Intrahepatic cholangiocarcinoma: can imaging phenotypes predict survival and tumor genetics?

Clinical and dosimetric considerations for yttrium-90 glass microspheres radioembolization of intrahepatic cholangiocarcinoma, metastatic colorectal carcinoma, and metastatic neuroendocrine carcinoma: recommendations from an international multidisciplinary working group

PURPOSE

The TheraSphere Global Steering Committee reconvened to review clinical data and address knowledge gaps related to treatment and dosimetry in non-HCC indications using Yttrium-90 (90Y) glass microspheres.

METHODS

A PubMed search was performed. References were reviewed and adjudicated by the Delphi method. Recommendations were graded according to the degree of recommendation and strength of consensus. Dosimetry focused on a mean dose approach, i.e., aiming for an average dose over either single or multicompartment volumes of interests. Committee discussion and consensus focused on optimal patient selection, disease presentation, liver function, tumour type, tumour vascularity, and curative/palliative treatment intent for intrahepatic cholangiocarcinoma (iCCA) and colorectal and neuroendocrine carcinoma liver metastases (mCRC, mNET).

RESULTS

For all indications, single compartment average perfused volume absorbed dose ≥ 400 Gy is recommended for radiation segmentectomy and 150 Gy for radiation lobectomy. Single compartment 120 Gy for uni- and bilobar treatment reflects current clinical practice, which results in variable tumour and normal tissue absorbed doses. Therefore, multicompartment dosimetry is recommended for uni- and bilobar treatment, aiming for maximum 75 Gy to normal tissue and 150-200 Gy (mCRC, mNET), ≥ 205 (iCCA) tumour absorbed doses. These dose thresholds are preliminary and should be used with caution accounting for patient specific characteristics.

CONCLUSION

Consensus recommendations are provided to guide clinical and dosimetry approaches for 90Y glass microsphere radioembolization in iCCA, mCRC and mNET.

CLINICAL TRIAL NUMBER

not applicable.

Radiogenomics of Intrahepatic Cholangiocarcinoma: Correlation of Imaging Features With BAP1 and FGFR Molecular Subtypes

PURPOSE

Clinical research has shown unique tumor behavioral characteristics of BRCA -associated protein-1- ( BAP1 -) and fibroblast growth factor receptor ( FGFR )-mutated intrahepatic cholangiocarcinomas (CCAs), with BAP1 -mutated tumors demonstrating more aggressive forms of disease and FGFR -altered CCAs showing more indolent behavior. We performed a retrospective case-control study to evaluate for unique imaging features associated with BAP1 and FGFR genomic markers in intrahepatic CCA (iCCA).

METHODS

Multiple imaging features of iCCA at first staging were analyzed by 2 abdominal radiologists blinded to genomic data. Growth and development of metastases at available follow-up imaging were also recorded, as were basic clinical cohort data. Types of iCCA analyzed included those with BAP1 , FGFR , or both alterations, as well as cases with low mutational burden or mutations with low clinical impact, which served as a control or "wild-type" group. There were 18 cases in the FGFR group, 10 with BAP1 mutations, and 31 wild types (controls).

RESULTS

Cases with BAP1 mutations showed significantly larger growth at first year of follow-up ( P = 0.03) and more frequent tumor-associated biliary ductal dilatation ( P = 0.04) compared with controls. FGFR -altered cases showed more infiltrative margins compared with controls ( P = 0.047) and demonstrated less enhancement between arterial to portal venous phases ( P = 0.02). BAP1 and FGFR groups had more cases with stage IV disease at presentation than controls ( P = 0.025, P = 0.006).

CONCLUSION

Compared with wild-type iCCAs, FGFR -mutated tumors often demonstrate infiltrative margins, and BAP1 tumors show increased biliary ductal dilatation at presentation. BAP1 -mutated cases had significantly larger growth at first-year restaging.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

Prediction of regional lymph node metastasis in intrahepatic cholangiocarcinoma: it's not all about size

OBJECTIVES

Lymph node metastases (LNM) are frequent in patients with intrahepatic cholangiocarcinoma (iCC) and worsen their prognosis even after surgery. Our aim was to investigate the predictive value of lymph node (LN) short axis, the most common discriminator for identifying LNM in tumor-imaging and to develop a predictive model for regional LNM in iCC taking computed tomography (CT) features of extranodal disease into account.

