 from 2-3, respectively. CONCLUSION: Diameters of the LGV and SV are associated with endoscopic grades of esophageal varices.

Validity of soft-tissue thickness of calf measured using MRI for assessing unilateral lower extremity lymphoedema secondary to cervical and endometrial cancer treatments.

AIM: To determine whether soft-tissue thickness of the calf measured using MRI could be valid for assessing unilateral lower extremity lymphoedema (LEL) secondary to cervical and endometrial cancer treatments. MATERIALS AND METHODS: Seventy women with unilateral LEL and 25 without LEL after cervical or endometrial cancer treatments underwent MRI examinations of their calves. Total thickness of soft-tissue (TT), muscle thickness (MT), and subcutaneous tissue thickness (STT) of the calf, and the difference between the affected and contralateral unaffected calf regarding TT (DTT), MT (DMT), and STT (DSTT) were obtained using fat-suppressed T2-weighted imaging in the middle of the calves. The volume of the calf and difference in volume (DV) between calves were obtained by the method of water displacement. Statistical analysis was performed to determine the validity of MRI measurements by volume measurements in staging LEL. RESULTS: There was a close correlation between volume and TT for the affected (r = 0.927) or unaffected calves (r = 0.896). STT of the affected calf, and DTT or DSTT of the calves were closely correlated with volume of the affected calf or DV of the calves (all p < 0.05). Multivariate analysis showed significant differences in TT, STT, volume of the affected calf, DTT, DSTT, and DV between stages except in volume of the affected calf or in DV between stage 0 and 1. For staging LEL, DSTT showed the best discrimination ability among all the parameters. CONCLUSIONS: Soft-tissue thickness of the calf measured at MRI could be valid for quantitatively staging unilateral LEL, and DSTT of the calves could be the best classifying factor.

Patterns of lymph node recurrence after radical surgery impacting on survival of patients with pT1-3N0M0 thoracic esophageal squamous cell carcinoma.

The aim of this study was to investigate how patterns of lymph nodes recurrence after radical surgery impact on survival of patients with pT1-3N0M0 thoracic esophageal squamous cell carcinoma. One hundred eighty consecutive patients with thoracic esophageal squamous cell carcinoma underwent radical surgery, and the tumors were staged as pT1-3N0M0 by postoperative pathology. Lymph nodes recurrence was detected with computed tomography 3-120 months after the treatment. The patterns of lymph nodes recurrence including stations, fields and locations of recurrent lymph nodes, and impacts on patterns of survival were statistically analyzed. There was a decreasing trend of overall survival with increasing stations or fields of postoperative lymph nodes involved (all P<0.05). Univariate analysis showed that stations or fields of lymph nodes recurrence, and abdominal or cervical lymph nodes involved were prognostic factors for survival (all P<0.05). Cox analyses revealed that the field was an independent factor (P<0.05, odds ratio=2.73). Lymph nodes involved occurred predominantly in cervix and upper mediastinum (P<0.05). In conclusion, patterns of lymph node recurrence especially the fields of lymph nodes involved are significant prognostic factors for survival of patients with pT1-3N0M0 thoracic esophageal squamous cell carcinoma.

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.

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).

A computed tomography-based nomogram for neoadjuvant chemotherapy plus immunotherapy response prediction in patients with advanced esophageal squamous cell carcinoma.

Objective: To develop a CT-based nomogram to predict the response of advanced esophageal squamous cell carcinoma (ESCC) to neoadjuvant chemotherapy plus immunotherapy. Methods: In this retrospective study, 158 consecutive patients with advanced ESCC receiving contrast-enhanced CT before neoadjuvant chemotherapy plus immunotherapy were randomized to a training cohort (TC, n = 121) and a validation cohort (VC, n = 37). Response to treatment was assessed with response evaluation criteria in solid tumors. Patients in the TC were divided into the responder (n = 69) and non-responder (n = 52) groups. For the TC, univariate analyses were performed to confirm factors associated with response prediction, and binary analyses were performed to identify independent variables to develop a nomogram. In both the TC and VC, the nomogram performance was assessed by area under the receiver operating characteristic curve (AUC), calibration slope, and decision curve analysis (DCA). Results: In the TC, univariate analysis showed that cT stage, cN stage, gross tumor volume, gross volume of all enlarged lymph nodes, and tumor length were associated with the response (all P < 0.05). Binary analysis demonstrated that cT stage, cN stage, and tumor length were independent predictors. The independent factors were imported into the R software to construct a nomogram, showing the discriminatory ability with an AUC of 0.813 (95% confidence interval: 0.735-0.890), and the calibration curve and DCA showed that the predictive ability of the nomogram was in good agreement with the actual observation. Conclusion: This study provides an accurate nomogram to predict the response of advanced ESCC to neoadjuvant chemotherapy plus immunotherapy.

