Characterization of liver nodules in patients with chronic liver disease by MRI: performance of the Liver Imaging Reporting and Data System (LI-RADS v.2018) scale and its comparison with the Likert scale

Constructing a Competency Evaluation Index System for Nursing Positions in a Chronic Kidney Disease Management Centre

Objective

The Chronic Kidney Disease Management Centre (CKDMC) primarily focuses on developing a new system for early screening, standardised diagnosis, treatment, and the long-term follow-up management of patients with chronic kidney disease (CKD) to enhance CKD prevention and management. Nurses play a pivotal role in the comprehensive management of CKD, contributing considerably to the improvement of patient survival. Consequently, this study constructs an evaluation index system for nursing positions in the CKDMC, delineating the required competencies of nurses and providing a foundation for their targeted training.

Methods

A literature review and semi-structured interviews were used to develop the competency evaluation index system for nursing positions at the CKDMC. The Delphi method, involving expert correspondence, was employed over two rounds of inquiry with 16 experts, focusing on screening, modifying, and refining the indicators at all levels.

Results

The response rates for the first and second rounds of the questionnaire were 100% and 93.8%, respectively, with expert authority coefficients of 0.73 for both rounds. The finalised competency evaluation index system includes 3 primary indicators (theoretical knowledge, practical skills, and professional attitude), 10 secondary indicators, and 44 tertiary indicators.

Conclusion

The study successfully established a CKD specialist nurse competency evaluation index system comprising 3 primary, 10 secondary, and 44 tertiary indicators. The consensus among experts was high, rendering the results scientific, objective, and reliable. This system can serve as a basis for the training, selection, and competency evaluation of nursing professionals in CKDMCs.

Ultrasound-Based Predictive Model to Assess the Risk of Orbital Malignancies

OBJECTIVE

Ultrasound (US) is widely used for evaluating various orbital conditions. However, accurately diagnosing malignant orbital masses using US remains challenging. We aimed to develop an ultrasonic feature-based model to predict the presence of malignant tumors in the orbit.

METHODS

A total of 510 patients with orbital masses were enrolled between January 2017 and April 2023. They were divided into a development cohort and a validation cohort. In the development cohort (n = 408), the ultrasonic and clinical features with differential values were identified. Based on these features, a predictive model and nomogram were constructed. The diagnostic performance of the model was compared with that of MRI or observers, and further validated in the validation cohort (n = 102).

RESULTS

The involvement of more than two quadrants, irregular shape, extremely low echo of the solid part, presence of echogenic foci, cast-like appearance, and two demographic characteristics (age and sex) were identified as independent features related to malignant tumors of the orbit. The predictive model constructed based on these features exhibited better performance in identifying malignant tumors compared to MRI (AUC = 0.78 [95% CI: 0.73, 0.82] vs. 0.69 [95% CI: 0.64, 0.74], p = 0.03) and observers (AUC = 0.93 [95% CI: 0.90, 0.95] vs. Observer 1, AUC = 0.80 [95% CI: 0.76, 0.84], p < 0.01; vs. Observer 2, AUC = 0.71 [95% CI: 0.66, 0.76], p < 0.01). In the validation cohort, the predictive model achieved an AUC of 0.88 (95% CI: 0.81, 0.94).

CONCLUSION

The ultrasonic-clinical feature-based predictive model can accurately identify malignant orbital tumors, offering a convenient approach in clinical practice.

Liver Imaging Reporting and Data System version 2018 category 5 for diagnosing hepatocellular carcinoma: an updated meta-analysis

OBJECTIVE

We performed an updated meta-analysis to determine the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS, LR) 5 category for hepatocellular carcinoma (HCC) using LI-RADS version 2018 (v2018), and to evaluate differences by imaging modalities and type of MRI contrast material.

METHODS

The MEDLINE and Embase databases were searched for studies reporting the performance of LR-5 using v2018 for diagnosing HCC. A bivariate random-effects model was used to calculate the pooled per-observation sensitivity and specificity. Subgroup analysis was performed based on imaging modalities and type of MRI contrast material.

