从事医疗器械、生物工程领域内的技术开发、技术咨询、技术服务、技术转让，医疗设备的经营性租赁，医疗设备及其软件和零部件（含医疗器械类体外诊断试剂），上述产品的辅料（不含危险化学品）、计算机软件及系统集成产品设备的批发、维修、零售（仅限分支机构经营）、进出口、佣金代理（拍卖除外）及其相关配套业务和售后服务，上述产品的仓储（除危险品）、分拨业务以及网上零售，受母公司委托为母公司及其所投资企业或者关联公司提供经营决策、资金运作和财务管理、研究开发和技术支持、承接本公司集团内部的共享服务及境外公司的服务外包、货物分拨等物流运作、市场营销服务及上述服务相关咨询服务，贸易咨询服务，商务信息咨询，商业性简单加工，转口贸易，区内企业间的贸易及其代理，商品展示。 【依法须经批准的项目，经相关部门批准后方可开展经营活动】
Objective:To compare dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)with intravoxel incoherent motion diffusion-weighted imaging(IVIM-DWI)in evaluating microvascular content in rectal cancer.Methods:This study was conducted on 23 patients with rectal adenocarcinoma confirmed by preoperative endoscopic pathology in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from December 2019 to December 2022.All subjects underwent IVIM-DWI and DCE-MRI scans before surgery.The DCE-MRI parameters calculated by the Tofts model were extracellular space volume fraction(Ve),space transport coefficient(Ktrans),and rate constant of extracellular space return to intravascular space(Kep).The IVIM-DWI parameters were calculated by the double exponential model:simple diffusion coefficient(D),perfusion related diffusion coefficient(D*),and perfusion fraction(f).The correlation between DCE-MRI and IVIM-DWI quantitative parameters and microvascular content in rectal cancer was analyzed and compared.Results:There were significant differences in Ktrans and D values of colorectal cancer in different pathological grades(F=9.159,P=0.002;F=5.106,P=0.016).Ktrans was superior to the D value in evaluating rectal cancer grading.Ktrans,Kep and D* were positively correlated with microvascular content in rectal cancer(r=0.734,P=0.000;r=0.617,P=0.002;r=0.456,P=0.029).DCE-MRI quantitative parameters were superior to IVIM-DWI quantitative parameters in evaluating microvascular content in rectal cancer.Conclusion:DCE-MRI was superior to IVIM-DWI in evaluating the differentiation degree and microcycle status of rectal cancer.However,considering that IVIM-DWI does not require injection of contrast agents,IVIM-DWI can still be recommended for the microcirculation of rectal cancer.
Objective: To evaluate the value of machine learning (ML) models based on biparametric magnetic resonance imaging (bpMRI) for diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). Methods: A total of 1 368 patients, aged from 30 to 92 (69.4±8.2) years, from 3 tertiary medical centers in Jiangsu Province were retrospectively collected from May 2015 to December 2020, including 412 cases of csPCa, 242 cases of clinically insignificant prostate cancer (ciPCa) and 714 cases of benign prostate lesions. The data of center 1 and center 2 were randomly divided into training cohort and internal testing cohort at a ratio of 7∶3 by random number sampling without replacement using Python Random package, and the data of center 3 were used as the independent external testing cohort. The training cohort includs 243 cases of csPCa, 135 cases of ciPCa and 384 cases of benign lesions, the internal testing cohort includs 104 cases of csPCa, 58 cases of ciPCa and 165 cases of benign lesions, and the external testing cohort includs 65 cases of csPCa, 49 cases of ciPCa and 165 cases of benign lesions. The radiomics features were extracted on T2-weighted imaging, diffusion-weighted imaging and apparent diffusion coefficient map, and optimal radiomics features were selected by using Pearson correlation coefficient method and analysis of variance. The ML models were built using two ML algorithms, including support vector machine and random forest (RF) and were further tested in the internal testing cohort and external testing cohort. Finally, the PI-RADS scores evaluated by the radiologists were adjusted by the ML models which had superior diagnostic performance, namely adjusted PI-RADS. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the ML models and PI-RADS. DeLong test was used to compare the areas under curve (AUC) of models with those of PI-RADS. Results: For PCa diagnosis, in internal testing cohort, the AUC of ML model using RF algorithm and PI-RADS were 0.869 (95%CI: 0.830-0.908) and 0.874 (95%CI: 0.836-0.913), respectively, and the difference between the model and PI-RADS did not reach to the statistical significance (P=0.793). In the external testing cohort, the AUC of model and PI-RADS were 0.845 (95%CI: 0.794-0.897) and 0.915 (95%CI: 0.880-0.951), respectively, and the difference was statistically significant (P=0.01). For csPCa diagnosis, the AUC of ML model using RF algorithm and PI-RADS were 0.874 (95%CI: 0.834-0.914) and 0.892 (95%CI: 0.857-0.927), respectively, in internal testing cohort, and the difference between the model and PI-RADS was not statistically significant (P=0.341). In the external testing cohort, the AUC of model and PI-RADS were 0.876 (95%CI: 0.831-0.920) and 0.884 (95%CI: 0.841-0.926), respectively, and the difference between the model and PI-RADS was not statistically significant (P=0.704). When PI-RADS assessment was adjusted with the assistance of ML models, the specificities increased from 63.0% to 80.0% in the internal testing cohort and from 92.7% to 93.3% in the external test group in diagnosing PCa. In diagnosing csPCa, the specificities increased from 52.5% to 72.6% in the internal testing cohort and from 75.2% to 79.9% in the external testing cohort. Conclusions: The ML models based on bpMRI showed comparable diagnostic performance to PI-RADS assessed by senior radiologists and achieved good generalization ability in both diagnosing PCa and csPCa. The specificities of the PI-RADS were improved by ML models.