MATERIALS AND METHODS

We enrolled 102 patients with pathologically proven iCC who underwent CT prior to hepatic resection and hilar lymph node dissection (LND) from 2005 to 2021. Two blinded radiologists assessed various imaging characteristics and LN diameters, which were analyzed by bivariate and multivariate logistic regression to develop a prediction model for LNM.

RESULTS

Prevalence of LNM was high (42.4 %) and estimated survival was shorter in LN-positive patients (p = 0.07). An LN short axis diameter of ≥ 9 mm demonstrated the highest predictive power for LNM. Three additional, statistically significant imaging features, presence of intrahepatic metastasis (p = 0.003), hilar tumor infiltration (p = 0.003), and tumor growth along the liver capsule (p = 0.004), were integrated into a prediction model, which substantially outperformed use of LN axis alone in ROC analysis (AUC 0.856 vs 0.701).

CONCLUSIONS

LN diameter alone proved to be a relevant but unreliable imaging-marker for LNM prediction in iCC. Our proposed prognostic model, which additionally considers intrahepatic metastases and hilar and capsular infiltration, significantly improves discriminatory power. Hilar and capsular involvement might indicate direct tumor extension to lymphatic liver structures.

What Role Does Radiotherapy Play in the Molecular Era for Intrahepatic Cholangiocarcinoma?

ABSTRACT

Intrahepatic cholangiocarcinoma is a rare disease, yet with rising incidence globally. Most patients are not eligible for potentially curative surgical resection, and many patients with unresectable disease die within 12 months of diagnosis, primarily due to liver failure from the primary tumor. Recent prospective and retrospective studies indicate that local control of the primary tumor can be achieved with hypofractionated radiotherapy in patients with unresectable disease, translating into prolonged survival of these patients. During the time that these encouraging reports for radiotherapy have been published, numerous concurrent studies have also shown that intrahepatic cholangiocarcinoma is a molecularly diverse disease with multiple targetable genetic alterations and a complex tumor microenvironment. These biological insights have translated into new drug approvals for subsets of patients. We review the current knowledge about the biology and targeted treatment of intrahepatic cholangiocarcinoma and describe these developments in the context of modern radiotherapy.

Imaging features based on CT and MRI for predicting prognosis of patients with intrahepatic cholangiocarcinoma: a single-center study and meta-analysis

BACKGROUND

To evaluate the prognostic role of imaging features based on CT and MRI in intrahepatic cholangiocarcinoma (ICC).

METHODS

Two hundred and four patients from a single-center database who underwent radical ICC surgery from 2010 to 2019 were enrolled in the study. Cox proportional hazard model was used for survival analysis of imaging features. A meta-analysis was performed to determine imaging features that predict overall survival (OS) and event-free survival (EFS) in ICC.

RESULTS

In the CT group of the retrospective cohort, tumor multiplicity, infiltrative tumor margin, lymph node metastasis, enhancement pattern in hepatic arterial phase and tumor necrosis correlated with poorer EFS and OS; moreover, enhancing capsules, high carcinoembryonic antigen levels contributed to poor OS. In the MRI group, tumor multiplicity and enhancement pattern were prognostic factors for OS; tumor multiplicity and enhancement pattern resulted in poor EFS. A total of 13 articles containing 1822 patients with ICC were enrolled in the adjusted hazard ratios meta-analysis. The results showed that enhancement pattern and infiltrative tumor margin were predictors of OS and EFS, whereas bile duct invasion was a predictor of OS.

CONCLUSIONS

Arterial enhancement patterns and tumor margin status were associated with both OS and EFS of ICC patients following resection.

Prognostic influence of multiple hepatic lesions in resectable intrahepatic cholangiocarcinoma: A systematic review and meta-analysis

BACKGROUND

Presence of multiple hepatic lesions in intrahepatic cholangiocarcinoma (iCCA) is included in staging as a negative prognostic factor, but both prognostic value and therapeutic implications remain debated. The aim of this study was to systematically review the prognostic influence of multiple lesions on survival after resection for iCCA, with stratification for distribution and number of lesions.