RESULTS

Forty-eight studies qualified for the meta-analysis, comprising 9031 patients, 10,547 observations, and 7216 HCCs. The pooled per-observation sensitivity and specificity of LR-5 for diagnosing HCC were 66% (95% CI, 61-70%) and 91% (95% CI, 89-93%), respectively. In the subgroup analysis, MRI with extracellular agent (ECA-MRI) showed significantly higher pooled sensitivity (77% [95% CI, 70-82%]) than CT (66% [95% CI, 58-73%]; p = 0.023) or MRI with gadoxetate (Gx-MRI) (65% [95% CI, 60-70%]; p = 0.001), but there was no significant difference between ECA-MRI and MRI with gadobenate (gadobenate-MRI) (73% [95% CI, 61-82%]; p = 0.495). Pooled specificities were 88% (95% CI, 80-93%) for CT, 92% (95% CI, 86-95%) for ECA-MRI, 93% (95% CI, 91-95%) for Gx-MRI, and 91% (95% CI, 84-95%) for gadobenate-MRI without significant differences (p = 0.084-0.803).

CONCLUSIONS

LI-RADS v2018 LR-5 provides high specificity for HCC diagnosis regardless of modality or contrast material, while ECA-MRI showed higher sensitivity than CT or Gx-MRI.

CLINICAL RELEVANCE STATEMENT

Refinement of the criteria for improving sensitivity while maintaining high specificity of LR-5 for HCC diagnosis may be an essential future direction.

KEY POINTS

• The pooled per-observation sensitivity and specificity of LR-5 for diagnosing HCC using LI-RADSv2018 were 66% and 91%, respectively. • ECA-MRI showed higher sensitivity than CT (77% vs 66%, p = 0.023) or Gx-MRI (77% vs 65%, p = 0.001). • LI-RADS v2018 LR-5 provides high specificity (88-93%) for HCC diagnosis regardless of modality or contrast material type.

Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning

Purpose

The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model.

Methods

115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model.

Results

For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001.

Conclusions

The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.

Structured Reporting in Radiological Settings: Pitfalls and Perspectives

Objective: The aim of this manuscript is to give an overview of structured reporting in radiological settings. Materials and Method: This article is a narrative review on structured reporting in radiological settings. Particularly, limitations and future perspectives are analyzed. RESULTS: The radiological report is a communication tool for the referring physician and the patients. It was conceived as a free text report (FTR) to allow radiologists to have their own individuality in the description of the radiological findings. However, this form could suffer from content, style, and presentation discrepancies, with a probability of transferring incorrect radiological data. Quality, datafication/quantification, and accessibility represent the three main goals in moving from FTRs to structured reports (SRs). In fact, the quality is related to standardization, which aims to improve communication and clarification. Moreover, a “structured” checklist, which allows all the fundamental items for a particular radiological study to be reported and permits the connection of the radiological data with clinical features, allowing a personalized medicine. With regard to accessibility, since radiological reports can be considered a source of research data, SR allows data mining to obtain new biomarkers and to help the development of new application domains, especially in the field of radiomics. Conclusions: Structured reporting could eliminate radiologist individuality, allowing a standardized approach.

Management of cutaneous melanoma: radiologists challenging and risk assessment

Melanoma patient remains a challenging for the radiologist, due to the difficulty related to the management of a patient more often in an advanced stage of the disease. It is necessary to determine a stratification of risk, optimizing the means, with diagnostic tools that should be optimized in relation to the type of patient, and improving knowledge. Staging and risk assessment procedures are determined by disease presentation at diagnosis. Melanoma staging is a critical tool to assist clinical decision-making and prognostic assessment. It is used for clinical trial design, eligibility, stratification, and analysis. The current standard for regional lymph nodes staging is represented by the sentinel lymph node excision biopsy procedure. For staging of distant metastases, PET-CT has the highest sensitivity and diagnostic odds ratio. Similar trend is observed during melanoma surveillance. The advent of immunotherapy, which has improved patient outcome, however, has determined new issues for radiologists, partly due to atypical response patterns, partly due to adverse reactions that must be identified as soon as possible for the correct management of the patient. The main objectives of the new ir-criteria are to standardize the assessment between different trials. However, these ir-criteria do not take into account all cases of atypical response patterns, as hyperprogression or dissociated responses. None of these criteria has actually been uniformly adopted in routine. The immune-related adverse events (irAEs) can involve various organs from head to toe. It is crucial for radiologists to know the imaging appearances of this condition, to exclude recurrent or progressive disease and for pneumonitis, since it could be potentially life-threatening toxicity, resulting in pneumonitis-related deaths in early phase trials, to allow a proper patient management.

