Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.

Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.

Barcelona Clinic Liver Cancer Classification and Treatment of Hepatocellular Carcinoma in a Côte d’Ivoire University Hospital

Context/Objectives: Hepatocellular carcinoma occurs mainly and increasingly in developing countries, where the prognosis is particularly poor. The Barcelona Clinic Liver Cancer classification is used to guide the treatment of hepatocellular carcinoma. The aim of this retrospective study was to describe the Barcelona Clinic Liver Cancer classification and the treatment of hepatocellular carcinoma in a University Hospital in Côte d’Ivoire. Methods: Patients with hepatocellular carcinoma hospitalized in the hepato-gastroenterology unit of the University Hospital of Yopougon from 01 January 2012 to 30 June 2017 were included. The diagnosis of hepatocellular carcinoma was based on the presence of hepatic nodules on the abdominal ultrasound scan, typical images with the helical scanner associated or not with an increase of the α-fetoprotein higher than 200 ng/ml or with histology. Demographic, clinical, biological and radiological data were determined at the time of diagnosis. Patients were classified according to the Barcelona Clinic Liver Cancer classification. Their treatment was specified. Results: There were 258 patients whose median age was 48.1 years. Viral hepatitis B virus was the primary cause of hepatocellular carcinoma in 64.7% of cases. The severity of the underlying cirrhosis was Child-Pugh A in 12.1%, B in 63.6% and C in 24.3% of cases. The median size of the tumor was 63 mm. The α-fetoprotein level was higher than 200 mg/ml in 56.03% of cases. The Eastern Cooperative Oncology Group (ECOG)/World Health Organization (WHO) system was ≥2 in 82.9%. The Barcelona Clinic Liver Cancer classification was A in 1.3%, B in 0%, C in 55.2% and D in 43.5% of patients. There was no transplantation or hepatic resection. Very few patients (1.9%) received radio-frequency curative therapy. The treatment was predominantly symptomatic in 97.8% of patients. During hospitalization 43.7% of patients died. Conclusion: Hepatocellular carcinoma occurs on a liver with severe cirrhosis at a late stage. This does not allow cure treatment and explains a high mortality rate during hospitalization. Hepatitis B virus is the main risk factor and immunization at birth will reduce the incidence of this cancer in Africa.

Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.

Performance Comparison of Vision Transformer- and CNN-Based Image Classification Using Cross Entropy: A Preliminary Application to Lung Cancer Discrimination from CT Images

This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images into four categories: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma (LULC), and normal. Although CNNs have made significant advancements in medical imaging, their limited capacity to capture long-range dependencies has led to the exploration of ViTs, which leverage self-attention mechanisms for a more comprehensive global understanding of images. The study utilized a dataset of 748 lung CT images to train both models with standardized input sizes, assessing their performance through conventional metrics—accuracy, precision, recall, F1 score, specificity, and AUC—as well as cross entropy, a novel metric for evaluating prediction uncertainty. Both models achieved similar accuracy rates (95%), with ViT demonstrating a slight edge over ResNet50 in precision and F1 scores for specific classes. However, ResNet50 exhibited higher recall for LULC, indicating fewer missed cases. Cross entropy analysis showed that the ViT model had lower average uncertainty, particularly in the LUAD, Normal, and LUSC classes, compared to ResNet50. This finding suggests that ViT predictions are generally more reliable, though ResNet50 performed better for LULC. The study underscores that accuracy alone is insufficient for model comparison, as cross entropy offers deeper insights into the reliability and confidence of model predictions. The results highlight the importance of incorporating cross entropy alongside traditional metrics for a more comprehensive evaluation of deep learning models in medical image classification, providing a nuanced understanding of their performance and reliability. While the ViT outperformed the CNN-based ResNet50 in lung cancer classification based on cross-entropy values, the performance differences were minor and may not hold clinical significance. Therefore, it may be premature to consider replacing CNNs with ViTs in this specific application.

Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

Cancer classification based on microarray gene expression data using a principal component accumulation method

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.

Multi-class cancer classification through gene expression profiles: microRNA versus mRNA

Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.

