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Objectives To develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm. Methods Between March 2012 and September 2019, 356 patients with pathologically confirmed solitary HCC ≤ 5 cm who underwent preoperative gadoxetate disodium–enhanced MRI were retrospectively enrolled. MVI was graded as M0, M1, or M2 according to the number and distribution of invaded vessels. Radiomics features were extracted from DWI, arterial, portal venous, and hepatobiliary phase images in regions of the entire tumor, peritumoral area ≤ 10 mm, and randomly selected liver tissue. Multivariate analysis identified the independent predictors for MVI and RFS, with nomogram visualized the ultimately predictive models. Results Elevated alpha-fetoprotein, total bilirubin and radiomics values, peritumoral enhancement, and incomplete or absent capsule enhancement were independent risk factors for MVI. The AUCs of MVI nomogram reached 0.920 (95% CI: 0.861–0.979) using random forest and 0.879 (95% CI: 0.820–0.938) using logistic regression analysis in validation cohort ( n = 106). With the 5-year RFS rate of 68.4%, the median RFS of MVI-positive (M2 and M1) and MVI-negative (M0) patients were 30.5 (11.9 and 40.9) and > 96.9 months ( p < 0.001), respectively. Age, histologic MVI, alkaline phosphatase, and alanine aminotransferase independently predicted recurrence, yielding AUC of 0.654 (95% CI: 0.538–0.769, n = 99) in RFS validation cohort. Instead of histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest achieved comparable accuracy in MVI stratification and RFS prediction. Conclusions Preoperative radiomics-based nomogram using random forest is a potential biomarker of MVI and RFS prediction for solitary HCC ≤ 5 cm. Key Points • The radiomics score was the predominant independent predictor of MVI which was the primary independent risk factor for postoperative recurrence. • The radiomics-based nomogram using either random forest or logistic regression analysis has obtained the best preoperative prediction of MVI in HCC patients so far. • As an excellent substitute for the invasive histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest (MVI-RF) achieved comparable accuracy in MVI stratification and outcome, reinforcing the radiologic understanding of HCC angioinvasion and progression.

Main recommendations Primary investigation of polypoid lesions of the gallbladder should be with abdominal ultrasound. Routine use of other imaging modalities is not recommended presently, but further research is needed. In centres with appropriate expertise and resources, alternative imaging modalities (such as contrast-enhanced and endoscopic ultrasound) may be useful to aid decision-making in difficult cases. Strong recommendation, low–moderate quality evidence. Cholecystectomy is recommended in patients with polypoid lesions of the gallbladder measuring 10 mm or more, providing the patient is fit for, and accepts, surgery. Multidisciplinary discussion may be employed to assess perceived individual risk of malignancy. Strong recommendation, low-quality evidence. Cholecystectomy is suggested for patients with a polypoid lesion and symptoms potentially attributable to the gallbladder if no alternative cause for the patient’s symptoms is demonstrated and the patient is fit for, and accepts, surgery. The patient should be counselled regarding the benefit of cholecystectomy versus the risk of persistent symptoms. Strong recommendation, low-quality evidence. If the patient has a 6–9 mm polypoid lesion of the gallbladder and one or more risk factors for malignancy, cholecystectomy is recommended if the patient is fit for, and accepts, surgery. These risk factors are as follows: age more than 60 years, history of primary sclerosing cholangitis (PSC), Asian ethnicity, sessile polypoid lesion (including focal gallbladder wall thickening > 4 mm). Strong recommendation, low–moderate quality evidence. If the patient has either no risk factors for malignancy and a gallbladder polypoid lesion of 6–9 mm, or risk factors for malignancy and a gallbladder polypoid lesion 5 mm or less, follow-up ultrasound of the gallbladder is recommended at 6 months, 1 year and 2 years. Follow-up should be discontinued after 2 years in the absence of growth. Moderate strength recommendation, moderate-quality evidence. If the patient has no risk factors for malignancy, and a gallbladder polypoid lesion of 5 mm or less, follow-up is not required. Strong recommendation, moderate-quality evidence. If during follow-up the gallbladder polypoid lesion grows to 10 mm, then cholecystectomy is advised. If the polypoid lesion grows by 2 mm or more within the 2-year follow-up period, then the current size of the polypoid lesion should be considered along with patient risk factors. Multidisciplinary discussion may be employed to decide whether continuation of monitoring, or cholecystectomy, is necessary. Moderate strength recommendation, moderate-quality evidence. If during follow-up the gallbladder polypoid lesion disappears, then monitoring can be discontinued. Strong recommendation, moderate-quality evidence. Source and scope These guidelines are an update of the 2017 recommendations developed between the European Society of Gastrointestinal and Abdominal Radiology (ESGAR), European Association for Endoscopic Surgery and other Interventional Techniques (EAES), International Society of Digestive Surgery–European Federation (EFISDS) and European Society of Gastrointestinal Endoscopy (ESGE). A targeted literature search was performed to discover recent evidence concerning the management and follow-up of gallbladder polyps. The changes within these updated guidelines were formulated after consideration of the latest evidence by a group of international experts. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was adopted to define the strength of recommendations and the quality of evidence. Key Point • These recommendations update the 2017 European guidelines regarding the management and follow-up of gallbladder polyps.

