Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis.

BACKGROUND AND AIMS: The recurrence and metastasis of hepatocellular carcinoma (HCC) are mainly caused by microvascular invasion (MVI). Our study aimed to uncover the cellular atlas of MVI+ HCC and investigate the underlying immune infiltration patterns with radiomics features. METHODS: Three MVI positive HCC and three MVI negative HCC samples were collected for single-cell RNA-seq analysis. 26 MVI positive HCC and 30 MVI negative HCC tissues were underwent bulk RNA-seq analysis. For radiomics analysis, radiomics features score (Radscore) were built using preoperative contrast MRI for MVI prediction and overall survival prediction. We deciphered the metabolism profiles of MVI+ HCC using scMetabolism and scFEA. The correlation of Radscore with the level of APOE+ macrophages and iCAFs was identified. Whole Exome Sequencing (WES) was applied to distinguish intrahepatic metastasis (IM) and multicentric occurrence (MO). Transcriptome profiles were compared between IM and MO. RESULTS: Elevated levels of APOE+ macrophages and iCAFs were detected in MVI+ HCC. There was a strong correlation between the infiltration of APOE+ macrophages and iCAFs, as confirmed by immunofluorescent staining. MVI positive tumors exhibited increased lipid metabolism, which was attributed to the increased presence of APOE+ macrophages. APOE+ macrophages and iCAFs were also found in high levels in IM, as opposed to MO. The difference of infiltration level and Radscore between two nodules in IM was relatively small. Furthermore, we developed Radscore for predicting MVI and HCC prognostication that were also able to predict the level of infiltration of APOE+ macrophages and iCAFs. CONCLUSION: This study demonstrated the interactions of cell subpopulations and distinct metabolism profiles in MVI+ HCC. Besides, MVI prediction Radscore and MVI prognostic Radscore were highly correlated with the infiltration of APOE+ macrophages and iCAFs, which helped to understand the biological significance of radiomics and optimize treatment strategy for MVI+ HCC.

Layer-specific strain in patients with cardiac amyloidosis using tissue tracking MR.

Background: Cardiac infiltration is the major predictor of poor prognosis in patients with systemic amyloidosis, thus it becomes of great importance to evaluate cardiac involvement. Purpose: We aimed to evaluate left ventricular myocardial deformation alteration in patients with cardiac amyloidosis (CA) using layer-specific tissue tracking MR. Material and Methods: Thirty-nine patients with CA were enrolled. Thirty-nine normal controls were also recruited. Layer-specific tissue tracking analysis was done based on cine MR images. Results: Compared with the control group, a significant reduction in LV whole layer strain values (GLS, GCS, and GRS) and layer-specific strain values was found in patients with CA (all P < 0.01). In addition, GRS and GLS, as well as subendocardial and subepicardial GLS, GRS, and GCS, were all diminished in patients with CA and reduced LVEF, when compared to those with preserved or mid-range LVEF (all P < 0.05). GCS showed the largest AUC (0.9952, P = 0.0001) with a sensitivity of 93.1% and specificity of 90% to predict reduced LVEF (<40%). Moreover, GCS was the only independent predictor of LV systolic dysfunction (Odds Ratio: 3.30, 95% CI:1.341-8.12, and P = 0.009). Conclusion: Layer-specific tissue tracking MR could be a useful method to assess left ventricular myocardial deformation in patients with CA.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.

Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

Sclerosing angiomatoid nodular transformation of the spleen: multimodality imaging features and literature review.