Integrative clustering methods of multi-omics data for molecule-based cancer classifications

One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories： direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis arc also discussed.

Smart MobiNet:A Deep Learning Approach for Accurate Skin Cancer Diagnosis

The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.

Classification of rectal cancer according to recurrence types-comparison of Japanese guidelines and Western guidelines

BACKGROUND Rectal cancer is characterized by more local recurrence(LR)and lung metastasis than colon cancer.However,the diagnosis of rectal cancer is not standardized as there is no global consensus on its definition and classification.The classification of rectal cancer differs between Japanese and Western guidelines.AIM To clarify the characteristics of rectal cancer by comparing the tumor location and characteristics of rectal cancer with those of colon cancer according to each set of guidelines.METHODS A total of 958 patients with Stage II and III colorectal cancer were included in the analysis:607 with colon cancer and 351 with rectal cancer.Localization of rectal cancers was assessed by enema examination and rigid endoscopy.According to Japan guidelines,rectal cancer is classified as Rb(below the peritoneal inversion),Ra(between the inferior margin of second sacral vertebrae and Rb)or RS(between Ra and sacral promontory).RESULTS There were no significant differences between RS rectal cancer and colon cancer in the rates of liver and lung metastasis or LR.Lung metastasis and LR were significantly more common among Rb rectal cancer(in Japan)than in colon cancer(P=0.0043 and P=0.0002,respectively).Lung metastases and LR occurred at significantly higher rates in rectal cancer measuring≤12 cm and≤10 cm than in colon cancers(P=0.0117,P=0.0467,P=0.0036,P=0.0010).Finally,the rates of liver metastasis,lung metastasis,and LR in rectal cancers measuring 11 cm to 15 cm were 6.9%,2.8%,and 5.7%,respectively.These were equivalent to the rates in colon cancer.CONCLUSION High rectal cancer may be treated with the same treatment strategies as colon cancer.There was no difference in the classification of colorectal cancer between Japan and Western countries.

Perioperative Treatment in Gastric Cancer—Developments in Patient Selection

Gastric cancer (GC) is a major public health issue. It is considered the 5th most common cancer diagnosed worldwide and it is one of the main causes of malignant disease-associated morbidity and mortality. The cornerstone of curative treatment is still surgery, and since the rate of relapse is high, a multidisciplinary approach is warranted in most developed countries. And while there have been recent developments in the perioperative scenario namely the FLOT regimen, little has advanced considering patient selection. We have reviewed the major trials in this setting and provide some insights from recently reported microsatellite instability (MSI) in a subgroup analysis in the MAGIC trial patients that seem to suggest an opportunity to patient selection. Furthermore, GC subtyping may prove helpful selecting candidates to immunotherapy or even multimodal therapy in the future. As the paradigm is moving towards a precision oncology model, GC patient selection remains one the biggest challenges in oncology but seems closer to clinical practice reality as new developments are being reported.

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.

National Comprehensive Cancer Network （NCCN） riSK classification in predicting biochemical recurrence after radical prostatectomy： a retrospective cohort study in Chinese prostate cancer patients

This study aimed to assess the role of the National Comprehensive Cancer Network （NCCN） risk classification in predicting biochemical recurrence （BCR） after radical prostatectomy （RP） in Chinese prostate cancer patients. We included a consecutive cohort of 385 patients with prostate cancer who underwent RP at Fudan University Shanghai Cancer Center （Shanghai, China） from March 2011 to December 2014. Gleason grade groups were applied at analysis according to the 2014 International Society of Urological Pathology Consensus. Risk groups were stratified according to the NCCN Clinical Practice Guidelines in Oncology： Prostate Cancer version 1, 2017. All 385 patients were divided into BCR and non-BCR groups. The clinicopathological characteristics were compared using an independent sample t-test, Chi-squared test, and Fisher＇s exact test. BCR-free survival was compared using the log-rank test and multivariable Cox proportional hazard analysis. During median follow-up of 48 months （range： 1-78 months）, 31 （8.05%） patients experienced BCR. The BCR group had higher prostate-specific antigen level at diagnosis （46.54 ± 39.58 ng m1-1 vs 21.02 ± 21.06 ng ml-1, P= 0.001）, more advanced pT stage （P= 0.002）, and higher pN1 rate （P〈 0.001）. NCCN risk classification was a significant predictor of BCR {P = 0.0006） and BCR-free survival （P = 0.003） after RP. As NCCN risk level increased, there was a significant decreasing trend in BCR-free survival rate （Ptrend = 0.0002）. This study confirmed and validated that NCCN risk classification was a significant predictor of BCR and BCR-free survival after RP.