METHODS

Medline and Embase were systematically searched to identify records (2010-2021) reporting survival for patients undergoing primary resection for iCCA. Included were original articles reporting overall survival, with data on multiple lesions including tumour distribution (satellites/other multiple lesions) and/or number. For meta-analysis, the random effects model and inverse variance method were used. PRISMA 2020 guidelines were followed.

RESULTS

Thirty-one studies were included for review. For meta-analysis, nine studies reporting data on the prognostic influence of satellite lesions (2737 patients) and six studies reporting data on multiple lesions other than satellites (1589 patients) were included. Satellite lesions (hazard ratio 1.89, 95% confidence interval 1.67-2.13) and multiple lesions other than satellites (hazard ratio 2.41, 95% confidence interval 1.72-3.37) were significant negative prognostic factors. Data stratified for tumour number, while limited, indicated increased risk per additional lesion.

CONCLUSION

Satellite lesions, as well as multiple lesions other than satellites, was a negative prognostic factor in resectable iCCA. Considering the prognostic impact, both tumour distribution and number of lesions should be evaluated together with other risk factors to allow risk stratification for iCCA patients with multiple lesions, rather than precluding resection for the entire patient group.

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Background

Nomograms have been established to predict survival in postoperative or elderly intrahepatic cholangiocarcinoma (ICC) patients. There are no models to predict postoperative survival in elderly ICC patients. Extreme gradient boosting (XGBoost) can adjust the errors generated by existing models. This retrospective cohort study aimed to develop and validate an XGBoost model to predict postoperative 5-year survival in elderly ICC patients.

Methods

The Surveillance, Epidemiology, and End Results (SEER) program provided data on elderly ICC patients aged 60 years or older and undergoing surgery. The median follow-up time was 20 months. Totally 1,055 patients were classified as training (n=738) and testing (n=317) sets at a ratio of 7:3. The outcome was postoperative 5-year survival. Demographic, tumor-related and treatment-related variables were collected. Variables were screened using the XGBoost model. The predictive performance of the model was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Kaplan-Meier curve. Cox regression analysis was conducted to estimate the risk of death in the predicted populations. The predictive abilities of the XGBoost model and the American Joint Commission on Cancer (AJCC) system (7th edition) were compared.

Results

The XGBoost model achieved an AUC of 0.811, a sensitivity of 0.573, a specificity of 0.890, and a PPV of 0.849 in the training set. In the testing set, the model had an AUC of 0.713, a sensitivity of 0.478, a specificity of 0.814, and a PPV of 0.726. The 5-year mortality risk of patients predicted to die was 2.91 times that of patients predicted to survive [hazard ratio (HR) =2.91, 95% confidence interval (CI): 2.42-3.50]. The XGBoost model showed a better predictive performance than the AJCC staging system both in the training and testing sets. AJCC stage, multiple (satellite) tumors/nodules, tumor-node-metastasis (TNM) stage, more than one lobe invaded, direct invasion of adjacent organs, tumor size, and radiotherapy were relatively important features in survival prediction.

Conclusions

The XGBoost model exhibited some predictive capacity, which may be applied to predict postoperative 5-year survival for elderly ICC patients.

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions

Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.

Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma

BACKGROUND

Most patients recur after resection of intrahepatic cholangiocarcinoma (IHC). We studied whether machine-learning incorporating radiomics and tumor size could predict intrahepatic recurrence within 1-year.

METHODS

This was a retrospective analysis of patients with IHC resected between 2000 and 2017 who had evaluable computed tomography imaging. Texture features (TFs) were extracted from the liver, tumor, and future liver remnant (FLR). Random forest classification using training (70.3%) and validation cohorts (29.7%) was used to design a predictive model.