A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls

Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm² and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.

Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases

PURPOSE

The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures.

RESULTS

The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model.

CONCLUSIONS

Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.

Imaging Features of Main Posthepatectomy Complications: A Radiologist’s Challenge

In the recent years, the number of liver resections has seen an impressive growth. Usually, hepatic resections remain the treatment of various liver diseases, such as malignant tumors, benign tumors, hydatid disease, and abscesses. Despite technical advancements and tremendous experience in the field of liver resection of specialized centers, there are moderately high rates of postoperative morbidity and mortality, especially in high-risk and older patient populations. Although ultrasonography is usually the first-line imaging examination for postoperative complications, Computed Tomography (CT) is the imaging tool of choice in emergency settings due to its capability to assess the whole body in a few seconds and detect all possible complications. Magnetic resonance cholangiopancreatography (MRCP) is the imaging modality of choice for delineating early postoperative bile duct injuries and ischemic cholangitis that may arise in the late postoperative phase. Moreover, both MDCT and MRCP can precisely detect tumor recurrence. Consequently, radiologists should have knowledge of these surgical procedures for better comprehension of postoperative changes and recognition of the radiological features of various postoperative complications.

Complications Risk Assessment and Imaging Findings of Thermal Ablation Treatment in Liver Cancers: What the Radiologist Should Expect

One of the major fields of application of ablation treatment is liver tumors. With respect to HCC, ablation treatments are considered as upfront treatments in patients with early-stage disease, while in colorectal liver metastases (CLM), they can be employed as an upfront treatment or in association with surgical resection. The main prognostic feature of ablation is the tumor size, since the goal of the treatment is the necrosis of all viable tumor tissue with an adequate tumor-free margin. Radiofrequency ablation (RFA) and microwave ablation (MWA) are the most employed ablation techniques. Ablation therapies in HCC and liver metastases have presented a challenge to radiologists, who need to assess response to determine complication-related treatment. Complications, defined as any unexpected variation from a procedural course, and adverse events, defined as any actual or potential injury related to the treatment, could occur either during the procedure or afterwards. To date, RFA and MWA have shown no statistically significant differences in mortality rates or major or minor complications. To reduce the rate of major complications, patient selection and risk assessment are essential. To determine the right cost-benefit ratio for the ablation method to be used, it is necessary to identify patients at high risk of infections, coagulation disorders and previous abdominal surgery interventions. Based on risk assessment, during the procedure as part of surveillance, the radiologists should pay attention to several complications, such as vascular, biliary, mechanical and infectious. Multiphase CT is an imaging tool chosen in emergency settings. The radiologist should report technical success, treatment efficacy, and complications. The complications should be assessed according to well-defined classification systems, and these complications should be categorized consistently according to severity and time of occurrence.

Electrochemotherapy of Primary Colon Rectum Cancer and Local Recurrence: Case Report and Prospective Analysis

Purpose: Surgery, radiotherapy, and oncological treatment (chemotherapy and antineoplastic antibodies) are standard treatments of rectal cancer. ECT has shown its effectiveness and suitability in deep solid tumors conducted in both preclinical and clinical studies. We show here an update and preliminary results with locally advanced rectum cancer (LARC) treated with ECT. Methods: Two patients with major clinical response to restaging after neoadjuvant treatment for LARC were subjected to ECT 12 weeks after completing chemo-radiation therapy. One patient was subjected to ECT on a colorectal local recurrence formed after neoadjuvant treatment for LARC and surgery. Computed Tomography and Magnetic Resonance Imaging were used to assess ECT response. Results: The results showed stable disease in two of the three patients treated, while one patient achieved a complete response. The local control of disease is maintained in the patient follow-up. For each patient, a reduction in pain was observed and for the patient with local recurrence, a reduction in bleeding present before ECT was also achieved. Conclusion: Preliminary results showed that ECT is a safe and effective treatment in patients with a major clinical response or local recurrence after neoadjuvant therapy for LARC and allows a reduction in pain and bleeding with a consequent improvement to quality of life.

