Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure

Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review

BACKGROUND

Partial hepatectomy continues to be the primary treatment approach for liver tumors, and post-hepatectomy liver failure (PHLF) remains the most critical life-threatening complication following surgery.

AIM

To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.

METHODS

This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Three databases were searched from November 2019 to December 2022, and references as well as cited literature in all included studies were manually screened in March 2023. Based on the defined inclusion criteria, articles on PHLF prognostic models were selected, and data from all included articles were extracted by two independent reviewers. The PROBAST was used to evaluate the quality of each included article.

RESULTS

A total of thirty-four studies met the eligibility criteria and were included in the analysis. Nearly all of the models (32/34, 94.1%) were developed and validated exclusively using private data sources. Predictive variables were categorized into five distinct types, with the majority of studies (32/34, 94.1%) utilizing multiple types of data. The area under the curve for the training models included ranged from 0.697 to 0.956. Analytical issues resulted in a high risk of bias across all studies included.

CONCLUSION

The validation performance of the existing models was substantially lower compared to the development models. All included studies were evaluated as having a high risk of bias, primarily due to issues within the analytical domain. The progression of modeling technology, particularly in artificial intelligence modeling, necessitates the use of suitable quality assessment tools.

Preoperative prediction of post hepatectomy liver failure after surgery for hepatocellular carcinoma on CT-scan by machine learning and radiomics analyses

INTRODUCTION

No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to predict the occurrence of PHLF preoperatively by radiomics and clinical data through machine-learning algorithms.

MATERIALS AND METHODS

Clinical data and 3-phases CT scans were retrospectively collected among 13 Italian centres between 2008 and 2022. Radiomics features were extracted in the non-tumoral liver area. Data were split between training(70 %) and test(30 %) sets. An oversampling was run(ADASYN) in the training set. Random-Forest(RF), extreme gradient boosting (XGB) and support vector machine (SVM) models were fitted to predict PHLF. Final evaluation of the metrics was run in the test set. The best models were included in an averaging ensemble model (AEM).

RESULTS

Five-hundred consecutive preoperative CT scans were collected with the relative clinical data. Of them, 17 (3.4 %) experienced a PHLF. Two-hundred sixteen radiomics features per patient were extracted. PCA selected 19 dimensions explaining >75 % of the variance. Associated clinical variables were: size, macrovascular invasion, cirrhosis, major resection and MELD score. Data were split in training cohort (70 %, n = 351) and a test cohort (30 %, n = 149). The RF model obtained an AUC = 89.1 %(Spec. = 70.1 %, Sens. = 100 %, accuracy = 71.1 %, PPV = 10.4 %, NPV = 100 %). The XGB model showed an AUC = 89.4 %(Spec. = 100 %, Sens. = 20.0 %, Accuracy = 97.3 %, PPV = 20 %, NPV = 97.3 %). The AEM combined the XGB and RF model, obtaining an AUC = 90.1 %(Spec. = 89.5 %, Sens. = 80.0 %, accuracy = 89.2 %, PPV = 21.0 %, NPV = 99.2 %).

CONCLUSIONS

The AEM obtained the best results in terms of discrimination and true positive identification. This could lead to better define patients fit or unfit for liver resection.

A virtual biopsy of liver parenchyma to predict the outcome of liver resection

The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females-median age 67 years-39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR.

A radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma

Background

Post-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively.

Aims

This study aimed to develop and validate a prediction model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC.

Methods

A total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination.

Results

The PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin-bilirubin (ALBI) score, indocyanine green retention test at 15 min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77-0.90) in the training cohort and 0.82 (95% CI: 0.72-0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer-Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF.

Conclusion

A model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC.

Radiomic applications in upper gastrointestinal cancer surgery

INTRODUCTION

Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care.

OBJECTIVE

Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy.

CONCLUSION

Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

Current status and future perspectives of radiomics in hepatocellular carcinoma

Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions

Purpose

This study aimed to investigate whether models built from radiomics features based on multiphase contrast-enhanced MRI can identify microscopic pre-hepatocellular carcinoma lesions.