Objectives The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for preoperative prediction of VETC and patient prognosis in hepatocellular cancer (HCC). Methods The study included 182 (training cohort: 128; validation cohort: 54) HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI. Volumes of interest including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase images, from which 1316 radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the useful features. Clinical, intratumoral, peritumoral, combined radiomics, and clinical radiomics models were established using machine learning algorithms. The Kaplan–Meier survival analysis was used to assess early recurrence and progression-free survival (PFS) in the VETC + and VETC- patients. Results In the validation cohort, the area under the curves (AUCs) of radiomics models were higher than that of the clinical model using random forest (all p  < 0.05). The peritumoral radiomics model (AUC = 0.972;95% confidence interval [CI]:0.887–0.998) had significantly higher AUC than intratumoral model (AUC = 0.919; 95% CI: 0.811–0.976) ( p  = 0.044). There were no significant differences in AUC between intratumoral or peritumoral radiomics model (PR) and combined radiomics model ( p  > 0.05). Early recurrence and PFS were significantly different between the PR-predicted VETC + and VETC- HCC patients ( p  < 0.05). PR-predicted VETC was independent predictor of early recurrence (hazard ratio [HR]: 2.08[1.31–3.28]; p  = 0.002) and PFS (HR: 1.95[1.20–3.17]; p  = 0.007). Conclusions The intratumoral or peritumoral radiomics model may be useful in predicting VETC and patient prognosis preoperatively. The peritumoral radiomics model may yield an incremental value over intratumoral model. Key Points • Radiomics models are useful for predicting vessels encapsulating tumor clusters (VETC) and patient prognosis preoperatively. • Peritumoral radiomics model may yield an incremental value over intratumoral model in prediction of VETC. • Peritumoral radiomics-model-predicted VETC was an independent predictor of early recurrence and progression-free survival.

ObjectivesThis study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.MethodsFor this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.ResultsThe radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.ConclusionsOur radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.Key Points• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.• Prognosis of high-risk patients remains dismal after curative-intent resection.• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

Objectives We aimed to compare the therapeutic outcomes of radiofrequency ablation (RFA) and microwave ablation (MWA) as first-line therapies in patients with small single perivascular hepatocellular carcinoma (HCC). Methods A total of 144 eligible patients with small (≤ 3 cm) single perivascular (proximity to hepatic and portal veins) HCC who underwent RFA ( N = 70) or MWA ( N = 74) as first-line treatment were included. The overall survival (OS), disease-free survival (DFS), and local tumor progression (LTP) rates between the two ablation modalities were compared. The inverse probability of treatment weighting (IPTW) method was used to reduce selection bias. Subgroup analysis was performed according to the type of hepatic vessels. Results After a median follow-up time of 38.2 months, there were no significant differences in OS (5-year OS: RFA 77.7% vs. MWA 74.6%; p = 0.600) and DFS (5-year DFS: RFA 24.7% vs. MWA 40.4%; p = 0.570). However, a significantly higher LTP rate was observed in the RFA group than the MWA group (5-year LTP: RFA 24.3% vs. MWA 8.4%; p = 0.030). IPTW-adjusted analyses revealed similar results. The treatment modality (RFA vs. MWA: HR 7.861, 95% CI 1.642–37.635, p = 0.010) was an independent prognostic factor for LTP. We observed a significant interaction effect of ablation modality and type of peritumoral vessel on LTP ( p = 0.034). For patients with periportal HCC, the LTP rate was significantly higher in the RFA group than in the MWA group ( p = 0.045). However, this difference was not observed in patients with perivenous HCC ( p = 0.116). Conclusions In patients with a small single periportal HCC, MWA exhibited better tumor control than RFA. Key Points • Microwave ablation exhibited better local tumor control than radiofrequency ablation for small single periportal hepatocellular carcinoma. • There was a significant interaction between the treatment effect of ablation modality and type of peritumoral vessel on local tumor progression. • The type of peritumoral vessel is vital in choosing ablation modalities for hepatocellular carcinoma.

