The bioaccessibilites of heavy metals in vegetables grown around a waste-incinerator site were estimated using the physiologically based extraction test (PBET) method, to assess potential health risk to the local consumers. The average gastric and intestinal bioaccessibilities of Cd, Cr, Cu, Ni, and Pb in vegetables varied within 3.2-9.4 and 0.8-5.3 %, 1.4-2.3 and 1.1-1.9 %, 25-46 and 13-26 %, 6.6-30 and 2.6-5.3 %, 11-29 and 7.1-23 %, respectively. Strong negative correlations were found between electrochemical potential (ΔE 0) and bioaccessibility for leaf mustard samples (r (2) = 0.857) and leaf lettuce samples (r (2) = 0.696). In addition, softness index (σp) and electrochemical potential (ΔE 0) exhibited a moderate but not significant relationship with bioaccessibilities on the basis of the multiple regression analysis (0.05 < p < 0.1). The total bioaccessible target hazard quotient (TBTHQ) of the five heavy metals was 2.5, with Pb being the major risk contributor. According to the TBTHQs of each group of vegetables, local consumers are experiencing adverse health effects by consuming most of the vegetables around waste-incinerator site.

In breast cancer, the survival rate is related to the size of the lesion. Since the spatial resolution of wholebody (WB)-PET is limited, it is difficult to evaluate small cancerous lesions. To improve resolution, highresolution dedicated breast PET (db-PET) scanners have been developed. However, the potential of db-PEThas not yet been established, and there has been no report of the application of db-PET on an Oncovision device (MAMMI) in breast cancer cases in Japan.The purpose of this study was to evaluate the potential of db-PET for assessing breast cancer. The influence of image reconstruction parameters was verified in a phantom evaluation, while we quantitatively investigated the ability of dB-PET to detect tumors, in comparison with WB-PET, with patients in the prone position, in a clinical evaluation. Despite a limited field of view in the vicinity of the chest wall, we show that db-PET has markedly better clinical value for diagnosing breast cancer as compared to WB-PET.

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PURPOSE: To investigate texture features of simultaneous indeterminate pulmonary nodules of breast cancer for predicting their potential metastasis. METHODS AND MATERIALS: 150 patients with simultaneous breast cancer diagnosed by biopsy and pulmonary nodules (diameter: 5-20mm) detected by preoperative CT were enrolled in this study. After surgery and breast cancer treatment, the patients were followed up for at least half a year or longer by CT to observe the changes of lung nodules, thereby inferring the potential of metastasis. We classify pulmonary nodules into two groups: the reduced or enlarged pulmonary nodules were defined as highly metastasis possibility (Group 1), and long-term stable pulmonary nodules were defined as low metastasis possibility (Group 2). In addition, pathologic proven primary lung cancer in this study (Group 3) was compared with Group 1. Therefore, we carried out a comparative analysis of the texture features between the groups, and additional statistical were used three regression testing to extract texture features. Finally, we construct a machine learning classifier and calculate the accuracy of cross-validation. RESULTS: We collected 106 features by the texture analysis(TA). There are 18 features with significant differences between Group 1 and the Group 2(p&lt;0.05), and 76 features with significant differences in the Group 1 and Group 3 (p&lt;0.05). We tried to find key features related to pathology in 106 features using three methods: lasso regression, ridge regression and forward stepwise regression. The accuracy in different regressions respectively is 94.5%,94.5%,89.7% using KNN between Group 1 and Group 2. The accuracy in different regressions respectively is 96.2%(KNN),96.2%(Tree),92.3%(Linear Discriminant)in the Group 1 and Group 3. CONCLUDES: The identified radiomics features have the potential to be used as a biomarker for metastasis prediction of simultaneous indeterminate pulmonary nodules in breast cancer patients, and it may contribute to preoperative treatment and postoperative follow-up planning. Citation Format: Xiao Q, Gu Y, Wu J, Wang Z, Huang Y. Machine learning based analysis of CT radiomics for the simultaneous indeterminate pulmonary nodules of breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-02-19.