Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer

Background

Posthepatectomy liver failure (PHLF) is a serious complication associated with high mortality rates. Machine learning (ML) has rapidly developed and may outperform traditional models in predicting PHLF in patients who have undergone hepatectomy. This study aimed to predict PHLF using ML and compare its performance with that of traditional scoring systems.

Methods

The clinicopathological data of 334 patients who underwent liver resection were retrospectively collected. The Pycaret library, a simple, open-source machine learning library, was used to compare multiple classification models for PHLF prediction. The predictive performance of 15 ML algorithms was compared using the mean area under the receiver operating characteristic curve (AUROC) and accuracy, and the best-fit model was selected among 15 ML algorithms. Next, the predictive performance of the selected ML-PHLF model was compared with that of routine scoring systems, the albumin-bilirubin score (ALBI) and the fibrosis-4 (FIB-4) index, using AUROC.

Results

The best model was extreme gradient boosting (accuracy:93.1%; AUROC:0.863) among the 15 ML algorithms. As compared with ALBI and FIB-4, the ML PHLF model had higher AUROC for predicting PHLF.

Conclusion

The novel ML model for predicting PHLF outperformed routine scoring systems.

A novel surgical scheme for hepatectomy in hepatocellular carcinoma patients with clinically significant portal hypertension

OBJECTIVE

Clinically significant portal hypertension (CSPH) seriously affects the feasibility and safety of surgical treatment for hepatocellular carcinoma (HCC) patients. The aim of this study was to establish a new surgical scheme defining risk classification of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among HCC patients with CSPH.

BACKGROUNDS

Hepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC.

METHODS

Multivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram.

RESULTS

This study included 924 patients, of whom 137 patients (14.8%) suffered from mild-CSPH and 66 patients suffered from (7.1%) with severe-CSPH confirmed preoperatively. Our data showed that preoperative prolonged prothrombin time, total bilirubin, indocyanine green retention rate at 15 min, CSPH grade, and standard future liver remnant volume were independent predictors of severe PHLF. By incorporating these factors, the nomogram achieved good prediction performance in assessing severe PHLF risk, and its concordance statistic was 0.891, 0.850 and 0.872 in the training cohort, internal validation cohort and external validation cohort, respectively, and good calibration curves were obtained. Moreover, the calculations of total points of diagnostic errors with 95% CI were concentrated in 110.5 (range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR.

CONCLUSION

This new surgical scheme established in our study is practical to stratify risk classification in assessing severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.

Major liver resections, perioperative issues and posthepatectomy liver failure: A comprehensive update for the anesthesiologist

Significant advances in surgical techniques and relevant medium- and long-term outcomes over the past two decades have led to a substantial expansion in the indications for major liver resections. To support these outstanding results and to reduce perioperative complications, anesthesiologists must address and master key perioperative issues (preoperative assessment, proactive intraoperative anesthesia strategies, and implementation of the Enhanced Recovery After Surgery approach). Intensive care unit monitoring immediately following liver surgery remains a subject of active and often unresolved debate. Among postoperative complications, posthepatectomy liver failure (PHLF) occurs in different grades of severity (A-C) and frequency (9%-30%), and it is the main cause of 90-d postoperative mortality. PHLF, recently redefined with pragmatic clinical criteria and perioperative scores, can be predicted, prevented, or anticipated. This review highlights: (1) The systemic consequences of surgical manipulations anesthesiologists must respond to or prevent, to positively impact PHLF (a proactive approach); and (2) the maximal intensive treatment of PHLF, including artificial options, mainly based, so far, on Acute Liver Failure treatment(s), to buy time waiting for the recovery of the native liver or, when appropriate and in very selected cases, toward liver transplant. Such a clinical context requires a strong commitment to surgeons, anesthesiologists, and intensivists to work together, for a fruitful collaboration in a mandatory clinical continuum.

Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis

BACKGROUND

Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery.

METHODS

A systematic search of Embase, MEDLINE, Cochrane, Web of Science, and Google Scholar was performed through December 2022. The primary outcome was the discriminatory performance of ML vs LR models as measured by the area under the receiver operating characteristic curve (AUC). A meta-analysis was then performed using a random effects model.

