Objective:To compare and analyze the chemical constituents of volatile oils extracted from the different parts ( flow-ers,leaves and roots) of Stelleropsis tianschanica by chromatography-mass spectrometry (GC-MS). Methods:The volatile oil was ex-tracted by diethyl ether-Soxhlet extraction method and analyzed by GC-MS with a capillary gas chromatographic column. The relative contents of the volatile compounds were calculated by chromatographic peak area normalization method.Results: Totally 179 volatile constituents in the different parts of Stelleropsis tianschanica were identified. Among them,81 compounds were identified in leaves,and the relative content accounted for 82.77% of the total volatile compounds;108 compounds were identified in flowers,and the relative content accounted for 82.85% of the total volatile compounds;112 compounds were identified in roots, and the relative content ac-counted for 85.98% of the total volatile compounds. Totally 33 compounds existed in all the three parts,and the content accounted for 39.24% of the total volatile components in leaves,35.86% in flowers and 48.89% in roots. The relative content of(Z,Z)-9,12-oc-tadecadienoic acid in leaves,flowers and roots of S. tianschanica was the highest,which accounted for 11.12%,9.8% and 22.49%, respectively. Conclusion:The different parts of S. tianschanica have similar volatile components, while the specific substances and the contents are different.

Objective:To explore the feasibility of predicting axillary lymph node metastasis of breast cancer using radiomics analysis based on dynamic contrast-enhanced (DCE) MRI.Methods:The retrospective study enrolled 163 patients (163 lesions) with breast cancer diagnosed by core needle biopsy from January 2013 to December 2013 in Peking University First Hospital. The status of axillary lymph nodes in all patients was pathologically confirmed, and they had complete preoperative breast MRI images. Among the 163 patients, 94 patients were confirmed with axillary lymph node metastasis, and 69 patients without axillary lymph node metastasis. They were randomly divided into the training dataset ( n=115) and testing dataset ( n=48) in a 7∶3 ratio. The radiomics analysis was performed in the training dataset, including image preprocessing and labeling, radiomics feature extraction, radiomics model establishment and model predictive performance inspection. Model performance was tested in the testing dataset. Receiver operating characteristic curve and area under curve (AUC) was used to analyze the model prediction performance. Results:Of the 1 075 features extracted from the training dataset, principal component analyses (PCA) features 8, 41 and 67 were selected by random forest classifier. The radiomics model including 3 PCA features reached an AUC of 0.956 (95%CI 0.907-0.988), with sensitivity of 91.2%, specificity of 100% and accuracy of 94.8%. In the testing dataset, the radiomics model including 3 PCA features reached an AUC of 0.767 (95%CI 0.652-0.890), with sensitivity of 80.8%, specificity of 72.7% and accuracy of 77.1%.Conclusion:It is feasible to predict axillary lymph node metastasis using radiomics features based on DCE-MRI of breast cancer.

Objective:To explore the feasibility of classification between carcinoma in situ and invasive carcinoma of breast using intratumoral and peritumoral radiomics based on breast dynamic contrast-enhanced (DCE) MRI.Methods:The retrospective study included consecutive invasive breast carcinoma pathological diagnosed by core needle biopsy or surgery from January 2013 to December 2013 and carcinoma in situ of breast diagnosed by surgery from January 2013 to December 2015 in Peking University First Hospital. All patients had pretreatment breast MRI images. A total of 251 cases (251 lesions) were included, with 208 invasive breast carcinoma and 43 carcinoma in situ of breast. They were all females and median age was 53 (23-82) years old. Patients were randomly divided into the training ( n=176) and testing dataset ( n=75) in a 7∶3 ratio. In the training dataset, combined with DCE mask and early enhancement images, intratumoral and peritumoral area were semi-automatic segmentation, and radiomics features were extracted and dimension reduction, finally a prediction model was established. Model performance was tested in the testing dataset. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to analyze the model prediction performance. Results:The prediction models established by intratumoral, peritumoral and intratumoral combined with peritumoral radiomics had good performance. The AUC of intratumoral, peritumoral and intratumoral combined with peritumoral radiomics prediction models in differentiating breast carcinoma in situ and invasive carcinoma were 0.865, 0.896 and 0.922 in the testing dataset, there was no significant difference in pairwise comparisons ( P>0.05). The sensitivity of intratumoral, peritumoral and intratumoral combined with peritumoral radiomics prediction models were 77.4%, 87.1%, 83.9%, the specificity were 92.3%, 84.6%, 100%, and the accuracy were 80.0%, 85.3%, 86.7%. Conclusion:It is potential feasible for classification between carcinoma in situ and invasive carcinoma of breast using intratumoral and peritumoral radiomics based on breast DCE MRI.