RESULTS

138 patients were included for analysis. Patients with early recurrence had a larger tumor size (7.25 cm [IQR 5.2-8.9] vs. 5.3 cm [IQR 4.0-7.2], P = 0.011) and a higher rate of lymph node metastasis (28.6% vs. 11.6%, P = 0.041), but were not more likely to have multifocal disease (21.4% vs. 17.4%, P = 0.643). Three TFs from the tumor, FD1, FD30, and IH4 and one from the FLR, ACM15, were identified by feature selection. Incorporation of TFs and tumor size achieved the highest AUC of 0.84 (95% CI 0.73-0.95) in predicting recurrence in the validation cohort.

CONCLUSION

This study demonstrates that radiomics and machine-learning can reliably predict patients at risk for early intrahepatic recurrence with good discrimination accuracy.

Morphomolecular Classification Update on Hepatocellular Adenoma, Hepatocellular Carcinoma, and Intrahepatic Cholangiocarcinoma

Hepatocellular adenomas (HCAs), hepatocellular carcinomas (HCCs), and intrahepatic cholangiocarcinomas (iCCAs) are a highly heterogeneous group of liver tumors with diverse pathomolecular features and prognoses. High-throughput gene sequencing techniques have allowed discovery of distinct genetic and molecular underpinnings of these tumors and identified distinct subtypes that demonstrate varied clinicobiologic behaviors, imaging findings, and complications. The combination of histopathologic findings and molecular profiling form the basis for the morphomolecular classification of liver tumors. Distinct HCA subtypes with characteristic imaging findings and complications include HNF1A-inactivated, inflammatory, β-catenin-activated, β-catenin-activated inflammatory, and sonic hedgehog HCAs. HCCs can be grouped into proliferative and nonproliferative subtypes. Proliferative HCCs include macrotrabecular-massive, TP53-mutated, scirrhous, clear cell, fibrolamellar, and sarcomatoid HCCs and combined HCC-cholangiocarcinoma. Steatohepatitic and β-catenin-mutated HCCs constitute the nonproliferative subtypes. iCCAs are classified as small-duct and large-duct types on the basis of the level of bile duct involvement, with significant differences in pathogenesis, molecular signatures, imaging findings, and biologic behaviors. Cross-sectional imaging modalities, including multiphase CT and multiparametric MRI, play an essential role in diagnosis, staging, treatment response assessment, and surveillance. Select imaging phenotypes can be correlated with genetic abnormalities, and identification of surrogate imaging markers may help avoid genetic testing. Improved understanding of morphomolecular features of liver tumors has opened new areas of research in the targeted therapeutics and management guidelines. The purpose of this article is to review imaging findings of select morphomolecular subtypes of HCAs, HCCs, and iCCAs and discuss therapeutic and prognostic implications. Online supplemental material is available for this article. ^©RSNA, 2022.

Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma

Background

This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC).

Methods

Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021.

Results

We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs.

Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance.

Conclusions

Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.

An update on radiomics techniques in primary liver cancers

BACKGROUND

Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.

METHODS

This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.

RESULTS

In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring.

CONCLUSIONS

Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.

TRIAL REGISTRATION

Not applicable.

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

OBJECTIVES

This study valued MR delayed enhancement pattern in predicting postoperative prognosis of intrahepatic mass-forming cholangiocarcinoma (IMCC).

METHODS

From 2011 to 2015, 231 patients of IMCC underwent DCE-MRI preoperatively. Enhancement patterns and MRI characteristics were evaluated. Recurrence and mortality data were compared among IMCCs with different enhancement patterns. Prognostic factor analysis was performed using preoperative and postoperative clinical-pathologic factors, as well as imaging findings.

RESULTS

Fifty-six (24.2%), 142 (61.5%) and 33 (14.3%) tumors showed hypo, peripheral rim and diffuse hyper enhancement in AP. Fifty-six (24.2%), 81 (35.1%) and 94 (40.7%) tumors showed hypo, heterogeneous and uniform enhancement in DP. Patients with arterial diffuse hyper enhancement or delayed uniform enhancement IMCCs had lower preoperative CA19-9 levels, smaller tumor sizes and minor operations than the rest patients (p < 0.05) and they were less associated with lymph nodes metastasis, vascular invasion, necrosis or poor tumor differentiation (p < 0.05), therefore with higher overall and disease-free survival rates (p < 0.05). The combination of AP and DP increased the detection rate of patients with good prognosis in the arterial rim enhancement group. Multivariate analysis revealed the delayed enhancement pattern (hypo HR = 6.304/10.028 for DFS/OS; heterogenous HR = 4.579/4.972 for DFS/OS), multitude of lesions (HR = 1.6/1.5 for DFS/OS) and tumor sizes (HR = 1.6 for DFS) were independent prognostic factors.