Methods

We retrospectively studied 54 small hepatocellular carcinoma (SHCC, diameter < 2 cm) patients and 70 patients with hepatocellular cysts or haemangiomas from September 2018 to June 2021. For the former, two MRI scans were collected within 12 months of each other; the 2^nd scan was used to confirm the diagnosis. The volumes of interest (VOIs), including SHCCs and normal liver tissues, were delineated on the 2^nd scans, mapped to the 1^st scans via image registration, and enrolled into the SHCC and internal-control cohorts, respectively, while those of normal liver tissues from patients with hepatocellular cysts or haemangioma were enrolled in the external-control cohort. We extracted 1132 radiomics features from each VOI and analysed their discriminability between the SHCC and internal-control cohorts for intra-group classification and the SHCC and external-control cohorts for inter-group classification. Five radial basis-function, kernel-based support vector machine (SVM) models (four corresponding single-phase models and one integrated from the four-phase MR images) were established.

Results

Among the 124 subjects, the multiphase models yielded better performance on the testing set for intra-group and inter-group classification, with areas under the receiver operating characteristic curves of 0.93 (95% CI, 0.85–1.00) and 0.97 (95% CI, 0.92–1.00), accuracies of 86.67% and 94.12%, sensitivities of 87.50% and 94.12%, and specificities of 85.71% and 94.12%, respectively.

Conclusion

The combined multiphase MRI-based radiomics feature model revealed microscopic pre-hepatocellular carcinoma lesions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05742-8.

Radiomics Analysis of Gd-EOB-DTPA Enhanced Hepatic MRI for Assessment of Functional Liver Reserve

Rationale and Objectives To evaluate the effectiveness of radiomics analysis based on Gd-EOB-DTPA enhanced hepatic MRI for functional liver reserve assessment in HCC patients. Materials and Methods Radiomics features were extracted from Gd-EOB-DTPA enhanced MRI images in 60 HCC patients. Boruta algorithm was performed to select features associated with indocyanine green retention rate at 15 min (ICG R15). Prediction and classification model were built by performing Random Forest regression analysis. Pearson correlation analysis and AUC of ROC were used to assess performance of the two models. Results A total of 165 radiomics features were extracted. Six radiomics features were selected to build the prediction model. A Predicted value of ICG R15 for each patient was calculated by the prediction model. Pearson correlation analysis revealed that predicted values were significantly associated with actual values of ICG R15 (R value = 0.90, p < 0.001). Nine radiomics features were selected to build the classification model. AUC of ROC revealed favorable performance of the classification model for identifying patients with ICG R15 <10% (AUC: 0.906, 95%CI: 0.900-0.913), <15% (AUC: 0.954, 95%CI: 0.950-0.958), and <20% (AUC: 0.996, 95%CI: 0.995-0.996). Conclusion Radiomics analysis of Gd-EOB-DTPA enhanced hepatic MRI can be used for assessment of functional liver reserve in HCC patients.

CT radiomics nomogram for the preoperative prediction of severe post-hepatectomy liver failure in patients with huge (≥ 10 cm) hepatocellular carcinoma

Background

This study aimed to establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥ 10 cm) hepatocellular carcinoma (HCC).

Methods

One hundred eighty-six patients with huge HCC (training dataset, n = 131 and test dataset, n = 55) that underwent curative hepatic resection were included in this study. The least absolute shrinkage and selection operator (LASSO) approach was applied to develop a radiomics signature for grade B or C PHLF prediction using the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. Decision tree analysis was performed to stratify the risk for severe PHLF.

Results

The radiomics signature consisting of nine features predicted severe PHLF with AUCs of 0.766 and 0.745 for the training and test datasets. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and the model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination ability, with AUCs of 0.842 and 0.863 for the training and test datasets, respectively. Based on decision tree analysis, patients were divided into three risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or radiomics score ≥ − 0.247 but underwent partial resections; intermediate-risk patients with radiomics score < − 0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ − 0.247 and underwent extended resections.

