Mass-forming Intrahepatic Cholangiocarcinoma Research Articles

Efficacy of oncolytic virus combined with HAIC of mFOLFOX for IMCC: A single-center, single-arm, prospective study.

e16265 Background: Hepatic arterial infusion chemotherapy (HAIC) is a well-established treatment for liver cancer. Our previous studies have demonstrated the effectiveness of HAIC with modified FOLFOX (mFOLFOX) for intrahepatic cholangiocarcinoma. However, intrahepatic mass-forming cholangiocarcinoma (IMCC) patients often lose the therapeutic opportunity due to obstructive jaundice caused by huge tumor compression. On the other hand, recombinant human adenovirus type 5 Injection (H101), an oncolytic virus (OVs), has direct anticancer effects and enhances cell-mediated immune responses. Therefor, this clinical study was designed to improve the prognosis of IMCC by the combination of OVs injection and HAIC. Methods: In this study, patients with unresectable IMCC and confirmed by biopsy were enrolled. H101 was used by ultrasound-guided injections 1-3 days before HAIC (a dosage of ≤10 cm, 1.0×1012 vp; ≥10 cm, 1.5×1012 vp). Then, patients were treated with HAIC, which is a night-administration regimen of 50 mg oxaliplatin plus 1.5g 5-fluorouracil and leucovorin, continuous infusion for 2-3 days. Patients received 4-6 cycles of treatment. The primary outcome was progression-free survival (PFS), the secondary outcomes were overall survival (OS), objective response rate (ORR), disease control rate (DCR), and quality of life (QOL). Efficacy assessments were conducted every four weeks following mRECIST v1.1 criteria. Quality of life was assessed by the WHOQOL-BREF scale. Results: From April 1, 2022 to February 1, 2024, 11 patients were enrolled. The median age was 57 years (range 36-73) and the mean tumor diameter was 8.6cm (range 4.3-11.7cm). The primary endpoint was met with a mPFS of 9.6 months. The mOS was 13.2 months. ORR was 36.3% (4/11), and DCR was 90.9% (10/11). CA-199 decreased from 2036.47 to 560.55 U/ml (P < 0.01). The CD8+ T cells increased significantly (306.94 VS. 448.7 cell/ul, P < 0.05), but the CD4+ T cells were with a steady tendency (425.41 VS. 583.50 cell/ul, P = 0.32). In addition, all patients were placed 1-3 biliary drains before enrollment. After OVs plus HAIC treatment, 7 patients had removed or closed their biliary tract drainage tubes, and the QOL score increased from 71.23 to 77.18. Significantly, during the puncture operation, the tumor texture became softer during the multi-cycle OVs plus HAIC treatment. It explains why the tumor pressure on the biliary tract was quickly relieved and the biliary drainage tube was removed in most enrolled IMCC patients. Conclusions: The combination of OVs injection and HAIC with mFOLFOX showed promising anti-tumor and immune-activation efficacy in IMCC. Meanwhile, this strategy has an effect on relieving the pain and malnutrition of long-term biliary drainage and improving the quality of life of patients. Even more, the OVs plus HAIC regimen may be more effective, if combined with the immune checkpoint inhibitors in the future. Clinical trial information: NCT05124002 .

Intrahepatic cholangiocarcinoma with arterial phase hyperenhancement and specialized tumor microenvironment associated with good prognosis after radical resection: A single-center retrospective study

BackgroundThis single-center retrospective study aimed to clarify the clinical and pathologic background of mass-forming intrahepatic cholangiocarcinomas. MethodsA total of 53 patients with mass-forming intrahepatic cholangiocarcinomas were selected from 2007 to 2021 and analyzed based on several parameters, including the preoperative computed tomography pattern (enhancement in the arterial phase of dynamic contrast-enhanced computed tomography), clinical data, and tumor microenvironment evaluated by immunohistochemistry. The hyperenhancement (n = 13) and hypoenhancement (n = 40) groups were defined using the 50% cutoff of tumors with higher attenuation than the liver parenchyma. ResultsThe hyperenhancement group was characterized by a better overall survival than the hypoenhancement group (5-year survival: 86% vs 27%, respectively; P < .001) and by a higher infiltration of peritumoral (92% vs 58%; P = .020) and intratumoral CD3-positive T lymphocytes (85% vs 35%; P = .002). Conversely, the hypoenhancement group was characterized by a higher infiltration versus peritumoral CD163-positive tumor-associated macrophages (60% vs 8%; P = .001), peritumoral pentraxin 3–positive tumor-associated macrophages (50% vs 15%; P = .024), and intratumoral α–smooth muscle actin–positive cancer-associated fibroblasts (15% vs 68%; P = .001). A multiple regression analysis was performed to predict overall survival from the microenvironment, and the independent poor predictor factors were low intratumoral CD3-positive T lymphocytes (hazard ratio = 2.75), high peritumoral (hazard ratio = 2.38), and intratumoral CD163-positive tumor-associated macrophages (hazard ratio = 2.81) (all P values < 0.05). ConclusionCompared with hypovascular, hypervascular mass-forming intrahepatic cholangiocarcinomas have better tumor immunity and prognosis.

