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.

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.

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.

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.

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.

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.

Evaluating microvascular invasion in hepatitis B virus-related hepatocellular carcinoma based on contrast-enhanced computed tomography radiomics and clinicoradiological factors

BACKGROUND Microvascular invasion(MVI)is a significant indicator of the aggressive behavior of hepatocellular carcinoma(HCC).Expanding the surgical resection margin and performing anatomical liver resection may improve outcomes in patients with MVI.However,no reliable preoperative method currently exists to predict MVI status or to identify patients at high-risk group(M2).AIM To develop and validate models based on contrast-enhanced computed tomo-graphy(CECT)radiomics and clinicoradiological factors to predict MVI and identify M2 among patients with hepatitis B virus-related HCC(HBV-HCC).The ultimate goal of the study was to guide surgical decision-making.METHODS A total of 270 patients who underwent surgical resection were retrospectively analyzed.The cohort was divided into a training dataset(189 patients)and a validation dataset(81)with a 7:3 ratio.Radiomics features were selected using intra-class correlation coefficient analysis,Pearson or Spearman’s correlation analysis,and the least absolute shrinkage and selection operator algorithm,leading to the construction of radscores from CECT images.Univariate and multivariate analyses identified significant clinicoradiological factors and radscores associated with MVI and M2,which were subsequently incorporated into predictive models.The models’performance was evaluated using calibration,discrimination,and clinical utility analysis.RESULTS Independent risk factors for MVI included non-smooth tumor margins,absence of a peritumoral hypointensity ring,and a high radscore based on delayed-phase CECT images.The MVI prediction model incorporating these factors achieved an area under the curve(AUC)of 0.841 in the training dataset and 0.768 in the validation dataset.The M2 prediction model,which integrated the radscore from the 5 mm peritumoral area in the CECT arterial phase,α-fetoprotein level,enhancing capsule,and aspartate aminotransferase level achieved an AUC of 0.865 in the training dataset and 0.798 in the validation dataset.Calibration and decision curve analyses confirmed the models’good fit and clinical utility.CONCLUSION Multivariable models were constructed by combining clinicoradiological risk factors and radscores to preoper-atively predict MVI and identify M2 among patients with HBV-HCC.Further studies are needed to evaluate the practical application of these models in clinical settings.

Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis

BACKGROUND Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications.However,most current studies predict the risk of esophageal variceal bleeding(EVB)based on image features at a single level,which results in incomplete data.Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis.AIM To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis.METHODS In this study,208 patients with cirrhosis were retrospectively evaluated and randomly split into training(n=145)and validation(n=63)cohorts.Three areas were chosen as regions of interest for extraction of multi-organ radiomic features:The whole liver,whole spleen,and lower esophagus–gastric fundus region.In the training cohort,radiomic score(Rad-score)was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method.Independent clinical risk factors were selected using multivariate logistic regression analyses.The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model(RC model).The established models were validated using the validation cohort.BACKGROUND Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications.However,most current studies predict the risk of esophageal variceal bleeding(EVB)based on image features at a single level,which results in incomplete data.Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis.AIM To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis.METHODS In this study,208 patients with cirrhosis were retrospectively evaluated and randomly split into training(n=145)and validation(n=63)cohorts.Three areas were chosen as regions of interest for extraction of multi-organ radiomic features:The whole liver,whole spleen,and lower esophagus–gastric fundus region.In the training cohort,radiomic score(Rad-score)was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method.Independent clinical risk factors were selected using multivariate logistic regression analyses.The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model(RC model).The established models were validated using the validation cohort.RESULTS The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis.Ascites,portal vein thrombosis,and plasma prothrombin time were identified as independent clinical risk factors.The area under the receiver operating characteristic curve(AUC)values for the RC model,Rad-score(liver+spleen+esophagus),Rad-score(liver),Rad-score(spleen),Rad-score(esophagus),and clinical model in the training cohort were 0.951,0.930,0.801,0.831,0.864,and 0.727,respectively.The corresponding AUC values in the validation cohort were 0.930,0.886,0.763,0.792,0.857,and 0.692.CONCLUSION In patients with cirrhosis,combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.

Preoperative CT radiomics models for predicting composition of in vivo urinary calculi

Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi.Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group(group A,n=373),anhydrous uric acid stone group(group B,n=86),carbonate apatite group(group C,n=30),ammonium urate stone group(group D,n=28)and ammonium magnesium phosphate hexahydrate stone group(group E,n=26)according to the composition of calculi,also divided into training set and test set at the ratio of 7∶3.Radiomics features were extracted and screened based on plain CT images of urinary system.Five binary task models(model A—E corresponding to group A—E)and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the predictive efficacy of binary task models,while the accuracy,precision,recall and F1 score were used to evaluate the predictive efficacy of the quinary task model.Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo,with AUC of 0.860—0.948 in training set and of 0.856—0.933 in test set.The accuracy,precision,recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25%,83.79%,46.23%and 0.596 in training set,respectively,while was 80.63%,75.26%,43.48%and 0.551 in test set,respectively.Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi,while the quinary task radiomics model had high accuracy but relatively poor stability.

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.

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.

Radiomics and molecular analysis:Bridging the gap for predicting hepatocellular carcinoma prognosis

This editorial examines a recent study that used radiomics based on computed tomography(CT)to predict the expression of the fibroblast-related gene enhancer of zeste homolog 2(EZH2)and its correlation with the survival of patients with hepatocellular carcinoma(HCC).By integrating radiomics with molecular analysis,the study presented a strategy for accurately predicting the expression of EZH2 from CT scans.The findings demonstrated a strong link between the radiomics model,EZH2 expression,and patient prognosis.This noninvasive approach provides valuable insights into the therapeutic management of HCC.

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.

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.

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.

Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

BACKGROUND The presence of perineural invasion(PNI)in patients with rectal cancer(RC)is associated with significantly poorer outcomes.However,traditional diagnostic modalities have many limitations.AIM To develop a deep learning radiomics stacking nomogram model to predict preoperative PNI status in patients with RC.METHODS We recruited 303 RC patients and separated them into the training(n=242)and test(n=61)datasets on an 8:2 scale.A substantial number of deep learning and hand-crafted radiomics features of primary tumors were extracted from the arterial and venous phases of computed tomography(CT)images.Four machine learning models were used to predict PNI status in RC patients:support vector machine,k-nearest neighbor,logistic regression,and multilayer perceptron.The stacking nomogram was created by combining optimal machine learning models for the arterial and venous phases with predicting clinical variables.RESULTS With an area under the curve(AUC)of 0.964[95%confidence interval(CI):0.944-0.983]in the training dataset and an AUC of 0.955(95%CI:0.900-0.999)in the test dataset,the stacking nomogram demonstrated strong performance in predicting PNI status.In the training dataset,the AUC of the stacking nomogram was greater than that of the arterial support vector machine(ASVM),venous SVM,and CT-T stage models(P<0.05).Although the AUC of the stacking nomogram was greater than that of the ASVM in the test dataset,the difference was not particularly noticeable(P=0.05137).CONCLUSION The developed deep learning radiomics stacking nomogram was effective in predicting preoperative PNI status in RC patients.

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.

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.