다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The updated version of the EFSUMB guidelines on the application of non-hepatic contrast-enhanced ultrasound (CEUS) deals with the use of microbubble ultrasound contrast outside the liver in the many established and emerging applications.

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The EFSUMB guidelines and recommendations for the clinical practice of contrast-enhanced ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version)

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.

IntroductionFollowing the trend of low-radiation dose computed tomographic (CT) imaging, concerns regarding the detectability of low-contrast lesions have been growing. The goal of this research was to evaluate whether a new image-based algorithm (Mono+) for virtual monoenergetic imaging with a dual

Assessment of an advanced image-based technique to calculate virtual monoenergetic computed tomographic images from a dual-energy examination to improve contrast-to-noise ratio in examinations using iodinated contrast media.

Purpose: To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC).Experimental Design: We enrolled 222 patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample t test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation.Results: The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model.Conclusions: Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy. Clin Cancer Res; 23(23); 7253-62. ©2017 AACR.

https://clincancerres.aacrjournals.org/content/clincanres/23/23/7253.full.pdf

Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Purpose To study the relationship between MRI-defined extramural venous invasion (EMVI) prior to treatment and prognosis in patients with locally advanced rectal cancer treated with neoadjuvant chemotherapy-radiation therapy followed by surgery. Materials and Methods This retrospective study included 517 patients with locally advanced rectal cancer evaluated from August 2008 to December 2014. Baseline and posttherapy MRI and follow-up data were retrieved for all patients. After training by using 328 cases with pathologic evaluation of EMVI after therapy, radiologists evaluated baseline MRI for EMVI status in addition to tumor size and characteristics, nodal status, and invasion of the mesorectal fascia. Reader reproducibility was determined by using κ coefficient. Kaplan-Meier curves and adjusted Cox models were used to determine the relationship of baseline MRI parameters to overall survival, metastasis-free survival, and local relapse-free survival. Results Among 517 patients, 335 (64.8%) were men; the mean age was 55.6 years ± 11.5 (standard deviation). At baseline, radiologists identified 259 of 517 (50%) patients with EMVI by using MRI. In adjusted analysis, EMVI and mesorectal fascial invasion at baseline MRI were predictors of metastasis-free survival (hazard ratio, 0.3 and 0.6; P ˂ .01 and P ˂ .02, respectively) and overall survival (hazard ratio, 0.5 and 0.5; P = .01 and P = .02, respectively). EMVI was the only factor associated with local relapse-free survival (hazard ratio, 0.3; P ˂ .01). The κ coefficient for determination of EMVI was 0.80. Conclusion Extramural venous invasion (EMVI) can be reliably evaluated with MRI. The presence of EMVI was associated with greater risk of local and distant tumor recurrence and overall death in patients with locally advanced rectal cancer treated with neoadjuvant chemotherapy-radiation therapy. © RSNA, 2018 Online supplemental material is available for this article.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018172889

MRI of Extramural Venous Invasion in Locally Advanced Rectal Cancer: Relationship to Tumor Recurrence and Overall Survival