CONCLUSIONS

The uniform enhancement pattern in delayed MRI was an independent optimal prognostic factor for IMCCs and increased the detection rate of patients with good prognosis compared to the arterial diffuse hyper enhancement pattern.

Cholangiocarcinoma: new perspectives for new horizons

INTRODUCTION

Biliary tract cancer represents a heterogeneous group of malignancies characterized by dismal prognosis and scarce therapeutic options.

AREA COVERED

In the last years, a growing interest in BTC pathology has emerged, thus highlighting a significant heterogeneity of the pathways underlying the carcinogenesis process, from both a molecular and genomic point of view. A better understanding of these differences is mandatory to deepen the behavior of this complex disease, as well as to identify new targetable target mutations, with the aim to improve the survival outcomes. The authors decided to provide a comprehensive overview of the recent highlights on BTCs, with a special focus on the genetic, epigenetic and molecular alterations, which may have an interesting clinical application in the next future.

EXPERT OPINION

In the last years, the efforts resulted from international collaborations have led to the identification of new promising targets for precision medicine approaches in the BTC setting. Further investigations and prospective trials are needed, but the hope is that these new knowledge in cooperation with the new technologies and procedures, including bio-molecular and genomic analysis as well radiomic studies, will enrich the therapeutic armamentarium thus improving the survival outcomes in a such lethal and complex disease.

Role of noninvasive imaging in the evaluation of intrahepatic cholangiocarcinoma: from diagnosis and prognosis to treatment response

INTRODUCTION

Intrahepatic cholangiocarcinoma is the second most common liver cancer. Desmoplastic stroma may be revealed as distinctive histopathologic findings favoring intrahepatic cholangiocarcinoma. Meanwhile, a range of imaging manifestations is often accompanied with rich desmoplastic stroma in intrahepatic cholangiocarcinoma, which can indicate large bile duct ICC, and a higher level of cancer-associated fibroblasts with poor prognosis and weak treatment response.

AREAS COVERED

We provide a comprehensive review of current state-of-the-art and recent advances in the imaging evaluation for diagnosis, staging, prognosis and treatment response of intrahepatic cholangiocarcinoma. In addition, we discuss precursor lesions, cells of origin, molecular mutation, which would cause the different histological classification. Moreover, histological classification and tumor microenvironment, which are related to the proportion of desmoplastic stroma with many imaging manifestations, would be also discussed.

EXPERT OPINION

The diagnosis, prognosis, treatment response of intrahepatic cholangiocarcinoma may be revealed as the presence and the proportion of desmoplastic stroma with a range of imaging manifestations. With the utility of radiomics and artificial intelligence, imaging is helpful for ICC evaluation. Multicentre, large-scale, prospective studies with external validation are in need to develop comprehensive prediction models based on clinical data, imaging findings, genetic parameters, molecular, metabolic, and immune biomarkers.

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Purpose

To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model.

Methods

Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared.

Results

Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2.

Conclusion

The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03183-9.

