Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma

BACKGROUND Microvascular invasion(MVI)of small hepatocellular carcinoma(sHCC)(≤3.0 cm)is an independent prognostic factor for poor progression-free and overall survival.Radiomics can help extract imaging information associated with tumor pathophysiology.AIM To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)-enhanced magnetic resonance imaging(MRI)for preoperative prediction of MVI in sHCC.METHODS In total,415 patients were diagnosed with sHCC by postoperative pathology.A total of 221 patients were retrospectively included from our hospital.In addition,we recruited 94 and 100 participants as independent external validation sets from two other hospitals.Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging(DWI)were constructed and validated using machine learning.As presented in the radiomics nomogram,a prediction model was developed using multivariable logistic regression analysis,which included radiomics scores,radiologic features,and clinical features,such as the alpha-fetoprotein(AFP)level.The calibration,decision-making curve,and clinical usefulness of the radiomics nomogram were analyzed.The radiomic nomogram was validated using independent external cohort data.The areas under the receiver operating curve(AUC)were used to assess the predictive capability.RESULTS Pathological examination confirmed MVI in 64(28.9%),22(23.4%),and 16(16.0%)of the 221,94,and 100 patients,respectively.AFP,tumor size,non-smooth tumor margin,incomplete capsule,and peritumoral hypointensity in hepatobiliary phase(HBP)images had poor diagnostic value for MVI of sHCC.Quantitative radiomic features(1409)of MRI scans)were extracted.The classifier of logistic regression(LR)was the best machine learning method,and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set(hospital A)and validation set(hospital B,C).The AUC of HBP was 0.979,0.970,and 0.803,respectively,and the AUC of DWI was 0.971,0.816,and 0.801(P<0.05),respectively.Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts(C-index of HBP and DWI were 0.971,0.912,0.808,and 0.970,0.843,0.869,respectively).The clinical usefulness of the nomogram was further confirmed using decision curve analysis.CONCLUSION AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC.Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI.The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.

Attention shifting during child-robot interaction:a preliminary clinical study for children with autism spectrum disorder

There is an increasing need to introduce socially interactive robots as a means of assistance in autism spectrum disorder(ASD) treatment and rehabilitation, to improve the effectiveness of rehabilitation training and the diversification of treatment, and to alleviate the shortage of medical personnel in China's Mainland and other places in the world. In this preliminary clinical study, three different socially interactive robots with different appearances and functionalities were tested in therapy-like settings in four different rehabilitation facilities/institutions in Shenzhen, China. Seventy-four participants, including 52 children with ASD, whose processes of interacting with robots were recorded by three different cameras, all received a single-session three-robot intervention. Data were collected from not only the videos recorded, but also the questionnaires filled mostly by parents of the participants. Some insights from the preliminary results were obtained. These can contribute to the research on physical robo it design and evaluations on robots in therapy-like settings. First, when doing physical robot design, some preferential focus should be on aspects of appearances and functionalities. Second, attention analysis using algorithms such as estimation of the directions of gaze and head posture of a child in the video clips can be adopted to quantitatively measure the prosocial behaviors and actions(e.g., attention shifting from one particular robot to other robots) of the children. Third, observing and calculating the frequency of the time children spend on exploring/playing with the robots in the video clips can be adopted to qualitatively analyze such behaviors and actions. Limitations of the present study are also presented.