Imaging and prognostic characterization of fat-containing hepatocellular carcinoma subtypes.

PURPOSE: Steatohepatitic hepatocellular carcinoma (SH-HCC) is characterized by intratumoral fat with > 50% inflammatory changes. However, intratumoral fat (with or without inflammation) can also be found in not-otherwise specified HCC (NOS-HCC). We compared the imaging features and outcome of resected HCC containing fat on pathology including SH-HCC (> 50% steatohepatitic component), NOS-HCC with < 50% steatohepatitic component (SH-NOS-HCC), and fatty NOS-HCC (no steatohepatitic component). MATERIAL AND METHODS: From September 2012 to June 2021, 94 patients underwent hepatic resection for fat-containing HCC on pathology. Imaging features and categories were assessed using LIRADS v2018. Fat quantification was performed on chemical-shift MRI. Recurrence-free and overall survival were estimated. RESULTS: Twenty-one patients (26%) had nonalcoholic steatohepatitis (NASH). The median intra-tumoral fat fraction was 8%, with differences between SH-HCC and SH-NOS-HCC (9.5% vs. 5% p = 0.03). There was no difference in major LI-RADS features between all groups; most tumors were classified as LR-4/5. A mosaic architecture on MRI was rare (7%) in SH-HCC, a fat in mass on CT was more frequently depicted (48%) in SH-HCC. A combination of NASH with no mosaic architecture on MRI or NASH with fat in mass on CT yielded excellent specificity for diagnosing SH-HCC (97.6% and 97.7%, respectively). The median recurrence-free and overall survival were 58 and 87 months, with no difference between groups (p = 0.18 and p = 0.69). CONCLUSION: In patients with NASH, an SH-HCC may be suspected in L4/LR-5 observations with no mosaic architecture at MRI or with fat in mass on CT. Oncological outcomes appear similar between fat-containing HCC subtypes.

Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning.

Background & Aims: The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Method: We selected 166 PLC biopsies divided into training, internal and external validation sets: 90, 29 and 47 samples, respectively. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, tiles of 256x256 pixels were extracted from the WSIs and used to train a ResNet18 neural network. The tumour/non-tumour annotations served as labels during training, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Results: Pathological review classified the training and validation sets into hepatocellular carcinoma (HCC, 33/90, 11/29 and 26/47), intrahepatic cholangiocarcinoma (iCCA, 28/90, 9/29 and 15/47), and cHCC-CCA (29/90, 9/29 and 6/47). In the two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the two-cluster model predictions (major contingent) in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (cluster 0: 5-97%; cluster 1: 2-94%). Conclusion: Our method applied to PLC HES biopsy could identify specific morphological features of HCC and iCCA. Although no specific features of cHCC-CCA were recognized, assessing the proportion of HCC and iCCA tiles within a slide could facilitate the identification of cHCC-CCA. Impact and implications: The diagnosis of primary liver cancers can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified primary liver cancers on routine-stained biopsies using a weakly supervised learning method. Our model identified specific features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Despite no specific features of cHCC-CCA being recognized, the identification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma tiles within a slide could facilitate the diagnosis of primary liver cancers, and particularly cHCC-CCA.

Association of LI-RADS and Histopathologic Features with Survival in Patients with Solitary Resected Hepatocellular Carcinoma.