Performance-weighted-voting model:An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation

Background:With improvements in next-generation DNA sequencing technology,lower cost is needed to collect genetic data.More machine learning techniques can be used to help with cancer analysis and diagnosis.Methods:We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer types.Our ensemble system consists of five weak classifiers(logistic regression,SVM,random forest,XGBoost and neural networks).We first used cross-validation to get the predicted results for the five classifiers.The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions.The final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted probability.Results:Using the somatic mutation count of each gene as the input feature,the overall accuracy of the performance-weighted-voting model reached 71.46%,which was significantly higher than the five weak classifiers and two other ensemble models:the hard-voting model and the soft-voting model.In addition,by analyzing the predictive pattern of the performance-weighted-voting model,we found that in most cancer types,higher tumor mutational burden can improve overall accuracy.Conclusion:This study has important clinical significance for identifying the origin of cancer,especially for those where the primary cannot be determined.In addition,our model presents a good strategy for using ensemble systems for cancer type classification.

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classi?cation

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine （IG-SVM） in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYLg, and GUCA2B.

InfiniumPurify: An R package for estimating and accounting for tumor purity in cancer methylation research

The proposition of cancer cells in a tumor sample,named as tumor purity,is an intrinsic factor of tumor samples and has potentially great influence in variety of analyses including differential methylation,subclonal deconvolution and subtype clustering.Infinium-Purify is an integrated R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450 k array data.InfiniumPurify has three main functions getPurity,InfiniumDMC and InfiniumClust,which could infer tumor purity,differential methylation analysis and tumor sample cluster accounting for estimated or user-provided tumor purities,respectively.The InfiniumPurify package provides a comprehensive analysis of tumor purity in cancer methylation research.

A nomogram predicting the recurrence of hepatocellular carcinoma in patients after laparoscopic hepatectomy

Background:Patients with hepatocellular carcinoma(HCC)undergoing surgical resection still have a high 5-year recurrence rate(~60%).With the development of laparoscopic hepatectomy(LH),few studies have compared the efficacy between LH and traditional surgical approach on HCC.The objective of this study was to establish a nomo-gram to evaluate the risk of recurrence in HCC patients who underwent LH.Methods:The clinical data of 432 patients,pathologically diagnosed with HCC,underwent LH as initial treatment and had surgical margin>1 cm were collected.The significance of their clinicopathological features to recurrence-free survival(RFS)was assessed,based on which a nomogram was constructed using a training cohort(n=324)and was internally validated using a temporal validation cohort(n=108).Results:Hepatitis B surface antigen(hazard ratio[HR],1.838;P=0.044),tumor number(HR,1.774;P=0.003),tumor thrombus(HR,2.356;P=0.003),cancer cell differentiation(HR,0.745;P=0.080),and microvascular tumor invasion(HR,1.673;P=0.007)were found to be independent risk factors for RFS in the training cohort,and were used for con-structing the nomogram.The C-index for RFS prediction in the training cohort using the nomogram was 0.786,which was higher than that of the 8th edition of the American Joint Committee on Cancer TNM classification(C-index,0.698)and the Barcelona Clinic Liver Cancer staging system(C-index,0.632).A high consistency between the nomogram prediction and actual observation was also demonstrated by a calibration curve.An improved predictive benefit in RFS and higher threshold probability of the nomogram were determined by receiver operating characteristic curve analysis,which was also confirmed in the validation cohort compared to other systems.Conclusions:We constructed and validated a nomogram able to quantify the risk of recurrence after initial LH for HCC patients,which can be clinically implemented in assisting the planification of individual postoperative surveil-lance protocols.