OBJECTIVE: The purpose of this study was to evaluate the CT and MRI findings, clinicopathologic features, and differential diagnosis of Sclerosing angiomatoid nodular transformation (SANT). METHODS AND MATERIALS: Seven men and seven women with pathological diagnoses of SANT were included in this retrospect study. Patients underwent at least one radiological examination before surgery. The number, shape, margin, size, attenuation, signal intensity, homogeneity, and enhancing pattern of the lesion were evaluated by two abdominal radiologists independently. Immunohistochemistry reports were available for 11 patients. The immunoreactivity to the vascular markers CD8, CD31, and CD34 was assessed. RESULTS: The 14 SANT patients (7 men, 7 women; mean age, 43.5 years; age range, 24-56 years) presented with a single lesion and showed no specific clinical symptoms. Among 14 patients, 12 patients underwent MR scan, 5 patients underwent CT scan and 3 patients underwent PET-CT. On CT, all 5 lesions showed hypodensity on non-contrast images and spoke-wheel enhancing pattern after contrast administration, and calcification was observed. On T2WI, 10 cases(83.3%)showed hypointensity and 2 cases (16.7%) showed hyperintensity with central hypointensity. On T1WI, 10 cases (83.3%) were isointense and 2 cases (16.7%) were slightly hypointense. 10 cases (83.3%) showed hypointensity on DWI and 2 cases (16.7%) showed slightly hyperintensity on DWI. After contrast administration, all 12 lesions showed progressive enhancement. 18 F-fluorodeoxyglucose (FDG) uptake in the tumor was seen in all three cases that underwent PET-CT. The maximum standardized uptake value (SUVmax) was 4.5, 5.1, and 3.8 respectively. RESULTS: Apart from the progressive spoke-wheel enhancing pattern, DWI and ADC findings will add value to the diagnosis of SANT.

Prognostic value of LI-RADS category on MRI in patients with primary hepatic lymphoepithelioma-like carcinoma.

OBJECTIVES: To compare the clinical and MRI features of primary hepatic lymphoepithelioma-like carcinoma (LELC) categorized as LR-M or LR-4/5 using the Liver Imaging Reporting and Data System (LI-RADS) version 2018 and to determine the prognostic factors for recurrence-free survival (RFS). METHODS: In this retrospective study, 37 patients with surgically confirmed LELC were included. Two independent observers evaluated preoperative MRI features according to the LI-RADS version 2018. Clinical and imaging features were compared between two groups. RFS and the associated factors were evaluated using Cox proportional hazards regression analysis, Kaplan-Meier analysis, and log-rank test. RESULTS: In total, 37 patients (mean age, 58.5 ± 10.3 years) were evaluated. Sixteen (43.2%) LELCs were categorized as LR-M and twenty-one (56.8%) LELCs were categorized as LR-4/5. In the multivariate analysis, the LR-M category was an independent factor for RFS (HR 7.908, 95% CI 1.170-53.437; p = 0.033). RFS rates were significantly lower in patients with LR-M LELCs than in patients with LR-4/5 LELCs (5-year RFS rate, 43.8% vs.85.7%; p = 0.002). CONCLUSION: The LI-RADS category was significantly associated with postsurgical prognosis of LELC, with tumor categorized as LR-M having a worse RFS than those categorized as LR-4/5. KEY POINTS: • Lymphoepithelioma-like carcinoma patients categorized as LR-M have worse recurrence-free survival than those categorized as LR-4/5. • MRI-based LI-RADS categorization was an independent factor for postoperative prognosis of primary hepatic lymphoepithelioma-like carcinoma.

Distinct radiological features of lymphoepithelioma-like intrahepatic cholangiocarcinoma: comparison with classical intrahepatic cholangiocarcinoma.