Complications after Thermal Ablation of Hepatocellular Carcinoma and Liver Metastases: Imaging Findings

Tumour ablation is a strategy of treatment of hepatic tumours in patients with small hepatocellular carcinoma (HCC) (<3 cm) or in patients unfit for surgical resection. Moreover, tumor ablation can be used as an adjuvant therapy or may be used in association with resection in case of patients with poor functional liver disease. These types of treatment usually could be performed percutaneously under image guidance. The most clinically verified and used ablation modalities are Radiofrequency Ablation (RFA) and microwave ablation (MWA). However, despite both of them are considered minimally invasive techniques, they could be related to post-procedural complications. The International Working Group on Image-Guided Tumor and the Society of Interventional Radiology (SIR) identified major and minor post-ablative complications. Major complications, as vascular complications, occur in 2.2% to 3.1% of cases and include all the high risk pathological conditions which could increase the level of care or result in hospital admission or substantially prolonged hospital stay (SIR classifications C−E). Minor complications, as biliary complications, occur in 5% to 8.9% and include self-limiting conditions that are considered to be of low risk for the patient’s outcome. The purpose of this review is to summarise the main pathological ultrasound (US) and Computed Tomography (CT) findings, that may arise after ablative treatment. To simplify the analysis, the pathological pictures are divided according to the site of damage into vascular, biliary and extrahepatic complications.

Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.

Magnetic Resonance Features of Liver Mucinous Colorectal Metastases: What the Radiologist Should Know

Purpose: The aim of this study is to assess MRI features of mucinous liver metastases compared to non-mucinous metastases and hepatic hemangioma. Methods: A radiological archive was assessed from January 2017 to June 2021 to select patients subjected to liver resection for CRCLM and MRI in the staging phase. We selected 20 patients with hepatic hemangioma (study group B). We evaluated (a) the maximum diameter of the lesions, in millimeters, on T1-W flash 2D in phase and out phase, on axial HASTE T2-W and on portal phase axial VIBE T1 W; and (b) the signal intensity (SI) in T1-W sequences, in T2-W sequences, Diffusion-Weighted Imaging (DWI) sequences and apparent diffusion coefficient (ADC) maps so as to observe (c) the presence and the type of contrast enhancement during the contrast study. The chi-square test was employed to analyze differences in percentage values of the categorical variable, while the non-parametric Kruskal−Wallis test was used to test for statistically significant differences between the median values of the continuous variables. A p-value < 0.05 was considered statistically significant. Results: The final study population included 52 patients (33 men and 19 women) with 63 years of median age (range 37−82 years) and 157 metastases. In 35 patients, we found 118 non-mucinous type metastases (control group), and in 17 patients, we found 39 mucinous type metastases (study group A). During follow-up, recurrence occurred in 12 patients, and three exhibited mucinous types among them. In the study group, all lesions (100%) showed hypointense SI on T1-W, very high SI (similar to hepatic hemangioma) in T2-W with restricted diffusion and iso-hypointense signals in the ADC map. During the contrast study, the main significant feature is the peripheral progressive enhancement.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

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 textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases

PURPOSE

To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients.

METHODS

This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered.

RESULTS

The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%.

CONCLUSIONS

Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

Simple Summary

The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences.

Abstract

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Simple Summary

Magnetic Resonance Imaging (MRI) is a consolidated imaging tool for the multiparametric assessment of tissues in various pathologies from degenerative and inflammatory diseases to cancer. In recent years, the continuous technological evolution of the equipment has led to the development of sequences that provide not only anatomical but also functional and metabolic information. In addition, there is a growing and emerging field of research in clinical applications using MRI to exploit the diagnostic and therapeutic capabilities of nanocompounds. This review illustrates the application of the most advanced magnetic resonance techniques in the field of nanomedicine.

Abstract

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.