Objectives To systematically review published studies on the use of radiomics of the pancreas. Methods The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study. Results A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions ( n  = 19), classification of pancreatic diseases ( n  = 23), and prediction of prognosis or treatment response ( n  = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features ( r  = 0.52, p  < 0.001) and the study sample size ( r  = 0.34, p  = 0.003). Conclusions Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice. Key Points • Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.

Objectives To analyze the effect of preoperative body composition on survival in patients with pancreatic cancer following pancreaticoduodenectomy (PD). Methods Between October 2005 and August 2018, 116 patients (68 men, 48 women, mean age 66.2 ± 11.9 years) diagnosed with pancreatic adenocarcinoma following PD were retrospectively enrolled. The preoperative CT on vertebral level L3 was assessed for total abdominal muscle area (TAMA), visceral adipose tissue area (VAT), subcutaneous adipose tissue area (SAT), and mean skeletal muscle attenuation (SMD). The clinical data and pathological findings of tumors were collected. The impact of these factors on disease-free survival (DFS) and overall survival (OS) was evaluated by the Kaplan–Meier method and by univariable and multivariable Cox proportional hazards models. Results The 3-year DFS and OS rates were 8% and 25%, respectively. Of 116 patients, 20 (17.2%), 3 (2.6%), and 46 (39.7%) patients were classified as having sarcopenia, sarcopenic obesity, and myosteatosis, respectively. The VAT–TAMA ratio (1.2 ± 0.7 vs 0.9 ± 0.5, p = 0.01) and the visceral to subcutaneous adipose tissue area ratio (1.3 ± 0.7 vs 0.9 ± 0.5, p = 0.04) were higher in sarcopenic patients than in the nonsarcopenic group. Preoperative sarcopenia and sarcopenic obesity were associated with shorter OS ( p = 0.012 and p = 0.041, respectively), but not shorter DFS. Myosteatosis was neither associated with DFS nor OS. On multivariable analysis, sarcopenia was the only significant prognostic factor for OS ( p = 0.039). Conclusions Preoperative sarcopenia assessed by CT is a poor prognostic factor for OS in pancreatic cancer patients after PD. Key Points • Sarcopenia and sarcopenic obesity can be evaluated by abdominal CT on L3 level. • Patients with diabetes mellitus (DM) had lower sex-standardized subcutaneous adipose tissue area index and skeletal muscle density and higher visceral to subcutaneous adipose tissue area ratio than did those without DM. • Preoperative sarcopenia, sarcopenic obesity, and new-onset diabetes mellitus may predict poor overall survival in pancreatic cancer patients following pancreaticoduodenectomy.

Objective To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on MRI and CT radiomics features. Methods This retrospective study included 85 patients aged 32 to 86 years with 86 histopathology-proven liver cancers: 24 cHCC-CC, 24 CC, and 38 HCC who had MRI and CT between 2004 and 2018. Initial CT reports and morphological evaluation of MRI features were used to assess the performance of radiologists read. Following tumor segmentation, 1419 radiomics features were extracted using PyRadiomics library and reduced to 20 principle components by principal component analysis. Support vector machine classifier was utilized to evaluate MRI and CT radiomics features for the prediction of cHCC-CC vs. non-cHCC-CC and HCC vs. non-HCC. Histopathology was the reference standard for all tumors. Results Radiomics MRI features demonstrated the best performance for differentiation of cHCC-CC from non-cHCC-CC with the highest AUC of 0.77 (SD 0.19) while CT was of limited value. Contrast-enhanced MRI phases and pre-contrast and portal-phase CT showed excellent performance for the differentiation of HCC from non-HCC (AUC of 0.79 (SD 0.07) to 0.81 (SD 0.13) for MRI and AUC of 0.81 (SD 0.06) and 0.71 (SD 0.15) for CT phases, respectively). The misdiagnosis of cHCC-CC as HCC or CC using radiologists read was 69% for CT and 58% for MRI. Conclusions Our results demonstrate promising predictive performance of MRI and CT radiomics features using machine learning analysis for differentiation of cHCC-CC from HCC and CC with potential implications for treatment decisions. Key Points • Retrospective study demonstrated promising predictive performance of MRI radiomics features in the differentiation of cHCC-CC from HCC and CC and of CT radiomics features in the differentiation of HCC from cHCC-CC and CC. • With future validation, radiomics analysis has the potential to inform current clinical practice for the pre-operative diagnosis of cHCC-CC and to enable optimal treatment decisions regards liver resection and transplantation.