BACKGROUND: Lymphoepithelioma-like intrahepatic cholangiocarcinoma (LEICC) has been recently introduced as a genetically distinct of intrahepatic cholangiocarcinoma (ICC). We aimed to investigate whether LEICC has distinct radiological characteristics in comparison with classical ICC, and to determine MRI features that can be used to differentiate LEICC from classical ICC. METHODS: Five hundred and sixty-seven consecutive patients who underwent surgical resection or liver transplantation for ICC between 2014 and 2021 were retrospectively identified. Among them, 30 patients with LEICC (LEICC-cohort) and 116 with stage-matched classical ICC (control-cohort) were finally included. Pre-operative MRI data were compared between the two cohorts. Multivariable logistic regression analysis was performed to determine relevant imaging features suggesting the diagnosis of LEICC over classical ICC. RESULTS: LEICCs showed significantly higher frequencies of a non-rim arterial phase hyperenhancement (APHE), washout on post-arterial images and a smooth margin, as well as less frequencies of perilesional enhancement and liver capsular retraction when compared with classical ICCs (P < 0.05 for all). The multivariate analysis revealed that non-rim APHE (odds ratio, 10.863; 95% CI [3.295-35.821]; P < 0.001) and the absence of perilesional enhancement (odds ratio, 3.350; 95% CI [1.167-9.619]; P = 0.025) are significant independent imaging features that suggest the diagnosis of LEICCs over classical ICCs. CONCLUSIONS: Compared with classical ICCs, LEICCs do have distinct radiological characteristics. A smooth margin, non-rim APHE, washout on post-arterial images, absent perilesional enhancement and absent liver capsular retraction are useful MRI features that could help to differentiate LEICCs from classical ICCs.

Magnetic Resonance Imaging and Diffusion Weighted Imaging-Based Histogram in Predicting Mesenchymal Transition High-Grade Serous Ovarian Cancer.

RATIONALE AND OBJECTIVES: To investigate the value of magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) findings in predicting mesenchymal transition (MT) high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm2) before surgery, and were assigned to the MT HGSOC or non-MT HGSOC group according to histopathology results. Clinical characteristics and MRI features including DWI-based histogram metrics were assessed and compared between the two groups. Univariate and multivariate analyses were performed to identify the significant variables associated with MT HGSOC - these variables were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: A total of 81 consecutive patients were recruited for pelvic MRI before surgery, including 37 (45.7%) MT patients and 44 (54.3%) non-MT patients. At univariate analysis, the features significantly related to MT HGSOC were identified as absence of discrete primary ovarian mass, pouch of Douglas implants, ovarian mass size, tumor volume, mean, SD, median, and 95th percentile apparent diffusion coefficient (ADC) values (all p < 0.05). At multivariate analysis, the absence of discrete primary ovarian mass {odds ratio (OR): 46.477; p = 0.025}, mean ADC value ≤ 1.105 (OR: 1.023; p = 0.009), and median ADC value ≤ 1.038 (OR: 0.982; p = 0.034) were found to be independent risk factors associated with MT HGSOC. The combination of all independent criteria yielded the largest AUC of 0.82 with a sensitivity of 83.87% and specificity of 66.67%, superior to any of the single predictor alone (p ≤ 0.012). The predictive C-index nomogram performance of the combination was 0.82. CONCLUSION: The combination of absence of discrete primary ovarian mass, lower mean ADC value, and median ADC value may be helpful for preoperatively predicting MT HGSOC.

Prognostic value of preoperative CT features for disease-free survival in patients with primary gastric gastrointestinal stromal tumors after resection.

PURPOSE: Tumor size is an important prognostic factor without consideration of the necrotic and cystic components within tumor for patients with gastrointestinal stromal tumors (GISTs). We aimed to extract the enhancing viable component from the tumor using computed tomography (CT) post-processing software and evaluate the value of preoperative CT features for predicting the disease-free survival (DFS) after curative resection for patients with primary gastric GISTs. METHODS: 132 Patients with primary gastric GISTs who underwent preoperative contrast-enhanced CT and curative resection were retrospectively analyzed. We used a certain CT attenuation of 30 HU to extract the enhancing tissue component from the tumor. Enhancing tissue volume and other CT features were assessed on venous-phase images. We evaluated the value of preoperative CT features for predicting the DFS after surgery. Univariate and multivariate Cox regression analyses were performed to find the independent risk factor for predicting the DFS. RESULTS: Of the 132 patients, 68 were males and 64 were females, with a mean age of 61 years. The median follow-up duration was 60 months, and 28 patients experienced disease recurrence and distant metastasis during the follow-up period. Serosal invasion (p < 0.001; HR = 5.277) and enhancing tissue volume (p = 0.005; HR = 1.447) were the independent risk factors for predicting the DFS after curative resection for patients with primary gastric GISTs. CONCLUSION: Preoperative contrast-enhanced CT could be useful for predicting the DFS after the surgery of gastric GISTs, and serosal invasion and enhancing tissue volume were the independent risk factors.