Intrahepatic Mass-Forming Cholangiocarcinoma: Is There Additional Prognostic Value in Using Gd-EOB Enhanced MRI?

To investigate the prognostic value of enhancement patterns of intrahepatic mass-forming cholangiocarcinomas (IMCCs) during the hepatobiliary phase (HBP) in gadoxetic acid (Gd-EOB)-enhanced MRI. We retrospectively identified 66 consecutive patients with histopathologically proven IMCCs (reference standard: resection) and preoperative Gd-EOB-enhanced MRI. Gd-EOB retention area was subjectively rated based on areas of intermediate signal intensity. Lesions were classified as either hypointense (0-25% retention area) or significantly-retaining (>25% retention area). Clinical, radiological, and prognostic features were compared between these groups. The primary endpoints were recurrence-free survival (RFS) and overall survival (OS) after primary surgical resection. 73% (48/66) of lesions were rated as hypointense and 29% (19/66) as significantly-retaining. While the hypointense subgroup more frequently featured local and distant intrahepatic metastases (p = 0.039 and p = 0.022) and an infiltrative growth pattern (p = 0.005), RFS, OS, and clinical features did not differ significantly with estimated Gd-EOB retention area or quantitatively measured HBP enhancement ratios. Lymph node metastasis was an independent predictor of poor RFS (p = 0.001). Gd-EOB-enhanced MRI revealed two subtypes of IMCC in the HBP: hypointense and signal-retaining. The hypointense subtype is associated with more frequent intrahepatic metastases and an infiltrative growth pattern, indicating potential tumor aggressiveness. However, this did not result in a significant difference in survival after the primary resection of IMCC.

Predicting the microvascular invasion and tumor grading of intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance imaging radiomics and morphological features.

Preoperative diagnosis of microvascular invasion (MVI) and tumor grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) using imaging findings can facilitate patient treatment decision-making. This study was conducted to establish and validate nomograms based on magnetic resonance imaging (MRI) radiomics and morphological features for predicting the MVI and tumor grading of IMCC before radical hepatectomy. A total of 235 patients with resected IMCC at the Chinese Academy of Medical Sciences and Peking Union Medical College were divided into a training set (n=167) and a validation set (n=68), retrospectively. Clinical data and MRI morphological features were recorded. Univariate and multivariate analyses were conducted to identify the significant features for the prediction of MVI and tumor grading. Radiomics features were extracted from T2-weighted imaging fat-suppressed and diffusion-weighted imaging (DWI). Radiomics signatures (rad_scores) were built based on the least absolute shrinkage and selection operator (LASSO) method. Then, the nomograms were constructed by combining the rad_scores and the significant clinical or MRI morphologic features. The predictive performances for MVI and tumor grading were evaluated by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility. Totals of 16 and 9 radiomics features were selected to build the rad_scores for the prediction of MVI and tumor grading for the training and validation set, respectively. The nomogram for the prediction of MVI comprised the morphologic features including number of tumors, tumor margin, and rad_score. For the prediction of tumor grading, the nomogram comprised the number of tumors, tumor necrosis, and rad_score. The best discriminations were observed in the training and validation sets for the MVI nomogram [AUCs of 0.874, 95% confidence interval (CI): (0.822-0.926) and 0.869 (0.783-0955)] and tumor grading nomogram [AUCs of 0.827 (0.763-0.891) and 0.848 (0.759-0.937)]. Decision curve analysis (DCA) further confirmed the clinical utilities of the nomograms. Nomograms based on MRI radiomics and morphological features can effectively predict the individualized risks of MVI and tumor grading for IMCC.

