Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT

Objective： To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas （MIAs）and invasive pulmonary adenocarcinomas （IPAs） based on radiomic feature analysis with computed tomography（CT）.Methods： A total of 109 patients with ground-glass opacity lesions （GGOs） in the lungs determined by CTexaminations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation andsegmentation of the GGOs as regions of interest （ROIs）, the patients were subdivided into three groups based onpathologic analyses： the preinvasive lesions （including atypical adenomatous hyperplasia and adenocarcinoma insitu） subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. Thedata analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguishany two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic （ROC） curve wasapplied to accurately evaluate the performances of the regression models.Results： We found that the voxel count feature （P〈0.001） could be used as a predictor for distinguishing IPAsfrom preinvasive lesions. However, the surface area feature （P=0.040） and the extruded surface area feature（P=0.013） could be predictors of IPAs compared with MIAs. In addition, the correlation feature （P=0.046） coulddistinguish preinvasive lesions from MIAs better.Conclusions： Preinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CTimages, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseasesare very important for clinical surgery.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

BACKGROUND The prognosis of hepatocellular carcinoma(HCC)remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy.In terms of recent studies,microvascular invasion(MVI)is one of the potential predictors of recurrence.Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.AIM To develop a radiomic analysis model based on pre-operative magnetic resonance imaging(MRI)data to predict MVI in HCC.METHODS A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation,among whom 73 were found to have MVI and 40 were not.All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy.We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI,namely,the regions of interest.Quantitative analyses included most discriminant factors(MDFs)developed using linear discriminant analysis algorithm and histogram analysis with MaZda software.Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis.Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic(ROC)curve analysis.Five-fold cross-validation was also applied via R software.RESULTS The area under the ROC curve(AUC)of the MDF(0.77-0.85)outperformed that of histogram parameters(0.51-0.74).After multivariate analysis,MDF values of the arterial and portal venous phase,and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI(P<0.05).The AUC value of the model was 0.939[95%confidence interval(CI):0.893-0.984,standard error:0.023].The result of internal five-fold cross-validation(AUC:0.912,95%CI:0.841-0.959,standard error:0.0298)also showed favorable predictive efficacy.CONCLUSION Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

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.