MR quantitative 3D shape analysis helps to distinguish mucinous cystic neoplasm from serous oligocystic adenoma.

PURPOSE We aimed to assess the performance of quantitative 3D shape analysis in the differential diagno- sis of pancreatic serous oligocystic adenoma (SOA) and mucinous cystic neoplasm (MCN). METHODS Four hundred thirty-two patients diagnosed with serous cystic neoplasms (SCNs) or MCNs were retrospectively reviewed from August 2014 to July 2019 and finally 87 patients with MCNs (n = 45) and SOAs (n = 42) were included. Clinical data and magnetic resonance morphologic fea- tures with 3D shape analysis of lesions (shape sphericity, compacity, and volume) were recorded and compared between MCNs and SOAs according to the pathology. Univariable and multivari- able regression analyses were used to identify independent impact factors for differentiating MCN from SOA. RESULTS The age of MCN patients was younger than SOAs (43.02 ± 10.83 years vs. 52.78 ± 12.31 years; OR = 0.275; 95% CI: 0.098-0.768; P = .014). MCN has a higher female/male ratio than SOA (43/2 vs. 27/15; OR = 40.418; 95% CI: 2.704-604.171; P = .007) and was more often located in the distal of pancreas (OR = 31.403; 95% CI: 2.985-330.342; P = .004). Shape_Sphericity derived from 3D shape analysis was a significant independent factor in the multivariable analysis and the value of MCN was closer to 1 than SOA (OR = 35.153; 95% CI: 5.301-237.585; P < .001). Area under the receiver operating characteristic curve (AUC) of Shape_Sphericity was 0.923 (optimal cutoff value was 0.964876). CONCLUSION Shape_Sphericity in combination with age, sex, and location could help to distinguish MCN from SOA.

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.

Comprehensive analysis of tumor immune microenvironment and prognosis of m6A-related lncRNAs in gastric cancer.

BACKGROUND: N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approaches of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between m6A and lncRNAs enrolling in the shaping of TIME remains unclear. METHODS: The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering method was implemented to classify GC patients into two clusters. Survival analysis, the infiltration level of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. A competing endogenous RNA (ceRNA) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which m6A-related lncRNAs enriched. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal lncRNAs, and risk model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the response to immune checkpoint inhibitors (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect expression patterns of the selected lncRNAs in the 35 tumor tissues and their paired adjacent normal tissues, and validated the prognostic value of risk model in our cohort (N = 35). RESULTS: The expression profiles of 15 lncRNAs were included to cluster patients into 2 subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and lower mutation rates of the genes. Different immune cell infiltration patterns were also displayed between the two clusters. GSEA showed that cluster1 preferentially enriched in tumor hallmarks and tumor-related biological pathways. KEGG pathway analysis found that the target mRNAs which m6A-related lncRNAs regulated by sponging miRNAs mainly enriched in vascular smooth muscle contraction, cAMP signaling pathway and cGMP-PKG signaling pathway. Next, eight lncRNAs were selected by LASSO regression algorithm to construct risk model. Patients in the high-risk group had poor prognoses, which were consistent in our cohort. As for predicting responses to ICIs therapy, patients from high-risk group were found to have lower tumor mutation burden (TMB) scores and account for large proportion in the Microsatellite Instability-Low (MSI-L) subtype. Moreover, patients had distinct immunophenoscores in different risk groups. CONCLUSION: Our study revealed that the interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to ICIs therapy.

Risk stratification of LI-RADS M and LI-RADS 4/5 combined hepatocellular cholangiocarcinoma: prognostic values of MR imaging features and clinicopathological factors.

