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Intrahepatic cholangiocarcinoma is a rare disease associated with a poor prognosis, primarily due to early recurrence and metastasis. An important feature of this condition is microvascular invasion (MVI). However, current predictive models based on imaging have limited efficacy in this regard. This study employed a random forest model to construct a predictive model for MVI identification and uncover its biological basis. Single-cell transcriptome sequencing, whole exome sequencing, and proteome sequencing were performed. The area under the curve of the prediction model in the validation set was 0.93. Further analysis indicated that MVI-associated tumor cells exhibited functional changes related to epithelial–mesenchymal transition and lipid metabolism due to alterations in the nuclear factor-kappa B and mitogen-activated protein kinase signaling pathways. Tumor cells were also differentially enriched for the interleukin-17 signaling pathway. There was less infiltration of SLC30A1+ CD8+ T cells expressing cytotoxic genes in MVI-associated intrahepatic cholangiocarcinoma, whereas there was more infiltration of myeloid cells with attenuated expression of the major histocompatibility complex II pathway. Additionally, MVI-associated intercellular communication was closely related to the SPP1–CD44 and ANXA1–FPR1 pathways. These findings resulted in a brilliant predictive model and fresh insights into MVI. [ABSTRACT FROM AUTHOR]

Background and Aim: There is a pressing need for non‐invasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study investigates the potential of exosome‐derived mRNA in plasma as a biomarker for diagnosing MVI. Methods: Patients with suspected HCC undergoing hepatectomy were prospectively recruited for preoperative peripheral blood collection. Exosomal RNA profiling was conducted using RNA sequencing in the discovery cohort, followed by differential expression analysis to identify candidate targets. We employed multiplexed droplet digital PCR technology to efficiently validate them in a larger sample size cohort. Results: A total of 131 HCC patients were ultimately enrolled, with 37 in the discovery cohort and 94 in the validation cohort. In the validation cohort, the expression levels of RSAD2, PRPSAP1, and HOXA2 were slightly elevated while CHMP4A showed a slight decrease in patients with MVI compared with those without MVI. These trends were consistent with the findings in the discovery cohort, although they did not reach statistical significance (P > 0.05). Notably, the expression level of exosomal PRPSAP1 in plasma was significantly higher in patients with more than 5 MVI than in those without MVI (0.147 vs 0.070, P = 0.035). Conclusion: This study unveils the potential of exosome‐derived PRPSAP1 in plasma as a promising indicator for predicting MVI status preoperatively. [ABSTRACT FROM AUTHOR]

Background: Postoperative recurrence is a vital reason for poor 5-year overall survival in hepatocellular carcinoma (HCC) patients. The ADV score is considered a parameter that can quantify HCC aggressiveness. This study aimed to identify HCC patients at high-risk of recurrence early using the ADV score. Methods: The medical data of consecutive HCC patients undergoing hepatectomy from The First Affiliated Hospital of Nanjing Medical University (TFAHNJMU) and Nanjing Drum Tower Hospital (NJDTH) were retrospectively reviewed. Based on the status of microvascular invasion and the Edmondson-Steiner grade, HCC patients were divided into three groups: low-risk group (group 1: no risk factor exists), medium-risk group (group 2: one risk factor exists), and high-risk group (group 3: coexistence of two risk factors). In the training cohort (TFAHNJMU), the R package nnet was used to establish a multi-categorical unordered logistic regression model based on the ADV score to predict three risk groups. The Welch's T-test was used to compare differences in clinical variables in three predicted risk groups. NJDTH served as an external validation center. At last, the confusion matrix was developed using the R package caret to evaluate the diagnostic performance of the model. Results: 350 and 405 patients from TFAHNJMU and NJDTH were included. HCC patients in different risk groups had significantly different liver function and inflammation levels. Density maps demonstrated that the ADV score could best differentiate between the three risk groups. The probability curve was plotted according to the predicted results of the multi-categorical unordered logistic regression model, and the best cut-off values of the ADV score were as follows: low-risk ≤ 3.4 log, 3.4 log < medium-risk ≤ 5.7 log, and high-risk > 5.7 log. The sensitivities of the ADV score predicting the high-risk group (group 3) were 70.2% (99/141) and 78.8% (63/80) in the training and external validation cohort, respectively. Conclusion: The ADV score might become a valuable marker for screening patients at high-risk of HCC recurrence with a cut-off value of 5.7 log, which might help surgeons, pathologists, and HCC patients make appropriate clinical decisions. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is the most frequent primary liver tumor globally and a leading cause of mortality in cirrhotic patients. Our study aimed to estimate the diagnostic performance of triphasic CT and inter-observer reliability in the preoperative detection of microvascular invasion (MVI) in HCC. Two independent radiologists accomplished a retrospective analysis for 99 patients with HCC to assess the CT features for MVI in each lesion. Postoperative histopathology was considered the gold standard. Results: Multivariate regression analysis revealed that incomplete or absent tumor capsules, presence of TTPV, and absence of hypodense halo were statistically significant independent predictors of MVI. There was excellent agreement among observers in evaluating peritumoral enhancement, identifying intratumoral arteries, hypodense halo, TTPV, and macrovascular invasion. Also, our results revealed moderate agreement in assessing the tumor margin and tumor capsule. Conclusion: Triphasic CT features of MVI are reliable imaging predictors that may be helpful for standard preoperative interpretation of HCC. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects. [ABSTRACT FROM AUTHOR]

Objective: To explore the application value of magnetic resonance imaging (MRI) multi-sequence scan in the diagnosis of hepatocellular carcinoma (HCC) and evaluation of microvascular invasion (MVI). Methods: A retrospective collection was performed on the clinical data of 100 patients with suspected HCC in the hospital between October 2019 and October 2022. All completed MRI multi-sequence scan and pathological examination. Taking pathological examination as the golden standard, the consistency between MRI multi-sequence scan and pathological examination in the diagnosis of HCC and evaluation of MVI was analyzed by Kappa test. Results: Among the 100 patients, pathological examination showed that there were 51 cases (51.00%) with HCC and 49 cases (49.00%) with benign liver lesions. In the 51 HCC patients, there were 17 cases (33.33%) with MVI. Taking pathological examination as the golden standard, sensitivity, specificity and Kappa values of T2WI in the diagnosis of HCC and MVI were (82.35%, 79.59%, 0.620) and (64.71%, 85.29%, 0.507), respectively. The sensitivity, specificity and Kappa values of TIWI in the diagnosis of HCC and MVI were (76.47%, 81.63%, 0.580) and (58.82%, 88.24%, 0.492), respectively. The sensitivity, specificity and Kappa values of LAVA in the diagnosis of HCC and MVI were (84.31%, 83.67%, 0.580) and (64.71%, 91.18%, 0.585), respectively. The sensitivity, specificity and Kappa values of MRI multi-sequence scan in the diagnosis of HCC and MVI were (96.08%, 79.59%, 0.759) and (94.12%, 82.35%, 0.712), respectively. The sensitivity of MRI multi-sequence scan was higher than that of single sequence (P<0.05). The proportions of unsmooth tumor edge and peritumoral enhancement in patients with MVI positive were higher than those with MVI negative (P<0.05). Conclusion: MRI multi-sequence scan is conductive to clinical diagnosis of HCC and evaluation of MVI. Unsmooth tumor edge and peritumoral enhancement have predictive significance in HCC and MVI. [ABSTRACT FROM AUTHOR]

