Gastric cancer is one of the most common malignant tumors of the digestive tract globally, causing a heavy burden on society due to its high incidence and mortality rates. Patients with gastric cancer and peritoneal metastasis suffer poor prognosis, and peritoneal metastasis is a major factor in the recurrence and metastasis of gastric cancer. Currently, the diagnosis of peritoneal metastasis mainly relies on contrast-enhanced CT, PET-CT, and other imaging techniques, but these technologies have a limited capability to detect small lesion of peritoneal metastases. Laparoscopic exploration is still considered as the most reliable method to diagnose peritoneal metastasis of gastric cancer. It allows direct visualization of the abdominal cavity and peritoneal regions, facilitating the detection of metastases. Furthermore, it enables the collection of histological or cytological specimens for definitive pathological diagnosis. However, the area formed by the parietal peritoneum and visceral peritoneum is large and has many folds in space. To explore the tumor and peritoneum as comprehensively as possible, our team has proposed the "Four-Step Procedure" of laparoscopic exploration for gastric cancer to standardize the operation process of laparoscopic exploration, which is characterized by its safety, comprehensiveness, precision and ease of implementation. This article primarily discusses the limitation of routine clinical techniques in diagnosing peritoneal metastasis in gastric cancer and introduce the significant value of the "Four-Step Procedure" in diagnosing peritoneal metastasis in gastric cancer.

In recent years,immunotherapy,particularly immune checkpoint inhibitors,has been widely used in the treatment of both solid and non-solid tumors,and it has become an important component of comprehensive gastric cancer treatment. The clinical application of immunotherapy in gastric cancer has gradually expanded from first-line treatment for advanced gastric cancer to perioperative treatment for resectable locally advanced gastric cancer. The use of immunotherapy combined with chemotherapy,targeted therapy,and other treatment modalities in the perioperative management of gastric cancer has injected new vitality into perioperative treatment strategies,establishing a novel model for comprehensive perioperative gastric cancer treatment. However,the application of immunotherapy in gastric cancer treatment still faces challenges such as limited high-level evidence for perioperative use,drug resistance,and adverse effects. In the future,efforts should focus on conducting high-level evidence-based clinical research on perioperative immunotherapy for gastric cancer,exploring and optimizing combined immunotherapy regimens,and delving into the molecular biological mechanisms of the tumor immune microenvironment in gastric cancer. By leveraging artificial intelligence and big data platform technologies,further optimization of screening strategies for potential beneficiaries should be pursued,and personalized precision treatment should be employed to address and overcome the challenges faced by immunotherapy in gastric cancer.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

Gastric cancer is one of the most common malignant tumors of the digestive tract globally, causing a heavy burden on society due to its high incidence and mortality rates. Patients with gastric cancer and peritoneal metastasis suffer poor prognosis, and peritoneal metastasis is a major factor in the recurrence and metastasis of gastric cancer. Currently, the diagnosis of peritoneal metastasis mainly relies on contrast-enhanced CT, PET-CT, and other imaging techniques, but these technologies have a limited capability to detect small lesion of peritoneal metastases. Laparoscopic exploration is still considered as the most reliable method to diagnose peritoneal metastasis of gastric cancer. It allows direct visualization of the abdominal cavity and peritoneal regions, facilitating the detection of metastases. Furthermore, it enables the collection of histological or cytological specimens for definitive pathological diagnosis. However, the area formed by the parietal peritoneum and visceral peritoneum is large and has many folds in space. To explore the tumor and peritoneum as comprehensively as possible, our team has proposed the "Four-Step Procedure" of laparoscopic exploration for gastric cancer to standardize the operation process of laparoscopic exploration, which is characterized by its safety, comprehensiveness, precision and ease of implementation. This article primarily discusses the limitation of routine clinical techniques in diagnosing peritoneal metastasis in gastric cancer and introduce the significant value of the "Four-Step Procedure" in diagnosing peritoneal metastasis in gastric cancer.

In recent years,immunotherapy,particularly immune checkpoint inhibitors,has been widely used in the treatment of both solid and non-solid tumors,and it has become an important component of comprehensive gastric cancer treatment. The clinical application of immunotherapy in gastric cancer has gradually expanded from first-line treatment for advanced gastric cancer to perioperative treatment for resectable locally advanced gastric cancer. The use of immunotherapy combined with chemotherapy,targeted therapy,and other treatment modalities in the perioperative management of gastric cancer has injected new vitality into perioperative treatment strategies,establishing a novel model for comprehensive perioperative gastric cancer treatment. However,the application of immunotherapy in gastric cancer treatment still faces challenges such as limited high-level evidence for perioperative use,drug resistance,and adverse effects. In the future,efforts should focus on conducting high-level evidence-based clinical research on perioperative immunotherapy for gastric cancer,exploring and optimizing combined immunotherapy regimens,and delving into the molecular biological mechanisms of the tumor immune microenvironment in gastric cancer. By leveraging artificial intelligence and big data platform technologies,further optimization of screening strategies for potential beneficiaries should be pursued,and personalized precision treatment should be employed to address and overcome the challenges faced by immunotherapy in gastric cancer.