Risk stratification for overall survival and recurrence-free survival after R0 resection for solitary intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features

PurposeThis study aimed to establish two nomograms for predicting overall survival (OS) and recurrence-free survival (RFS) in patients with solitary intrahepatic mass-forming cholangiocarcinoma (IMCC) based on preoperative magnetic resonance imaging (MRI) features. MethodsThis retrospective study included 120 consecutive patients who were diagnosed with solitary IMCC. Preoperative MRI and clinical features were collected. Based on the univariate and multivariate Cox regression analyses, two nomograms were constructed to predict OS and RFS, respectively. The effective performance of the nomograms was evaluated using concordance index (C-index). The prognostic stratification systems for OS and RFS were developed and used to classify patients into high- and low-risk groups. ResultsSuspicious lymph nodes, arterial phase (AP) enhancement patterns, and bile duct dilatation were independent predictors of OS, while suspicious lymph nodes, AP enhancement patterns, and necrosis were independent predictors of RFS. The nomograms achieved the C-index values of 0.705/0.710 for OS and 0.721/0.759 for RFS in the development/validation cohorts, which were significantly higher than those of the T and TNM stages (P < 0.05). Patients were stratified into high- and low-risk groups, the 1-year OS and RFS rates of high-risk patients were poorer than those of patients with low-risk in the development cohort (OS: 93.5% vs 76.3%, P < 0.001; RFS: 74.5% vs 22.4%, P < 0.001). Similar results were observed in the validation cohort. ConclusionsTwo nomograms were constructed based on preoperative MRI features in patients with solitary IMCC for predicting the OS and RFS and facilitate further prognostic stratification.

A nomogram based on ultrasonographic features and clinical indicators for differentiating mass-forming intrahepatic cholangiocarcinoma and liver metastatic colorectal adenocarcinoma.

This study aimed to develop and validate a nomogram based on ultrasonographic features and clinical indicators to differentiate mass-forming intrahepatic cholangiocarcinoma (MF-ICC) from hepatic metastatic colorectal adenocarcinoma. A total of 343 patients with pathologically confirmed MF-ICC or metastatic colorectal adenocarcinoma were enrolled between October 2018 and July 2022. Patients were randomly assigned to training and validation sets at a ratio of 7:3. Preoperative ultrasound features and clinical indicators were retrieved. Univariate logistic regression analysis was employed to select relevant features. Multivariate logistic regression analysis was used to establish a predictive model, which was presented as a nomogram in training sets. The model's performance was assessed in terms of discrimination, calibration, and clinical usefulness. The study included 169 patients with MF-ICC and 174 with liver metastatic colorectal adenocarcinoma, assigned to training (n=238) and validation (n=105) cohorts. The nomogram incorporated ultrasound features (tumor size, lesion number, echogenicity, tumor necrosis, and rim arterial phase hyperenhancement) and clinical information (serum levels of CEA, CA19-9, CA125). The nomogram demonstrated promising performance in differentiating these two entities in both training and validation sets, with an AUC value of 0.937 (95%CI: 0.907,0.969) and 0.916 (95%CI: 0.863,0.968), respectively. The Hosmer-Lemeshow test and calibration curves confirmed good consistency between predictions and observations. Additionally, decision curve analysis confirmed the nomogram's high clinical practicability. The nomogram based on ultrasound features and clinical indicators demonstrated good discrimination performance in differentiating MF-ICC from metastatic colorectal adenocarcinoma, which may enhance clinical decision-making process in managing these challenging diagnostic scenarios.

Nomogram based on preoperative clinical and MRI features to estimate the microvascular invasion status and the prognosis of solitary intrahepatic mass-forming cholangiocarcinoma.

To develop a nomogram based on preoperative clinical and magnetic resonance imaging (MRI) features for the microvascular invasion (MVI) status in solitary intrahepatic mass-forming cholangiocarcinoma (sIMCC) and to evaluate whether it could predict recurrence-free survival (RFS). We included 115 cases who experienced MRI examinations for sIMCC with R0 resection. The preoperative clinical and MRI features were extracted. Independent predictors related to MVI+ were evaluated by stepwise multivariate logistic regression, and a nomogram was constructed. A receiver operating characteristic (ROC) curve was used to assess the predictive ability. All patients were classified into high- and low-risk groups of MVI. Then, the correlations of the nomogram with RFS in patents with sIMCC were analyzed by Kaplan-Meier method. The occurrence rate of MVI+ was 38.3% (44/115). The preoperative independent predictors of MVI+ were carbohydrate antigen 19-9 >37U/ml, tumor size >5cm, and an ill-defined tumor boundary. Integrating these predictors, the nomogram exerted a favorable diagnostic performance with areas under the ROC curve of 0.767 (95% confidence interval [CI] 0.654-0.881) in the development cohort, and 0.760 (95% CI 0.591-0.929) in the validation cohort. In the RFS analysis, significant differences were observed between the high- and low-risk MVI groups (6-month RFS rates: 64.5% vs. 78.8% and 46.7% vs. 82.4% in the development and validation cohorts, respectively) (P<0.05). A nomogram based on clinical and MRI features is a potential biomarker of MVI and may be a potent method to classify the risk of recurrence in patients with sIMCC.

