Accuracy evaluation of combining gastroscopy, multi-slice spiral CT, Her-2, and tumor markers in gastric cancer staging diagnosis

Zhao, Shunyun; Bi, Yangang; Wang, Zhenfang; Zhang, Fantao; Zhang, Yang; Xu, Yongyun

doi:10.1186/s12957-022-02616-z

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…However, the sensitivity and speci city of existing tumor markers are not su ciently effective, and the search for optimal tumor markers for detecting gastric cancer continues. [14] Recent years have seen the emergence of new tumor markers, such as CST4, which have been shown to have high diagnostic value for gastrointestinal cancers [15] . Some studies have found that combining certain tumor markers can enhance the diagnostic value for gastric cancer [16] , such as the combination of gastrin-17 with CEA, CA125, and CA19-9 [17] .…”

Section: Discussionmentioning

confidence: 99%

Serum CST4 Combined with CEA, CA72-4, CA125, CA19-9 Improves the Diagnosis Value of Gastric cancer and Changes in pre-operation and post-operation

Sun,

Zhao

et al. 2023

Preprint

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Objective: This study aims to investigate the potential enhancement of diagnostic accuracy in gastric cancer through the combined detection of serum tumor markers (CST4, CEA, CA72-4, CA125, CA19-9). Additionally, we examine CST4 levels in both preoperative and postoperative gastric cancer patients to evaluate the correlation between CST4 levels and tumor burden. Results: In gastric cancer, the positive rates of CST4, CEA, CA72-4, CA125, and CA19-9 were notably higher (20.34%, 20.76%, 14.41%, 8.9%, 11.44%, respectively) compared to those in the control group (4.35%, 4.35%, 0%, 1.45%, 7.25%, respectively). The sensitivity of these markers were as follows: CST4 (0.20), CEA (0.21), CA72-4 (0.25), CA125 (0.09), and CA19-9 (0.11). Their respective specificities were 0.96, 0.96, 1.0, 0.99, and 0.93. When these five tumor markers were combined, the AUC area increased to 0.74, indicating that the combined use of these markers significantly improved diagnostic efficacy compared to individual tests. Furthermore, significant differences were observed in CST4 levels pre- and post-operation in gastric cancer patients with varying stages, tumor sizes, and lymph node metastases. Notably, a significant difference in CST4 levels was found before and after surgery in patients with poorly differentiated gastric cancer (P < 0.001), but no such difference was observed in well-differentiated gastric cancer (P = 0.438). Conclusion: CST4, CEA, CA72-4, CA125, and CA19-9 all hold significant value in the diagnosis of gastric cancer. Specifically, serum CST4 may serve as a suitable tumor marker for gastric cancer screening. The combined use of CST4, CEA, CA72-4, CA125, and CA19-9 can significantly enhance the diagnostic accuracy for gastric cancer. Importantly, postoperative CST4 levels in gastric cancer patients were significantly lower than preoperative levels, with the exception of patients with well-differentiated tumors.

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Section: Discussionmentioning

confidence: 99%

Serum CST4 Combined with CEA, CA72-4, CA125, CA19-9 Improves the Diagnosis Value of Gastric cancer and Changes in pre-operation and post-operation

Sun,

Zhao

et al. 2023

Preprint

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“…The results of this study reveal that the later the stage, the higher the risk ratio (HR), and the later the stage indicates that the deeper the tumor invasion of the intestinal wall, the greater the chance of vascular and nerve invasion and the increased probability of metastasis in the distant organs of the tumor cells. CEA is a gastrointestinal cancer marker that is involved in disease diagnosis and the evaluation of disease prognosis ( 15 – 17 ). Several previous studies have found CEA positivity to be a poor prognostic factor in the gastrointestinal tract ( 18 ), which is consistent with the results of this study.…”

Section: Discussionmentioning

confidence: 99%

Establishment and validation of the survival prediction risk model for appendiceal cancer

Liu

Junli²,

Wang³

et al. 2022

Front. Med.

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ObjectiveEstablishing a risk model of the survival situation of appendix cancer for accurately identifying high-risk patients and developing individualized treatment plans.MethodsA total of 4,691 patients who were diagnosed with primary appendix cancer from 2010 to 2016 were extracted using Surveillance, Epidemiology, and End Results (SEER) * Stat software. The total sample size was divided into 3,283 cases in the modeling set and 1,408 cases in the validation set at a ratio of 7:3. A nomogram model based on independent risk factors that affect the prognosis of appendix cancer was established. Single-factor Cox risk regression, Lasso regression, and multifactor Cox risk regression were used for analyzing the risk factors that affect overall survival (OS) in appendectomy patients. A nomogram model was established based on the independent risk factors that affect appendix cancer prognosis, and the receiver operating characteristic curve (ROC) curve and calibration curve were used for evaluating the model. Survival differences between the high- and low-risk groups were analyzed through Kaplan–Meier survival analysis and the log-rank test. Single-factor Cox risk regression analysis found age, ethnicity, pathological type, pathological stage, surgery, radiotherapy, chemotherapy, number of lymph nodes removed, T stage, N stage, M stage, tumor size, and CEA all to be risk factors for appendiceal OS. At the same time, multifactor Cox risk regression analysis found age, tumor stage, surgery, lymph node removal, T stage, N stage, M stage, and CEA to be independent risk factors for appendiceal OS. A nomogram model was established for the multifactor statistically significant indicators. Further stratified with corresponding probability values based on multifactorial Cox risk regression, Kaplan–Meier survival analysis found the low-risk group of the modeling and validation sets to have a significantly better prognosis than the high-risk group (p < 0.001).ConclusionThe established appendix cancer survival model can be used for the prediction of 1-, 3-, and 5-year OS and for the development of personalized treatment options through the identification of high-risk patients.

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“…Studies have reported that EUS achieves a high overall accuracy in T stage assessment, with a sensitivity of 86% (32). Zhao et al demonstrated that the accuracy of staging using multi-slice spiral CT images and gastroscope can reach 83.67% through statistical analysis (33). Guan et al employed Yolov5-based DetectionNet for staging gastric cancer on arterial phase CT images, achieving an average accuracy of 0.909 (34).…”

Section: Preoperative Diagnosis Of Gc Has Been a Focal Point Of Researchmentioning

confidence: 99%

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Gao,

Yu,

Zhang

et al. 2023

Front. Oncol.

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BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

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Accuracy evaluation of combining gastroscopy, multi-slice spiral CT, Her-2, and tumor markers in gastric cancer staging diagnosis

Cited by 12 publications

References 24 publications

Serum CST4 Combined with CEA, CA72-4, CA125, CA19-9 Improves the Diagnosis Value of Gastric cancer and Changes in pre-operation and post-operation

Serum CST4 Combined with CEA, CA72-4, CA125, CA19-9 Improves the Diagnosis Value of Gastric cancer and Changes in pre-operation and post-operation

Establishment and validation of the survival prediction risk model for appendiceal cancer

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

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