OBJECTIVES: To investigate the role of clinicopathological factors and MR imaging factors in risk stratification of combined hepatocellular cholangiocarcinoma (cHCC-CCA) patients who were classified as LR-M and LR-4/5. METHODS: We retrospectively identified consecutive patients who were confirmed as cHCC-CCA after surgical surgery in our institution from June 2015 to November 2020. Two radiologists evaluated the preoperative MR imaging features independently, and each lesion was assigned with a LI-RADS category. Preoperative clinical data were also collected. Multivariate Cox proportional hazards model was applied to separately identify the independent factors correlated with the recurrence of cHCC-CCAs in LR-M and LR-4/5. Risk stratifications were conducted separately in LR-M and LR-4/5. Recurrence-free survival (RFS) rates and overall survival (OS) rates were analyzed by using the Kaplan-Meier survival curves and log-rank test. RESULTS: A total of 131 patients with single primary lesion which met the 2019 WHO classification criteria were finally included. Corona enhancement, delayed central enhancement, and microvascular invasion (MVI) were identified as predictors of RFS in LR-M. Mosaic architecture, CA19-9, and MVI were independently associated with RFS in LR-4/5. Based on the number of these independent predictors, patients were stratified into favorable-outcome groups (LR-ML subgroup and LR-4/5L subgroup) and dismal-outcome groups (LR-MH subgroup and LR-4/5H subgroup). The corresponding median RFS for LR-ML, LR-MH, LR-5L, and LR-5H were 25.6 months, 8.2 months, 51.7 months, and 18.1 months. CONCLUSION: Our study explored the prognostic values of imaging and clinicopathological factors for LR-M and LR-4/5 cHCC-CCA patients, and different survival outcomes were observed among four subgroups after conducting risk stratifications. KEY POINTS: • Corona enhancement, delayed central enhancement, and MVI were identified as predictors of RFS in cHCC-CCAs which were classified into LR-M. Mosaic architecture, CA19-9, and MVI were independently associated with RFS in cHCC-CCAs which were classified into LR-4/5. • Based on the identified risk factors, LR-M and LR-4/5 cHCC-CCA patients could be stratified into two subgroups respectively, with significantly different RFS and OS. • cHCC-CCA patients from LR-M did not always have worse RFS and OS than those from LR-4/5 in some cases.

Microvascular invasion of small hepatocellular carcinoma can be preoperatively predicted by the 3D quantification of MRI.

OBJECTIVES: To explore the importance of three-dimensional (3D) quantitative analysis during gadoxetic acid-enhanced magnetic resonance imaging (MRI) of microvascular invasion (MVI) and early recurrence (< 2 years) after surgery of single hepatocellular carcinoma (HCC) ≤ 3 cm. METHODS: Two hundred fourteen patients with pathologically confirmed HCC (training cohort: n = 169; validation cohort: n = 45) were included retrospectively. The 3D quantitative parameters (volume, sphericity, and compacity) and conventional MRI features were analyzed. The significant predictors for MVI were identified using univariate and multivariate logistic regression analyses. Nomograms were constructed from the prediction model, and the relationship between the significant predictors and early recurrence rates was evaluated using the Kaplan-Meier method. RESULTS: Tumor sphericity (odds ratio [OR] = 0.000; p < 0.001), non-smooth tumor margin (OR = 3.353; p = 0.015), and peritumoral hypointensity on hepatobiliary phase (HBP) (OR = 14.067; p = 0.003) were independent significant factors for MVI. When these three factors were combined, the diagnostic specificity of the training and validation cohorts was 97.0 (128/132) and 87.9 (29/33), respectively. The nomogram based on the predictive model performed satisfactorily in the training (C-index: 0.885) and validation (C-index: 0.869) cohorts. Early recurrence rates of patients with two or three significant factors were significantly higher than those with none in the training (29.1% vs. 10.2%, p = 0.007) and validation (36.4% vs. 6.7%, p = 0.037) cohorts. CONCLUSIONS: Lower sphericity combined with non-smooth tumor margin and peritumoral hypointensity on HBP are potential predictive factors for MVI and associated with early recurrence after surgery of HCC ≤ 3 cm. KEY POINTS: • Lower sphericity, non-smooth tumor margin, and peritumoral hypointensity on HBP were important indicators of the occurrence of MVI in HCC. • The combinational model prepared from these findings satisfactorily predicted MVI, and the presence of these predictors was associated with an early recurrence rate after surgical resection in HCC patients. • This model could help clinicians in the preoperative management of small HCC ≤ 3 cm.

