Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma:A quantitative review with Radiomics Quality Score

BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implementation in clinical practice is still far,with many issues related to the methodological quality of radiomic studies.AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score(RQS).METHODS A systematic literature search of PubMed,Google Scholar,and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023.The methodological quality of radiomic studies was assessed using the RQS tool.Spearman’s correlation(ρ)analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies.The level of statistical significance was set at P<0.05.RESULTS One hundred and twenty-seven articles were included,of which 43 focused on HCC prognosis,39 on prediction of pathological findings,16 on prediction of the expression of molecular markers outcomes,18 had a diagnostic purpose,and 11 had multiple purposes.The mean RQS was 8±6.22,and the corresponding percentage was 24.15%±15.25%(ranging from 0.0% to 58.33%).RQS was positively correlated with journal impact factor(IF;ρ=0.36,P=2.98×10^(-5)),5-years IF(ρ=0.33,P=1.56×10^(-4)),number of patients included in the study(ρ=0.51,P<9.37×10^(-10))and number of radiomics features extracted in the study(ρ=0.59,P<4.59×10^(-13)),and time of publication(ρ=-0.23,P<0.0072).CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients,our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model:A dual-center study

BACKGROUND Perineural invasion(PNI)has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer(RC).Preoperative prediction of PNI status is helpful for individualized treatment of RC.Recently,several radiomics studies have been used to predict the PNI status in RC,demonstrating a good predictive effect,but the results lacked generalizability.The preoperative prediction of PNI status is still challenging and needs further study.AIM To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers.The patients underwent preoperative high-resolution magnetic resonance imaging(MRI)between May 2019 and August 2022.Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging(T2WI)and contrast-enhanced T1WI(T1CE)sequences.The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared(T2WI,T1CE and T2WI+T1CE fusion sequences).A clinical-radiomics(CR)model was established by combining the radiomics features and clinical risk factors.The internal and external validation groups were used to validate the proposed models.The area under the receiver operating characteristic curve(AUC),DeLong test,net reclassification improvement(NRI),integrated discrimination improvement(IDI),calibration curve,and decision curve analysis(DCA)were used to evaluate the model performance.RESULTS Among the radiomics models,the T2WI+T1CE fusion sequences model showed the best predictive performance,in the training and internal validation groups,the AUCs of the fusion sequence model were 0.839[95%confidence interval(CI):0.757-0.921]and 0.787(95%CI:0.650-0.923),which were higher than those of the T2WI and T1CE sequence models.The CR model constructed by combining clinical risk factors had the best predictive performance.In the training and internal and external validation groups,the AUCs of the CR model were 0.889(95%CI:0.824-0.954),0.889(95%CI:0.803-0.976)and 0.894(95%CI:0.814-0.974).Delong test,NRI,and IDI showed that the CR model had significant differences from other models(P<0.05).Calibration curves demonstrated good agreement,and DCA revealed significant benefits of the CR model.CONCLUSION The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively,which facilitates individualized treatment of RC patients.

Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease

BACKGROUND Due to similar clinical manifestations and imaging signs,differential diagnosis of primary intestinal lymphoma(PIL)and Crohn's disease(CD)is a challenge in clinical practice.AIM To investigate the ability of radiomics combined with machine learning methods to differentiate PIL from CD.METHODS We collected contrast-enhanced computed tomography(CECT)and clinical data from 120 patients form center 1.A total of 944 features were extracted singlephase images of CECT scans.Using the last absolute shrinkage and selection operator model,the best predictive radiographic features and clinical indications were screened.Data from 54 patients were collected at center 2 as an external validation set to verify the robustness of the model.The area under the receiver operating characteristic curve,accuracy,sensitivity and specificity were used for evaluation.RESULTS A total of five machine learning models were built to distinguish PIL from CD.Based on the results from the test group,most models performed well with a large area under the curve(AUC)(>0.850)and high accuracy(>0.900).The combined clinical and radiomics model(AUC=1.000,accuracy=1.000)was the best model among all models.CONCLUSION Based on machine learning,a model combining clinical data with radiologic features was constructed that can effectively differentiate PIL from CD.