// Dan Sun 1 , Xiaoting Li 2 , Yingjian He 2 , Wenhui Li 1 , Ying Wang 1 , Huan Wang 1 , Shanshan Jiang 1 , Yan Xin 1 1 Laboratory of Gastrointestinal Onco-Pathology, Cancer Institute and General Surgery Institute, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China 2 Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China Correspondence to: Yan Xin, email: yxin@mail.cmu.edu.cn Keywords: yes-associated protein1/YAP1, gastric cancer, proliferation, migration, invasion Received: March 15, 2016      Accepted: October 31, 2016      Published: November 07, 2016 ABSTRACT Yes-associated protein 1 (YAP1) plays an important role in the development of carcinomas such as breast, colorectal, and gastric (GC) cancers, but the role of YAP1 in GC has not been investigated comprehensively. The present study strongly suggests that YAP1 and P62 were significantly up-regulated in GC specimens, compared with normal gastric mucosa. In addition, the YAP1 high P62 high expression was independently associated with poor prognosis in GC (hazard ratio: 1.334, 95% confidence interval: 1.045–1.704, P = 0.021). Stable YAP1 silencing inhibited the proliferation, migration, and invasion of BGC-823 GC cells in vitro and inhibited the growth of xenograft tumor and hematogenous metastasis of BGC-823 GC cells in vivo . The mechanism was associated with inhibited extracellular signal-regulated kinases (ERK)1/2 phosphorylation, elevated E-cadherin protein expression and decreased vimentin protein expression, down-regulated β-catenin protein expression and elevated α-catenin protein expression, and down-regulated long non-coding RNA (lncRNA) expressions including HOX transcript antisense RNA (HOTAIR), H19, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), human large tumor suppressor-2 (LATS2)-AS1-001, and LATS2. YAP1 over-expression promoted the proliferation, migration, and invasion of human immortalized normal gastric mucosa GES-1 cells in vitro by reversing the above signal molecules. Subcutaneous inoculation of GES-1 cells and YAP1-over-expressing GES-1 cells into nude mice did not generate tumors. We successfully established the xenograft tumor models using MKN-45 GC cells, but immunochemistry showed that there was no YAP1 expression in MKN-45 cells. These results suggest that YAP1 is not a direct factor affecting tumor formation, but could accelerate tumor growth and metastasis. Collectively, this study highlights an important role for YAP1 as a promoter of GC growth and metastasis, and suggests that YAP1 could possibly be a potential treatment target for GC.

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YAP1 enhances cell proliferation, migration, and invasion of gastric cancer in vitro and in vivo

Objectives The objective of this study was to compare the image quality and radiation dose of chest CT images reconstructed with a blend of adaptive statistical iterative reconstruction (ASIR) and filtered back-projection (FBP) with images generated using conventional FBP. Methods Patients with chest CT re-examinations were alternately assigned to two scanners with different reconstruction techniques. The study groups included noise index (NI) 11 with 30% ASIR (A30), NI 13 with 40% ASIR (A40), NI 15 with 50% ASIR (A50) and NI 17 with 60% ASIR (A60), sequentially changed every 2 months. The control images were obtained using FBP and NI 11. All acquisitions were performed with automatic dose modulation. Paired t-test and non-parameter test were applied to compare the difference. Results The radiation doses were significantly lower in the examinations that used ASIR (p<0.001). The mean dose reduction rate was 27.7%, 45.2%, 57.1% and 71.8% for Groups A30, A40, A50 and A60, respectively. The image quality of G...

/pdf/evaluation-of-dose-reduction-and-image-quality-in-chest-ct-3djcx2r4ka.pdf

Evaluation of dose reduction and image quality in chest CT using adaptive statistical iterative reconstruction with the same group of patients

BACKGROUND The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. METHODS A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1-weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max-pooling layers and ended with three dense layers. The pre-NAC model and post-NAC model inputted six phases of pre-NAC and post-NAC images, respectively. The combined model used 12 channels from six phases of pre-NAC and six phases of post-NAC images. All models above included three indexes of molecular type as one additional input channel. RESULTS The training set contained 137 non-pCR and 107 pCR participants. The validation set contained 33 non-pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre-NAC, 0.968 for post-NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre-NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post-NAC model (100% vs. 82.8%, P = 0.033). CONCLUSION This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data. The model performed better than using pre-NAC data only, and also performed better than using post-NAC data only. KEY POINTS Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre-NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1759-7714.13309

Xiao-Ting Li

Papers

Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

MRI of Extramural Venous Invasion in Locally Advanced Rectal Cancer: Relationship to Tumor Recurrence and Overall Survival

YAP1 enhances cell proliferation, migration, and invasion of gastric cancer in vitro and in vivo

Evaluation of dose reduction and image quality in chest CT using adaptive statistical iterative reconstruction with the same group of patients

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.