Combined arterial and delayed enhancement patterns of MRI assist in prognostic prediction for intrahepatic mass-forming cholangiocarcinoma (IMCC).

OBJECTIVES: This study valued MR delayed enhancement pattern in predicting postoperative prognosis of intrahepatic mass-forming cholangiocarcinoma (IMCC). METHODS: From 2011 to 2015, 231 patients of IMCC underwent DCE-MRI preoperatively. Enhancement patterns and MRI characteristics were evaluated. Recurrence and mortality data were compared among IMCCs with different enhancement patterns. Prognostic factor analysis was performed using preoperative and postoperative clinical-pathologic factors, as well as imaging findings. RESULTS: Fifty-six (24.2%), 142 (61.5%) and 33 (14.3%) tumors showed hypo, peripheral rim and diffuse hyper enhancement in AP. Fifty-six (24.2%), 81 (35.1%) and 94 (40.7%) tumors showed hypo, heterogeneous and uniform enhancement in DP. Patients with arterial diffuse hyper enhancement or delayed uniform enhancement IMCCs had lower preoperative CA19-9 levels, smaller tumor sizes and minor operations than the rest patients (p < 0.05) and they were less associated with lymph nodes metastasis, vascular invasion, necrosis or poor tumor differentiation (p < 0.05), therefore with higher overall and disease-free survival rates (p < 0.05). The combination of AP and DP increased the detection rate of patients with good prognosis in the arterial rim enhancement group. Multivariate analysis revealed the delayed enhancement pattern (hypo HR = 6.304/10.028 for DFS/OS; heterogenous HR = 4.579/4.972 for DFS/OS), multitude of lesions (HR = 1.6/1.5 for DFS/OS) and tumor sizes (HR = 1.6 for DFS) were independent prognostic factors. CONCLUSIONS: The uniform enhancement pattern in delayed MRI was an independent optimal prognostic factor for IMCCs and increased the detection rate of patients with good prognosis compared to the arterial diffuse hyper enhancement pattern.

Hepatobiliary phase images of gadoxetic acid-enhanced MRI may improve accuracy of predicting the size of hepatocellular carcinoma at pathology.

BACKGROUND: Gadoxetic acid-enhanced magnetic resonance imaging (MRI) has been widely used in clinical practice. However, scientific evidence is lacking for recommending a particular sequence for measuring tumor size. PURPOSE: To retrospectively compare the size of hepatocellular carcinoma (HCC) measured on different gadoxetic acid-enhanced MRI sequences using pathology as a reference. MATERIAL AND METHODS: A total of 217 patients with single HCC who underwent gadoxetic acid-enhanced MRI before surgery were included. The size of the HCC was measured by two abdominal radiologists independently on the following sequences: T1-weighted; T2-weighted; b-500 diffusion-weighted imaging (DWI); and arterial, portal venous, transitional, and hepatobiliary phases. Tumor size measured on MRI was compared with pathological size by using Pearson correlation coefficient, independent-sample t test, and Bland-Altman plot. Agreement between two readers was evaluated with intraclass correlation coefficient (ICC). RESULTS: Correlation between the MR images and pathology was high for both readers (0.899-0.955). Absolute error between MRI and pathologic assessment was lowest on hepatobiliary phase images for both readers (reader 1, 2.8±4.2 mm; reader 2, 3.2±3.4 mm) and highest on arterial phase images for reader 1 (4.9±4.4 mm) and DWI phase images for reader 2 (5.1±4.9 mm). Absolute errors were significantly different for hepatobiliary phase compared with other sequences for both readers (reader 1, P≤0.012; reader 2, P≤0.037). Inter-reader agreements for all sequence measurements were strong (0.971-0.997). CONCLUSION: The performance of gadoxetic acid-enhanced MRI sequences varied with HCC size, and the hepatobiliary phase may be optimal among these sequences.

