Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features

Primary lung adenocarcinoma harboring upper mediastinal lymphatic skip metastasis of cervical squamous cell carcinoma: A case report and literature review

The upper mediastinal lymph nodes are a rare site of metastasis in early-stage cervical cancer, but they are a common site of metastasis in lung cancer. Notably, standard approaches for identifying the source of metastasis and subsequent treatment are currently lacking. The present study describes the case of a patient with primary lung adenocarcinoma harboring upper mediastinal lymphatic skip metastasis from cervical squamous cell carcinoma 2 years after a radical hysterectomy. During video-assisted thoracoscopic surgery, it was indicated that the patient had a tendency for metastasis to the upper mediastinal lymph nodes from the lung tumor. Pathological examination confirmed the presence of metastasis; however, it was confirmed to originate from cervical carcinoma, rather than lung adenocarcinoma. In conclusion, for patients with lung cancer and concurrent malignancies, metastatic lymph nodes discovered during surgery may originate from the previous malignancy. Surgical management of oligometastatic lymph nodes in the mediastinum can be a potential treatment option, albeit one that may necessitate the integration of adjuvant treatment modalities as warranted by the individual case.

Surgical management, including the role of transplantation, for intrahepatic and peri-hilar cholangiocarcinoma

Intrahepatic and peri-hilar cholangiocarcinoma are life threatening disease with poor outcomes despite optimal treatment currently available (5-year overall survival following resection 20-35%, and <10% cured at 10-years post resection). The insidious onset makes diagnosis difficult, the majority do not have a resection option and the high recurrence rate post-resection suggests that occult metastatic disease is frequently present. Advances in perioperative management, such as ipsilateral portal vein (and hepatic vein) embolisation methods to increase the future liver remnant volume, genomic profiling, and (neo)adjuvant therapies demonstrate great potential in improving outcomes. However multiple areas of controversy exist. Surgical resection rate and outcomes vary between centres with no global consensus on how 'resectable' disease is defined - molecular profiling and genomic analysis could potentially identify patients unlikely to benefit from resection or likely to benefit from targeted therapies. FDG-PET scanning has also improved the ability to detect metastatic disease preoperatively and avoid futile resection. However tumours frequently invade major vasculo-biliary structures, with resection and reconstruction associated with significant morbidity and mortality even in specialist centres. Liver transplantation has been investigated for very selected patients for the last decade and yet the selection algorithm, surgical approach and both value of both neoadjuvant and adjuvant therapies remain to be clarified. In this review, we discuss the contemporary management of intrahepatic and peri-hilar cholangiocarcinoma.

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

PURPOSE

This study aimed to establish a nomogram based on preoperative magnetic resonance imaging (MRI) features to predict the very early recurrence (VER, less than 6 months) of intrahepatic mass-forming cholangiocarcinoma (IMCC) after R0 resection.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer

OBJECTIVE

Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.

MATERIALS AND METHODS

A total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Through the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801.

CONCLUSION

Radiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.

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

PURPOSE

This 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.

METHODS

This 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.

RESULTS

Suspicious 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.

CONCLUSIONS

Two nomograms were constructed based on preoperative MRI features in patients with solitary IMCC for predicting the OS and RFS and facilitate further prognostic stratification.

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.

Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model

Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion. On the preoperative CT, we segmented the ICC (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenchyma rim around the tumor (Margin-VOI). We considered two types of pathology data: tumor grading (G) and microvascular invasion (MVI). The predictive models were internally validated. Overall, 244 patients were analyzed: 82 (34%) had G3 tumors and 139 (57%) had MVI. For G3 prediction, the clinical model had an AUC = 0.69 and an Accuracy = 0.68 at internal cross-validation. The addition of radiomic features extracted from the portal phase of CT improved the model performance (Clinical data+Tumor-VOI: AUC = 0.73/Accuracy = 0.72; +Tumor-/Margin-VOI: AUC = 0.77/Accuracy = 0.77). Also for MVI prediction, the addition of portal phase radiomics improved the model performance (Clinical data: AUC = 0.75/Accuracy = 0.70; +Tumor-VOI: AUC = 0.82/Accuracy = 0.73; +Tumor-/Margin-VOI: AUC = 0.82/Accuracy = 0.75). The permutation tests confirmed that a combined clinical-radiomic model outperforms a purely clinical one (p < 0.05). The addition of the textural features extracted from the arterial phase had no impact. In conclusion, the radiomic features of the tumor and peritumoral tissue extracted from the portal phase of preoperative CT improve the prediction of ICC grading and MVI.