Background Both Liver Imaging Reporting and Data System (LI-RADS) and histopathologic features provide prognostic information in patients with hepatocellular carcinoma (HCC), but whether LI-RADS is independently associated with survival is uncertain. Purpose To assess the association of LI-RADS categories and features with survival outcomes in patients with solitary resected HCC. Materials and Methods This retrospective study included patients with solitary resected HCC from three institutions examined with preoperative contrast-enhanced CT and/or MRI between January 2008 and December 2019. Three independent readers evaluated the LI-RADS version 2018 categories and features. Histopathologic features including World Health Organization tumor grade, microvascular and macrovascular invasion, satellite nodules, and tumor capsule were recorded. Overall survival and disease-free survival were assessed with Cox regression models. Marginal effects of nontargetoid features on survival were estimated using propensity score matching. Results A total of 360 patients (median age, 64 years [IQR, 56-70 years]; 280 male patients) were included. At CT and MRI, the LI-RADS LR-M category was associated with increased risk of recurrence (CT: hazard ratio [HR] = 1.83 [95% CI: 1.26, 2.66], P = .001; MRI: HR = 2.22 [95% CI: 1.56, 3.16], P < .001) and death (CT: HR = 2.47 [95% CI: 1.72, 3.55], P < .001; MRI: HR = 1.80 [95% CI: 1.32, 2.46], P < .001) independently of histopathologic features. The presence of at least one nontargetoid feature was associated with an increased risk of recurrence (CT: HR = 1.80 [95% CI: 1.36, 2.38], P < .001; MRI: HR = 1.93 [95% CI: 1.81, 2.06], P < .001) and death (CT: HR = 1.51 [95% CI: 1.10, 2.07], P < .010) independently of histopathologic features. In matched samples, recurrence was associated with the presence of at least one nontargetoid feature at CT (HR = 2.06 [95% CI: 1.15, 3.66]; P = .02) or MRI (HR = 1.79 [95% CI: 1.01, 3.20]; P = .048). Conclusion In patients with solitary resected HCC, LR-M category and nontargetoid features were negatively associated with survival independently of histopathologic characteristics. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kartalis and Grigoriadis in this issue.

Differential diagnosis of small hepatocellular nodules in cirrhosis: surrogate histological criteria of TERT promoter mutations.

AIMS: The differential diagnosis of small hepatocellular nodules in cirrhosis between dysplastic nodules and hepatocellular carcinoma (HCC) remains challenging on biopsy. As TERT promoter (pTERT) mutations may indicate the nodules already engaged in the malignant process, the aim of this study was to identify histological criteria associated with pTERT mutations by detecting these mutations by ddPCR in small formalin-fixed paraffin-embedded (FFPE) hepatocellular nodules arising in cirrhosis. METHODS AND RESULTS: We built a bicentric cohort data set of 339 hepatocellular nodules < 2 cm from cirrhotic samples, divided into a test cohort of 299 resected samples and a validation cohort of 40 biopsies. Pathological review, based on the evaluation of 14 histological criteria, classified all nodules. pTERT mutations were identified by ddPCR in FFPE samples. Among the 339 nodules, ddPCR revealed pTERT mutations in 105 cases (31%), including 90 and 15 cases in the test and validation cohorts, respectively. On multivariate analysis, three histological criteria were associated with pTERT mutations in the test cohort: increased cell density (P = 0.003), stromal invasion (P = 0.036) and plate-thickening anomalies (P < 0.001). With the combination of at least two of these major criteria, the AUC for predicting pTERT mutations was 0.84 in the test cohort (sensitivity: 86%, specificity: 83%) and 0.81 in the validation cohort (sensitivity: 87%, specificity: 76%). CONCLUSIONS: We identified three histological criteria as surrogate markers of pTERT mutations that may be used in routine biopsy to more clearly classify small hepatocellular nodules arising in cirrhosis.

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.

Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study.