Decoding Genes: Current Update on Radiogenomics of Select Abdominal Malignancies

Technologic advances in chromosomal analysis and DNA sequencing have enabled genome-wide analysis of cancer cells, yielding considerable data on the genetic basis of malignancies. Evolving knowledge of tumor genetics and oncologic pathways has led to a better understanding of histopathologic features, tumor classification, tumor biologic characteristics, and imaging findings and discovery of targeted therapeutic agents. Radiogenomics is a rapidly evolving field of imaging research aimed at correlating imaging features with gene mutations and gene expression patterns, and it may provide surrogate imaging biomarkers that may supplant genetic tests and be used to predict treatment response and prognosis and guide personalized treatment options. Multidetector CT, multiparametric MRI, and PET with use of multiple radiotracers are some of the imaging techniques commonly used to assess radiogenomic associations. Select abdominal malignancies demonstrate characteristic imaging features that correspond to gene mutations. Recent advances have enabled us to understand the genetics of steatotic and nonsteatotic hepatocellular adenomas, a plethora of morphologic-molecular subtypes of hepatic malignancies, a variety of clear cell and non-clear cell renal cell carcinomas, a myriad of hereditary and sporadic exocrine and neuroendocrine tumors of the pancreas, and the development of targeted therapeutic agents for gastrointestinal stromal tumors based on characteristic KIT gene mutations. Mutations associated with aggressive phenotypes of these malignancies can sometimes be predicted on the basis of their imaging characteristics. Radiologists should be familiar with the genetics and pathogenesis of common cancers that have associated imaging biomarkers, which can help them be integral members of the cancer management team and guide clinicians and pathologists. Online supplemental material is available for this article. ^©RSNA, 2020 See discussion on this article by Luna (pp 1627-1630).

Genomic profiling of multifocal intrahepatic cholangiocarcinoma reveals intraindividual concordance of genetic alterations

In multifocal intrahepatic cholangiocarcinoma (IHC), intrahepatic metastases (IM) represent a contraindication to surgical resection, whereas satellite nodules (SN) do not. However, no consensus criteria exist to distinguish IM from SN. The purpose of this study was to determine genetic alterations and clonal relationships in surgically resected multifocal IHC. Next-generation sequencing of 34 spatially separated IHC tumors was performed using a targeted panel of 201 cancer-associated genes. Proposed definitions in the literature were applied of SN located in the same liver segment and ≤2 cm from the primary tumor; and IM located in a different liver segment and/or >2 cm from the primary tumor. Somatic point mutations concordant across tumors from individual patients included BAP1, SMARCA4 and IDH1. Small insertions and deletions (indels) present at the same genome positions among all tumors from individuals included indels in DNA repair genes, CHEK1, ERCC5, ATR and MSH6. Copy number alterations were also similar between all tumors in each patient. In this cohort of multifocal IHC, genomic profiles were concordant across all tumors in each patient, suggesting a common progenitor cell origin, regardless of the location of tumors in the liver. The decision to perform surgery should not be based upon a perceived distinction between IM and SN.

Intrahepatic cholangiocarcinoma: Introducing the preoperative prediction score based on preoperative imaging

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) still has a poor long-term outcome, even after complete resection. We investigated different parameters gathered in preoperative imaging and analyzed their influence on resectability, recurrence, and survival.

METHODS

All patients who underwent exploration due to ICC between January 2008 and June 2018 were analyzed retrospectively. Kaplan-Meier model, log-rank test and Cox regression were used.

RESULTS

Out of 184 patients, 135 (73.4%) underwent curative intended resection. Median overall survival (OS) was 22.2 months with a consecutive 1-, 3- and 5-year OS of 73%, 29%, and 17%. Median recurrence-free survival (RFS) was 9.3 months with a consecutive 1-, 3- and 5-year RFS of 36%, 15%, and 11%. Site of tumor, parenchymal localization, tumor configuration/dissemination, and estimated tumor volume had significant influence on resectability. Univariate analyses showed that site of tumor, tumor configuration/dissemination, number of nodules, and estimated tumor volume had predictive values for OS and RFS. Together with tumor size the preoperative prediction (POP) score was created showing significance for OS and RFS (all P < 0.001). In multivariate analysis, POP score (HR = 1.779; 95% CI: 1.268-2.495; P = 0.001), T stage (HR = 1.255; 95% CI: 1.040-1.514; P = 0.018) and N stage (HR = 1.334; 95% CI: 1.081-1.645; P = 0.007) were the independent predictors for OS. For RFS, POP score (HR = 1.733; 95% CI: 1.300-2.311; P < 0.001) and M stage (HR = 3.036; 95% CI: 1.376-6.697; P = 0.006) were the independent predictors.