Preoperative MRI features predicting very early recurrence of intrahepatic mass-forming cholangiocarcinoma after R0 resection: a comparison with the AJCC 8th edition staging system.

This study aimed to establish a nomogram based on preoperative magnetic resonance imaging (MRI) features to predict the very early recurrence (VER, less than 6months) of intrahepatic mass-forming cholangiocarcinoma (IMCC) after R0 resection. This study enrolled a group of 193 IMCC patients from our institution between March 2010 and January 2022. Patients were allocated into the development cohort (n=137) and the validation cohort (n=56), randomly, and the preoperative clinical and MRI features were collected. Univariate and multivariate stepwise logistic regression assessments were adopted to assess predictors of VER. Nomogram was constructed and certificated in the validation cohort. The performance of the prediction nomogram was evaluated by its discrimination, calibration, and clinical utility. The performance of the nomogram was compared with the T stage of the American Joint Committee on Cancer (AJCC) 8th edition staging system. Fifty-three patients (27.5%) experienced VER of the tumor and 140 patients (72.5%) with non-VER, during the follow-up period. After multivariate stepwise logistic regression, number of lesions, diffuse hypoenhancement on arterial phase, necorsis and suspicious lymph nodes were independently associated with VER. The nomogram demonstrated significantly higher area under the curve (AUC) of 0.813 than T stage (AUC=0.666, P=0.006) in the development cohort, whereas in the validation cohort, the nomogram showed better discrimination performance, with an AUC of 0.808 than T stage (0.705) with no significantly difference (P=0.230). Decision curve analysis reflected the clinical net benefit of the nomogram. The nomogram based on preoperative MRI features is a reliable tool to predict VER for patients with IMCC after R0 resection. This nomogram will be helpful to improve survival prediction and individualized treatment.

Differentiating mass-forming intrahepatic cholangiocarcinoma from atypical hepatocellular carcinoma using Gd-EOB-DTPA-enhanced magnetic resonance imaging combined with serum markers in at-risk patients with hepatitis B virus.

The precise differentiation of intrahepatic cholangiocarcinoma (ICC) from atypical hepatocellular carcinoma (HCC) is vital for treatment strategy and prognostic prediction. In clinical practice, nearly 40% of HCCs demonstrate atypical manifestations, particularly HCCs with rim arterial phase hyperenhancement (APHE), which is challenging to differentiate from mass-forming ICC. Thus, we aimed to develop a diagnostic regimen of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) contrast-enhanced magnetic resonance imaging (MRI) combined with serum tumor markers in differentiating mass-forming ICC from atypical HCC in at-risk patients with the hepatitis B virus (HBV). This study enrolled 129 patients with pathologically proven mass-forming ICCs (n=53) and atypical HCCs (n=76) who had undergone preoperative Gd-EOB-DTPA contrast-enhanced MRI. The clinical data and imaging findings were analyzed. Univariate and multivariate logistic analyses were performed to identify the independent predictors for differentiating mass-forming ICCs from atypical HCCs. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, and DeLong test was used to compare the areas under curves of all independent predictors. Univariate logistic regression analysis revealed normal alpha fetoprotein (AFP), elevated carbohydrate antigen 19-9 (CA19-9) level, elevated carcinoma embryonic antigen (CEA) level, central hyperintensity on T2-weighted imaging (T2WI), central hypointensity on T2WI, and targetoid sign on hepatobiliary phase (HBP) and targetoid restriction on diffusion-weighted imaging (DWI) were more likely to be significant predictors favoring mass-forming ICCs (all P values <0.05). In contrast, multifocal hyperintensity on T2WI and capsule sign were more frequently seen in patients with atypical HCC (all P values <0.05). Multivariate analysis revealed normal AFP, elevated CA19-9 level, targetoid sign on HBP, and targetoid restriction on DWI (all P=0.001) were independent predictors for differentiating mass-forming ICCs from atypical HCCs; DeLong test showed that the area under curve (AUC) increased to 0.949 when the above predictors were combined (all P values <0.05), and the sensitivity, specificity, and accuracy of the combined independent predictors were 88.7%, 93.4%, and 91.5%, respectively. A diagnostic regimen integrating tumor markers (AFP, CA19-9) and imaging biomarkers (targetoid restriction on DWI and/or targetoid sign on HBP) using Gd-EOB-DTPA-enhanced MRI could help to differentiate mass-forming ICCs from atypical HCCs and achieve high diagnostic performance of mass-forming ICCs in at-risk patients with the HBV. Mass-forming intrahepatic cholangiocarcinoma (mass-forming ICC); atypical hepatocellular carcinoma (atypical HCC); magnetic resonance imaging (MRI); gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA); hepatobiliary phase (HBP).