BACKGROUND: Clinical benefits of atezolizumab plus bevacizumab (atezolizumab-bevacizumab) are observed only in a subset of patients with hepatocellular carcinoma and the development of biomarkers is needed to improve therapeutic strategies. The atezolizumab-bevacizumab response signature (ABRS), assessed by molecular biology profiling techniques, has been shown to be associated with progression-free survival after treatment initiation. The primary objective of our study was to develop an artificial intelligence (AI) model able to estimate ABRS expression directly from histological slides, and to evaluate if model predictions were associated with progression-free survival. METHODS: In this multicentre retrospective study, we developed a model (ABRS-prediction; ABRS-P), which was derived from the previously published clustering-constrained attention multiple instance learning (or CLAM) pipeline. We trained the model fit for regression analysis using a multicentre dataset from The Cancer Genome Atlas (patients treated by surgical resection, n=336). The ABRS-P model was externally validated on two independent series of samples from patients with hepatocellular carcinoma (a surgical resection series, n=225; and a biopsy series, n=157). The predictive value of the model was further tested in a series of biopsy samples from a multicentre cohort of patients with hepatocellular carcinoma treated with atezolizumab-bevacizumab (n=122). All samples in the study were from adults (aged ≥18 years). The validation sets were sampled between Jan 1, 2008, to Jan 1, 2023. For the multicentre validation set, the primary objective was to assess the association of high versus low ABRS-P values, defined relative to cross-validation median split thresholds in the first biopsy series, with progression-free survival after treatment initiation. Finally, we performed spatial transcriptomics and matched prediction heatmaps with in situ expression profiles. FINDINGS: Of the 840 patients sampled, 641 (76%) were male and 199 (24%) were female. Across the development and validation datasets, hepatocellular carcinoma risk factors included alcohol intake, hepatitis B and C virus infections, and non-alcoholic steatohepatitis. Using cross-validation in the development series, the mean Pearson's correlation between ABRS-P values and ABRS score (mean expression of ABRS genes) was r=0·62 (SD 0·09; mean p<0·0001, SD<0·0001). The ABRS-P generalised well on the external validation series (surgical resection series, r=0·60 [95% CI 0·51-0·68], p<0·0001; biopsy series, r=0·53 [0·40-0·63], p<0·0001). In the 122 patients treated with atezolizumab-bevacizumab, those with ABRS-P-high tumours (n=74) showed significantly longer median progression-free survival than those with ABRS-P-low tumours (n=48) after treatment initiation (12 months [95% CI 7-not reached] vs 7 months [4-9]; p=0·014). Spatial transcriptomics showed significantly higher ABRS score, along with upregulation of various other immune effectors, in tumour areas with high ABRS-P values versus areas with low ABRS-P values. INTERPRETATION: Our study indicates that AI applied on hepatocellular carcinoma digital slides is able to serve as a biomarker for progression-free survival in patients treated with atezolizumab-bevacizumab. This approach could be used in the development of inexpensive and fast biomarkers for targeted therapies. The combination of AI heatmaps with spatial transcriptomics provides insight on the molecular features associated with predictions. This methodology could be applied to other cancers or diseases and improve understanding of the biological mechanisms that drive responses to treatments. FUNDING: Institut National du Cancer, Fondation ARC, China Scholarship Council, Ligue Contre le Cancer du Val de Marne, Fondation de l'Avenir, Ipsen, and Fondation Bristol Myers Squibb Pour la Recherche en Immuno-Oncologie.

Impact of induction therapy in locally advanced intrahepatic cholangiocarcinoma.

INTRODUCTION: After local or systemic treatment, a small number of patients with primarily unresectable intrahepatic cholangiocarcinoma (ICC) may benefit from secondary resection. This study aimed to analyze the oncological outcome of patients who underwent radical surgery after preoperative treatment. METHODS: From 2000 to 2021, all patients who underwent curative-intent liver resection for ICC in three tertiary centers were selected. Patients were divided into two groups: upfront surgery (US) and preoperative treatment (POT). Oncologic data (preoperative treatment, histologic data, adjuvant chemotherapy, overall survival, and recurrence-free survival) were compared between the two groups. RESULTS: Among 198 included patients, 31 (15.7%) received POT including chemotherapy (74.2%), radioembolization (12.9%), chemoembolization (9.7%), or combined radiotherapy and chemotherapy (3.2%). Major resection was performed in 156 (78.8%) patients, and 53 (26.8%) had vascular and/or biliary reconstruction. Histological findings were similar between US and POT group and were not affected by the type of POT. After a median follow-up of 23 months, recurrence rate (58.1% POT vs. 55.1% US, p = 0.760) and type were similar between groups. Recurrence-free survival at 1 and 3 years (41.9% and 22.6% vs. 46.7 and 21.6% in the POT and US, respectively, p = 0.989) and overall survival at 1 and 3 years (77.4% and 32.3% vs. 69.5% and 34.7% in the POT and US respectively, p = 0.323) were similar and independent of the POT type. CONCLUSION: After POT, downstaged patients who underwent curative-intent resection for initially unresectable ICC have similar long-term outcomes as those undergoing upfront surgery.

Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation?

OBJECTIVES: To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application. METHODS: This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS). RESULTS: In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set. CONCLUSIONS: The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes. CLINICAL RELEVANCE STATEMENT: Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate. KEY POINTS: • The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.

Pathological overview of steatohepatitic hepatocellular carcinoma in a surgical series.