A multidimensional nomogram combining imaging features and clinical factors to predict the invasiveness and metastasis of combined hepatocellular cholangiocarcinoma.

BACKGROUND: Combined hepatocellular cholangiocarcinoma (CHCC-CCA) is a rare type of primary liver cancer having aggressive behavior. Few studies have investigated the prognostic factors of CHCC-CCA. Therefore, this study aimed to establish a nomogram to evaluate the risk of microvascular invasion (MVI) and the presence of satellite nodules and lymph node metastasis (LNM), which are associated with prognosis. METHODS: One hundred and seventy-one patients pathologically diagnosed with CHCC-CCA were divided into a training set (n=116) and validation set (n=55). Logistic regression analysis was used to assess the relative value of clinical factors associated with the presence of MVI and satellite nodules. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish the imaging model of all outcomes, and to build clinical model of LNM. Nomograms were constructed by incorporating clinical risk factors and imaging features. The model performance was evaluated on the training and validation sets to determine its discrimination ability, calibration, and clinical utility. Kaplan Meier analysis and time dependent receiver operating characteristic (ROC) were displayed to evaluate the prognosis value of the predicted nomograms of MVI and satellite nodule. RESULTS: A nomogram comprising the platelet to lymphocyte ratio (PLR), albumin-to-alkaline phosphatase ratio (AAPR) and imaging model was established for the prediction of MVI. Carcinoembryonic antigen (CEA) level and size were combined with the imaging model to establish a nomogram for the prediction of the presence of satellite nodules. Favorable calibration and discrimination were observed in the training and validation sets for the MVI nomogram (C-indexes of 0.857 and 0.795), the nomogram for predicting satellite nodules (C-indexes of 0.919 and 0.883) and the LNM nomogram (C-indexes of 0.872 and 0.666). Decision curve analysis (DCA) further confirmed the clinical utility of the nomograms. The preoperatively predicted MVI and satellite nodules by the combined nomograms achieved satisfactory performance in recurrence-free survival (RFS) and overall survival (OS) prediction. CONCLUSIONS: The proposed nomograms incorporating clinical risk factors and imaging features achieved satisfactory performance for individualized preoperative predictions of MVI, the presence of satellite nodules, and LNM. The prediction models were demonstrated to be good indicator for predicting the prognosis of CHCC-CCA, facilitating treatment strategy optimization for patients with CHCC-CCA.

Analysis of m6A-Related lncRNAs for Prognosis Value and Response to Immune Checkpoint Inhibitors Therapy in Hepatocellular Carcinoma.

INTRODUCTION: N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in the progression of hepatocellular carcinoma (HCC). However, how their interaction is involved in the prognostic value of HCC and immune checkpoint inhibitors (ICIs) therapy remains unclear. METHODS: The RNA sequencing and clinical data of HCC patients were collected from TCGA database. The prognostic m6A-related lncRNAs were screened out with Pearson correlation test, univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. Patients with HCC were classified into 2 subtypes by consensus clustering. Survival analyses were performed to assess the prognostic value of different clusters and risk models. Potential tumor correlated biological pathways correlated with different clusters were explored through gene set enrichment analysis. We also identified the relationship of the risk model and clusters with response to immune checkpoint inhibitors (ICIs) therapy and tumor microenvironment (TME). Furthermore, the prognostic value of the 9 m6A-related lncRNAs was validated in the external cohort. Finally, the role of SNHG4 was explored by silencing and overexpression of SNHG4 through conducting proliferation, migration and invasion experiments. RESULTS: Patients from 2 clusters and different risk groups based on m6A-related lncRNAs had significantly different clinicopathological characteristics and overall survival outcomes. Tumor-correlated biological pathways were found to be correlated with Cluster 2 through GSEA. Moreover, we found that patients from different clusters and risk groups expressed higher levels of immune checkpoint genes and had distinct TME and different responses for ICIs therapy. Prognostic value of this risk model was further confirmed in the external cohort. Finally, consistent with the discovery, SNHG4 played an oncogenic role in vitro. CONCLUSION: Our study demonstrated that the 9 m6A-related lncRNA signature may serve as a novel predictor in the prognosis of HCC and optimize (ICIs) therapy. SNHG4 plays an oncogenic role in HCC.