RESULTS

A total of 62 LR models and 143 ML models were included across 38 studies. On average, the best-performing ML models had a significantly higher AUC than the LR models (ΔAUC, 0.07; 95% CI, 0.04-0.09; P < .001). Similarly, on average, the best-performing ML models had a significantly higher logit (AUC) than the LR models (Δlogit [AUC], 0.41; 95% CI, 0.23-0.58; P < .001). Approximately half of studies (44%) were found to have a low risk of bias. Upon a subset analysis of only low-risk studies, the difference in logit (AUC) remained significant (ML vs LR, Δlogit [AUC], 0.40; 95% CI, 0.14-0.66; P = .009).

CONCLUSION

We found a significant improvement in discriminatory ability when using ML over LR algorithms in predicting postoperative outcomes for patients undergoing GI surgery. Subsequent efforts should establish standardized protocols for both developing and reporting studies using ML models and explore the practical implementation of these models.

Artificial Intelligence in Perioperative Planning and Management of Liver Resection

Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of "PubMed" and "Google Scholar". The period for the search was from 2000 to June 2023. The search terms included "artificial intelligence", "machine learning", "liver cancers", "liver tumors", "hepatectomy", "perioperative" and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI's integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients' quality of life and survival rates.

C-Reactive Protein-Albumin Ratio (CAR): A More Promising Inflammation-Based Prognostic Marker for Patients Undergoing Curative Hepatectomy for Hepatocellular Carcinoma

Background

Systemic inflammatory response is a hallmark of cancer and plays a significant role in the development and progression of various malignant tumors. This research aimed to estimate the prognostic function of the C-reactive protein-albumin ratio (CAR) in patients undergoing hepatectomy for hepatocellular carcinoma (HCC) and compare it with other inflammation-based prognostic scores, including the neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, systemic immune inflammation index, prognostic index, Glasgow prognostic score, and modified Glasgow prognostic score.

Methods

Retrospective analysis was conducted on data from 1039 HCC cases who underwent curative liver resection. The prognostic performance of CAR was compared with other scores using the area under the time-dependent receiver operating characteristic (t-ROC) curve. Multivariable Cox regression analyses were performed to confirm independent predictors for disease-free survival (DFS) and overall survival (OS).

Results

The area under the t-ROC curve for CAR in the evaluation of DFS and OS was significantly greater than that of other scores and alpha-fetoprotein (AFP). Patients were stratified based on the optimal cut-off value of CAR, and the data revealed that both DFS and OS were remarkably worse in the high-CAR set compared to the low-CAR set. Multivariable Cox analysis demonstrated that CAR was an independent prognostic parameters for assessing DFS and OS. Regardless of AFP levels, all patients were subsequently divided into significantly different subgroups of DFS and OS based on CAR risk stratification. Similar results were observed when applying CAR risk stratification to other scoring systems. CAR also showed good clinical applicability in patients with different clinical features.

Conclusion

CAR is a more effective inflammation-based prognostic marker than other scores and AFP in predicting DFS as well as OS among patients with HCC after curative hepatectomy.

Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence

(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.

A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks

BACKGROUND

Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease.

OBJECTIVE

This study introduces a deep learning approach that combines fully connected and convolutional neural networks for effective ALF prediction. The goal is to overcome limitations of traditional machine learning methods and enhance predictive model performance and generalization.

METHODS

The proposed model integrates a fully connected neural network for handling basic patient features and a convolutional neural network dedicated to capturing temporal patterns in patient data. The combination allows automatic learning of complex patterns and abstract features present in highly dynamic medical data associated with ALF.

RESULTS

The model's effectiveness is demonstrated through comprehensive experiments and performance evaluations. It outperforms traditional machine learning methods, achieving 94.8% accuracy and superior generalization capabilities.

CONCLUSIONS

The study highlights the potential of deep learning in ALF prediction, emphasizing the importance of considering individualized medical factors. Future research should focus on improving model robustness, addressing imbalanced data, and further exploring personalized features for enhanced predictive accuracy in real-world clinical scenarios.

Clinical study of standard residual liver volume and transient elastography in predicting poor prognosis of patients after hemihepatectomy

BACKGROUND

Liver cancer resection, especially in patients with hemihepatectomy or extended hemihepatectomy, often leads to poor prognosis, such as liver insufficiency and even liver failure and death, because the standard residual liver volume (SRLV) cannot be fully compensated after surgery.

AIM

To explore the risk factors of poor prognosis after hemihepatectomy for hepatocellular carcinoma and evaluate the application value of related prognostic approaches.