Magnetic resonance imaging features of bile duct adenoma

Objectives

To evaluate the magnetic resonance imaging (MRI) features of bile duct adenoma.

Methods

The data of 28 patients [with 32 pathologically confirmed bile duct adenomas, including 15 with malignant change (malignant group) and 17 without malignant change (benign adenoma group)] were retrospectively reviewed. Abdominal MRI was performed for all patients; in addition, dynamic enhanced MRI was performed for 18 lesions. The MRI features, including lesion location, maximum size, morphology, signal characteristics, enhancement type, and appearance of the bile duct, were assessed by two abdominal radiologists. Apparent diffusion coefficient (ADC) values were measured and compared.

Results

Of the 32 bile duct adenomas, 22 (68.75%) involved the common bile duct (CBD). While 14/32 (43.75%) lesions presented as focal eccentric-type masses, 9/32 (28.13%) presented as plaque-like masses, 4/32 (12.50%) as bile duct casting masses, and 5/32 (15.62%) as infiltrative masses. A frond-like superficial appearance was seen in 8/32 (25%) lesions. Infiltrative masses were significantly more common in the malignant group than in the benign adenoma group (P = 0.015). While 23/32 (71.88%) lesions were isointense on T1-weighted imaging (T1WI), 24/32 (75%) were hyperintense on T2-weighted imaging (T2WI). Bile duct dilatation was present upstream of the lesion in all cases. Bile duct dilatation at the lesion was seen in 24/32 (75%) cases and downstream of the lesion in 6/32 (18.75%) cases. Of the 18 lesions that underwent dynamic enhanced MRI, 14 (77.78%) showed moderate enhancement and 13 (72.22%) showed persistent enhancement. On diffusion-weighted imaging (DWI), 27/32 (84.37%) lesions showed hyperintensity. Mean ADC value was comparable between the malignant group and the benign adenoma group (P = 0.156).

Conclusions

Bile duct adenoma primarily presents as intraductal growth in the CBD, usually with bile duct dilatation at the lesion site or upstream to it. Most lesions are isointense on T1WI, are hyperintense on T2WI and DWI, and show moderate enhancement. A superficial frond-like appearance of the lesion and bile duct dilatation at the lesion or downstream to it might be characteristics of bile duct adenoma. An infiltrative appearance might indicate malignant transformation.

Immuno-genomic-radiomics to predict response of biliary tract cancer to camrelizumab plus GEMOX in a single-arm phase II trial

Background & Aims

Immunotherapy is an option for the treatment of advanced biliary tract cancer (BTC), although it has a low response rate. In this post hoc analysis, we investigated the predictive value of an immuno-genomic-radiomics (IGR) analysis for patients with BTC treated with camrelizumab plus gemcitabine and oxaliplatin (GEMOX) therapy.

Methods

Thirty-two patients with BTC treated with camrelizumab plus GEMOX were prospectively enrolled. The relationship between high-throughput computed tomography (CT) radiomics features with immuno-genomic expression was tested and scaled with a full correlation matrix analysis. Odds ratio (OR) of IGR expression for objective response to camrelizumab plus GEMOX was tested with logistic regression analysis. Association of IGR expression with progression-free survival (PFS) and overall survival (OS) was analysed with a Cox proportional hazard regression.

