Effect of visceral adipose tissue on the accuracy of preoperative T-staging of gastric cancer

Ma, Teng; Li, Xiaojiao; Zhang, Tong; Duan, Mingguang; Ma, Qianli; Cong, Lin; Huang, Zhaoqin; Wang, Ximing; Chen, Yunchao

doi:10.1016/j.ejrad.2022.110488

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Although the accuracy of single-detector CT (69–84%) and multi-detector CT (80–89%) for gastric cancer staging overall has been reported [ 50 ], the ability of CT to separate early T-stages is limited [ 51 , 52 ], and in our cohort, the T-stage was set as Tx in the majority of cases and correctly in less than 3%. Similarly, EUS analysis provided accurate T-staging in one-third of the patients who underwent the examination.…”

Section: Discussionmentioning

confidence: 70%

Early Gastric Cancers in Central Norway 2001 to 2016—A Population-Based Study

Kvamme,

Stillingen,

Sandø

et al. 2024

Cancers

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Early gastric cancers (EGCs) are confined to the gastric mucosa and submucosa irrespective of lymph node metastases and constitute only a minor proportion of gastric cancer in Western countries. We aimed to characterize EGCs and assess the survival of EGC in Central Norway during 2001–2016. A retrospective population-based study on 1205 patients with gastric cancer was performed. At the time, surgical resection was the standard treatment, and 88 (7.3%) EGCs were identified. Histopathological specimens were re-examined, and the eCura score and survival were evaluated. The number of gastric cancers declined (p = 0.010), but the relative proportion of EGC was unchanged during the study period. EGCs were more often of the Lauren intestinal type (p < 0.001) compared with controls. A significant proportion (9.4%, n = 5) of the patients with a low-risk eCura had lymph node metastases, whereas further exclusion of tumors with histological ulceration or SM2 invasion identified an N0 cohort. The median survival for EGC patients was 117.1 months (95% CI 99.8–134.3) and the 5-year overall survival was 75%. Twelve deaths were cancer-related, either due to postoperative complications (5.7%, n = 5) or cancer recurrence (8%, n = 7). In conclusion, EGCs constituted a minor but constant proportion of gastric cancers. eCura alone was insufficient in predicting patients with pN0 disease.

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Section: Discussionmentioning

confidence: 70%

Early Gastric Cancers in Central Norway 2001 to 2016—A Population-Based Study

Kvamme,

Stillingen,

Sandø

et al. 2024

Cancers

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“…According to reports, CT's overall diagnostic accuracy for Tstaging ranges from 73.8% to 88.9%. Radiologist evaluation of its T1 stage diagnostic accuracy, however, was average from 63% to 82.7% (7,8). In certain postoperative cases of EGC (9,10), it was discovered, based on several published research and our experience, that some EGC was over-staged as advanced GC in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images

et al. 2022

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BackgroundEarly gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images.MethodsWe retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance.ResultsThe deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC.ConclusionsThese results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

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