Objective: To validate a risk score model to predict MVI using Total tumor volume (TTV) and AFP. Study Design: Cross-sectional. Setting: Department of Gastroenterology, Liaquat National Hospital, Karachi, Pakistan. Period: June 2022 to December, 2023. Methods: Patients of either gender, older than 18 years and above with confirmed diagnosis of HCC were enrolled. When there were several HCC tumors, the largest tumor’s axis was measured and used as the representative HCC diameter. When microscopic tumor invasion was detected in the portal, hepatic vein, or biliary vein of the surrounding liver tissue that was adjacent to the tumor, it was referred to as micro vascular invasion. Results: Total 302 patients were studied with mean age of 62.1 ± 9.2 years. Majority of patients were males (64.2%). Around one-fifth patients had more than one tumor (19.9%). Out of 302 patients, MVI was seen in 30.1% patients. In multivariable analysis number of tumors (aOR=220.65, 95% CI: 128.91-922.67), tumor volume (aOR=1.01, 95% CI: 1.01-1.02), and AFP (aOR=1.01, 95% CI: 1-1.02) were found to be significant predictors of MVI. Conclusion: This study validated a prediction model for forecasting of MVI in HCC patients with highly significant variables including number of tumors, tumor volume and alpha feto protein. Timely decision making could be done using this model. [ABSTRACT FROM AUTHOR]

This study aims to evaluate and compare the predictive accuracy of Sonazoid-contrast-enhanced ultrasound (CEUS) and Gd-EOB-DTPA-enhanced MRI for detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). In this single-center prospective study, we included 64 patients with histopathologically confirmed single HCC lesions. Based on post-operative pathologic data, patients were categorized into two groups: those with MVI (n = 21) and those without MVI (n = 43). The diagnostic efficacy of CEUS was compared with that of MRI in predicting MVI. Multifactorial analysis revealed that US features (tumor size > 4.35 cm, peritumoral enhancement, post-vascular ring enhancement, peak energy in the arterial phase of the difference between the margin area of HCC and distal liver parenchyma <−1.0 × 106 a.u), MRI features (rim enhancement, irregular tumor margin, and the halo sign) were all independent predictors of MVI (p < 0.05). The sensitivity and specificity of CEUS features in predicting MVI ranged from 61.9% to 86.4% and from 42.9% to 71.4%, respectively. For MRI features, the sensitivity and specificity ranged from 33.3% to 76.3% and from 54.7% to 90.5%, respectively. No statistically significant differences were observed in the area under the curve between CEUS and MRI (p > 0.05). Notably, peak energy of the difference showed the highest sensitivity at 86.4%, while the halo sign in MRI exhibited the highest specificity at 90.5%. Sonazoid-CEUS and Gd-EOB-DTPA-enhanced MRI demonstrate potential in predicting MVI in HCC lesions. Notably, CEUS showed higher sensitivity, whereas MRI displayed greater specificity in predicting MVI. [ABSTRACT FROM AUTHOR]

Background and Objectives: Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients. Materials and Methods: A total of 489 patients with a pathological diagnosis of HCC were enrolled from our hospital. Those registered from 2012–2015 formed the derivation cohort, and those from 2016–2019 formed the validation cohort for pre-operative prediction of MVI. A nomogram model for prediction was created using a regression model, with risk factors derived from clinical and tumor-related features before surgery. Results: Using the nomogram model to predict the odds ratio of MVI before hepatectomy, the AFP, platelet count, GOT/GPT ratio, albumin–alkaline phosphatase ratio, ALBI score, and GNRI were identified as significant variables for predicting MVI. The Youden index scores for each risk variable were 0.287, 0.276, 0.196, 0.185, 0.115, and 0.112, respectively, for the AFP, platelet count, GOT/GPT ratio, AAR, ALBI, and GNRI. The maximum value of the total nomogram scores was 220. An increase in the number of nomogram points indicated a higher probability of MVI occurrence. The accuracy rates ranged from 55.9% to 64.4%, and precision rates ranged from 54.3% to 68.2%. Overall survival rates were 97.6%, 83.4%, and 73.9% for MVI(−) and 80.0%, 71.8%, and 41.2% for MVI(+) (p < 0.001). The prognostic effects of MVI(+) on tumor-free survival and overall survival were poor in both the derivation and validation cohorts. Conclusions: Our nomogram model, which integrates clinical factors, showed reliable calibration for predicting MVI and provides a useful tool enabling surgeons to estimate the probability of MVI before resection. Consequently, surgical strategies and post-operative care programs can be adapted to improve the prognosis of HCC patients where possible. [ABSTRACT FROM AUTHOR]

Objective To investigate the efficacy and safety of drug-eluting bead transarterial chemoembolization (DEB- TACE) as neoadjuvant therapy before liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) beyond Milan criteria with microvascular invasion (MVI) and its impact on the prognosis after LT. Methods From May 2016 to December 2022, 141 patients with HCC beyond Milan criteria underwent LT. A total of 55 patients with MVI were enrolled in the study and were divided into DEB-TACE before LT group (n=26) and control group (n=29) that did not receive any treatment before LT. The Kaplan-Meier method was utilized to estimate the overall survival (OS) rate and recurrence-free survival (RFS) rate after LT after potential prognostic factors for RFS and OS were identified. Results The objective response rate after DEB-TACE was 88%, and the main adverse event was embolization syndrome. The 1-, 2- and 3-year RFS rates in DEB-TACE group (88.5%, 79.2% and 73.9%) were higher than control group (69%, 50% and 44.4%). The 3-year RFS rate was significantly different between the two groups (χ²= 4.428, P=0.035). The 1-, 2- and 3-year OS rates in DEB-TACE group (100%, 91.7% and 82.6%) were not significantly (χ²=3.224, P=0.073) higher than control group (96.6%, 82.1% and 59.3%). Conclusion Preoperative DEB-TACE neoadjuvant therapy can improve the prognosis of LT in patients with HCC beyond Milan criteria with MVI. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive. Methods: In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA). Results: Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model. Conclusions: Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making. [ABSTRACT FROM AUTHOR]