METHODS

The clinical data of 35 patients with primary liver cancer in Nantong Third People's Hospital from February 2016 to July 2020 were retrospectively analyzed. The receiver operating characteristic curve was created using medcac19.0.4 to compare the critical values of the SRLV in different stages of liver fibrosis after hemihepatectomy with those of liver dysfunction after hemihepatectomy. It was constructed by combining the Child-Pugh score to evaluate its application value in predicting liver function compensation.

RESULTS

The liver stiffness measure (LSM) value and SRLV were associated with liver dysfunction after hemihepatectomy. Logistic regression analysis showed that an LSM value ≥ 25 kPa [odds ratio (OR) = 6.254, P < 0.05] and SRLV ≤ 0.290 L/m2 (OR = 5.686, P < 0.05) were independent risk factors for postoperative liver dysfunction. The accuracy of the new liver reserve evaluation model for predicting postoperative liver function was higher than that of the Child-Pugh score (P < 0.05).

CONCLUSION

SRLV and LSM values can be used to evaluate the safety of hemihepatectomy. The new liver reserve evaluation model has good application potential in the evaluation of liver reserve function after hemihepatectomy.

Current status of preoperative risk assessment for posthepatectomy liver failure in patients with hepatocellular carcinoma

Liver resection is an effective therapeutic option for patients with hepatocellular carcinoma. However, posthepatectomy liver failure (PHLF) remains a major cause of hepatectomy-related mortality, and the accurate prediction of PHLF based on preoperative assessment of liver functional reserve is a critical issue. The definition of PHLF proposed by the International Study Group for Liver Surgery has gained acceptance as a standard grading criterion. Liver function can be estimated using a variety of parameters, including routine blood biochemical examinations, clinical scoring systems, dynamic liver function tests, liver stiffness and fibrosis markers, and imaging studies. The Child-Pugh score and model for end-stage liver disease scores are conventionally used for estimating liver decompensation, although the alternatively developed albumin-bilirubin score shows superior performance for predicting hepatic dysfunction. Indocyanine green clearance, a dynamic liver function test mostly used in Japan and other Asian countries, serves as a quantitative estimation of liver function reserve and helps determine indications for surgical procedures according to the estimated risk of PHLF. In an attempt to improve predictive accuracy, specific evaluation of liver fibrosis and portal hypertension has gained popularity, including liver stiffness measurements using ultrasonography or magnetic resonance elastography, as well as noninvasive fibrosis markers. Imaging modalities, including Tc-99m-labeled galactosyl serum albumin scintigraphy and gadolinium-enhanced magnetic resonance imaging, are used for preoperative evaluation in combination with liver volume. This review aims to provide an overview of the usefulness of current options for the preoperative assessment of liver function in predicting PHLF.

Perioperative predictors of outcome of hepatectomy for HBV-related hepatocellular carcinoma

Hepatitis B virus (HBV) is identified as a major risk factor for hepatocellular carcinoma (HCC), resulting in so-called hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). Hepatectomy for HCC is acknowledged as an efficient treatment strategy, especially for early HCC. Furthermore, patients with advanced HCC can still obtain survival benefits through surgical treatment combined with neoadjuvant therapy, adjuvant therapy, transcatheter arterial chemoembolization, and radiofrequency ablation. Therefore, preoperative and postoperative predictors of HBV-related HCC have crucial indicative functions for the follow-up treatment of patients with feasible hepatectomy. This review covers a variety of research results on preoperative and postoperative predictors of hepatectomy for HBV-related HCC over the past decade and in previous landmark studies. The relevant contents of Hepatitis C virus-related HCC, non-HBV non-HCV HCC, and the artificial intelligence application in this field are briefly addressed in the extended content. Through the integration of this review, a large number of preoperative and postoperative factors can predict the prognosis of HBV-related HCC, while most of the predictors have no standardized thresholds. According to the characteristics, detection methods, and application of predictors, the predictors can be divided into the following categories: 1. serological and hematological predictors, 2. genetic, pathological predictors, 3. imaging predictors, 4. other predictors, 5. analysis models and indexes. Similar results appear in HCV-related HCC, non-HBV non-HCV HCC. Predictions based on AI and big biological data are actively being applied. A reasonable prediction model should be established based on the economic, health, and other levels in specific countries and regions.

A deep learning model for prediction of post hepatectomy liver failure after hemihepatectomy using preoperative contrast-enhanced computed tomography: a retrospective study

Objective

Post-hepatectomy liver failure (PHLF) remains clinical challenges after major hepatectomy. The aim of this study was to establish and validate a deep learning model to predict PHLF after hemihepatectomy using preoperative contrast-enhancedcomputed tomography with three phases (Non-contrast, arterial phase and venous phase).