Morphological, dynamic and functional characteristics of liver pseudolesions and benign lesions

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide and one of the most common causes of death among patients with cirrhosis, developing in 1-8% of them every year, regardless of their cirrhotic stage. The radiological features of HCC are almost always sufficient for reaching the diagnosis; thus, histological confirmation is rarely needed. However, the study of cirrhotic livers remains a challenge for radiologists due to the developing of fibrous and regenerative tissue that cause the distortion of normal liver parenchyma, changing the typical appearances of benign lesions and pseudolesions, which therefore may be misinterpreted as malignancies. In addition, a correct distinction between pseudolesions and malignancy is crucial to allow appropriate targeted therapy and avoid treatment delays.The present review encompasses technical pitfalls and describes focal benign lesions and pseudolesions that may be misinterpreted as HCC in cirrhotic livers, providing the imaging features of regenerative nodules, large regenerative nodules, siderotic nodules, hepatic hemangiomas (including rapidly filling and sclerosed hemangiomas), segmental hyperplasia, arterioportal shunts, focal confluent fibrosis and focal fatty changes. Lastly, the present review explores the most promising new imaging techniques that are emerging and that could help radiologists differentiate benign lesions and pseudolesions from overt HCC.

Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is the second most common type of primary hepatic malignancy. Aim of this work is to analyse the features of ICC and its differential diagnosis at MRI, assessing two categories intraparenchymal and peribiliary lesions.

METHODS

The study population included 88 patients with histological diagnosis of ICCs: 61 with mass-forming type, 23 with periductal-infiltrating tumours and 4 with intraductal-growing type. As a control study groups, we identified: 86 consecutive patients with liver colorectal intrahepatic metastases (mCRC) (groups A); 35 consecutive patients with peribiliary metastases (groups B); 62 consecutive patients (groups C) with hepatocellular carcinoma (HCC); 18 consecutive patients (groups D) with combined hepatocellular cholangiocarcinoma (cHCC-CCA); and 26 consecutive patients (groups E) with hepatic hemangioma. For all lesions, magnetic resonance (MR) features were assessed according to Liver Imaging Reporting and Data System (LI-RADS) version 2018. The liver-specific gadolinium ethoxybenzyl dimeglumine-EOB (Primovist, Bayer Schering Pharma, Germany), was employed. Chi-square test was employed to analyse differences in percentage values of categorical variable, while the nonparametric Kruskal-Wallis test was used to test for statistically significant differences between the median values of the continuous variables. However, false discovery rate adjustment according to Benjamin and Hochberg for multiple testing was considered.

RESULTS

T1- and T2-weighted signal intensity (SI), restricted diffusion, transitional phase (TP) and hepatobiliary phase (HP) aspects allowed the differentiation between study group (mass-forming ICCs) and each other control group (A, C, D, E) with statistical significance, while arterial phase (AP) appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D with statistical significance. Instead, no MR feature allowed the differentiation between study group (periductal-infiltrating type) and control group B.

CONCLUSION

T1 and T2 W SI, restricted diffusion, TP and HP appearance allowed the differentiation between mass-forming ICCs and mimickers with statistical significance, while AP appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D.

Optimizing diagnostic imaging data using LI-RADS and the Likert scale in patients with hepatocellular carcinoma

Purpose

The study aimed to compare the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS), which incorporates fixed criteria, and the Likert scale (LS), which mainly depends on an overall impression in liver lesion diagnosis.

Material and methods

Diagnostic data of 110 hepatic nodules in 103 high-risk patients for hepatocellular carcinoma (HCC) were included. Data including diameter, arterial hyperenhancement, washout, and capsule were reviewed by 2 readers using LI-RADS and LS (range, score 1-5). Inter-reader agreement (IRA), intraclass agreement (ICA), and diagnostic performance were determined by Fleiss, Cohen’s k, and logistic regression, respectively.