Computed tomography-based radiomics predicts the fibroblastrelated gene EZH2 expression level and survival of hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is the most common subtype of liver cancer.The primary treatment strategies for HCC currently include liver transplantation and surgical resection.However,these methods often yield unsatisfactory outcomes,leading to a poor prognosis for many patients.This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.AIM To construct a radiomics model that can accurately predict the EZH2 expression in HCC.METHODS Gene expression,clinical parameters,HCC-related radiomics,and fibroblastrelated genes were acquired from public databases.A gene model was developed,and its clinical efficacy was assessed statistically.Drug sensitivity analysis was conducted with identified hub genes.Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes.A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.RESULTS EZH2 and NRAS were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model.This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis.A negative correlation was observed between EZH2 expression and drug sensitivity.Elevated EZH2 expression was linked to poorer prognosis,and its diagnostic value in HCC surpassed that of the risk model.A radiomics model,developed using a logistic algorithm,also showed superior efficiency in predicting EZH2 expression.The Radscore was higher in the group with high EZH2 expression.A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and EZH2 expression in predicting the overall survival of HCC patients.CONCLUSION EZH2 plays significant roles in diagnosing HCC and therapeutic efficacy.A radiomics model,developed using a logistic algorithm,efficiently predicted EZH2 expression and exhibited strong correlation with HCC prognosis.

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to predict the risk grade of EC.AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.METHODS The study comprised 112 EC patients.The participants were randomly separated into training and validation groups with a 7:3 ratio.Logistic regression analysis was applied to uncover independent clinical predictors.These predictors were then used to create a clinical nomogram.Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images,the Mann-Whitney U test,Pearson test,and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features,which were subsequently utilized to generate a radiomic signature.Seven machine learning strategies were used to construct radiomic models that relied on the screening features.The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.RESULTS Having an accuracy of 0.82 along with an area under the curve(AUC)of 0.915[95%confidence interval(CI):0.806-0.986],the random forest method trained on radiomics characteristics performed better than expected.The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram(AUC:0.75,95%CI:0.611-0.899)and the combined nomogram(AUC:0.869,95%CI:0.702-0.986)that integrated clinical parameters and radiomic signature.CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer

BACKGROUND Lymph node(LN)staging in rectal cancer(RC)affects treatment decisions and patient prognosis.For radiologists,the traditional preoperative assessment of LN metastasis(LNM)using magnetic resonance imaging(MRI)poses a challenge.AIM To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs.METHODS In this retrospective study,270 LNs(158 nonmetastatic,112 metastatic)were randomly split into training(n=189)and validation sets(n=81).LNs were classified based on pathology-MRI matching.Conventional MRI features[size,shape,margin,T2-weighted imaging(T2WI)appearance,and CE-T1-weighted imaging(T1WI)enhancement]were evaluated.Three radiomics models used 3D features from T1WI and T2WI images.Additionally,a nomogram model combining conventional MRI and radiomics features was developed.The model used univariate analysis and multivariable logistic regression.Evaluation employed the receiver operating characteristic curve,with DeLong test for comparing diagnostic performance.Nomogram performance was assessed using calibration and decision curve analysis.RESULTS The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM.In the training set,the nomogram model achieved an area under the curve(AUC)of 0.92,which was significantly higher than the AUCs of 0.82(P<0.001)and 0.89(P<0.001)of the conventional MRI and radiomics models,respectively.In the validation set,the nomogram model achieved an AUC of 0.91,significantly surpassing 0.80(P<0.001)and 0.86(P<0.001),respectively.CONCLUSION The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study

BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography(CT)image radiomics.METHODS This study included 181 stage II/III gastric cancer patients,141 from Lichuan People's Hospital,and 40 from the Cancer Imaging Archive(TCIA).Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest(ROI),and approximately 1700 radiomics features were extracted from each ROI.The skeletal muscle index(SMI)and skeletal muscle density(SMD)were measured using CT images from the lower margin of the third lumbar vertebra.Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation,36 radiomics features were identified as important predictors,and the OS-associated CT image radiomics score(OACRS)was cal-culated for each patient using these important predictors.RESULTS Patients with a high OACRS had a poorer prognosis than those with a low OACRS score(P<0.05)and those in the TCIA cohort.Univariate and multivariate analyses revealed that OACRS was a risk factor[RR=3.023(1.896-4.365),P<0.001]independent of SMI,SMD,and pathological features.Moreover,OACRS outperformed SMI and SMD and could improve OS prediction(P<0.05).CONCLUSION A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early-and late-stage pancreatic ductal adenocarcinoma

BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

BACKGROUND The prognosis for hepatocellular carcinoma(HCC)in the presence of cirrhosis is unfavourable,primarily attributable to the high incidence of recurrence.AIM To develop a machine learning model for predicting early recurrence(ER)of posthepatectomy HCC in patients with cirrhosis and to stratify patients’overall survival(OS)based on the predicted risk of recurrence.METHODS In this retrospective study,214 HCC patients with cirrhosis who underwent curative hepatectomy were examined.Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods.Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses.Five machine learning methods were used for model comparison,aiming to identify the optimal model.The model’s performance was evaluated using the receiver operating characteristic curve[area under the curve(AUC)],calibration,and decision curve analysis.Additionally,the Kaplan-Meier(K-M)curve was used to evaluate the stratification effect of the model on patient OS.RESULTS Within this study,the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features.In the training cohort,this model attained an AUC of 0.844,while in the validation cohort,it achieved a value of 0.790.The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients’OS.CONCLUSION The combined model,integrating both radiomics and clinical-radiologic characteristics,exhibited excellent performance in HCC with cirrhosis.The K-M curves assessing OS revealed statistically significant differences.

Ultrasomics in liver cancer: Developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound

BACKGROUND Hepatocellular carcinoma(HCC)and intrahepatic cholangiocarcinoma(ICC)represent the predominant histological types of primary liver cancer,comprising over 99%of cases.Given their differing biological behaviors,prognoses,and treatment strategies,accurately differentiating between HCC and ICC is crucial for effective clinical management.Radiomics,an emerging image processing technology,can automatically extract various quantitative image features that may elude the human eye.Reports on the application of ultrasound(US)-based radiomics methods in distinguishing HCC from ICC are limited.METHODS In our retrospective study,we included a total of 280 patients who were diagnosed with ICC(n=140)and HCC(n=140)between 1999 and 2019.These patients were divided into training(n=224)and testing(n=56)groups for analysis.US images and relevant clinical characteristics were collected.We utilized the XGBoost method to extract and select radiomics features and further employed a random forest algorithm to establish ultrasomics models.We compared the diagnostic performances of these ultrasomics models with that of radiologists.RESULTS Four distinct ultrasomics models were constructed,with the number of selected features varying between models:13 features for the US model;15 for the contrast-enhanced ultrasound(CEUS)model;13 for the combined US+CEUS model;and 21 for the US+CEUS+clinical data model.The US+CEUS+clinical data model yielded the highest area under the receiver operating characteristic curve(AUC)among all models,achieving an AUC of 0.973 in the validation cohort and 0.971 in the test cohort.This performance exceeded even the most experienced radiologist(AUC=0.964).The AUC for the US+CEUS model(training cohort AUC=0.964,test cohort AUC=0.955)was significantly higher than that of the US model alone(training cohort AUC=0.822,test cohort AUC=0.816).This finding underscored the significant benefit of incorporating CEUS information in accurately distin-guishing ICC from HCC.CONCLUSION We developed a radiomics diagnostic model based on CEUS images capable of quickly distinguishing HCC from ICC,which outperformed experienced radiologists.