Objectives To investigate the association of sarcopenia, myosteatosis, and sarcopenic obesity with survival outcomes among patients who underwent immunotherapy for advanced hepatocellular carcinoma (HCC). Methods In this retrospective analysis, patients who initiated immunotherapy for advanced HCC were enrolled. Sarcopenia and myosteatosis were evaluated on pretreatment CT at L3 level by skeletal muscle index and mean muscle attenuation using predefined cutoff values. Sarcopenic obesity was defined as concurrent sarcopenia and body mass index > 25 kg/m 2 . The log-rank test and the Cox proportional hazards model were used to compare overall survival (OS) and progression-free survival (PFS). Results A total of 138 patients was included (discovery cohort n = 111, validation cohort n = 27). In the discovery cohort, patients with sarcopenia exhibited significantly poorer PFS ( p = 0.048) and OS ( p = 0.002) than patients without sarcopenia. Patients with myosteatosis exhibited significantly poorer PFS ( p < 0.001) and OS ( p < 0.001) than patients without myosteatosis. Patients with sarcopenic obesity compared to patients without sarcopenic obesity exhibited significantly poorer OS ( p = 0.006) but not PFS ( p = 0.31). In multivariate analysis adjusting for patient demographics, tumor extent, and liver function reserve, myosteatosis remained an independent predictor of poor PFS ( p = 0.014) and OS ( p = 0.007); sarcopenia remained an independent predictor for poor OS ( p = 0.007). The prediction models for survival outcomes built by the discovery cohort showed similar performance in the validation cohort. Conclusions Sarcopenia and myosteatosis are independent prognostic factors in patients who received immunotherapy for advanced HCC. Key Points • Sarcopenia and myosteatosis can be evaluated by CT at L3 level. • Sarcopenia, myosteatosis, and sarcopenic obesity were associated with poor survival outcomes in patients who underwent immunotherapy for advanced HCC. • Myosteatosis was an independent predictor of PFS and OS, and sarcopenia was independent for OS in these patients.

Objective To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images to preoperatively differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from mass-forming intrahepatic cholangiocarcinoma (IMCC). Methods A total of 151 training cohort patients (45 cHCC-CC and 106 IMCC) and 65 validation cohort patients (19 cHCC-CC and 46 IMCC) were enrolled. Findings of clinical characteristics and MR features were analyzed. Radiomics features were extracted from the DCE-MR images. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator algorithm. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical model. The radiomics signature and significant clinicoradiological variables were then incorporated into the radiomics nomogram by multivariate logistic regression analysis. Performance of the radiomics nomogram, radiomics signature, and clinical model was assessed by receiver operating characteristic and area under the curve (AUC) was compared. Results Eleven radiomics features were selected to develop the radiomics signature. The radiomics nomogram integrating the alpha fetoprotein, background liver disease (cirrhosis or chronic hepatitis), and radiomics signature showed favorable calibration and discrimination performance with an AUC value of 0.945 in training cohort and 0.897 in validation cohort. The AUCs for the radiomics signature and clinical model were 0.848 and 0.856 in training cohort and 0.792 and 0.809 in validation cohort, respectively. The radiomics nomogram outperformed both the radiomics signature and clinical model alone ( p < 0.05). Conclusion The radiomics nomogram based on DCE-MRI may provide an effective and noninvasive tool to differentiate cHCC-CC from IMCC, which could help guide treatment strategies. Key Points • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging is useful to preoperatively differentiate cHCC-CC from IMCC. • The radiomics nomogram showed the best performance in both training and validation cohorts for differentiating cHCC-CC from IMCC.