Results

CT radiomics correlated with CD8⁺ T cells (r = -0.72-0.71, p = 0.004-0.047), tumour mutation burden (TMB) (r = 0.59, p = 0.039), and ARID1A mutation (r = -0.58-0.57, p = 0.020-0.034). There was no significant correlation between radiomics and programmed cell death protein ligand 1 expression (p >0.96). Among all IGR biomarkers, only four radiomics features were independent predictors of objective response (OR = 0.09-3.81; p = 0.011-0.044). Combining independent radiomics features into an objective response prediction model achieved an area under the curve of 0.869. In a Cox analysis, radiomics signature [hazard ratio (HR) = 6.90, p <0.001], ARID1A (HR = 3.31, p = 0.013), and blood TMB (HR = 1.13, p = 0.023) were independent predictors of PFS. Radiomics signature (HR = 6.58, p <0.001) and CD8⁺ T cells (HR = 0.22, p = 0.004) were independent predictors of OS. Prognostic models integrating these features achieved concordance indexes of 0.677 and 0.681 for PFS and OS, respectively.

Conclusions

Radiomics could act as a non-invasive immuno-genomic surrogate of BTC, which could further aid in response prediction for patients with BTC treated with immunotherapy. However, multicenter and larger sample studies are required to validate these results.

Impact and implications

Immunotherapy is an alternative for the treatment of advanced BTC, whereas tumour response is heterogeneous. In a post hoc analysis of the single-arm phase II clinical trial (NCT03486678), we found that CT radiomics features were associated with the tumour microenvironment and that IGR expression was a promising marker for tumour response and long-term survival.

Clinical trial number

Post hoc analysis of NCT03486678.

Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative

OBJECTIVES

To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies.

METHODS

A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients.

RESULTS

A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1-13) for reader 1, 8 (22.2%, IQR 3-12) for reader 2, and 10 (27.8%; IQR 5-14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62-0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p < 0.001 for reader 1).

CONCLUSIONS

Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.

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.

Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend

Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also been numerous radiomics investigations on intrahepatic cholangiocarcinoma in recent years, but no pertinent review materials are readily available. This work discusses the modeling analysis of radiomics for the prediction of lymph node metastasis, microvascular invasion, and early recurrence of intrahepatic cholangiocarcinoma, as well as the use of deep learning. This paper briefly reviews the current status of radiomics research to provide a reference for future studies.

Apparent Diffusion Coefficient MRI Shows Association With Early Progression of Unresectable Intrahepatic Cholangiocarcinoma With Combined Targeted-Immunotherapy

BACKGROUND

Most intrahepatic cholangiocarcinomas (ICCs) are diagnosed at advanced stage with an extremely poor prognosis. For these patients, combining targeted therapies and immunotherapy may have a promising therapeutic effect, and current Response Evaluation Criteria in Solid Tumors (RECIST) criteria have limited applicability.

PURPOSE

To investigate the associations between pretreatment MRI features and the efficacy of combined targeted-immunotherapy by estimating the risk of early progression (EP) in unresectable ICC, with special emphasis on diffusion-weighted imaging.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 43 unresectable ICC patients (24 with EP [disease progression ≤12 months after treatment] and 19 with nonearly progression [NEP, disease progression >12 months]), who received first-line systemic therapy with lenvatinib plus PD1 antibody combination.

FIELD STRENGTH/SEQUENCE

The 0-T scanner, including T1- and T2-weighted imaging, diffusion-weighted imaging, and dynamic gadopentetate dimeglumine-enhanced imaging.

ASSESSMENT

Clinical characteristics and MR imaging features including apparent diffusion coefficient (ADC), as well as survival analysis of EP were evaluated.

STATISTICAL TESTS

Features between EP and NEP groups were compared by univariate analyses and multivariate logistic regression analysis. Diagnostic performance was analyzed by receiver operating characteristic curve. Univariate and multivariate Cox regression models were applied for survival analysis of EP. The progression-free survival (PFS) rates were estimated using the Kaplan-Meier analysis and compared by the log-rank test. The significance threshold was set at P < 0.05.