Results

There were 53 triphasic enhanced computed tomography (CT) and 50 dynamic magnetic resonance (MR) examinations. Overall, IRA was excellent (k = 0.898). IRA was good for arterial hyperenhancement (k = 0.705), washout (k = 0.763), and capsule (k = 0.771) and excellent for diameter (k = 0.981) and tumour embolus (k = 0.927). Overall, ICA between LI-RADS and LS was fair 0.32; ICA was good for scores of 1 (k = 0.682), fair for scores of 2 (k = 0.36), moderate for scores of 5 (k = 0.52), but no agreement was found for scores of 3 (k = –0.059) and 4 (k = –0.022). LIRADS produced relatively high accuracy (87.3% vs. 80%), relatively low sensitivity (84.3% vs. 98%), and significantly higher specificity (89.83% vs. 64.4%) and positive likelihood ratio (+LR: 8.29 vs. 2.75) compared to LS approach.

Conclusions

LI-RADS revealed higher diagnostic accuracy as compared to LS with statistical proof higher specificity and +LR showing its ability to foretell malignancy in high-risk patients. We recommend the practical application of the LI-RADS system in the detection and treatment response monitoring of patients with HCC.

Firework Optimization Algorithm-Based Diagnosis of Hepatocellular Carcinoma and Hepatic Cavernous Hemangioma Using MRI Images

This study was aimed to explore the diagnostic features of magnetic resonance imaging (MRI) on hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HCH). A fireworks algorithm optimization (FAO) was proposed based on the fireworks algorithm (FA), and it was compared with the maximum between-class variance method (OTSU) and the maximum entropy threshold method (KSW) for analysis. In addition, it was applied to the diagnosis of MRI images of 55 HCC patients in the experimental group (group E) and 55 HCH patients in the control group (group C). It was found that the FAO showed a greatly lower difference function (DF) and a shorter running time in contrast to the OTSU and KSW algorithms (P < 0.05); the diagnostic accuracy (DA) of the T1-weighted image (T1WI) for patients in groups E and C was 85.31% and 95.85%, respectively, and the DA of the T2-weighted image (T2WI) was 97.84% (group E) and 89.71% (group C), respectively. In short, FAO showed an excellent performance in segmentation and reconstruction of MRI images for liver tissue, and T1WI and T2WI of MRI images showed high accuracy in diagnosing the HCC and HCH, respectively.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

Abbreviated MRI Protocol for the Assessment of Ablated Area in HCC Patients

Background: Liver Imaging Reporting and Data Systems (LI-RADS) Treatment Response Algorithm (TRA) was created to provide a standardized assessment of hepatocellular carcinoma (HCC) following loco regional therapy. The aim of this study was to compare sensitivity of standard MRI protocol versus abbreviated protocol (only T1-Weigthed fat suppressed (FS) sequences pre- and post-contrast phase) in the detection of ablated area according to LI-RADS Treatment Response (LR-TR) categories. Methods: From January 2015 to June 2020, we selected 64 patients with HCC, who underwent Radiofrequency ablation (RFA) or Microwave ablation (MWA) treatment. According to inclusion criteria, 136 pathologically proven treated HCC (median 2, range 1–3 per patient; mean size 20.0 mm; range 15–30 mm) in 58 patients (26 women, 32 men; median age, 74 years; range, 62–83 years) comprised our study population. For each ablated area, abbreviated protocol, and standard Magnetic Resonance Imaging (MRI) studies were independently and blindly assessed in random order within and between three expert radiologists. Each radiologist assessed the ablated area by using the following categories: “LR-TR Non-viable” = 1; “LR-TR Equivocal” = 2 and “LR-TR Viable” = 0. Results: According to the concordance between MRI and Contrast enhancement ultrasound (CEUS) among 136 treated HCCs, 115 lesions were assessed as non-viable or totally ablate and 21 as viable or partially ablate. The accuracy for standard MRI protocol and abbreviated MRI protocol for predicting pathologic tumor viability of a consensus reading was 98.6% (sensitivity = 100%; specificity = 98.3%; positive predictive value = 91.3% and negative predictive value = 100%). No differences were found in sensitivity or specificity between standard MRI LR-TR viable and abbreviated MRI LR-TR viable categories (p value > 0.05 at McNemar test). Conclusion: The abbreviated dynamic protocol showed similar diagnostic accuracy to conventional MRI study in the assessment of treated HCCs, with a reduction of the acquisition study time of 30% respect to conventional MRI.