AIMS: According to the last WHO classification, steatohepatitic hepatocellular carcinoma (SH-HCC) is recognized as a distinct HCC subtype, even though a consensual definition is still lacking. The objectives of the study were to carefully describe the morphological features of SH-HCC and evaluate its impact on prognosis. METHODS AND RESULTS: We conducted a single-centre retrospective study including 297 surgically resected HCC. Pathological features including SH criteria (steatosis, ballooning, Mallory-Denk bodies, fibrosis, and inflammation) were assessed. SH-HCC was defined by the presence of at least four of the five SH criteria and the SH component represented >50% of the tumour area. According to this definition, 39 (13%) HCC cases corresponded to SH-HCC and 30 cases (10%) corresponded to HCC with an SH component (<50%). SH criteria in SH-HCC and non-SH-HCC were distributed as follows: ballooning (100% versus 11%), fibrosis (100% versus 81%), inflammation (100% versus 67%), steatosis (92% versus 8%), and Mallory-Denk bodies (74% versus 3%). Inflammation markers (c-reactive protein [CRP] and serum amyloid A [SAA]) were significantly more expressed in SH-HCC compared to non-SH-HCC (82% versus 14%, P = <0.001). Five-year recurrence-free survival (RFS) and 5-year overall survival (OS) were similar for SH-HCC and non-SH-HCC (P = 0.413 and P = 0.866, respectively). The percentage of SH component does not impact OS and RFS. CONCLUSION: We confirm in a large cohort the relatively high prevalence (13%) of SH-HCC. Ballooning is the most specific criteria for this subtype. The percentage of the SH component does not impact prognosis.

MALDI Imaging, a Powerful Multiplex Approach to Decipher Intratumoral Heterogeneity: Combined Hepato-Cholangiocarcinomas as Proof of Concept.

Combined hepato-cholangiocarcinomas (cHCC-CCA) belong to the spectrum of primary liver carcinomas, which include hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (iCCA) at both ends of the spectrum. Mainly due to the high intratumor heterogeneity of cHCC-CCA, its diagnosis and pathological description remain challenging. Taking advantage of in situ non-targeted molecular mapping provided by MALDI (Matrix Assisted Laser Desorption Ionization) imaging, we sought to develop a multiscale and multiparametric morphological approach, integrating molecular and conventional pathological analysis. MALDI imaging was applied to five representative cases of resected cHCC-CCA. Principal component analysis and segmentations with MALDI imaging techniques identified areas related to either iCCA or HCC and also hidden tumor areas not visible microscopically. In addition, the overlap between MALDI segmentation and immunostaining provided a comprehensive description of cHCC-CCA tumor heterogeneity by identifying transitional and micro-metastatic areas. Moreover, a list of peptides derived from in silico digestion was obtained for each immunohistochemical marker and was matched within the peptide peak list acquired by MALDI. Comparison of immunostaining images with ions from in silico digestion revealed an accurate identification of iCCA and HCC areas. Our study provides further evidence on the performance of MALDI imaging in exploring intratumor heterogeneity and offering virtual multiplex immunostaining through a single acquisition.

Nestin as a diagnostic and prognostic marker for combined hepatocellular-cholangiocarcinoma.

BACKGROUND & AIMS: Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLCs. METHODS: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCCs) and 221 intrahepatic cholangiocarcinomas (iCCAs). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver-operating characteristic curves and Cox regression modeling. RESULTS: Nestin was able to distinguish cHCC-CCA from HCC with AUCs of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from iCCA (AUCs of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA ("Nestin High", >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies. CONCLUSION: We show in different clinical settings that Nestin has diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy. LAY SUMMARY: There are different types of primary liver cancers (i.e. cancers that originate in the liver). Accurately identifying a specific subtype of primary liver cancer (and determining its associated prognosis) is important as it can have a major impact on treatment allocation. Herein, we show that a protein called Nestin could be used to refine risk stratification and improve treatment allocation for patients with combined hepatocellular carcinoma, a rare but highly aggressive subtype of primary liver cancer.

Factors of Early Recurrence After Resection for Intrahepatic Cholangiocarcinoma.

