Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible,non-curable,and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention.It is a frequently occurring mental illness that occurs in about 60%–80%of cases of dementia.It is usually observed between people in the age group of 60 years and above.Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal(CN),Mild Cognitive Impairment(MCI)and Alzheimer’s Disease(AD).Alzheimer’s disease is the last phase of the disease where the brain is severely damaged,and the patients are not able to live on their own.Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms.Here,105 number of radiomic features are extracted and used to predict the alzhimer’s.This paper uses Support Vector Machine,K-Nearest Neighbour,Gaussian Naïve Bayes,eXtreme Gradient Boosting(XGBoost)and Random Forest to predict Alzheimer’s disease.The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%.This proposed approach also achieved 88%accuracy,88%recall,88%precision and 87%F1-score for AD vs.CN,it achieved 72%accuracy,73%recall,72%precisionand 71%F1-score for AD vs.MCI and it achieved 69%accuracy,69%recall,68%precision and 69%F1-score for MCI vs.CN.The comparative analysis shows that the proposed approach performs better than others approaches.

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma:Machine learning model based on contrast-enhanced computed tomography

BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.

Analysis of the Relationship between Image and Blood Examinations in an Artificial Intelligence System for the Molecular Diagnosis of Breast Cancer

Molecular subtype classification based on tumor genotype has recently been used for differential diagnosis of breast cancer. The shift from conventional tissue classification to molecular genetics-based classification is primarily because objective genetic information can ensure a biologically clear classification system and patient groups may be created for a given set of diagnoses and suitable treatments. Given the stressful nature of biopsy, radiomic studies are conducted to determine breast cancer subtypes using non-invasive imaging tests. Minimally invasive blood tests using microRNAs (miRNAs) contained in exosomes have been developed. We investigated the usefulness of radiomic features and miRNAs in distinguishing triple-negative breast cancer (TNBC) from other cancer types. Fat suppression T2-weighted magnetic resonance images and miRNAs of 60 cases (9 TNBC and 51 others) were retrieved from the Cancer Genome Atlas Breast Invasive Carcinoma. Six radiomic features and six miRNAs were selected by least absolute shrinkage and selection operator. Linear discriminant analysis was employed to distinguish between TNBC and others. With miRNAs, TNBC and others were completely separated, whereas with radiomic features, TNBC overlapped with other types of breast cancer. Receiver operating characteristic curve analysis results showed that the area under the curve of radiomic features and miRNAs was 0.85 and 1.0, respectively. miRNAs showed a higher discrimination performance than radiomic features. Although gene analysis is expensive and facilities for performing it are limited, miRNAs for blood tests may be useful in artificial intelligence systems for the molecular diagnosis of breast cancer.

Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning

Background and Aims:The relationship between quanti-tative magnetic resonance imaging(MRI)imaging features and gene-expression signatures associated with the recur-rence of hepatocellular carcinoma(HCC)is not well studied.Methods:In this study,we generated multivariable regres-sion models to explore the correlation between the preoper-ative MRI features and Golgi membrane protein 1(GOLM1),SET domain containing 7(SETD7),and Rho family GTPase 1(RND1)gene expression levels in a cohort study including 92 early-stage HCC patients.A total of 307 imaging features of tumor texture and shape were computed from T2-weighted MRI.The key MRI features were identified by performing a multi-step feature selection procedure including the cor-relation analysis and the application of RELIEFF algorithm.Afterward,regression models were generated using kernel-based support vector machines with 5-fold cross-validation.Results:The features computed from higher specificity MRI better described GOLM1 and RND1 gene-expression levels,while imaging features computed from lower specificity MRI data were more descriptive for the SETD7 gene.The GOLM1 regression model generated with three features demon-strated a moderate positive correlation(p<0.001),and the RND1 model developed with five variables was positively as-sociated(p<0.001)with gene expression levels.Moreover,RND1 regression model integrating four features was mod-erately correlated with expressed RND1 levels(p<0.001).Conclusions:The results demonstrated that MRI radiomics features could help quantify GOLM1,SETD7,and RND1 ex-pression levels noninvasively and predict the recurrence risk for early-stage HCC patients.

Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan

OBJECTIVE:To summarize the potential characteristics of convalescent patients with coronavirus disease 2019(COVID-19)in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.METHODS:In this study,we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients.In addition,this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values,including thick and thin,fur,peeling,fat and lean,tooth marks and cracked,and greasy and putrid fur.RESULTS:We included 2164 tongue images in total(34%from day 0,35.4%from day 14 and 30.6%from day 28)from convalescent patients.The significance results are shown as follows.Firstly,as the recovery time prolongs,the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively.Secondly,the decrease of abnormality rate of tongue coat,included greasy tongue fur,putrid fur,teeth-mark,thick-thin fur,are of significant statistical difference(P<0.05).Thirdly,the average value of gray-level cooccurrence matrices increases from 0.173 to 0.194,the average value of entropy increases from 0.606 to 0.665,the average value of inverse difference normalized decrease from 0.981 to 0.979,and the average value of dissimilarity decrease from 0.1576 to 0.1828.The details of other radiomics features are describe in results section.CONCLUSIONS:Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images,including L color space of the tongue and coat radiomics features analysis.This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease:diagnosis,longitudinal progress and biological basis

Hippocampal morphological change is one of the main hallmarks of Alzheimer’s disease(AD).However,whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment(MCI)to AD dementia and whether these features provide any neurobiological foundation remains unclear.The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging(MRI)markers for AD.Multivariate classifier-based support vector machine(SVM)analysis provided individual-level predictions for distinguishing AD patients(n=261)from normal controls(NCs;n=231)with an accuracy of 88.21%and intersite crossvalidation.Further analyses of a large,independent the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset(n=1228)reinforced these findings.In MCI groups,a systemic analysis demonstrated that the identified features were significantly associated with clinical features(e.g.,apolipoprotein E(APOE)genotype,polygenic risk scores,cerebrospinal fluid(CSF)Ab,CSF Tau),and longitudinal changes in cognition ability;more importantly,the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI,and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus.The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.