Application Progress of Ultrasound Radiomics in the Evaluation and Prediction of Neoadjuvant Chemotherapy for Breast Cancer

Breast cancer is a malignant tumor with the highest incidence in women. In recent years, the incidence of breast cancer has shown an increasing trend, especially in younger patients, which seriously threatens the life and health of women. In order to improve the treatment effect of breast cancer, neoadjuvant chemotherapy has become a reliable strategy to cooperate with surgical treatment and improve the prognosis of advanced breast cancer, which is conducive to quickly and accurately curbing the growth of cancer cells, controlling the patients’ condition, reducing their pain, and improving the cure rate of breast cancer patients. This paper analyzes the development history of ultrasound radiomics, explores its application in the evaluation and prediction of neoadjuvant chemotherapy for breast cancer, and clarifies the research results of multimodal ultrasound radiomics in the analysis of high-order characteristics of breast cancer tumors and the evaluation of tumor heterogeneity, so as to provide references for the clinical treatment of breast cancer.

Development and validation of tongue imaging-based radiomics tool for the diagnosis of insomnia degree:a two-center study

Background:Traditional Chinese medicine(TCM)is commonly used for the diagnosis and treatment of insomnia,with tongue diagnosis being particularly important.The aim of our study was to develop and validate a novel tongue imaging-based radiomics(TIR)method for accurately diagnosing insomnia severity.Methods:This two-center analysis prospectively enrolled 399 patients who underwent tongue imaging between July and October 2021 and divided them into primary and validation cohorts by study center.Here,we referred to the Insomnia Severity Index(ISI)standard and the degree of insomnia was evaluated as absent,subthreshold,moderate,or severe.For developed the TIR diagnostic tool,a U-Net algorithm was used to segment tongue images.Subsequently,seven imaging features were selected from the extracted high-throughout radiomics features using the least absolute shrinkage and selection operator algorithm.Then,the final radiomics model was developed in the primary cohort and tested in the independent validation cohort.Finally,we assessed and compared the diagnostic performance differences between TCM tongue diagnosis and our TIR diagnostic tool with the ISI gold standard.The confusion matrix was calculated to evaluate the diagnostic performance.Results:Seven tongue imaging features were selected to build the TIR tool,with showing good correlations with the insomnia degree.The TIR method had an accuracy of 0.798,a macro-average sensitivity of 0.78,a macro-average specificity of 0.906,a weighted-average sensitivity of 0.798,and a weighted specificity of 0.916,showing a significantly better performance compared to the average performance of three experienced TCM physicians(mean accuracy of 0.458,P<0.01).Conclusions:The preliminary study demonstrates the potential application of TIR in the diagnosis of insomnia degree and measurement of sleep health.The integration of quantitative imaging analysis and machine learning algorithms holds promise for advancing both of TCM and precision sleep medicine.

Research Progress of Ultrasound Radiomics in The Diagnosis and Treatment of Breast Cancer

With the advancement of medical research in recent years and the frequent occurrence of different types of cancer, breast cancer has gradually attracted the public’s attention. The incidence of breast cancer is rising, mainly affecting women with a high mortality rate. According to the clinical treatment effect, early diagnosis and early treatment can effectively control the mortality of breast cancer and improve patient’s quality of life. Ultrasound radiomics is an emerging field that can extract quantitative high-dimensional data from ultrasound images. Recently, ultrasound radiomics has been widely used in the clinical treatment of breast cancer. This paper analyzed the research progress of ultrasound radiomics in the diagnosis and treatment of breast cancer.

Clinical-radiomics nomogram for predicting esophagogastric variceal bleeding risk noninvasively in patients with cirrhosis