Objectives: Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction. Methods: This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score. Results: Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively. Conclusions: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively. Advances in knowledge: Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study aimed to investigate the feasibility of using dual-layer spectral CT multi-parameter feature to predict microvascular invasion of hepatocellular carcinoma. METHODS: This retrospective study enrolled 50 HCC patients who underwent multiphase contrast-enhanced spectral CT studies preoperatively. Combined clinical data, radiological features with spectral CT quantitative parameter were constructed to predict MVI. ROC was applied to identify potential predictors of MVI. The CT values obtained by simulating the conventional CT scans with 70 keV images were compared with those obtained with 40 keV images. RESULTS: 50 hepatocellular carcinomas were detected with 30 lesions (Group A) with microvascular invasion and 20 (Group B) without. There were significant differences in AFP,tumer size, IC, NIC,slope and effective atomic number in AP and ICrr in VP between Group A ((1000(10.875,1000),4.360±0.3105, 1.7750 (1.5350,1.8825) mg/ml, 0.1785 (0.1621,0.2124), 2.0362±0.2108,8.0960±0.1043,0.2830±0.0777) and Group B (4.750(3.325,20.425),3.190±0.2979,1.4700 (1.4500,1.5775) mg/ml, 0.1441 (0.1373,0.1490),1.8601±0.1595, 7.8105±0.7830 and 0.2228±0.0612) (all p < 0.05). Using 0.1586 as the threshold for NIC, one could obtain an area-under-curve (AUC) of 0.875 in ROC to differentiate between tumours with and without microvascular invasion. AUC was 0.625 with CT value at 70 keV and improved to 0.843 at 40 keV. CONCLUSION: Dual-layer spectral CT provides additional quantitative parameters than conventional CT to enhance the differentiation between hepatocellular carcinoma with and without microvascular invasion. Especially, the normalized iodine concentration (NIC) in arterial phase has the greatest potential application value in determining whether microvascular invasion exists, and can offer an important reference for clinical treatment plan and prognosis assessment. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm. Methods: We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort. Results: In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively. Conclusions: Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy. [ABSTRACT FROM AUTHOR]

Objectives: To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). Methods: A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). Results: The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. Conclusion: Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. Clinical relevance statement: The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. Key Points: We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI. [ABSTRACT FROM AUTHOR]

Background: In this study, we aimed to establish nomograms to predict the microvascular invasion (MVI) and early recurrence in patients with small hepatocellular carcinoma (SHCC), thereby guiding individualized treatment strategies for prognosis improvement. Methods: This study retrospectively analyzed 326 SHCC patients who underwent radical resection at Wuhan Union Hospital between April 2017 and January 2022. They were randomly divided into a training set and a validation set at a 7:3 ratio. The preoperative nomogram for MVI was constructed based on univariate and multivariate logistic regression analysis, and the prognostic nomogram for early recurrence was constructed based on univariate and multivariate Cox regression analysis. We used the receiver operating characteristic (ROC) curves, area under the curves (AUCs), and calibration curves to estimate the predictive accuracy and discriminability of nomograms. Decision curve analysis (DCA) and Kaplan-Meier survival curves were employed to further confirm the clinical effectiveness of nomograms. Results: The AUCs of the preoperative nomogram for MVI on the training set and validation set were 0.749 (95%CI: 0.684–0.813) and 0.856 (95%CI: 0.805–0.906), respectively. For the prognostic nomogram, the AUCs of 1-year and 2-year RFS respectively reached 0.839 (95%CI: 0.775–0.903) and 0.856 (95%CI: 0.806–0.905) in the training set, and 0.808 (95%CI: 0.719–0.896) and 0.874 (95%CI: 0.804–0.943) in the validation set. Subsequent calibration curves, DCA analysis and Kaplan-Meier survival curves demonstrated the high accuracy and efficacy of the nomograms for clinical application. Conclusions: The nomograms we constructed could effectively predict MVI and early recurrence in SHCC patients, providing a basis for clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance. Purpose: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients. Materials and methods: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation. Results: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239–5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178–4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007–4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948). Conclusion: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits. [ABSTRACT FROM AUTHOR]

Abstract Background Hepatocellular carcinoma (HCC) is the most frequent primary liver tumor globally and a leading cause of mortality in cirrhotic patients. Our study aimed to estimate the diagnostic performance of triphasic CT and inter-observer reliability in the preoperative detection of microvascular invasion (MVI) in HCC. Two independent radiologists accomplished a retrospective analysis for 99 patients with HCC to assess the CT features for MVI in each lesion. Postoperative histopathology was considered the gold standard. Results Multivariate regression analysis revealed that incomplete or absent tumor capsules, presence of TTPV, and absence of hypodense halo were statistically significant independent predictors of MVI. There was excellent agreement among observers in evaluating peritumoral enhancement, identifying intratumoral arteries, hypodense halo, TTPV, and macrovascular invasion. Also, our results revealed moderate agreement in assessing the tumor margin and tumor capsule. Conclusion Triphasic CT features of MVI are reliable imaging predictors that may be helpful for standard preoperative interpretation of HCC.

Abstract Background Postoperative recurrence is a vital reason for poor 5-year overall survival in hepatocellular carcinoma (HCC) patients. The ADV score is considered a parameter that can quantify HCC aggressiveness. This study aimed to identify HCC patients at high-risk of recurrence early using the ADV score. Methods The medical data of consecutive HCC patients undergoing hepatectomy from The First Affiliated Hospital of Nanjing Medical University (TFAHNJMU) and Nanjing Drum Tower Hospital (NJDTH) were retrospectively reviewed. Based on the status of microvascular invasion and the Edmondson-Steiner grade, HCC patients were divided into three groups: low-risk group (group 1: no risk factor exists), medium-risk group (group 2: one risk factor exists), and high-risk group (group 3: coexistence of two risk factors). In the training cohort (TFAHNJMU), the R package nnet was used to establish a multi-categorical unordered logistic regression model based on the ADV score to predict three risk groups. The Welch’s T-test was used to compare differences in clinical variables in three predicted risk groups. NJDTH served as an external validation center. At last, the confusion matrix was developed using the R package caret to evaluate the diagnostic performance of the model. Results 350 and 405 patients from TFAHNJMU and NJDTH were included. HCC patients in different risk groups had significantly different liver function and inflammation levels. Density maps demonstrated that the ADV score could best differentiate between the three risk groups. The probability curve was plotted according to the predicted results of the multi-categorical unordered logistic regression model, and the best cut-off values of the ADV score were as follows: low-risk ≤ 3.4 log, 3.4 log 5.7 log. The sensitivities of the ADV score predicting the high-risk group (group 3) were 70.2% (99/141) and 78.8% (63/80) in the training and external validation cohort, respectively. Conclusion The ADV score might become a valuable marker for screening patients at high-risk of HCC recurrence with a cut-off value of 5.7 log, which might help surgeons, pathologists, and HCC patients make appropriate clinical decisions.