CONCLUSIONS

The POP score showed to have a highly significant influence on OS and RFS. The score is easy to assess through preoperative imaging. For patients in the high risk group at least staging laparoscopy or preoperative chemotherapy should be evaluated, because they showed equal outcome compared to the irresectable group.

Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

BACKGROUND

Cholangiocarcinoma (CCA) is a relatively rare malignant biliary system tumor, and yet it represents the second most common primary hepatic neoplasm, following hepatocellular carcinoma. Regardless of the type, location, or etiology, the survival prognosis of these tumors remains poor. The only method of cure for CCA is complete surgical resection, but part of patients with complete resection are still subject to local recurrence or distant metastasis.

SUMMARY

Over the last several decades, our understanding of the molecular biology of CCA has increased tremendously, diagnostic and evaluative techniques have evolved, and novel therapeutic approaches have been established. Key Messages: This review provides an overview of preoperative imaging evaluations of CCA. Furthermore, relevant information about artificial intelligence (AI) in medical imaging is discussed, as well as the development of AI in CCA treatment.

Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.

Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features

BACKGROUND

To assess the performance of imaging features, including radiomics texture features, in predicting histopathologic tumor grade, AJCC stage, and outcomes [time to recurrence (TTR) and overall survival (OS)] in patients with intrahepatic cholangiocarcinoma (ICC).

METHODS

Seventy-three patients (26 M/47F, mean age 63y) with pre-operative imaging (CT, n = 37; MRI, n = 21; CT and MRI, n = 15] within 6 months of resection were included in this retrospective study. Qualitative imaging traits were assessed by 2 observers. A 3rd observer measured tumor apparent diffusion coefficient (ADC), enhancement ratios (ERs), and Haralick texture features. Blood biomarkers and imaging features were compared with histopathology (tumor grade and AJCC stage) and outcomes (TTR and OS) using log-rank, generalized Wilcoxon, Cox proportional hazards regression, and Fisher exact tests.

RESULTS

Median TTR and OS were 53.9 and 79.7 months. ICC recurred in 64.4% (47/73) of patients and 46.6% (34/73) of patients died. There was fair accuracy for some qualitative imaging features in the prediction of worse tumor grade (maximal AUC of 0.68 for biliary obstruction on MRI, p = 0.032, observer 1) and higher AJCC stage (maximal AUC of 0.73 for biliary obstruction on CT, p = 0.002, observer 2; and AUC of 0.73 for vascular involvement on MRI, p = 0.01, observer 2). Cox proportional hazards regression analysis showed that CA 19-9 [hazard ratio (HR) 2.44/95% confidence interval (CI) 1.31-4.57/p = 0.005)] and tumor size on imaging (HR 1.13/95% CI 1.04-1.22/p = 0.003) were significant predictors of TTR, while CA 19-9 (HR 4.08/95% CI 1.75-9.56, p = 0.001) and presence of metastatic lymph nodes at histopathology (HR 2.86/95% CI 1.35-6.07/p = 0.006) were significant predictors of OS. On multivariable analysis, satellite lesions on CT (HR 2.79/95%CI 1.01-7.15/p = 0.032, observer 2), vascular involvement on MRI (HR 0.10/95% CI 0.01-0.85/p = 0.032, observer 1), and texture feature MRI variance (HR 0.55/95% CI 0.31-0.97, p = 0.040) predicted TTR once adjusted for the independent predictors CA 19-9 and tumor size on imaging. Several qualitative and quantitative features demonstrated associations with TTR, OS, and AJCC stage at univariable analysis (range: HR 0.35-19; p < 0.001-0.045), however none were predictive of OS at multivariable analysis when adjusted for CA 19-9 and metastatic lymph nodes (p > 0.088).

CONCLUSIONS

There was reasonable accuracy in predicting tumor grade and higher AJCC stage in ICC utilizing certain qualitative and quantitative imaging traits. Serum CA 19-9, tumor size, presence of metastatic lymph nodes, and qualitative imaging traits of satellite lesions and vascular involvement are predictors of patient outcomes, along with a promising predictive ability of certain quantitative texture features.

Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma

RATIONALE AND OBJECTIVES

The purpose of this study was to establish and validate radiomics signatures based on ultrasound (US) medicine images to assess the biological behaviors of intrahepatic cholangiocarcinoma (ICC) in a noninvasive manner.

MATERIALS AND METHODS

This study consisted of 128 ICC patients. We focused on evaluating six pathological features: microvascular invasion, perineural invasion, differentiation, Ki-67, vascular endothelial growth factor, and cytokeratin 7. Region of interest (ROI) of ICC was identified by manually plotting the tumor contour on the grayscale US image. We extracted radiomics features from medical US imaging. Then, dimensionality reduction methods and classifiers were used to develop radiomic signatures for evaluating six pathological features in ICC. Finally, independent validation datasets were used to assess the radiomic signatures performance.

RESULTS

We extracted 1076 quantitative characteristic parameters on the US medicine images. Based on extracted radiomics features, the best performing radiomic signatures for evaluating microvascular invasion features were produced by hypothetical test + support vector machine (SVM), perineural invasion subgroup were least absolute shrinkage and selection operator + principal component analysis + support vector machine, differentiation subgroup were hypothetical test + decision tree, Ki-67 subgroup were hypothetical test + logistic regression, vascular endothelial growth factor subgroup were hypothetical test + Gradient Boosting Decision Tree (GBDT), and cytokeratin 7 subgroup were hypothetical test + bagging, respectively.

CONCLUSION

Through the high-throughput radiomics analysis based on US medicine images, we proposed radiomics signatures that have moderate efficiency in predicting the biological behaviors of ICC noninvasively.

Radiomics based on artificial intelligence in liver diseases: where we are?

Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

Radiogenomics: bridging imaging and genomics

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

Gadoxetic acid enhanced magnetic resonance imaging for prediction of the postoperative prognosis of intrahepatic mass-forming cholangiocarcinoma

PURPOSE

To identify imaging markers that independently predict the post-operative outcome of intrahepatic mass-forming cholangiocarcinoma (IMCC) using gadoxetate disodium-enhanced magnetic resonance imaging (MRI).

METHODS

Data from 54 patients who underwent pre-operative gadoxetate disodium-enhanced MRI and curative surgery for IMCC were retrospectively evaluated. The prognostic power of various imaging and pathological features reportedly associated with recurrence-free survival (RFS) and overall survival (OS) was analyzed using Cox regression models. A model combining imaging and pathological features was developed and its performance was evaluated using the Harrell C-index and Akaike information criterion.

RESULTS

Capsule penetration (P = 0.016) and tumor size (P = 0.015) were independent markers for worse RFS, while capsule penetration (P = 0.012) and hepatic vein obstruction (HVO, P = 0.016) were independent markers for worse OS, respectively, in the imaging-based model. Capsule penetration was the only imaging marker identified in the combined prediction model of RFS, and the combined model showed a higher C-index and lower AIC value compared with the model based on pathological features alone.

CONCLUSIONS

Capsule penetration and HVO on MRI are significantly worse imaging prognostic factors for post-operative outcomes in patients with IMCC. Incorporation of capsule penetration and HVO into a surgical staging system may improve prediction of the post-operative prognosis of IMCC.

Radiogenomics and Radiomics in Liver Cancers

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor’s molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.

Radiomics and radiogenomics of primary liver cancers

Concurrent advancements in imaging and genomic biomarkers have created opportunities to identify non-invasive imaging surrogates of molecular phenotypes. In order to develop such imaging surrogates radiomics and radiogenomics/imaging genomics will be necessary; there has been consistent progress in these fields for primary liver cancers. In this article we evaluate the current status of the field specifically with regards to hepatocellular carcinoma and intrahepatic cholangiocarcinoma, highlighting some of the up and coming results that were presented at the annual Radiological Society of North America Conference in 2017. There are an increasing number of studies in this area with a bias towards quantitative feature measurement, which is expected to benefit reproducibility of the findings and portends well for the future development of biomarkers for diagnosis, prognosis, and treatment response assessment. We review some of the advancements and look forward to some of the exciting future applications that are anticipated as the field develops.