Translating imaging traits of mass-forming intrahepatic cholangiocarcinoma into the clinic: From prognostic to therapeutic insights

Background & AimsThe progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour underlying imaging traits. We developed and validated imaging-based prognostic systems for resected MICCs with an appraisal of the tumour immune microenvironment (TIME) underpinning patient-specific imaging traits. MethodsBetween January 2009 and December 2019, a total of 322 patients who underwent dynamic computed tomography/magnetic resonance imaging and curative-intent resection for MICC at three hepatobiliary institutions were retrospectively recruited, divided into training (n = 193) and validation (n = 129) datasets. Two radiological and clinical scoring (RACS) systems, one integrating preoperative variables and one integrating preoperative and postoperative variables, were developed using Cox regression analysis. We then prospectively analysed the TIME of tissue samples from 20 patients who met study criteria from January 2021 to December 2021 using multiplexed immunofluorescence. ResultsPreoperative and postoperative MICC-RACS systems built on carbohydrate antigen 19-9, albumin, tumour number, radiological/pathological nodal status, pathological necrosis, and three radiological traits (arterial enhancement pattern, tumour boundary, and capsular retraction) demonstrated good performance in predicting disease-specific (C-statistic >0.80) and disease-free (C-statistic >0.75) survival that outperformed rival models and staging systems across study cohorts (P <0.05 for all). Patients with MICC-RACS score of 0–2 (low risk), 3–5 (medium risk), and ≥6 (high risk) had incrementally worse prognosis after surgery. Significant differences in spatial distribution and infiltration level of immune cells were identified between arterial enhancement patterns. Enhanced infiltration of immunosuppressive regulatory T cells and M2-like macrophages at the invasive margin were noted in tumours with distinct boundary and capsular retraction, respectively. ConclusionsOur MICC-RACS systems are simple but powerful prognostic tools that may facilitate the understanding of spatially distinct TIMEs and patient-tailored immunotherapy approach. Impact and ImplicationsThe progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour of MICC underlying imaging traits. In this study, we proposed novel and easy-to-use tools, built on radiological and clinical features, that demonstrated good performance in predicting the prognosis either before or after surgery and outperformed rival models/systems across major imaging modalities. The characteristic radiological traits integrated into prognostic systems (arterial enhancement pattern, tumour boundary, and capsular retraction) were highly correlated with heterogeneous tumour-immune microenvironments, thereby renovating treatment paradigms for this difficult-to-treat disease.

MRI-Based Radiomics Nomogram for Preoperatively Differentiating Intrahepatic Mass-Forming Cholangiocarcinoma From Resectable Colorectal Liver Metastases

To establish a radiomics nomogram based on multiparameter magnetic resonance(MR)images for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from colorectal cancer liver metastasis (CRLM). A total of 133 patients in training cohort (64 IMCC and 69 CRLM), 57 patients in internal validation cohort (29 IMCC and 28 CRLM), and 51 patients (23 IMCC and 28 CRLM) in external validation cohort were included. Radiomics features were extracted from the multiparameter MR images and selected by the least absolute shrinkage and selection operator algorithm to establish the radiomics model. Clinical variables and magnetic resonance imaging (MRI) findings were selected by univariate and multivariate analyses to construct a clinical model. The radiomics nomogram was combined with radiomics model and clinical model. Six features were selected to construct the radiomics model. The radiomics signature showed better discrimination than the clinical model in the training cohort (Area Under the Curve (AUC), 0.92; 95% confidence interval (CI), 0.87-0.96 vs. AUC, 0.74; 95% CI, 0.66-0.83) and the external validation cohort (AUC, 0.90; 95% CI, 0.82-0.98 vs. AUC, 0.81; 95% CI, 0.69-0.93). The radiomics nomogram showed the best discrimination performance with favorable calibration in the training cohort (AUC, 0.94; 95% CI, 0.90-0.97) and the external validation cohort (AUC, 0.92; 95% CI, 0.84-1.00). The radiomics nomogram combining radiomics signatures based on multiparameter MRI with clinical factors (serum carcinoembryonic antigen level and tumor diameter) may provide a reliable and noninvasive tool to discriminate IMCC from CRLM, which could help guide treatment strategies and prognosis preoperatively prediction.