Abstract Background In this study, we aimed to establish nomograms to predict the microvascular invasion (MVI) and early recurrence in patients with small hepatocellular carcinoma (SHCC), thereby guiding individualized treatment strategies for prognosis improvement. Methods This study retrospectively analyzed 326 SHCC patients who underwent radical resection at Wuhan Union Hospital between April 2017 and January 2022. They were randomly divided into a training set and a validation set at a 7:3 ratio. The preoperative nomogram for MVI was constructed based on univariate and multivariate logistic regression analysis, and the prognostic nomogram for early recurrence was constructed based on univariate and multivariate Cox regression analysis. We used the receiver operating characteristic (ROC) curves, area under the curves (AUCs), and calibration curves to estimate the predictive accuracy and discriminability of nomograms. Decision curve analysis (DCA) and Kaplan-Meier survival curves were employed to further confirm the clinical effectiveness of nomograms. Results The AUCs of the preoperative nomogram for MVI on the training set and validation set were 0.749 (95%CI: 0.684–0.813) and 0.856 (95%CI: 0.805–0.906), respectively. For the prognostic nomogram, the AUCs of 1-year and 2-year RFS respectively reached 0.839 (95%CI: 0.775–0.903) and 0.856 (95%CI: 0.806–0.905) in the training set, and 0.808 (95%CI: 0.719–0.896) and 0.874 (95%CI: 0.804–0.943) in the validation set. Subsequent calibration curves, DCA analysis and Kaplan-Meier survival curves demonstrated the high accuracy and efficacy of the nomograms for clinical application. Conclusions The nomograms we constructed could effectively predict MVI and early recurrence in SHCC patients, providing a basis for clinical decision-making.

Abstract Objectives To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). Methods A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). Results The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. Conclusion Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. Clinical relevance statement The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. Key Points We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI. Graphical Abstract

Abstract Background Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors in China. Microvascular invasion (MVI) often indicates poor prognosis and metastasis in HCC patients. 18F-FDG PET–CT is a new imaging method commonly used to screen for tumor occurrence and evaluate tumor stage. Purpose This study attempted to predict the occurrence of MVI in early-stage HCC through 18F-FDG positron emission tomography (PET)/computed tomography (CT) imaging findings and laboratory data. Patients and methods A total of 113 patients who met the inclusion criteria were divided into two groups based on postoperative pathology: the MVI-positive group and MVI-negative group. We retrospectively analyzed the imaging findings and laboratory data of 113 patients. Imaging findings included tumor size, tumor maximum standard uptake value (SUVmaxT), and normal liver maximum standard uptake value (SUVmaxL). The ratios of SUVmaxT to SUVmaxL (SUVmaxT/L) and an SUVmaxT/L > 2 were defined as active tumor metabolism. The tumor size was indicated by the maximum diameter of the tumor, and a diameter greater than 5 cm was defined as a mass lesion. The laboratory data included the alpha-fetoprotein (AFP) level and the HBeAg level. An AFP concentration > 20 ng/mL was defined as a high AFP level. A HBeAg concentration > 0.03 NCU/mL was defined as HB-positive. Results The SUVmaxT/L (p = 0.003), AFP level (p = 0.008) and tumor size (p = 0.015) were significantly different between the two groups. Patients with active tumor metabolism, mass lesions and high AFP levels tended to be MVI positive. Binary logistic regression analysis verified that active tumor metabolism (OR = 4.124, 95% CI, 1.566–10.861; p = 0.004) and high AFP levels (OR = 2.702, 95% CI, 1.214–6.021; p = 0.015) were independent risk factors for MVI. The sensitivity of the combination of these two independent risk factors predicting HCC with MVI was 56.9% (29/51), the specificity was 83.9% (52/62) and the accuracy was 71.7% (81/113). Conclusion Active tumor metabolism and high AFP levels can predict the occurrence of MVI in HCC patients.

Robin Schmidt,1,2 Charlie Alexander Hamm,1,3 Christopher Rueger,1 Han Xu,1 Yubei He,1,2 Luzie Alexandra Gottwald,4 Bernhard Gebauer,1 Lynn Jeanette Savic1– 3 1Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany; 2Experimental Clinical Research Center (ECRC) at Charité - Universitätsmedizin Berlin and Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, 13125, Germany; 3Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany; 4Vivantes Humboldt-Klinikum, Berlin, 13509, GermanyCorrespondence: Lynn Jeanette Savic, Charité Universitätsmedizin Berlin, Campus Virchow Klinikum, Klinik für Radiologie, Augustenburger Platz 1, Berlin, D-13353, Germany, Email lynn-jeanette.savic@charite.dePurpose: Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE).Patients and Methods: Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher’s exact and t-test, Kaplan–Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson’s correlation.Results: OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed.Conclusion: The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.Keywords: cancer imaging, hepatocellular carcinoma, microvascular invasion, magnetic resonance tomography, predictive imaging biomarkers

Abstract Aim Tumor budding (TB) has excellent prognostic value in many solid tumors, but there is little research on it in hepatocellular carcinoma (HCC). This study assessed the prognostic value of TB in patients with HCC who received hepatectomy. Methods This retrospective study included 210 patients with HCC who received curative hepatectomy at the First Affiliated Hospital of Xi'an Jiaotong University, between 2016 and 2018. TB was evaluated on hematoxylin‐ and eosin‐stained slides according to the criteria established by the 2016 International Tumor Budding Consensus Conference. t‐tests, Chi‐squared tests, and rank‐sum tests were used to correlate the extent of TB with clinicopathological parameters. Prognostic analysis was performed using Cox regression models and the Kaplan–Meier method. Results The positive detection rate of TB was 45.2% (95/210) in 210 patients with HCC. Patients positive for TB always exhibit lower tumor differentiation, higher hepatitis B virus DNA levels, and more severe liver fibrosis. Multivariate Cox analysis identified TB (hazard ratio [HR]: 2.232, 95% confidence interval [CI]: 1.479–3.368, p

Zhao Zhang, Xiu-Fen Jia, Xiao-Yu Chen, Yong-Hua Chen, Ke-Hua Pan Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of ChinaCorrespondence: Ke-Hua Pan, Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, No. 1 of Xuefu North Road, Wenzhou, Ouhai District, 325000, People’s Republic of China, Tel +86-577-8806 9618, Fax +86-577-8806 9655, Email pankehuapan@126.comObjective: The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC).Methods: A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix.Results: There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75– 0.88) and 0.73 (0.64– 0.80) in the training group and an AUC of 0.74 (0.61– 0.85) and 0.62 (0.48– 0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78– 0.91) and 0.77 (0.64– 0.87) in the training and test groups, respectively.Conclusion: A radiomics model utilizing-contrast-enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision-making processes.Keywords: hepatocellular carcinoma, magnetic resonance imaging, microvascular invasion, radiomics