BACKGROUND: Two-thirds of patients undergoing liver resection for intrahepatic cholangiocarcinoma experience recurrence after surgery. Our aim was to identify factors associated with early recurrence after resection for intrahepatic cholangiocarcinoma. METHODS: Patients with intrahepatic cholangiocarcinoma undergoing curative intent resection (complete resection and lymphadenectomy) were included in two centers between 2005 and 2021 and were divided into three groups: early recurrence (< 12 months after resection), delayed recurrence (> 12 months), and no recurrence. Patients experiencing early (< 90 days) postoperative mortality were excluded. RESULTS: Among 120 included patients, 44 (36.7%) experienced early recurrence, 24 (20.0%) experienced delayed recurrence, and 52 (43.3%) did not experience recurrence after a median follow-up of 59 months (IQR: 26-113). The median recurrence-free survival was 16 months (95% CI: 9.6-22.4). Median overall survival was 55 months (95% CI: 45.7-64.3), while it was 25 months for patients with early recurrence (p < 0.001). Patients with early recurrence had significantly larger tumors (59.1% of tumors > 70 mm in early vs. 58.3% in delayed vs. 26.9% in no recurrence group, p = 0.002), multiple lesions (65.9% vs. 29.2% vs. 11.5%, p < 0.001), and positive lymph nodes (N +) (38.6% vs. 37.5% vs. 11.5%, p = 0.005). In multivariable analysis, presence of multiple lesions (OR: 9.324; 95% CI: 3.051-28.489; p < 0.001) and positive lymph nodes (OR: 3.307. 95% CI: 1.001-11.011. p = 0.05) were associated with early recurrence. CONCLUSION: Early recurrence after curative resection of intrahepatic cholangiocarcinoma is frequent and is associated with the presence of multiple lesions and positive lymph nodes, raising the question of surgery's futility in this context.

Differentiation of hepatocellular adenoma by subtype and hepatocellular carcinoma in non-cirrhotic liver by fractal analysis of perfusion MRI.

BACKGROUND: To investigate whether fractal analysis of perfusion differentiates hepatocellular adenoma (HCA) subtypes and hepatocellular carcinoma (HCC) in non-cirrhotic liver by quantifying perfusion chaos using four-dimensional dynamic contrast-enhanced magnetic resonance imaging (4D-DCE-MRI). RESULTS: A retrospective population of 63 patients (47 female) with histopathologically characterized HCA and HCC in non-cirrhotic livers was investigated. Our population consisted of 13 hepatocyte nuclear factor (HNF)-1α-inactivated (H-HCAs), 7 ß-catenin-exon-3-mutated (bex3-HCAs), 27 inflammatory HCAs (I-HCAs), and 16 HCCs. Four-dimensional fractal analysis was applied to arterial, portal venous, and delayed phases of 4D-DCE-MRI and was performed in lesions as well as remote liver tissue. Diagnostic accuracy of fractal analysis was compared to qualitative MRI features alone and their combination using multi-class diagnostic accuracy testing including kappa-statistics and area under the receiver operating characteristic curve (AUC). Fractal analysis allowed quantification of perfusion chaos, which was significantly different between lesion subtypes (multi-class AUC = 0.90, p < 0.001), except between I-HCA and HCC. Qualitative MRI features alone did not allow reliable differentiation between HCA subtypes and HCC (κ = 0.35). However, combining qualitative MRI features and fractal analysis reliably predicted the histopathological diagnosis (κ = 0.89) and improved differentiation of high-risk lesions (i.e., HCCs, bex3-HCAs) and low-risk lesions (H-HCAs, I-HCAs) from sensitivity and specificity of 43% (95% confidence interval [CI] 23-66%) and 47% (CI 32-64%) for qualitative MRI features to 96% (CI 78-100%) and 68% (CI 51-81%), respectively, when adding fractal analysis. CONCLUSIONS: Combining qualitative MRI features with fractal analysis allows identification of HCA subtypes and HCCs in patients with non-cirrhotic livers and improves differentiation of lesions with high and low risk for malignant transformation.

Performance of non-invasive biomarkers compared with invasive methods for risk prediction of posthepatectomy liver failure in hepatocellular carcinoma.