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.

Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma.

BACKGROUND: Esophagectomy is the main treatment for esophageal squamous cell carcinoma (ESCC), and patients with histopathologically negative margins still have a relatively higher recurrence rate. Contrast-enhanced CT (CECT) radiomics might noninvasively obtain potential information about the internal heterogeneity of ESCC and its adjacent tissues. This study aimed to develop CECT radiomics models to preoperatively identify the differences between tumor and proximal tumor-adjacent and tumor-distant tissues in ESCC to potentially reduce tumor recurrence. METHODS: A total of 529 consecutive patients with ESCC from Centers A (n = 447) and B (n = 82) undergoing preoperative CECT were retrospectively enrolled in this study. Radiomics features of the tumor, proximal tumor-adjacent (PTA) and proximal tumor-distant (PTD) tissues were individually extracted by delineating the corresponding region of interest (ROI) on CECT and applying the 3D-Slicer radiomics module. Patients with pairwise tissues (ESCC vs. PTA, ESCC vs. PTD, and PTA vs. PTD) from Center A were randomly assigned to the training cohort (TC, n = 313) and internal validation cohort (IVC, n = 134). Univariate analysis and the least absolute shrinkage and selection operator were used to select the core radiomics features, and logistic regression was performed to develop radiomics models to differentiate individual pairwise tissues in TC, validated in IVC and the external validation cohort (EVC) from Center B. Diagnostic performance was assessed using area under the receiver operating characteristics curve (AUC) and accuracy. RESULTS: With the chosen 20, 19 and 5 core radiomics features in TC, 3 individual radiomics models were developed, which exhibited excellent ability to differentiate the tumor from PTA tissue (AUC: 0.965; accuracy: 0.965), the tumor from PTD tissue (AUC: 0.991; accuracy: 0.958), and PTA from PTD tissue (AUC: 0.870; accuracy: 0.848), respectively. In IVC and EVC, the models also showed good performance in differentiating the tumor from PTA tissue (AUCs: 0.956 and 0.962; accuracy: 0.956 and 0.937), the tumor from PTD tissue (AUCs: 0.990 and 0.974; accuracy: 0.952 and 0.970), and PTA from PTD tissue (AUCs: 0.806 and 0.786; accuracy: 0.760 and 0.786), respectively. CONCLUSION: CECT radiomics models could differentiate the tumor from PTA tissue, the tumor from PTD tissue, and PTA from PTD tissue in ESCC.

Computed tomography-based nomogram of Siewert type II/III adenocarcinoma of esophagogastric junction to predict response to docetaxel, oxaliplatin and S-1.

BACKGROUND: Neoadjuvant chemotherapy (NAC) has become the standard care for advanced adenocarcinoma of esophagogastric junction (AEG), although a part of the patients cannot benefit from NAC. There are no models based on baseline computed tomography (CT) to predict response of Siewert type II or III AEG to NAC with docetaxel, oxaliplatin and S-1 (DOS). AIM: To develop a CT-based nomogram to predict response of Siewert type II/III AEG to NAC with DOS. METHODS: One hundred and twenty-eight consecutive patients with confirmed Siewert type II/III AEG underwent CT before and after three cycles of NAC with DOS, and were randomly and consecutively assigned to the training cohort (TC) (n = 94) and the validation cohort (VC) (n = 34). Therapeutic effect was assessed by disease-control rate and progressive disease according to the Response Evaluation Criteria in Solid Tumors (version 1.1) criteria. Possible prognostic factors associated with responses after DOS treatment including Siewert classification, gross tumor volume (GTV), and cT and cN stages were evaluated using pretherapeutic CT data in addition to sex and age. Univariate and multivariate analyses of CT and clinical features in the TC were performed to determine independent factors associated with response to DOS. A nomogram was established based on independent factors to predict the response. The predictive performance of the nomogram was evaluated by Concordance index (C-index), calibration and receiver operating characteristics curve in the TC and VC. RESULTS: Univariate analysis showed that Siewert type (52/55 vs 29/39, P = 0.005), pretherapeutic cT stage (57/62 vs 24/32, P = 0.028), GTV (47.3 ± 27.4 vs 73.2 ± 54.3, P = 0.040) were significantly associated with response to DOS in the TC. Multivariate analysis of the TC also showed that the pretherapeutic cT stage, GTV and Siewert type were independent predictive factors related to response to DOS (odds ratio = 4.631, 1.027 and 7.639, respectively; all P < 0.05). The nomogram developed with these independent factors showed an excellent performance to predict response to DOS in the TC and VC (C-index: 0.838 and 0.824), with area under the receiver operating characteristic curve of 0.838 and 0.824, respectively. The calibration curves showed that the practical and predicted response to DOS effectively coincided. CONCLUSION: A novel nomogram developed with pretherapeutic cT stage, GTV and Siewert type predicted the response of Siewert type II/III AEG to NAC with DOS.