Abstract Objectives To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy. Methods Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (C DCE) were constructed to predict MVI. The combined model incorporating C DCE and CR features was developed and evaluated. Kaplan–Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches. Results A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. C DCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and C DCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p

Juan Zhang,1,&ast; Yinqiao Li,1,2,&ast; Jinju Xia,1 Xingpeng Pan,1 Lun Lu,1 Jiazhao Fu,3 Ningyang Jia1 1Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, China; 2School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; 3Department of Organ Transplantation, Changhai Hospital, First Affiliated Hospital of Naval Medical University, Shanghai, China&ast;These authors contributed equally to this workCorrespondence: Jiazhao Fu; Ningyang Jia, Email fujiazhao@126.com; ningyangjia@163.comObjective: This study aims to investigate the predictive value of Gadobenate dimeglumine (Gd-BOPTA) enhanced MRI features on microvascular invasion (MVI) and recurrence in patients with Liver Imaging Reporting and Data System (LI-RADS) category 5 hepatocellular carcinoma (HCC).Methods: A total of 132 patients with LI-RADS category 5 HCC who underwent curative resection and Gd-BOPTA enhanced MRI at our hospital between January 2016 and December 2018 were retrospectively analyzed. Qualitative evaluation based on LI-RADS v2018 imaging features was performed. Logistic regression analyses were conducted to assess the predictive significance of these features for MVI, and the Cox proportional hazards model was used to identify postoperative risk factors of recurrence. The recurrence-free survival (RFS) was analyzed by using the Kaplan–Meier curve and Log rank test.Results: Multivariate logistic regression analysis identified that corona enhancement (odds ratio [OR] = 3.217; p < 0.001), internal arteries (OR = 4.147; p = 0.004), and peritumoral hypointensity on hepatobiliary phase (HBP) (OR = 5.165; p < 0.001) were significantly associated with MVI. Among the 132 patients with LR-5 HCC, 62 patients experienced postoperative recurrence. Multivariate Cox regression analysis showed that mosaic architecture (hazard ratio [HR] = 1.982; p = 0.014), corona enhancement (HR = 1.783; p = 0.039), and peritumoral hypointensity on HBP (HR = 2.130; p = 0.009) were risk factors for poor RFS.Conclusion: MRI features based on Gd-BOPTA can be noninvasively and effectively predict MVI and recurrence of LR-5 HCC patients.Keywords: hepatocellular carcinoma, magnetic resonance imaging, microvascular invasion, prognosis

Yang Yu,1,&ast; Xiao-Hui Wang,2,&ast; Wen-Jie Hu,1 De-Hua Chen,1 Zi-Li Hu,3 Shao-Qiang Li1 1Hepatic Pancreatobiliary Surgery Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China; 2Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University) Changsha, Hunan Province, 410005, People’s Republic of China; 3Department of Liver Surgery, Sun Yat-Sen University Cancer Center, Guangdong, 510060, Guangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shao-Qiang Li, Hepatic Pancreatobiliary Surgery Center, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China, Tel +86-020-87755766-8214, Fax +86-020-87755766-8661, Email lishaoq@mail.sysu.edu.cnPurpose: The patterns and risk factors of postsurgical recurrence of patient with hepatocellular carcinoma (HCC) with microvascular invasion (MVI) are not clarified. This study aimed to decipher and compare the postoperative recurrent patterns and the risk factors contributing to recurrence between MVI positive (MVI(+)) and MVI negative (MVI(-)) HCC after hepatectomy.Patients and methods: Patients with HCC who underwent hepatectomy in three Chinese academic hospitals between January 1, 2009, and December 31, 2018, were enrolled. Recurrent patterns included early (≤ 2 years) or late (> 2 years) recurrence, recurrent sites and number, and risk factors of recurrence were compared between the MVI(+)and MVI(-) groups by propensity score–matching (PSM).Results: Of 1756 patients included, 581 (33.1%) were MVI(+), and 875 (49.8%) patients developed early recurrence. Compared with the MVI(-) group, the MVI(+) group had a higher 2-year recurrence rate in the PSM cohort (hazard ratio [HR], 1.82; 95% confidence interval [CI], 1.59– 2.10; P < 0.001), and more patients with multiple tumor recurrence. Patients with early recurrence in the MVI(+) group had a worse overall survival (OS) than those in the MVI(-) group (HR, 1.24; 95% CI, 1.02– 1.50; P = 0.034). Resection margin (RM) ≤ 1.0 cm is a surgical predictor of early recurrence for the MVI(+) group (HR, 0.68; 95% CI, 0.54– 0.87; P = 0.002), but not for the MVI(-) group.Conclusion: Compared to MVI(-) HCC, MVI(+) HCC tends to be early, multiple recurrence and lung and lymph node metastasis after resection. RM ≤ 1.0 cm is a surgical risk factor of early recurrence for patient with MVI.Keywords: hepatocellular carcinoma, microvascular invasion, recurrence patterns, risk factors, outcomes

Background: Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors in China. Microvascular invasion (MVI) often indicates poor prognosis and metastasis in HCC patients. 18F-FDG PET–CT is a new imaging method commonly used to screen for tumor occurrence and evaluate tumor stage. Purpose: This study attempted to predict the occurrence of MVI in early-stage HCC through 18F-FDG positron emission tomography (PET)/computed tomography (CT) imaging findings and laboratory data. Patients and methods: A total of 113 patients who met the inclusion criteria were divided into two groups based on postoperative pathology: the MVI-positive group and MVI-negative group. We retrospectively analyzed the imaging findings and laboratory data of 113 patients. Imaging findings included tumor size, tumor maximum standard uptake value (SUVmaxT), and normal liver maximum standard uptake value (SUVmaxL). The ratios of SUVmaxT to SUVmaxL (SUVmaxT/L) and an SUVmaxT/L > 2 were defined as active tumor metabolism. The tumor size was indicated by the maximum diameter of the tumor, and a diameter greater than 5 cm was defined as a mass lesion. The laboratory data included the alpha-fetoprotein (AFP) level and the HBeAg level. An AFP concentration > 20 ng/mL was defined as a high AFP level. A HBeAg concentration > 0.03 NCU/mL was defined as HB-positive. Results: The SUVmaxT/L (p = 0.003), AFP level (p = 0.008) and tumor size (p = 0.015) were significantly different between the two groups. Patients with active tumor metabolism, mass lesions and high AFP levels tended to be MVI positive. Binary logistic regression analysis verified that active tumor metabolism (OR = 4.124, 95% CI, 1.566–10.861; p = 0.004) and high AFP levels (OR = 2.702, 95% CI, 1.214–6.021; p = 0.015) were independent risk factors for MVI. The sensitivity of the combination of these two independent risk factors predicting HCC with MVI was 56.9% (29/51), the specificity was 83.9% (52/62) and the accuracy was 71.7% (81/113). Conclusion: Active tumor metabolism and high AFP levels can predict the occurrence of MVI in HCC patients. [ABSTRACT FROM AUTHOR]