BACKGROUND Esophagogastric variceal bleeding(EGVB)is a serious complication of patients with decompensated cirrhosis and is associated with high mortality and morbidity.Early diagnosis and screening of cirrhotic patients at risk for EGVB is crucial.Currently,there is a lack of noninvasive predictive models widely available in clinical practice.AIM To develop a nomogram based on clinical variables and radiomics to facilitate the noninvasive prediction of EGVB in cirrhotic patients.METHODS A total of 211 cirrhotic patients hospitalized between September 2017 and December 2021 were included in this retrospective study.Patients were divided into training(n=149)and validation(n=62)groups at a 7:3 ratio.Participants underwent three-phase computed tomography(CT)scans before endoscopy,and radiomic features were extracted from portal venous phase CT images.The independent sample t-test and least absolute shrinkage and selection operator logistic regression were used to screen out the best features and establish a radiomics signature(RadScore).Univariate and multivariate analyses were performed to determine the independent predictors of EGVB in clinical settings.A noninvasive predictive nomogram for the risk of EGVB was built using independent clinical predictors and RadScore.Receiver operating characteristic,calibration,clinical decision,and clinical impact curves were applied to evaluate the model’s performance.RESULTS Albumin(P=0.001),fibrinogen(P=0.001),portal vein thrombosis(P=0.002),aspartate aminotransferase(P=0.001),and spleen thickness(P=0.025)were selected as independent clinical predictors of EGVB.RadScore,constructed with five CT features of the liver region and three of the spleen regions,performed well in training(area under the receiver operating characteristic curve(AUC)=0.817)as well as in validation(AUC=0.741)cohorts.There was excellent predictive performance in both the training and validation cohorts for the clinical-radiomics model(AUC=0.925 and 0.912,respectively).Compared with the existing noninvasive models such as ratio of aspartate aminotransferase to platelets and Fibrosis-4 scores,our combined model had better predictive accuracy with the Delong's test less than 0.05.The Nomogram had a good fit in the calibration curve(P>0.05),and the clinical decision curve further supported its clinical utility.CONCLUSION We designed and validated a clinical-radiomics nomogram able to noninvasively predict whether cirrhotic patients will develop EGVB,thus facilitating early diagnosis and treatment.

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.

Radiomics in the diagnosis and treatment of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)is a common malignant tumor.At present,early diagnosis of HCC is dif-ficult and therapeutic methods are limited.Radiomics can achieve accurate quantitative evaluation of the lesions without invasion,and has important value in the diagnosis and treatment of HCC.Radiomics fea-tures can predict the development of cancer in patients,serve as the basis for risk stratification of HCC patients,and help clinicians distinguish similar diseases,thus improving the diagnostic accuracy.Further-more,the prediction of the treatment outcomes helps determine the treatment plan.Radiomics is also helpful in predicting the HCC recurrence,disease-free survival and overall survival.This review summa-rized the role of radiomics in the diagnosis,treatment and prognosis of HCC.

Clinical-radiomics predictors to identify the suitability of transarterial chemoembolization treatment in intermediate-stage hepatocellular carcinoma:A multicenter study

Background: Although transarterial chemoembolization(TACE) is the first-line therapy for intermediatestage hepatocellular carcinoma(HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. Methods: A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator(LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting(XGBoost) with 5-fold cross-validation. The Shapley additive explanations(SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model’s performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. Results: A third of the patients(81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve(AUC) of 0.759, 0.885, 0.906 [95% confidence interval(CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894(95% CI: 0.815-0.972) in the testing cohort, respectively. Conclusions: The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.

Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease.However,the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging.Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques,working on numerical information coded within Digital Imaging and Communications in Medicine files:This image numerical analysis has been named“radiomics”.Radiomics allows the extraction of quantitative features from radiological images,mainly invisible to the naked eye,that can be further analyzed by artificial intelligence algorithms.Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis,staging,and prognosis,prediction of treatment response and diseases monitoring and surveillance.Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography(CT)images with different aims:The preoperative prediction of lymph node metastasis,detecting BRAF and RAS gene mutations.Moreover,the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints.Most published studies concerning radiomics and magnetic resonance imaging(MRI)mainly focused on the response of advanced tumors that under-went neoadjuvant therapy.Nodes status is the main determinant of adjuvant chemotherapy.Therefore,several radiomics model based on MRI,especially on T2-weighted images and ADC maps,for the preoperative prediction of nodes metastasis in rectal cancer has been developed.Current studies mostly focused on the applications of radiomics in positron emission tomogra-phy/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy.Since colorectal liver metastases develop in about 25%of patients with colorectal carcinoma,the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions.Radiomics could be an additional tool in clinical setting,especially in identifying patients with high-risk disease.Nevertheless,radiomics has numerous shortcomings that make daily use extremely difficult.Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.