A convenient scoring system to distinguish intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis based on magnetic resonance imaging features

ObjectivesTo develop and validate a diagnostic scoring system to differentiate intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM).MethodsA total of 366 patients (263 in the training cohort, 103 in the validation cohort) who underwent MRI examination with pathologically proven either IMCC or CRLM from two centers were included. Twenty-eight MRI features were collected. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors for distinguishing IMCC from solitary CRLM. The independent predictors were weighted over based on regression coefficients to build a scoring system. The overall score distribution was divided into three groups to show the diagnostic probability of CRLM.ResultsSix independent predictors, including hepatic capsular retraction, peripheral hepatic enhancement, vessel penetrating the tumor, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, and rim enhancement at the portal venous phase were included in the system. All predictors were assigned 1 point. At a cutoff of 3 points, AUCs for this score model were 0.948 and 0.903 with sensitivities of 96.5% and 92.0%, specificities of 84.4% and 71.7%, positive predictive values of 87.7% and 75.4%, negative predictive values of 95.4% and 90.5%, and accuracies of 90.9% and 81.6% for the training and validation cohorts, respectively. An increasing trend was shown in the diagnostic probability of CRLM among the three groups based on the score.ConclusionsThe established scoring system is reliable and convenient for distinguishing IMCC from solitary CRLM using six MRI features.Clinical relevance statementA reliable and convenient scoring system was developed to differentiate between intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis using six MRI features.Key Points• Characteristic MRI features were identified to distinguish intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM).• A model to distinguish IMCC from solitary CRLM was created based on 6 features, including hepatic capsular retraction, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, rim enhancement at the portal venous phase, peripheral hepatic enhancement, and vessel penetrating the tumor.

Machine learning based on gadoxetic acid-enhanced MRI for differentiating atypical intrahepatic mass-forming cholangiocarcinoma from poorly differentiated hepatocellular carcinoma.

The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for differentiating atypical intrahepatic mass-forming cholangiocarcinoma (aIMCC) from poorly differentiated hepatocellular carcinoma (pHCC). A total of 134 patients (51 aIMCC and 83 pHCC) who underwent Gadoxetic acid-enhanced MRI between March 2016 and March 2022 were enrolled in this study and then randomly assigned to the training and validation cohorts by 7:3 (93 patients and 41 patients, respectively). The radiomics features were extracted from the hepatobiliary phase of Gadoxetic acid-enhanced MRI. In the training cohort, the SelectKBest and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features. The clinical, radiomics, and clinical-radiomics model were established using four machine learning algorithms. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve. Comparison of the radiomics and clinical-radiomics model was done by the Delong test. The clinical usefulness of the model was evaluated using decision curve analysis (DCA). In 1132 extracted radiomic features, 15 were selected to develop radiomics signature. For identifying aIMCC and pHCC, the radiomics model constructed by random forest algorithm showed the high performance (AUC = 0.90) in the training cohort. The performance of the clinical-radiomics model (AUC = 0.89) was not significantly different (P = 0.88) from that of the radiomics model constructed by random forest algorithm (AUC = 0.86) in the validation cohort. DCA demonstrated that the clinical-radiomics model constructed by random forest algorithm had a high net clinical benefit. The clinical-radiomics model is an effective tool to distinguish aIMCC from pHCC and may provide additional value for the development of treatment plans.

Predictive factors of microvascular invasion in patients with intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance images.

The aim of this retrospective study was to develop and validate a preoperative nomogram for predicting microvascular invasion (MVI) in patients with intrahepatic mass-forming cholangiocarcinoma (IMCC) based on magnetic resonance imaging (MRI). In this retrospective study, 224 consecutive patients with clinicopathologically confirmed IMCC were enrolled. Patients whose data were collected from February 2010 to December 2020 were randomly divided into the training (131 patients) and internal validation (51 patients) datasets. The data from January 2021 to November 2021 (42 patients) were allocated to the time-independent validation dataset. Univariate and multivariate forward logistic regression analyses were used to identify preoperative MRI features that were significantly related to MVI, which were then used to develop the nomogram. We used the area under the receiver operating characteristic curve (AUC) and calibration curve to evaluate the performance of the nomogram. Interobserver agreement of MRI qualitative features was good to excellent, with κ values of 0.613-0.882. Multivariate analyses indicated that the following variables were independent predictors of MVI: multiple tumours (odds ratio [OR]) = 4.819, 95% confidence interval [CI] 1.562-14.864, P = 0.006), ill-defined margin (OR = 6.922, 95% CI 2.883-16.633, P < 0.001), and carbohydrate antigen 19-9 (CA 19-9) > 37U/ml (OR = 2.890, 95% CI 1.211-6.897, P = 0.017). A nomogram incorporating these factors was established using well-fitted calibration curves. The nomogram showed good diagnostic efficacy for MVI, with AUC values of 0.838, 0.819, and 0.874 for the training, internal validation, and time-independent validation datasets, respectively. A nomogram constructed using independent factors, namely the presence of multiple tumours, ill-defined margins, and CA 19-9 > 37U/ml could predict the presence of MVI. This can facilitate personalised therapeutic strategy and clinical management in patients with IMCC.