Background: Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision‐making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. Purpose: Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Method: The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI‐positive group (MVI+) and MVI‐negative group (MVI‐) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf‐SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf‐SVM with the best predictive performance was selected to construct the radiomics score (R‐score). Finally, we combined R‐score and clinical‐pathology‐image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). Result: Alpha‐fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920–1.000) constructed by radiometry score (R‐score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. Conclusion: This multi‐parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis. [ABSTRACT FROM AUTHOR]

Objective: To analyze the predictive value of contrast-enhanced ultrasound (CEUS) combined with serum alpha-fetoprotein (AFP), γ-glutamyl transpeptidase (GGT) and lymphocyte count ratio (GLR) for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Methods: 177 HCC patients who underwent hepatectomy in The 73rd Group Army Hospital of the Chinese People's Liberation Army from October 2018 to October 2023 were selected, all patients underwent CEUS and serum AFP, GGT and GLR examinations 1 week before operation. According to the results of postoperative pathological examination of MVI, patients were divided into MVI positive group (n=52) and MVI negative group (n=125). The risk factors affecting MVI in HCC patients were analyzed by multivariate Logistic regression analysis, the predictive value of CEUS combine with AFP, GGT and GLR for MVI in HCC patients were analyzed by receiver operating characteristic (ROC) curve. Results: The onset time, peak time and clearance time in MVI positive group were shorter than those in MVI negative group (P<0.05). AFP, GGT and GLR in MVI positive group were significantly higher than those in MVI negative group(P<0.05). Multivariate Logistic regression analysis showed that elevated AFP, elevated GGT, elevated GLR, irregular lesion morphology, shortened onset time, shortened peak time, and shortened clearance time were independent risk factors for MVI in HCC patients (P<0.001). The ROC results showed that, the area under the curve of MVI in HCC patients predicted by the combination of serum AFP, GGT and GLR was 0.901, which was significantly larger than that of single detection(P<0.05). Conclusion: The levels of serum AFP, GGT and GLR in patients with MVI after HCC operation increased, and the time of initial increase, peak time and clearance time were shortened, which were independent risk factors for MVI in HCC patients. CEUS combined with AFP, GGT and GLR has a high predictive value for MVI in HCC patients. [ABSTRACT FROM AUTHOR]

Objectives: To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy. Methods: Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (CDCE) were constructed to predict MVI. The combined model incorporating CDCE and CR features was developed and evaluated. Kaplan–Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches. Results: A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. CDCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and CDCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p < 0.05). Conclusion: The combined model integrating DCE perfusion parameters and CR features could be used for MVI prediction in HCC patients and assist clinical decision-making. Critical relevance statement: The combined model incorporating bi-regional DCE-MRI perfusion parameters and CR features predicted MVI preoperatively, which could stratify the risk of recurrence and aid in optimizing treatment strategies. Key Points: Microvascular invasion (MVI) is a significant predictor of prognosis for hepatocellular carcinoma (HCC). Quantitative DCE-MRI could predict MVI in solitary BCLC stage A HCC; the combined model improved performance. The combined model could help stratify the risk of recurrence and aid treatment planning. [ABSTRACT FROM AUTHOR]

Objectives: Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size. Results: The results suggested a significantly higher fraction of impeded diffusion (FID) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, FID, tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by FID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164). Conclusion: IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy. [ABSTRACT FROM AUTHOR]

Conversion therapy for initially unresectable hepatocellular carcinoma (iuHCC) using lenvatinib combined with transcatheter arterial chemoembolization (TACE) plus a PD-1 inhibitor (LTP) has achieved promising results. However, further comparative research is necessary to evaluate the effectiveness and safety of conversion surgery (CS) for iuHCC. Data for 32 consecutive patients with iuHCC receiving CS and 419 consecutive patients with resectable HCC receiving initial surgery (IS) between November 2019 and September 2022 were collected retrospectively. After propensity score matching (PSM), 65 patients were selected. Before matching, the CS group had longer EFS (not reached vs. 12.9 months, P < 0.001) and similar OS (not reached vs. not reached, P = 0.510) compared with the IS group. Similar results for EFS (P = 0.001) and OS (P = 0.190) were obtained after matching. The multivariable Cox model (HR = 0.231, 95% CI: 0.105–0.504; P < 0.001) and subgroup analyses confirmed that CS could improve EFS. The CS group had significantly lower incidence of microvascular invasion (MVI) than the IS group (3.1% vs. 50.4%, P < 0.001). Moreover, the two groups had similar safety profiles. CS is effective and safe for patients with iuHCC receiving LTP. LTP has the potential to reduce risk factors for postoperative recurrence, especially MVI, which may influence surgical decision-making. [ABSTRACT FROM AUTHOR]

Objectives Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. Methods We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. Results 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P  =   0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C -index of 0.884 (95% CI: 0.829-0.927). Conclusions The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. Advances in knowledge GCN based on MRI could predict MVI on HCC. [ABSTRACT FROM AUTHOR]

Purpose: Microvascular invasion (MVI) is a critical factor in predicting the recurrence and prognosis of hepatocellular carcinoma (HCC) after liver transplantation (LT). However, there is a lack of reliable preoperative predictors for MVI. The purpose of this study is to evaluate the potential of an 18F-FDG PET/CT-based nomogram in predicting MVI before LT for HCC. Methods: 83 HCC patients who obtained 18F-FDG PET/CT before LT were included in this retrospective research. To determine the parameters connected to MVI and to create a nomogram for MVI prediction, respectively, Logistic and Cox regression models were applied. Analyses of the calibration curve and receiver operating characteristic (ROC) curves were used to assess the model's capability to differentiate between clinical factors and metabolic data from PET/CT images. Results: Among the 83 patients analyzed, 41% were diagnosed with histologic MVI. Multivariate logistic regression analysis revealed that Child–Pugh stage, alpha-fetoprotein, number of tumors, CT Dmax, and Tumor-to-normal liver uptake ratio (TLR) were significant predictors of MVI. A nomogram was constructed using these predictors, which demonstrated strong calibration with a close agreement between predicted and actual MVI probabilities. The nomogram also showed excellent differentiation with an AUC of 0.965 (95% CI 0.925–1.000). Conclusion: The nomogram based on 18F-FDG PET/CT metabolic characteristics is a reliable preoperative imaging biomarker for predicting MVI in HCC patients before undergoing LT. It has demonstrated excellent efficacy and high clinical applicability. [ABSTRACT FROM AUTHOR]