CT and MRI Liver Imaging Reporting and Data System Version 2018 for Hepatocellular Carcinoma: A Systematic Review With Meta-Analysis

PURPOSE

The aim of this study was to determine the diagnostic performance of the LR-5 category for hepatocellular carcinoma (HCC) and the pooled proportion of HCC in each Liver Imaging Reporting and Data System (LI-RADS) category with CT and MRI, using LI-RADS version 2018.

METHODS

The MEDLINE, Embase, and Scopus databases were searched from inception to December 7, 2019, for studies reporting the diagnostic accuracy of LI-RADS version 2018 for HCC. Risk for bias and concerns regarding applicability were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Random-effects models were used to determine the summary estimates of the diagnostic performance of LR-5 and the pooled proportion of HCC for each LI-RADS category.

RESULTS

Fourteen studies were included in the final analysis, consisting of 2,708 observations with 1,841 HCCs. The pooled per-observation sensitivity and specificity of the LR-5 category for diagnosing HCC were 70% (95% confidence interval [CI], 61%-78%) and 91% (95% CI, 89%-93%), respectively. No HCCs were reported for LR-1 and LR-2. The pooled proportions of HCC were 31% (95% CI, 12%-50%) for LR-3, 64% (95% CI, 47%-80%) for LR-4, 95% (95% CI, 93%-96%) for LR-5, 54% (95% CI, 30%-77%) for LR-TIV, and 33% (95% CI, 21%-46%) for LR-M. The proportions of HCC were significantly different among the LI-RADS categories (P = .022).

CONCLUSIONS

The LR-5 category of LI-RADS version 2018 provided moderate sensitivity and high specificity for diagnosing HCC. Higher LI-RADS categories from LR-3 to LR-5 included greater proportions of HCC.

Distinguishing intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma by computed tomography and magnetic resonance imaging using the Bayesian method: a bi-center study

OBJECTIVES

To determine imaging hallmarks for distinguishing intrahepatic mass-forming biliary carcinomas (IMBCs) from hepatocellular carcinoma (HCC) and to validate their diagnostic ability using Bayesian statistics.

METHODS

Study 1 retrospectively identified clinical and imaging hallmarks that distinguish IMBCs (n = 41) from HCC (n = 247) using computed tomography (CT) and magnetic resonance imaging (MRI). Study 2 retrospectively assessed the diagnostic ability of these hallmarks to distinguish IMBCs (n = 37) from HCC (n = 111) using Bayesian statistics with images obtained from a different institution. We also assessed the diagnostic ability of the hallmarks in the patient subgroup with high diagnostic confidence (≥ 80% of post-test probability). Two radiologists independently evaluated the imaging findings in studies 1 and 2.

RESULTS

In study 1, arterial phase peritumoral parenchymal enhancement on CT/MRI, delayed enhancement on CT/MRI, diffusion-weighted imaging peripheral hyperintensity, and bile duct dilatation were hallmarks indicating IMBCs, whereas chronic liver disease, non-rim arterial phase hyperenhancement on CT/MRI, enhancing capsule on CT/MRI, and opposed-phase signal drop were hallmarks indicating HCC (p = 0.001-0.04). In study 2, Bayesian statistics-based post-test probability combining all hallmark features had a diagnostic accuracy of 89.2% (132/148) in distinguishing IMBCs from HCC for both readers. In the high diagnostic confidence subgroup (n = 120 and n = 124 for readers 1 and 2, respectively), the accuracy improved (95.0% (114/120) and 93.5% (116/124) for readers 1 and 2, respectively).

CONCLUSIONS

Combined interpretation of CT and MRI to identify hallmark features is useful in discriminating IMBCs from HCCs. High post-test probability by Bayesian statistics allows for a more reliable non-invasive diagnosis.

KEY POINTS

• Combined interpretation of CT and MRI to identify hallmark features was useful in discriminating intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma. • Bayesian method-based post-test probability combining all hallmark features determined in study 1 showed high (> 90%) sensitivity and specificity for distinguishing intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma. • If the post-test probability or the confidence was ≥ 80% when combining the imaging features of CT and MRI, the high specificity of > 95% was achieved without any loss of sensitivity to distinguish hepatocellular carcinoma from intrahepatic mass-forming biliary carcinomas.