2020
DOI: 10.1016/j.ejso.2020.04.002
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The additive value of restaging-CT during neoadjuvant chemotherapy for gastric cancer

Abstract: Introduction: Computed tomography (CT) is used for restaging of gastric cancer patients during neoadjuvant chemotherapy (NAC). The treatment strategy could be altered after detection of distant interval metastases, possibly leading to a reduction in unnecessary chemotherapy cycles, its related toxicity, and surgical procedures. The aim of this study was to evaluate the additive value of restaging-CT during NAC in guiding clinical decision making in gastric cancer. Materials and methods: This retrospective, mul… Show more

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Cited by 21 publications
(11 citation statements)
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“…3 b, some yrT3 or yrT4 cancers are found to be ypT0-2. These observations are in line with findings in a similar, recently published study from the Netherlands [ 35 ].…”
Section: Discussionsupporting
confidence: 93%
“…3 b, some yrT3 or yrT4 cancers are found to be ypT0-2. These observations are in line with findings in a similar, recently published study from the Netherlands [ 35 ].…”
Section: Discussionsupporting
confidence: 93%
“…Another restriction is associated with ambiguous restaging before potential gastrectomy. According to Gertsen et al, restaging CT prevents only 1% of unnecessary laparotomies, although preoperative assessment was performed before completing NAC [29]. High volume centres present contrasting numbers with negative prognostic value (NPV) of LN involvement at a level of 90.1% among patients with early GC [21,30].…”
Section: Computed Tomographymentioning
confidence: 99%
“…With the continuous development of intelligent segmentation algorithms, they are often used to learn original images and are extensively used in image segmentation, image classification, and target image positioning [ 9 ]. Some scholars have proposed in research that combining pixel information of different scales can extract the best size information [ 10 ]. In the study, a decision tree was built based on the feature ring and the segmentation position and then used to extract the features of the CT image to obtain the optimal segmentation boundary, providing a new method for clinical prediction and diagnosis of the occurrence and development of gastric cancer.…”
Section: Introductionmentioning
confidence: 99%