Objectives: Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)–based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Methods: Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (Dt), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). Results: MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f4) and a significantly lower fraction of habitat 2 (f2) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. Conclusions: DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. Clinical relevance statement: The proposed strategy, diffusion-weighted imaging–based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. Key Points: • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging–derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion. [ABSTRACT FROM AUTHOR]

Objective: To observe the effects of carrelizumab combined with transcatheter arterial chemoembolization (TACE) on tumor markers, angiogenic factors, serum programmed cell death protein-1 (PD-1) and programmed death ligand-1 (PD-L1) in patients with hepatocellular carcinoma (HCC) with microvascular invasion. Methods: 121 HCC patients with microvascular invasion who were admitted to our hospital from June 2021 to July 2023 were divided into control group (n=60, receiving radical resection of liver cancer and TACE treatment) and study group (n=61, receiving carrelizumab treatment on the basis of control group) according to the random number table method. The efficacy, serum tumor marker levels [alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), abnormal prothrombin (PIVKA-II), carbohydrate antigen 199 (CA199)], angiogenesis factor levels [vascular endothelial growth factor receptor 2 (VEGF-R2), angiopoietin-2 (Ang-2), vascular endothelial growth factor (VEGF)], serum PD-1 and PD-L1 levels, adverse reactions were compared between two groups (P>0.05). Results: Compared with control group, the total clinical effective rate in study group was higher (P<0.05). Compared with control group after treatment, CEA, AFP, CA199, PIVKA-II, VEGF, VEGF-R2, Ang-2, PD-1 and PD-L1 in study group were lower(P<0.05). There was no difference in the incidence of adverse reactions between two groups(P>0.05). Conclusion: Carrelizumab combined with TACE in the treatment of HCC patients with microvascular invasion, which can improve the serum tumor markers, inhibit angiogenesis and reduce serum PD-1 and PD-L1 levels, and effectively control disease progression. [ABSTRACT FROM AUTHOR]

The study was designed to evaluate microvascular invasion (MVI) using three-dimensional (3D) morphological indicators prior to surgery. This retrospective study included 156 patients with hepatocellular carcinoma (HCC) at our hospital from 2017 to 2018. Through thin-layer CT scanning and 3D reconstruction, the tumor surface inclination angles can be quantitatively analyzed to determine the surface irregularity rate (SIR), which serves as a comprehensive assessment method for tumor irregularity based on preoperative 3D morphological evaluation. Univariate and multivariate logistic regression analyses were employed to investigate the correlation with MVI. The SIR was related to MVI (OR: 10.667, P < 0.001). Multivariate logistic regression analysis showed that the SIR was an independent risk factor for MVI. The area under the receiver operating characteristic curve (ROC) of prediction model composed of the morphological indicator SIR was 0.831 (95% confidence interval: 0.759–0.895). The preoperative 3D morphological indicator SIR of a tumor is an accurate predictor of MVI, providing a valuable tool in clinical decision-making. [ABSTRACT FROM AUTHOR]

BackgroundIn recent years, studies have confirmed the predictive capability of spectral computer tomography (CT) in determining microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Discrepancies in the predicted MVI values between conventional CT imaging features and spectral CT parameters necessitate additional validation.MethodsIn this retrospective study, 105 cases of small HCC were reviewed, and participants were divided into MVI-negative (n=53, Male:48 (90.57%); mean age, 59.40 ± 11.79 years) and MVI-positive (n=52, Male:50(96.15%); mean age, 58.74 ± 9.21 years) groups using pathological results. Imaging features and iodine density (ID) obtained from three-phase enhancement spectral CT scans were gathered from all participants. The study evaluated differences in imaging features and ID values of HCC between two groups, assessing their diagnostic accuracy in predicting MVI occurrence in HCC patients. Furthermore, the diagnostic efficacy of imaging features and ID in predicting MVI was compared.ResultsSignificant differences were noted in the presence of mosaic architecture, nodule-in-nodule architecture, and corona enhancement between the groups, all with p-values < 0.001. There were also notable disparities in IDs between the two groups across the arterial phase, portal phase, and delayed phases, all with p-values < 0.001. The imaging features of nodule-in-nodule architecture, corona enhancement, and nonsmooth tumor margin demonstrate significant diagnostic efficacy, with area under the curve (AUC) of 0.761, 0.742, and 0.752, respectively. In spectral CT analysis, the arterial, portal, and delayed phase IDs exhibit remarkable diagnostic accuracy in detecting MVI, with AUCs of 0.821, 0.832, and 0.802, respectively. Furthermore, the combined models of imaging features, ID, and imaging features with ID reveal substantial predictive capabilities, with AUCs of 0.846, 0.872, and 0.904, respectively. DeLong test results indicated no statistically significant differences between imaging features and IDs.ConclusionsSubstantial differences were noted in imaging features and ID between the MVI-negative and MVI-positive groups in this study. The ID and imaging features exhibited a robust diagnostic precision in predicting MVI. Additionally, our results suggest that both imaging features and ID showed similar predictive efficacy for MVI.

BackgroundThis retrospective cohort study and meta-analysis aims to explore the association between microvascular invasion (MVI) and clinicopathologiccal features, as well as survival outcomes of patients with renal cell carcinoma (RCC).Material and methodsThe retrospective cohort study included 30 RCC patients with positive MVI and another 75 patients with negative MVI as controls. Clinicopathological features and follow-up data were compiled. The meta-analysis conducted searches on PubMed, Cochrane Library, Web of Science, Embase, and WanFang Data from the beginning to 30 September 2023, for comparative studies relevant to MVI patients. The Newcastle-Ottawa Scale and Egger Test were used to assess the risk of biases and certainty of evidence in the included studies.ResultsThe cohort study showed that MVI was associated with advanced primary tumor stage, high pathological grades, high tumor size, high clinical symptoms and lymph node invasion (P 0.05). The meta-analysis part included 11 cohort studies. The results confirmed that patients with MVI positive had worse 12 and 60 mo CSS rates (HR12mo = 0.86, 95%CI 0.80–0.92; HR60mo = 0.63, 95% CI 0.55–0.72; P < 0.00001). Moreover, the meta-analysis also confirmed that MVI group was associated with higher rate of advanced tumor stage, pathological grades, tumor size diameter, higher rate of clinical symptoms and lymph node invasion (P

ObjectiveThe early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC.Materials and methods173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves.ResultsInflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively.ConclusionNLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.