RESULTS

Tumor number, tumor margin, arterial peritumoral enhancement, lymphatic metastasis, and apparent diffusion coefficient (ADC) value were significantly different between EP and NEP groups. At multivariate logistic regression analysis, ADC was the only independent variable associated with EP (odds ratio = 0.012), with an area under the curve of 0.774 (optimal cutoff value was 1.028 × 10^-3  mm² /sec). Multivariate Cox regression model proved that ADC value (hazard ratio = 0.140) and ill-defined margin (hazard ratio = 2.784) were independent risk factors. ICCs with low ADC values showed shorter PFS than those with high values (χ²  = 9.368).

DATA CONCLUSION

Pretreatment MRI features were associated with EP for unresectable ICC treated with combined targeted-immunotherapy, and decreased ADC value was an independent variable.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 4.

Functional Investigation of the Tumoural Heterogeneity of Intrahepatic Cholangiocarcinoma by In Vivo PET-CT Navigation: A Proof-of-Concept Study

Intra-tumoural heterogeneity (IH) is a major determinant of resistance to therapy and outcomes but remains poorly translated into clinical practice. Intrahepatic cholangiocarcinoma (ICC) often presents as large heterogeneous masses at imaging. The present study proposed an innovative in vivo technique to functionally assess the IH of ICC. Preoperative 18F-FDG PET-CT and intraoperative ultrasonography were merged to perform the intraoperative navigation of functional tumour heterogeneity. The tumour areas with the highest and the lowest metabolism (SUV) at PET-CT were selected, identified during surgery, and sampled. Three consecutive patients underwent the procedure. The areas with the highest uptake at PET-CT had higher proliferation index (KI67) values and higher immune infiltration compared to areas with the lowest uptake. One of the patients showed a heterogeneous presence of FGFR2 translocation within the samples. Tumour heterogeneity at PET-CT may drive biopsy to sample the most informative ICC areas. Even more relevant, these preliminary data show the possibility of achieving a non-invasive evaluation of IH in ICC, paving the way for an imaging-based precision-medicine approach.

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.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography

BACKGROUND

Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC.

METHODS

A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts.

RESULTS

Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts.

CONCLUSIONS

Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Radiomics of Biliary Tumors: A Systematic Review of Current Evidence

Biliary tumors are rare diseases with major clinical unmet needs. Standard imaging modalities provide neither a conclusive diagnosis nor robust biomarkers to drive treatment planning. In several neoplasms, texture analyses non-invasively unveiled tumor characteristics and aggressiveness. The present manuscript aims to summarize the available evidence about the role of radiomics in the management of biliary tumors. A systematic review was carried out through the most relevant databases. Original, English-language articles published before May 2021 were considered. Three main outcome measures were evaluated: prediction of pathology data; prediction of survival; and differential diagnosis. Twenty-seven studies, including a total of 3605 subjects, were identified. Mass-forming intrahepatic cholangiocarcinoma (ICC) was the subject of most studies (n = 21). Radiomics reliably predicted lymph node metastases (range, AUC = 0.729−0.900, accuracy = 0.69−0.83), tumor grading (AUC = 0.680−0.890, accuracy = 0.70−0.82), and survival (C-index = 0.673−0.889). Textural features allowed for the accurate differentiation of ICC from HCC, mixed HCC-ICC, and inflammatory masses (AUC > 0.800). For all endpoints (pathology/survival/diagnosis), the predictive/prognostic models combining radiomic and clinical data outperformed the standard clinical models. Some limitations must be acknowledged: all studies are retrospective; the analyzed imaging modalities and phases are heterogeneous; the adoption of signatures/scores limits the interpretability and applicability of results. In conclusion, radiomics may play a relevant role in the management of biliary tumors, from diagnosis to treatment planning. It provides new non-invasive biomarkers, which are complementary to the standard clinical biomarkers; however, further studies are needed for their implementation in clinical practice.