Preoperative subcategorization based on magnetic resonance imaging in intrahepatic cholangiocarcinoma

BackgroundAppropriate preoperative identification of iCCA subtype is essential for personalized management, so the aim of this study is to investigate the role of MR imaging features in preoperatively differentiating the iCCA subtype.MethodsNinety-three patients with mass-forming intrahepatic cholangiocarcinoma (iCCA, 63 small duct type and 30 large duct type) were retrospectively enrolled according to the latest 5th WHO classification (mean age, males vs. females: 60.66 ± 10.53 vs. 61.88 ± 12.82, 50 men). Significant imaging features for differentiating large duct iCCA and small duct iCCA were identified using univariate and multivariate logistic regression analyses, and a regression-based predictive model was then generated. Furthermore, diagnostic performance parameters of single significant imaging features and the predictive model were obtained, and corresponding receiver operating characteristic (ROC) curves were subsequently presented.ResultsThe univariate analysis showed that tumor in vein, arterial phase hypoenhancement, intrahepatic duct dilatation, lack of targetoid restriction and lack of targetoid appearance in T2 were predictors of large duct type iCCA. Arterial phase hypoenhancement, intrahepatic duct dilatation and lack of targetoid restriction were independent predictors for large duct type iCCA in multivariate analysis. The regression-based predictive model has achieved the best preoperative prediction performance in iCCA subcategorization so far. The area under the ROC curve of the regression-based predictive model was up to 0.91 (95% CI: 0.85, 0.98), and it was significantly higher than every single significant imaging feature.ConclusionsArterial phase hypoenhancement, intrahepatic duct dilatation and lack of targetoid restriction could be considered reliable MR imaging indicators of large duct type iCCA. MR imaging features can facilitate noninvasive prediction of iCCA subtype with satisfactory predictive performance.

Fat-free Muscle Index Predicts Overall Survival in Mass-forming Intrahepatic Cholangiocarcinoma Patients: A Sex-stratified Analysis

IntroductionSarcopenia is associated with adverse outcomes in patients with malignancies, it remains unclear whether the sex discrepancy of sarcopenia or muscle loss leads to different prognoses in patients with mass-forming intrahepatic cholangiocarcinoma (MICC).Material and methodsPatients who were pathologically diagnosed with MICC and underwent partial hepatectomy between 2012 and 2019 were recruited. Fat-free muscle areas (FFMA) were defined as areas of bilateral erector spinae muscles on dual-echo T1-weighted gradient-recalled-echo images at the level of radix of the superior mesenteric artery. FFMI (fat-free skeletal muscle index) was calculated by dividing FFMA by individual’s height squared. The prognostic value of FFMI was investigated using Kaplan-Meier method and multivariable Cox regressions in both the entire cohort and sex-specific subgroups.ResultsA total of 157 patients were finally included. Cutoff values for the definition of low FFMI were 14.19 cm2/m2 in males and 11.07 cm2/m2 in females. Patients with low FFMI had significantly shorter overall survival (OS) than those with high FFMI in the entire cohort (19.42 vs 35.29 months, p=0.0067) and male patients (17.25 vs 37.19 months, p=0.006), but not females. Multivariate analyses showed that low FFMI was a significant predictor of OS in the entire cohort (HR 1.725, 95%CI 1.054-2.821, p=0.03) and male patients (HR 2.145, 95%CI 1.113-4.133, p=0.023). No association between FFMI and disease-free survival was found in both the entire cohort and sex-specific subgroups.ConclusionsPreoperative FFMI measured by MRI is a prognostic biomarker for OS in MICC patients, and it is more sensitive in males than in females.

CT-based Radiomics of Mass-forming Intrahepatic Cholangiocarcinoma and its Peritumoral Tissue Improve the Prediction of Survival after Surgery

CT-based Radiomics of Mass-forming Intrahepatic Cholangiocarcinoma and its Peritumoral Tissue Improve the Prediction of Survival after Surgery

A Deep Learning Workflow for Mass-Forming Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma Classification Based on MRI.