Background: Microvascular invasion (MVI) is associated with an unfavorable prognosis and early recurrence of hepatocellular carcinoma (HCC), which is the crucial pathological hallmark of immunotherapy. While microvascular invasion (MVI) in hepatocellular carcinoma (HCC) currently lacks a detailed single-cell analysis of the tumor microenvironment (TME), it holds significant promise for immunotherapy using immune checkpoint inhibitors (ICI). Methods: We performed single-cell RNA sequencing (scRNA-seq) on 3 MVI positive (MVIP) and 14 MVI-negative (MVIN) tumor tissues, as well as their paired adjacent non-tumoral tissues. Results: We identified SPP1+ macrophages and CD4+ proliferative T cells as intertumoral populations critical for the formation of cold tumors and immunosuppressive environments in MVI-positive patients and verified their prognostic value in correlation with MVIP HCC patients. Additionally, we identified SPP1+ dominated interactions between SPP1+ macrophages and the immunosuppressive T population as contributors to MVI destruction and tumorigenesis. Conclusions: We provide a comprehensive single-cell atlas of HCC patients with MVI, shedding light on the immunosuppressive ecosystem and upregulated signaling associated with MVI. These findings demonstrate that intercellular mechanisms drive MVI and provide a potential immunotherapeutic target for HCC patients with HCC and underlying MVI.

Background and aim: Accurately predicting microvascular invasion (MVI) before surgery is beneficial for surgical decision-making, and some high-risk hepatocellular carcinoma (HCC) patients may benefit from postoperative adjuvant transarterial chemoembolization (PA-TACE). The purpose of this study was to develop and validate a novel nomogram for predicting MVI and assessing the survival benefits of selectively receiving PA-TACE in HCC patients. Methods: The 1372 HCC patients who underwent hepatectomy at four medical institutions were randomly divided into training and validation datasets according to a 7:3 ratio. We developed and validated a nomogram for predicting MVI using preoperative clinical data and further evaluated the survival benefits of selective PA-TACE in different risk subgroups. Results: The nomogram for predicting MVI integrated alpha-fetoprotein, tumor diameter, tumor number, and tumor margin, with an area under the curve of 0.724, which was greater than that of any single predictive factor. The calibration curve, decision curve, and clinical impact curve demonstrated that the nomogram had strong predictive performance. Risk stratification based on the nomogram revealed that patients in the low-risk group did not achieve better DFS and OS with PA-TACE (all p > 0.05), while patients in the medium-to-high risk groups could benefit from higher DFS (Medium-risk, p = 0.039; High-risk, p = 0.027) and OS (Medium-risk, p = 0.001; High-risk, p = 0.019) with PA-TACE. Conclusions: The nomogram predicting MVI demonstrated strong predictive performance, and its risk stratification aided in identifying different subgroups of HCC patients who may benefit from PA-TACE with improved survival outcomes.

ObjectiveTo investigate the value of the combined application of intravoxel incoherent motion (IVIM) and enhanced T2*-weighted angiography (ESWAN) for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).Materials and methods76 patients with pathologically confirmed HCC were retrospectively enrolled and divided into the MVI-positive group (n=26) and MVI-negative group (n=50). Conventional MRI, IVIM, and ESWAN sequences were performed. Three region of interests (ROIs) were placed on the maximum axial slice of the lesion on D, D*, and f maps derived from IVIM sequence, and R2* map derived from ESWAN sequence, and intratumoral susceptibility signal (ITSS) from the phase map derived from ESWAN sequence was also automatically measured. Receiver operating characteristic (ROC) curves were drawn to evaluate the ability for predicting MVI. Univariate and multivariate logistic regression were used to screen independent risk predictors in clinical and imaging information. The Delong’s test was used to compare the differences between the area under curves (AUCs).ResultsThe D and D* values of MVI-negative group were significantly higher than those of MVI-positive group (P=0.038, and P=0.023), which in MVI-negative group were 0.892×10-3 (0.760×10-3, 1.303×10-3) mm2/s and 0.055 (0.025, 0.100) mm2/s, and in MVI-positive group were 0.591×10-3 (0.372×10-3, 0.824×10-3) mm2/s and 0.028 (0.006, 0.050)mm2/s, respectively. The R2* and ITSS values of MVI-negative group were significantly lower than those of MVI-positive group (P=0.034, and P=0.005), which in MVI-negative group were 29.290 (23.117, 35.228) Hz and 0.146 (0.086, 0.236), and in MVI-positive group were 43.696 (34.914, 58.083) Hz and 0.199 (0.155, 0.245), respectively. After univariate and multivariate analyses, only AFP (odds ratio, 0.183; 95% CI, 0.041–0.823; P = 0.027) was the independent risk factor for predicting the status of MVI. The AUCs of AFP, D, D*, R2*, and ITSS for prediction of MVI were 0.652, 0.739, 0.707, 0.798, and 0.657, respectively. The AUCs of IVIM (D+D*), ESWAN (R2*+ITSS), and combination (D+D*+R2*+ITSS) for predicting MVI were 0.772, 0.800, and, 0.855, respectively. When IVIM combined with ESWAN, the performance was improved with a sensitivity of 73.1% and a specificity of 92.0% (cut-off value: 0.502) and the AUC was significantly higher than AFP (P=0.001), D (P=0.038), D* (P=0.023), R2* (P=0.034), and ITSS (P=0.005).ConclusionThe IVIM and ESWAN parameters showed good efficacy in prediction of MVI in patients with HCC. The combination of IVIM and ESWAN may be useful for noninvasive prediction of MVI before clinical operation.

Abstract Objective To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic–static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature. Results In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751). Conclusions The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.

Yuhua Wen,1,2,&ast; Lianghe Lu,1,2,&ast; Jie Mei,1,2 Yihong Ling,2,3 Renguo Guan,1,2 Wenping Lin,1,2 Wei Wei,1,2 Rongping Guo1,2 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 2Department of Liver Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 3Department of Pathology of Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Rongping Guo; Wei Wei, Department of Hepatobiliary Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China, Email guorp@sysucc.org.cn; weiwei@sysucc.org.cnBackground: Microvascular invasion (MVI) is a significant pathological feature in hepatocellular carcinoma (HCC), adjuvant hepatic arterial infusion chemotherapy (a-HAIC) and adjuvant transcatheter arterial chemoembolization (a-TACE), are commonly used for HCC patients with MVI. This study aims to evaluate the efficacies of two adjuvant therapies after surgical treatment for HCC, compare them, and identify the significant factors.Methods: Clinical data from two randomized controlled trials involving HCC patients with MVI after surgical treatment were retrospectively reviewed. Propensity score matching (PSM) analysis was performed to balance baseline differences between patients who received a-HAIC or a-TACE, and control groups who underwent hepatectomy alone. Disease-free survival (DFS) and overall survival (OS) rates were compared.Results: In total of 549 patients were collected from two randomized controlled trials. Using the PSM and Kaplan-Meier method, the median DFS of the a-HAIC, a-TACE, and control groups was 63.2, 21.7, and 11.2 months (P

Abstract Objective We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm. Methods Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients’ preoperative MVI status. Results The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application. Conclusion The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value. Critical relevance statement The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm. Key points • Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals. Graphical Abstract