Utility of preoperative computed tomography features in predicting the Ki-67 labeling index of gastric gastrointestinal stromal tumors.

PURPOSE: To evaluate the value of preoperative computed tomography (CT) features including morphologic and quantitative features for predicting the Ki-67 labeling index (Ki-67LI) of gastric gastrointestinal stromal tumors (GISTs). METHODS: We retrospectively included 167 patients with gastric GISTs who underwent preoperative contrast-enhanced CT. We assessed the morphologic features of preoperative CT images and the quantitative features including the maximum diameter of tumor, total tumor volume, mean total tumor CT value, necrosis volume, necrosis volume ratio, enhanced tissue volume, and mean CT value of enhanced tissue. Potential predictive parameters to distinguish the high-level Ki-67LI group (>4%, n = 125) from the low-level Ki-67LI group (≤4%, n = 42) were compared and subsequently determined in multivariable logistic regression analysis. RESULTS: Growth pattern (p = 0.036), shape (p = 0.000), maximum diameter (p = 0.018), total tumor volume (p = 0.021), mean total tumor CT value (p = 0.009), necrosis volume (p = 0.006), necrosis volume ratio (p = 0.000), enhanced tissue volume (p = 0.027), and mean CT value of enhanced tissue (p = 0.004) were significantly different between the two groups. Multivariate logistic regression analysis indicated that lobulated/irregular shape (odds ratio [OR] = 3.817; p = 0.000) and high necrosis volume ratio (OR = 1.935; p = 0.024) were independent factors of high-level Ki-67LI. CONCLUSIONS: Higher necrosis volume ratio in combination with lobulated/irregular shape could potentially predict high expression of Ki-67LI for gastric GISTs.

Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI.

OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hold (MBH) T2WI. METHODS: One hundred and fifty-two adult patients with suspected liver disease were prospectively enrolled. Two independent readers reviewed images acquired with conventional MBH-T2WI and SBH-T2WI at 3.0 T MR scanner. For image quality analyses, motion artifacts scores and boundary sharpness scores were compared using nonparametric Wilcoxon matched pairs tests between MBH-T2WI and SBH-T2WI. With the reference standard, 89 patients with 376 index lesions were included for lesion analyses. The lesion detection rates were compared by chi-square test, the lesion conspicuity scores and lesion-liver contrast ratio (CR) were compared using nonparametric Wilcoxon matched pairs tests between the two sequences. RESULTS: For both readers, motion artifacts scores of SBH-T2WI were significantly lower than MBH-T2WI (P < 0.001). Boundary sharpness scores of SBH-T2WI were significantly higher than MBH-T2WI (P < 0.001). The lesion detection rates for SBH-T2WI were significantly higher than MBH-T2WI (P < 0.001); the differences of lesion detection rates between the two sequences were statistically significant for small (≤ 10 mm) liver lesions (P < 0.001), while not significant for larger (> 10 mm) lesions (P > 0.05). Lesion conspicuity scores were significantly higher on SBH-T2WI than MBH-T2WI in the entire cohort as well as in both stratified subgroups of lesions ≤10 mm and > 10 mm (P < 0.001 for all). CRs for focal liver lesions were also significantly higher with SBH-T2WI (P < 0.001). CONCLUSION: The SBH-T2WI sequence with deep-learning based reconstruction showed promising performance as it provided significantly better image quality, lesion detectability, lesion conspicuity and contrast within a single breath-hold, compared with the conventional MBH-T2WI.