Precise classification of mass-forming intrahepatic cholangiocarcinoma (MF-ICC) and hepatocellular carcinoma (HCC) based on magnetic resonance imaging (MRI) is crucial for personalized treatment strategy. The purpose of the present study was to differentiate MF-ICC from HCC applying a novel deep-learning-based workflow with stronger feature extraction ability and fusion capability to improve the classification performance of deep learning on small datasets. To retain more effective lesion features, we propose a preprocessing method called semi-segmented preprocessing (Semi-SP) to select the region of interest (ROI). Then, the ROIs were sent to the strided feature fusion residual network (SFFNet) for training and classification. The SFFNet model is composed of three parts: the multilayer feature fusion module (MFF) was proposed to extract discriminative features of MF-ICC/HCC and integrate features of different levels; a new stationary residual block (SRB) was proposed to solve the problem of information loss and network instability during training; the attention mechanism convolutional block attention module (CBAM) was adopted in the middle layer of the network to extract the correlation of multi-spatial feature information, so as to filter the irrelevant feature information in pixels. The SFFNet model achieved an overall accuracy of 92.26% and an AUC of 0.9680, with high sensitivity (86.21%) and specificity (94.70%) for MF-ICC. In this paper, we proposed a specifically designed Semi-SP method and SFFNet model to differentiate MF-ICC from HCC. This workflow achieves good MF-ICC/HCC classification performance due to stronger feature extraction and fusion capabilities, which provide complementary information for personalized treatment strategy.

Preoperative differentiation of hepatocellular carcinoma with peripheral rim-like enhancement from intrahepatic mass-forming cholangiocarcinoma on contrast-enhanced MRI.

The present study aimed to determine the reliable imaging features to distinguish atypical hepatocellular carcinoma (HCC) with peripheral rim-like enhancement from intrahepatic mass-forming cholangiocarcinoma (IMCC) on contrast-enhanced magnetic resonance imaging (MRI). A total of 168 patients (130 male, 57.10 ± 10.53 years) pathological confirmed HCC or IMCC who underwent contrast-enhanced MRI between July 2019 and February 2022 were retrospectively included. Univariate and multivariate logistic regression analyses were used to determine independent differential factors for distinguishing HCC from IMCC, and the model was established. Bootstrap resampling 1000 times was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility. Radiological capsule (OR 0.024, 95% CI: 0.006, 0.095, P<0.001), heterogeneous signal intensity (SI) on T1WI (OR 0.009, 95%CI: 0.001,0.056, P<0.001) were independent differential factors for predicting HCC over IMCC. A lobulated contour (OR 11.732, 95%CI: 2.928,47.007, P = 0.001), target sign on DP (OR 14.269, 95%CI: 2.849,82.106, P = 0.007), bile duct dilatation (OR 12.856, 95%CI: 2.013, P = 0.001) were independent differential factors for predicting IMCCs over HCCs. The independent differential factors constituted a model to distinguish atypical HCCs and IMCCs. The area under receiver operating characteristic (ROC) curve, sensitivity, and specificity values of the model were 0.964(0.940,0.987), 0.88, and 0.906, indicating that the model had an excellent differential diagnostic performance. The decision curve analysis (DCA) curve showed that the model obtained a better net clinical benefit. The present study identified reliable imaging features for distinguishing atypical HCCs with peripheral rim-like enhancement from IMCCs on contrast-enhanced MRI. Our findings may help radiologists provide clinicians with more accurate preoperative imaging diagnoses to select appropriate treatment options.

Left hepatectomy with suprahepatic inferior vena cava resection and reconstruction under veno-arterial extracorporeal membrane oxygenation for intrahepatic cholangiocarcinoma: a case report

BackgroundCurative surgery is the most effective treatment for intrahepatic cholangiocarcinoma (ICC). When an ICC involves the suprahepatic inferior vena cava (IVC), hepatectomy with suprahepatic IVC resection and reconstruction is challenging. For reconstruction of the suprahepatic IVC, total hepatic vascular exclusion (THVE), veno-venous bypass, and/or in situ hypothermic portal perfusion are required, but mortality and morbidity remain high.Case presentationWe present a 73-year-old woman with mass-forming ICC which invaded the suprahepatic IVC and middle hepatic vein. Left hepatectomy, suprahepatic IVC resection, and reconstruction with an artificial graft were successfully performed during veno-arterial extracorporeal membrane oxygenation (V-A ECMO) to maintain blood pressure. While clamping the IVC diagonally, the right hepatic vein confluence could be preserved. No congestion in the right liver was seen; therefore, there was no requirement for the Pringle maneuver or THVE during reconstruction. No morbidity or mortality was seen after surgery.ConclusionsHepatectomy with suprahepatic IVC resection and reconstruction under V-A ECMO can be performed safely. When an ICC invades the suprahepatic IVC, V-A ECMO during resection and reconstruction of the suprahepatic IVC with an artificial graft is recommended as one of the options.