Conclusions

The radiomics nomogram could predict severe PHLF in huge HCC patients. A decision tree may be useful in surgical decision-making for huge HCC hepatectomy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02459-0.

Predictive value of gadoxetic acid-enhanced MRI for posthepatectomy liver failure: a systematic review

Objectives

Effective and non-invasive biomarkers to predict and avoid posthepatectomy liver failure (PHLF) are urgently needed. This systematic review aims to evaluate the efficacy of gadoxetic acid–enhanced MRI-derived parameters as an imaging biomarker in preoperative prediction of PHLF.

Methods

A systematic literature search was performed in the databases of PubMed/Medline, Web of Science, Embase, and Cochrane Library up to 11 December 2020. Studies evaluating the incidence of PHLF on patients who underwent hepatectomy with preoperative liver function assessment using gadoxetic acid–enhanced MRI were included. Data was extracted using pre-designed tables. The Quality In Prognostic Studies (QUIPS) tool was adopted to evaluate the risk of bias.

Results

A total of 15 studies were identified for qualitative synthesis and most studies were marked as low to moderate risk of bias in each domain of QUIPS. The most commonly used parameter was relative liver enhancement or its related parameters. The reported incidence of PHLF ranged from 3.9 to 40%. The predictive sensitivity and specificity of gadoxetic acid–enhanced MRI parameters varied from 75 to 100% and from 54 to 93% in ten reported studies. A majority of the studies revealed that the gadoxetic acid–enhanced MRI parameter was a predictor for PHLF.

Conclusions

Gadoxetic acid–enhanced MRI showed a high predictive capacity for PHLF and represents a promising imaging biomarker in prediction of PHLF. Multicenter, prospective trials with large sample size and reliable, unified liver function parameters are required to validate the efficacy of individual liver function parameters.

Key Points

• There is an obvious heterogeneity of the published studies, not only in variance of MRI liver function parameters but also in indication and extent of the liver resection.

• Signal intensity (SI)–based parameters derived from gadoxetic acid–enhanced MRI are the commonly used method for PHLF prediction.

• Gadoxetic acid–enhanced MRI-derived parameters showed high predictive efficacy for PHLF and can potentially serve as a predictor for the incidence of PHLF.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08297-8.

Progress of MRI Radiomics in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

OBJECTIVE

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

METHODS

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

RESULTS

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

CONCLUSION

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Artificial intelligence for hepatitis evaluation

Recently, increasing attention has been paid to the application of artificial intelligence (AI) to the diagnosis of diverse hepatic diseases, which comprises traditional machine learning and deep learning. Recent studies have shown the possible value of AI based data mining in predicting the incidence of hepatitis, classifying the different stages of hepatitis, diagnosing or screening for hepatitis, forecasting the progression of hepatitis, and predicting response to antiviral drugs in chronic hepatitis C patients. More importantly, AI based on radiology has been proven to be useful in predicting hepatitis and liver fibrosis as well as grading hepatocellular carcinoma (HCC) and differentiating it from benign liver tumors. It can predict the risk of vascular invasion of HCC, the risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis, and the risk of liver failure after hepatectomy in HCC patients. In this review, we summarize the application of AI in hepatitis, and identify the challenges and future perspectives.

Radiomics in Hepatocellular Carcinoma

BACKGROUND

Radiomics is an emerging field that extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes ina non-invasive manner. This field currently is in the initial growth phase and lacks standardized evaluation criteria but remains a very promising tool for the future todevelop suitable biomarkers for diagnosis, prognosis, and treatment response assessments. The analysis of hepatocellular carcinoma by radiomics will contribute toearly diagnosis and treatment of tumors and improve survival and cure rates.

AIM

Herein, we aimed to provide an up-to-date overview of the principles of radiomics specifically regarding hepatocellular carcinoma (HCC) and discuss the current challenges and future advancements of radiomics for HCC.