Abstract Background Triple negative breast cancer (TNBC) occurs in approximately 10% to 25% of all patients with breast cancer and is associated with poor prognosis. Neo-adjuvant chemotherapy has been reported to produce a higher pathologic complete response (pCR) rate in TNBC. If pCR is achieved, patients with TNBC had a similar survival with non-TNBC patients. The aim of our study was to investigate the protein expression of epithelial growth factor receptor (EGFR) and response to neo-adjuvant chemotherapy and clinical outcome in patients with TNBC compared with non-TNBC. Methods A total of 198 locally advanced breast cancer patients who received neo-adjuvant chemotherapy were studied. Immunohistochemistry (IHC) was carried out to detect the protein expression of EGFR in tumor samples. Clinical and pathological parameters, pCR rate and survival data were compared between 40 TNBCs and 158 non-TNBCs. Results In 198 cases who received neo-adjuvant chemotherapy, significant differences exist in surgical therapy (P=0.005) and pCR rate (P=0.012) between patients with TNBCs and non-TNBCs. Overexpression of EGFR was significantly associated with pCR rate in patients with TNBCs (P < 0.001). Survival analysis revealed that patients with TNBCs had worse DFS and OS than those with non-TNBCs (P = 0.001, P < 0.001 respectively). Furthermore, for patients with non-TNBCs, those who acheived pCR had better DFS and OS than those who acheived RD (both P < 0.001). Conclusions Our results suggested that patients with TNBCs had increased pCR rates compared with non-TNBC. Overexpression of EGFR predicted better response to neo-adjuvant chemotherapy in patients with TNBCs.
The teacher-oriented education research centers on the practical classroom teaching activities whereby teachers continually reassess.This helps teachers with professionalism and realization of values.The paper also discusses the contents and approaches of the education researches.
In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need to obtain a high image contrast between bone and soft tissue to facilitate target delineation and patient positioning for NPC radiotherapy. In this paper, our aim is to develop a novel image conversion between the CT and MRI modalities to obtain clear bone and soft tissue images simultaneously, here called bone-enhanced MRI (BeMRI).Thirty-five patients were retrospectively selected for this study. All patients underwent clinical CT simulation and 1.5T MRI within the same week in Shenzhen Second People's Hospital. To synthesize BeMRI, two deep learning networks, U-Net and CycleGAN, were constructed to transform MRI to synthetic CT (sCT) images. Each network used 28 patients' images as the training set, while the remaining 7 patients were used as the test set (~1/5 of all datasets). The bone structure from the sCT was then extracted by the threshold-based method and embedded in the corresponding part of the MRI image to generate the BeMRI image. To evaluate the performance of these networks, the following metrics were applied: mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR).In our experiments, both deep learning models achieved good performance and were able to effectively extract bone structure from MRI. Specifically, the supervised U-Net model achieved the best results with the lowest overall average MAE of 125.55 (P<0.05) and produced the highest SSIM of 0.89 and PSNR of 23.84. These results indicate that BeMRI can display bone structure in higher contrast than conventional MRI.A new image modality BeMRI, which is a composite image of CT and MRI, was proposed. With high image contrast of both bone structure and soft tissues, BeMRI will facilitate tumor localization and patient positioning and eliminate the need to frequently check between separate MRI and CT images during NPC radiotherapy.
Chemical freezing check material can reduce the freezing point of surface water on pavement,which checks the freezing of snow on pavement to a certain degree.It introduces the development process and function principles of this material based on the research fruit of Japan and European countries.Meanwhile according to the follow-up investigation of this kind of pavement in Japan,it can be deduced that the freezing check effect would keep 6 ～7 years and precipitation quantity in summer is less than that in winter.