Radiomics models of contrast-enhanced computed tomography for predicting the activity and prognosis of acute pancreatitis.

BACKGROUND: The modified pancreatitis activity scoring system (mPASS) was proposed to assess the activity of acute pancreatitis (AP) while it doesn't include indicators that directly reflect pathophysiology processes and imaging characteristics. OBJECTIVES: To determine the threshold of admission mPASS and investigate radiomics and laboratory parameters to construct a model to predict the activity of AP. METHODS: AP inpatients at institution 1 were randomly divided into training and validation groups based on a 5:5 ratio. AP inpatients at Institution 2 were served as test group. The cutoff value of admission mPASS scores in predicting severe AP was selected to divide patients into high and low level of disease activity group. LASSO was used in screening features. Multivariable logistic regression was used to develop radiomics model. Meaningful laboratory parameters were used to construct combined model. RESULTS: There were 234 (48 years ± 10, 155 men) and 101 (48 years ± 11, 69 men) patients in two institutions. The threshold of admission mPASS score was 112.5 in severe AP prediction. The AUC of the radiomics model was 0.79, 0.72, and 0.76 and that of the combined model incorporating rad-score and white blood cell were 0.84, 0.77, and 0.80 in three groups for activity prediction. The AUC of the combined model in predicting disease without remission was 0.74. CONCLUSIONS: The threshold of admission mPASS was 112.5 in predicting severe AP. The model based on CECT radiomics has the ability to predict AP activity. Its ability to predict disease without remission is comparable to mPASS. CRITICAL RELEVANCE STATEMENT: This work is the first attempt to assess the activity of acute pancreatitis using contrast-enhanced CT radiomics and laboratory parameters. The model provides a new method to predict the activity and prognosis of AP, which could contribute to further management. KEY POINTS: Radiomics features and laboratory parameters are associated with the activity of acute pancreatitis. The combined model provides a new method to predict the activity and prognosis of AP. The ability of the combined model is comparable to the modified Pancreatitis Activity Scoring System.

Tumor volume of resectable adenocarcinoma of the esophagogastric junction at multidetector CT: association with regional lymph node metastasis and N stage.

PURPOSE: To determine whether the volume of resectable adenocarcinoma of the esophagogastric junction (AEG) measured at multidetector computed tomography (CT) is associated 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 from each participant. Two hundred sixteen patients with resectable AEG prospectively underwent contrast material-enhanced thoracoabdominal multidetector CT less than 2 weeks before curative resection. Gross tumor volume was retrospectively measured on CT scans. Univariate and multivariate analyses were performed to identify whether gross tumor volume is associated with regional lymph node metastasis. The Mann-Whitney U test was performed to compare gross tumor volume among N stages, with Bonferroni correction for multigroup comparisons. Receiver operating characteristic analysis was performed to determine if gross tumor volume could help classify N stage. RESULTS: Univariate analysis showed that gross tumor volume is associated with regional lymph node metastasis (P < .0001). Multivariate analysis revealed that gross tumor volume is an independent risk factor of lymph node metastasis (P = .023, odds ratio = 2.791). The Mann-Whitney U test showed that gross tumor volume could help differentiate between stage N0 and stages N1-N2 or N1-N3 disease and between stages N1-N2 and stage N3 disease (P < .0001 for all). In patients with stage T1-T3 AEG, gross tumor volume could help differentiate between stage N0 and stages N1-N2 (cutoff, 15.23 cm(3)) or N1-N3 (cutoff, 17.16 cm(3)) disease and between stages N1-N2 and stage N3 disease (cutoff, 33.96 cm(3)). In patients with stage T3 AEG, gross tumor volume could help differentiate stage N0 from stages N1-N2 (cutoff, 18.41 cm(3)) or N1-N3 (cutoff, 19.30 cm(3)) disease and stages N1-N2 from stage N3 disease (cutoff, 33.96 cm(3)). CONCLUSION: Gross tumor volume of AEG measured with multidetector CT is associated with regional lymph node metastasis and N stage.