BACKGROUND: Posthepatectomy liver failure (PHLF) is a rare but dreaded complication. The aim was to test whether a combination of non-invasive biomarkers (NIBs) and CT data could predict the risk of PHLF in patients who underwent resection of hepatocellular carcinoma (HCC). METHODS: Patients with HCC who had liver resection between 2012 and 2020 were included. A relevant combination of NIBs (NIB model) to model PHLF risk was identified using a doubly robust estimator (inverse probability weighting combined with logistic regression). The adjustment variables were body surface area, ASA fitness grade, male sex, future liver remnant (FLR) ratio, difficulty of liver resection, and blood loss. The reference invasive biomarker (IB) model comprised a combination of pathological analysis of the underlying liver and hepatic venous pressure gradient (HVPG) measurement. Various NIB and IB models for prediction of PHLF were fitted and compared. NIB model performances were validated externally. Areas under the curve (AUCs) were corrected using bootstrapping. RESULTS: Overall 323 patients were included. The doubly robust estimator showed that hepatitis C infection (odds ratio (OR) 4.33, 95 per cent c.i. 1.29 to 9.20; P = 0.001), MELD score (OR 1.26, 1.04 to 1.66; P = 0.001), fibrosis-4 score (OR 1.36, 1.06 to 1.85; P = 0.001), liver surface nodularity score (OR 1.55, 1.28 to 4.29; P = 0.031), and FLR volume ratio (OR 0.99, 0.97 to 1.00; P = 0.014) were associated with PHLF. Their combination (NIB model) was fitted externally (2-centre cohort, 165 patients) to model PHLF risk (AUC 0.867). Among 129 of 323 patients who underwent preoperative HVPG measurement, NIB and IB models had similar performances (AUC 0.753 versus 0.732; P = 0.940). A well calibrated nomogram was drawn based on the NIB model (AUC 0.740). The risk of grade B/C PHLF could be ruled out in patients with a cumulative score of less than 160 points. CONCLUSION: The NIB model provides reliable preoperative evaluation with performance at least similar to that of invasive methods for PHLF risk prediction.

Immune Profiling of Combined Hepatocellular- Cholangiocarcinoma Reveals Distinct Subtypes and Activation of Gene Signatures Predictive of Response to Immunotherapy.

PURPOSE: Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare malignancy associated with an overall poor prognosis. We aimed to investigate the immune profile of cHCC-CCA and determine its impact on disease outcome. EXPERIMENTAL DESIGN: We performed a multicenter study of 96 patients with cHCC-CCA. Gene expression profile was analyzed using nCounter PanCancer IO 360 Panel. Densities of main immune cells subsets were quantified from digital slides of IHC stainings. Genetic alterations were investigated using targeted next-generation sequencing. RESULTS: Two main immune subtypes of cHCC-CCA were identified by clustering analysis: an "immune-high" (IH) subtype (57% of the cases) and an "immune-low" (IL) subtype (43% of the cases). Tumors classified as IH showed overexpression of genes related to immune cells recruitment, adaptive and innate immunity, antigen presentation, cytotoxicity, immune suppression, and inflammation (P < 0.0001). IH cHCC-CCAs also displayed activation of gene signatures recently shown to be associated with response to immunotherapy in patients with HCC. Quantification of immunostainings confirmed that IH tumors were also characterized by higher densities of immune cells. Immune subtypes were not associated with any genetic alterations. Finally, multivariate analysis showed that the IH subtype was an independent predictor of improved overall survival. CONCLUSIONS: We have identified a subgroup of cHCC-CCA that displays features of an ongoing intratumor immune response, along with an activation of gene signatures predictive of response to immunotherapy in HCC. This tumor subclass is associated with an improved clinical outcome. These findings suggest that a subset of patients with cHCC-CCA may benefit from immunomodulating therapeutic approaches.

Cystic fibrosis-related liver disease: Clinical presentations, diagnostic and monitoring approaches in the era of CFTR modulator therapies.

Cystic fibrosis (CF) is the most common autosomal recessive disease in the Caucasian population. Cystic fibrosis-related liver disease (CFLD) is defined as the pathogenesis related to the underlying CFTR defect in biliary epithelial cells. CFLD needs to be distinguished from other liver manifestations that may not have any pathological significance. The clinical/histological presentation and severity of CFLD vary. The main histological presentation of CFLD is focal biliary fibrosis, which is usually asymptomatic. Portal hypertension develops in a minority of cases (about 10%) and may require specific management including liver transplantation for end-stage liver disease. Portal hypertension is usually the result of the progression of focal biliary fibrosis to multilobular cirrhosis during childhood. Nevertheless, non-cirrhotic portal hypertension as a result of porto-sinusoidal vascular disease is now identified increasingly more frequently, mainly in young adults. To evaluate the effect of new CFTR modulator therapies on the liver, the spectrum of hepatobiliary involvement must first be precisely classified. This paper discusses the phenotypic features of CFLD, its underlying physiopathology and relevant diagnostic and follow-up approaches, with a special focus on imaging.