Methods

265 patients undergoing hemihepatectomy in Sir Run Run Shaw Hospital were enrolled in this study. The primary endpoint was PHLF, according to the International Study Group of Liver Surgery's definition. In this study, to evaluate the proposed method, 5-fold cross-validation technique was used. The dataset was split into 5 folds of equal size, and each fold was used as a test set once, while the other folds were temporarily combined to form a training set. Performance metrics on the test set were then calculated and stored. At the end of the 5-fold cross-validation run, the accuracy, precision, sensitivity and specificity for predicting PHLF with the deep learning model and the area under receiver operating characteristic curve (AUC) were calculated.

Results

Of the 265 patients, 170 patients with left liver resection and 95 patients with right liver resection. The diagnosis had 6 types: hepatocellular carcinoma, intrahepatic cholangiocarcinoma, liver metastases, benign tumor, hepatolithiasis, and other liver diseases. Laparoscopic liver resection was performed in 187 patients. The accuracy of prediction was 84.15%. The AUC was 0.7927. In 170 left hemihepatectomy cases, the accuracy was 89.41% (152/170), and the AUC was 82.72%. The accuracy was 77.47% (141/182) with liver mass, 78.33% (47/60) with liver cirrhosis and 80.46% (70/87) with viral hepatitis.

Conclusion

The deep learning model showed excellent performance in prediction of PHLF and could be useful for identifying high-risk patients to modify the treatment planning.

Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients

BACKGROUND

Liver cirrhosis patients are at risk for esophagogastric variceal bleeding (EGVB). Herein, we aimed to estimate the EGVB risk in patients with liver cirrhosis using an artificial neural network (ANN).

METHODS

We included 999 liver cirrhosis patients hospitalized at the Beijing Ditan Hospital, Capital Medical University in the training cohort and 101 patients from Shuguang Hospital in the validation cohort. The factors independently affecting EGVB occurrence were determined via univariate analysis and used to develop an ANN model.

RESULTS

The 1-year cumulative EGVB incidence rates were 11.9 and 11.9% in the training and validation groups, respectively. A total of 12 independent risk factors, including gender, drinking and smoking history, decompensation, ascites, location and size of varices, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), hematocrit (HCT) and neutrophil-lymphocyte ratio (NLR) levels as well as red blood cell (RBC) count were evaluated and used to establish the ANN model, which estimated the 1-year EGVB risk. The ANN model had an area under the curve (AUC) of 0.959, which was significantly higher than the AUC for the North Italian Endoscopic Club (NIEC) (0.669) and revised North Italian Endoscopic Club (Rev-NIEC) indices (0.725) (all P <  0.001). Decision curve analyses revealed improved net benefits of the ANN compared to the NIEC and Rev-NIEC indices.

CONCLUSIONS

The ANN model accurately predicted the 1-year risk for EGVB in liver cirrhosis patients and might be used as a basis for risk-based EGVB surveillance strategies.

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

Introduction

Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF.

Methods

A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models.

Results

The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models.

Conclusion

A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

Promoting Surgical Resection through Future Liver Remnant Hypertrophy

An inadequate future liver remnant (FLR) can preclude curative-intent surgical resection for patients with primary or secondary hepatic malignancies. For patients with normal baseline liver function and without risk factors, an FLR of 20% is needed to maintain postsurgical hepatic function. However, the FLR requirement is higher for patients who are exposed to systemic chemotherapy (FLR, >30%) or have cirrhosis (FLR, >40%). Interventional radiologic and surgical methods to achieve FLR hypertrophy are evolving, including portal vein ligation, portal vein embolization, radiation lobectomy, hepatic venous deprivation, and associating liver partition and portal vein ligation for staged hepatectomy. Each technique offers particular advantages and disadvantages. Knowledge of these procedures can help clinicians to choose the suitable technique for each patient. The authors review the techniques used to develop FLR hypertrophy, focusing on technical considerations, outcomes, and the advantages and disadvantages of each approach. Online supplemental material is available for this article. ^©RSNA, 2022.

Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease

Background: Alzheimer’s disease (AD) is the most common form of dementia in old age and poses a severe threat to the health and life of the elderly. However, traditional diagnostic methods and the ATN diagnostic framework have limitations in clinical practice. Developing novel biomarkers and diagnostic models is necessary to complement existing diagnostic procedures.