MRI-only radiotherapy is expected to be safer and more precise compared with conventional CT-based radiotherapy. But unlike CT, MR images is not related with electron density for radiotherapy planning. To use MR images for treatment planning, we proposed a residual learning based u-shaped deep neural network (RUN) to synthesize CT images from MR images. The RUN network is an encoding-decoding network consisted of 16 residual learning based units which are constructed with parameter-free shortcuts to alleviate the degradation problem of deep network and strengthen feature propagation and reuse. We input MR and corresponding CT images to the RUN network to learn a voxel-by-voxel mapping from MR to CT images. Synthetic CT images are generated for newly input MR images through the learned mapping model. Our datasets contain 35 patients with more than 5000 T1/T2-weighted MR and CT images pairs. We compare Hounsfield Unit (HU) discrepancies between synthetic CT and original CT images. The mean absolute error (MAE) was 66.12±5.95 HU, peak signal noise ratio (PSNR) was 28.52±0.64 dB, and structural similarity index (SSIM) was 0.97±0.005 for T1-weighted MR. The comparison results of the same metrics for T2-weighted MR were 63.79±4.18 HU, 28.8±0.55 dB and 0.973±0.004 respectively. Converting CT images from MR images for one patient takes about 3 seconds on average. Experimental results show that the proposed RUN network is accurate, robust, and efficient for predicting synthetic CT from MR images for MRI-only radiotherapy.
A new MIS model is presented in this paper in order to overcome the drawbacks of current MIS for commerce sale corporations.The advanced computer technology and the new ideas such as the support chain optimization,business process reengineering and data warehouse are introduced to the new MIS model,which can meet the management needs of large commerce corporations now and in future.The MIS based the new model has been successfully applied to a large drug sale group in Dalian and good economy benefit has been gained.
Purpose: The objective of this study was to evaluate the American Joint Committee on Cancer (AJCC) pathological prognostic stage among patients with invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) and to propose a modified score system if necessary. Methods: Women diagnosed with IDC and ILC during 2010-2015 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively identified. Disease-specific survival (DSS) and overall survival (OS) were estimated by Kaplan-Meier method. Predictive performances of different staging systems were evaluated based on Harrell concordance index (C-index) and Akaike Information Criterion (AIC). Multivariate Cox models were conducted to build preferable score systems. Results: A total of 184,541 female patients were included in the final analyses, with a median follow-up of 30.0 months. In IDC cohort, the pathological prognostic stage (C-index, 0.8281; AIC, 110274.5) was superior to the anatomic stage (C-index, 0.8125; AIC, 112537.0; P < 0.001 for C-index) in risk stratification with respect to DSS. In ILC cohort, the prognostic stage (C-index, 0.8281; AIC, 7124.423) didn't outperform the anatomic stage (C-index, 0.8324; AIC, 7144.818; P = 0.748 for C-index) with respect to DSS. Similar results were observed with respect to OS. The score system defined by anatomic stage plus grade plus estrogen receptor and progesterone receptor (AS+GEP) allows for better staging (C-index, 0.8085; AIC, 7178.448) for ILC patients. Conclusion: Compared with anatomic stage, the pathological prognostic stage provided more accurate stratification for patients with IDC, but not for patients with ILC. The AS+GEP score system may fit ILC tumors better.
To investigate the accuracy of core needle biopsy (CNB) in evaluating breast cancer estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67 status and to identify factors which might be associated with Ki67 value change after CNB. A retrospective study was carried out on 276 patients with paired CNB and surgically removed samples (SRS). Clinico-pathological factors as well as the surgery time interval (STI) between CNB and surgery were analyzed to determine whether there were factors associated with Ki67 value change after CNB. Five tumor subtypes were classified as follows: Luminal A, Luminal B-HER2-, Luminal B-HER2+, Triple Negative (TN), and HER2+. Ki67 value change was calculated as SRS minus CNB. Mean STI after CNB was 4.5 (1-37) days. Good agreement was achieved for ER, PR, and HER2 evaluation between CNB and SRS. However, Ki67 expression level was significantly higher in SRS compared with CNB samples: 29.1 % vs. 26.2 % (P < 0.001). Both univariate and multivariate analysis demonstrated that STI and molecular subtype were associated with a Ki67 change after CNB. Luminal A tumors experienced more Ki67 elevation than Luminal B-HER2- diseases (6.2 % vs -0.1 %, P = 0.014). Patients with longer STI after CNB had a higher Ki67 increase: -1.1 % within 1-2 days, 2.1 % with 3-4 days, and 5.6 % more than 4 days, respectively (P = 0.007). For TN and HER2+ tumors, the Ki67 change was apt to be 0 with STI ≤ 4 days, while a >7 % Ki67 increase was noticed in patients with STI ≥ 5 days. CNB was accurate in evaluating ER, PR, HER2, and molecular subtype status. Ki67 value significantly increased after CNB, which was associated with STI and molecular subtype. Further translational research needs to consider Ki67 changes following CNB among different breast cancer molecular subtypes.
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