Gene expression signature as a surrogate marker of microvascular invasion on routine hepatocellular carcinoma biopsies.

BACKGROUND & AIMS: Microvascular invasion (MVI), a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma (HCC), is only detectable by microscopic examination of the surgical specimen. We aimed to define a transcriptomic signature associated with MVI in HCC than can be applied to formalin-fixed paraffin-embedded (FFPE) biopsies for use in clinical practice. METHODS: To identify a gene expression signature related to MVI by using NanoString technology, we selected a set of 200 genes according to the literature and RNA-sequencing data obtained from a cohort of 150 frozen HCC samples previously published. We used 178 FFPE-archived HCC samples, including 109 surgical samples for the training set and 69 paired pre-operative biopsies for the validation set. In 14 cases of the training set, a paired biopsy was available and was also analyzed. RESULTS: We identified a 6-gene signature (ROS1, UGT2B7, FAS, ANGPTL7, GMNN, MKI67) strongly associated with MVI in the training set of FFPE surgical HCC samples, with 82% accuracy (sensitivity 82%, specificity 81%, AUC 0.82). The NanoString gene expression was highly correlated in 14 paired surgical/biopsy HCC samples (mean R: 0.97). In the validation set of 69 FFPE HCC biopsies, the 6-gene NanoString signature predicted MVI with 74% accuracy (sensitivity 73%, specificity 76%, AUC 0.74). Moreover, on multivariate analysis, the MVI signature was associated with overall survival in both sets (hazard ratio 2.29; 95% CI 1.03-5.07; p = 0.041). CONCLUSION: We defined a 6-gene signature that can accurately predict MVI in FFPE HCC biopsy samples, which is also associated with overall survival, although its survival impact must be confirmed by extensive study with further clinical data. LAY SUMMARY: Microvascular invasion, a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma, is only detectable by microscopic examination of a surgical specimen. In this study, we defined a relevant surrogate signature of microvascular invasion in hepatocellular carcinoma that may be applied in clinical practice with routine tumor biopsy and integrated into the therapeutic strategy.

Liver surface nodularity on non-contrast MRI identifies advanced fibrosis in patients with NAFLD.

OBJECTIVES: To evaluate the diagnostic performance of liver surface nodularity (LSN) for the assessment of advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). METHODS: We retrospectively analysed patients with pathologically proven NAFLD who underwent liver MRI. Demographic, clinical, and laboratory data (including FIB-4 scores) were gathered. The SAF score was used to assess NAFLD. MRI-proton density fat fraction (PDFF) and LSN were determined on pre-contrast MR sequences. ROC curve analysis was performed to evaluate the diagnostic performance of MRI-LSN for the diagnosis of advanced (F3-F4) liver fibrosis. RESULTS: The final population included 142 patients. Sixty-seven (47%) patients had non-alcoholic steatohepatitis (NASH), and 52 (37%) had advanced fibrosis. The median MRI-PDFF increased with the grades of steatosis: 8.1%, 18.1%, and 31% in S1, S2, and S3 patients, respectively (p < 0.001). The area under the ROC curve (AUC) of MRI-LSN ≥ 2.50 was 0.838 (95%CI 0.767-0.894, sensitivity 67.3%, specificity 88.9%, positive and negative predictive values 77.8% and 82.5%, respectively) for the diagnosis of advanced fibrosis. Combining FIB-4 and MRI-LSN correctly classified 103/142 (73%) patients. This was validated in an external cohort of 75 patients. CONCLUSIONS: MRI-LSN has good diagnostic performance in diagnosis of advanced fibrosis in NAFLD patients. A combination of FIB-4 and MRI-LSN derived from pre-contrast MRI could be helpful to detect advanced fibrosis. KEY POINTS: • MRI-LSN ≥ 2.5 was accurate for the diagnosis of advanced hepatic fibrosis in NAFLD patients. • The combination of FIB-4 and MRI-LSN improved the detection of advanced fibrosis. • MRI-LSN can be easily derived by unenhanced MRI sequences that are routinely acquired.