Methods: The AD expression profile dataset GSE63060 was downloaded from the NCBI GEO public database for preprocessing. AD-related differentially expressed genes were screened using a weighted co-expression network and differential expression analysis, and functional enrichment analysis was performed. Subsequently, we screened hub genes by random forest, analyzed the correlation between hub genes and immune cells using ssGSEA, and finally built an AD diagnostic model using an artificial neural network and validated it.

Results: Based on the random forest algorithm, we screened a total of seven hub genes from AD-related DEGs, based on which we confirmed that hub genes play an essential role in the immune microenvironment and successfully established a novel diagnostic model for AD using artificial neural networks, and validated its effectiveness in the publicly available datasets GSE63060 and GSE97760.

Conclusion: Our study establishes a reliable model for screening and diagnosing AD that provides a theoretical basis for adding diagnostic biomarkers for the AD gene.

Developmental artificial neural network model to evaluate the preoperative safe limit of future liver remnant volume for HCC combined with clinically significant portal hypertension

Background & aims: Finding a way to comprehensively integrate the presence and grade of clinically significant portal hypertension, amount of preserved liver function and extent of hepatectomy into the guidelines for choosing appropriate candidates to hepatectomy remained challenging. This study sheds light on these issues to facilitate precise surgical decisions for clinicians. Methods: Independent risk factors associated with grade B/C post-hepatectomy liver failure were identified by stochastic forest algorithm and logistic regression in hepatitis B virus-related hepatocellular carcinoma patients. Results: The artificial neural network model was generated by integrating preoperative pre-ALB, prothrombin time, total bilirubin, AST, indocyanine green retention rate at 15 min, standard future liver remnant volume and clinically significant portal hypertension grade. In addition, stratification of patients into three risk groups emphasized significant distinctions in the risk of grade B/C post-hepatectomy liver failure. Conclusion: The authors' artificial neural network model could provide a reasonable therapeutic option for clinicians to select optimal candidates with clinically significant portal hypertension for hepatectomy and supplement the hepatocellular carcinoma surgical treatment algorithm.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.

Surgical data science - from concepts toward clinical translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research

The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.

Machine learning in gastrointestinal surgery

Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.

A systematic review of prediction models for post-hepatectomy liver failure in patients undergoing liver surgery

BACKGROUND

The aim of this systematic review is to evaluate the current evidence in the context of clinical prediction model for post-hepatectomy liver failure (PHLF).

METHODS

A systematic search of the English literature for a period from December 2005 to September 2020 was conducted. Primary outcome was defined using the three common PHLF criteria (50-50 criteria, peak bilirubin>7 mg/dl criteria, and ≥ grade B PHLF criteria by the International Study Group of Liver Surgery). Studies that reported the value of area under receiver operative characteristic curve (AUC) for the occurrence of PHLF were included.

RESULTS

Twenty eight of 1327 screened articles were eligible for inclusion. Eighteen studies developed the prediction models. The median AUC was found to be 0.79 (0.65-0.933). The parameters related to the amount of future liver remnant volume were most commonly identified as significant predictors for PHLF in statistical analysis (24 studies) and were most frequently incorporated in the prediction models (18 studies). The parameters associated with portal hypertension were significant for predicting PHLF in 16 studies and were adopted in the prediction models in 14 studies.

CONCLUSION

Parameters related to future liver remnant volume and portal hypertension seem to be facilitating in predicting PHLF.

Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion

BACKGROUND

The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion.

METHODS

Nine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort.

RESULTS

PHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox's proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups.

CONCLUSION

When compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.

Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.

Role of artificial intelligence in hepatobiliary and pancreatic surgery

Over the past decade, enhanced preoperative imaging and visualization, improved delineation of the complex anatomical structures of the liver and pancreas, and intra-operative technological advances have helped deliver the liver and pancreatic surgery with increased safety and better postoperative outcomes. Artificial intelligence (AI) has a major role to play in 3D visualization, virtual simulation, augmented reality that helps in the training of surgeons and the future delivery of conventional, laparoscopic, and robotic hepatobiliary and pancreatic (HPB) surgery; artificial neural networks and machine learning has the potential to revolutionize individualized patient care during the preoperative imaging, and postoperative surveillance. In this paper, we reviewed the existing evidence and outlined the potential for applying AI in the perioperative care of patients undergoing HPB surgery.