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.

Radiomics in medical imaging: pitfalls and challenges in clinical management

BACKGROUND

Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye.

METHODS

This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed.

RESULTS

Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting.

CONCLUSIONS

Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.

Contrast-enhanced MRI could predict response of systemic therapy in advanced intrahepatic cholangiocarcinoma

OBJECTIVE

To investigate whether pre-treatment contrast-enhanced MRI could predict the therapeutic response of systemic treatment in advanced intrahepatic cholangiocarcinoma (ICC).

METHODS

This retrospective study enrolled 61 ICC participants with contrast-enhanced MRI before combined systemic therapy. Clinical characteristics and MRI features were compared between patients with and without therapeutic response by univariate and multivariate logistic regression analyses. Then, a combined MRI-based model and the nomogram were established based on the results of the multivariate analysis. The diagnostic performances of significant findings and the combined model were evaluated and compared. The progression-free survival (PFS) rates between patients with high and low combined index values were compared.

RESULTS

Thirty (49.18%) patients showed overall response after therapy. In multivariate analysis, tumor margin (odds ratio (OR) = 5.004, p = 0.014), T2 homogeneity (OR = 14.93, p = 0.019), and arterial peritumoral enhancement (OR = 5.076, p = 0.042) were independent predictive factors associated with therapeutic response. The C-index with the formulated nomogram incorporating the three independent imaging features was 0.828 (95% CI 0.710-0.913). Diagnostic characteristics of the combined index were superior to any single feature alone (p = 0.0007-0.0141). ICCs with high combined index values showed higher PFS rates than those with low values (χ² = 13.306, p < 0.0001).

CONCLUSIONS

Pre-treatment contrast-enhanced MRI can be used to predict therapeutic response in advanced ICC with systemic therapy. The combination model incorporating significant MRI features achieved an improved predictive value, which may play an important role in identifying appropriate therapeutic candidates.

KEY POINTS

• Contrast-enhanced MRI can predict response of systemic therapy in advanced ICC. • MRI features of tumor margin, T2 homogeneity, and arterial peritumoral enhancement are related to therapeutic response. • The combined MRI-based model may help to identify appropriate therapeutic candidates.

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.

The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma

Background

Intrahepatic cholangiocarcinoma is an aggressive liver carcinoma with increasing incidence and mortality. A good auxiliary prognostic prediction tool is desperately needed for the development of treatment strategies. The purpose of this study was to explore the prognostic value of the radiomics nomogram based on enhanced CT in intrahepatic cholangiocarcinoma.

Methods

In this retrospective study, 101 patients with pathological confirmation of intrahepatic cholangiocarcinoma were recruited. A radiomics nomogram was developed by radiomics score and independent clinical risk factors selecting from multivariate Cox regression. All patients were stratified as high risk and low risk by a nomogram. Model performance and clinical usefulness were assessed by calibration curve, ROC curve, and survival curve.

Results

A total of 101patients (mean age, 58.2 years old; range 36–79 years old) were included in the study. The 1-year, 3-year, and 5-year overall survival rates were 49.5%, 26.6%, and 14.4%, respectively, with a median survival time of 12.2 months in the whole set. The least absolute shrinkage and selection operator (LASSO) method selected 3 features. Multivariate Cox analysis found three independent prognostic factors. The radiomics nomogram showed a significant prognosis value with overall survival. There was a significant difference in the 1-year and 3-year survival rates of stratified high-risk and low-risk patients in the whole set (30.4% vs. 56.4% and 13.0% vs. 30.6%, respectively, p = 0.018).

Conclusions

This radiomics nomogram has potential application value in the preoperative prognostic prediction of intrahepatic cholangiocarcinoma and may facilitate in clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02162-0.

CT texture analysis of abdominal lesions – Part I.: Liver lesions

Artificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.