Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.

Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.

A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, P < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.

The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.

Immune checkpoint inhibitors (ICIs) plus chemotherapy have become a new first-line treatment option for patients with locally advanced or metastatic gastric cancer. However, it is still controversial whether to choose chemotherapy alone or ICIs plus chemotherapy as the first-line treatment option due to a lack of ideal predictive biomarkers for the efficacy. This study intended to explore the predictive value of inflammatory markers for the efficacy of first-line ICIs plus chemotherapy in this disease.

This retrospective study included 131 patients with locally advanced or metastatic gastric cancer who received first-line treatment between July, 2020 and June, 2023. Among them, 76 received first-line ICIs plus chemotherapy and 55 received chemotherapy alone. Firstly, Kaplan-Meier and Cox regression analyses were used to explore the correlation between inflammatory markers and efficacy of first-line ICIs plus chemotherapy. Subsequently, the predictive value of combined baseline and dynamic changes in inflammatory markers was explored. Moreover, the predictive value of baseline inflammatory markers was further verified by comparing efficacy of ICIs plus chemotherapy with that of chemotherapy alone.

In patients receiving first-line ICIs plus chemotherapy, low baseline monocyte-to-lymphocyte ratio (MLR) in peripheral blood was significantly associated with better progression-free survival (PFS) and overall survival (OS), and was an independent prognostic factor for OS. In addition, dynamic early changes of MLR also played predictive role. Patients whose MLR was lower at baseline and after two cycles of treatment had better OS (P = 0.009). Furthermore, compared to chemotherapy alone, patients with a lower baseline MLR were more likely to benefit from first-line ICIs plus chemotherapy.

MLR could serve as a new biomarker to predict the efficacy of first-line ICIs plus chemotherapy in patients with locally advanced or metastatic gastric cancer. And it is helpful to select the candidates for first-line ICIs plus chemotherapy, which is worthy of further study.

Gastric cancer is a major global health concern, often diagnosed at advanced stages, leading to poor prognosis. Proximal and distal gastric cancers exhibit distinct clinicopathological features.

To investigate the diagnostic value of hematological and inflammatory markers in differentiating proximal and distal gastric cancers and to evaluate their association with clinical outcomes.

A retrospective cohort study was conducted on 150 patients diagnosed with gastric adenocarcinoma through histopathological analysis. Patients were categorized into proximal gastric cancer and distal gastric cancer groups. Laboratory parameters were analyzed.

Of the 150 patients, 84 had proximal gastric cancer and 66 had distal gastric cancer. Dysphagia was significantly more common in the proximal gastric cancer group, while anemia and higher platelet-to-lymphocyte ratio values were observed in the distal gastric cancer group (P = 0.031). Tumor stage and neutrophil-to-lymphocyte ratio emerged as independent predictors of all-cause mortality. No significant differences were found in other laboratory or biochemical parameters between the groups.

Proximal and distal gastric cancers demonstrate distinct clinical and laboratory profiles. The platelet-to-lymphocyte ratio may serve as a valuable marker in differentiating cancer localization, while the neutrophil-to-lymphocyte ratio is a prognostic indicator for mortality. These findings highlight the potential of hematological markers in optimizing diagnosis and treatment strategies for gastric cancer.

The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic status before treatment and determine appropriate treatment strategies.

To develop positive lymph node ratio (LNR) and machine learning (ML)-based staging systems for GC patients with varying differentiation.

This multicenter retrospective cohort study included 11772 GC patients, with 5612 in the training set (Harbin Medical University Cancer Hospital) and 6160 in the validation set (Surveillance, Epidemiology, and End Results Program database). X-tile software identified optimal cutoff values for the positive LNR, and five ML models were developed using pT and LNR staging. Risk scores were divided into seven stages, constructing new staging systems tailored to different tumor differentiation levels.

In both the training and validation sets, regardless of the tumor differentiation level, LNR staging demonstrated superior prognostic stratification compared to pN. Extreme Gradient Boosting exhibited better predictive performance than the other four models. Compared to tumor node metastasis staging, the new staging systems, developed for patients with different degrees of differentiation, showed significantly better predictive performance.

The new positive lymph nodes ratio staging and integrated staging systems constructed for GC patients with different differentiation grades exhibited better prognostic stratification capabilities.

Currently, immune checkpoint inhibitors (ICIs) have excellent performance in the clinical treatment of advanced gastric cancer (AGC). However, precisely selecting AGC patients who can benefit from immunotherapy is an urgent difficulty. In this study, we investigated the immunoprognostic role of myeloid-to-lymphocyte ratio (M:L) in AGC patients.

We collected information on 268 AGC patients who were hospitalized in the Department of Medical Oncology of PLA General Hospital from December 2014 to May 2021. The patients were divided into low M: L group (< 3.76) and high M:L group (≥ 3.76). Survival differences between different M: L level groups at baseline and after treatment were analyzed by methods such as Kaplan-Meier, Cox or Logistic regression model.

Progression free survival (PFS) (5.8 months vs. 3.4 months, p = 0.001) and overall survival (OS) (14.1 months vs. 9.0 months, p = 0.001) were significantly longer in the low M:L group than in the high M:L group. After analyses of Cox regression modeling it was concluded that M:L was an independent prognostic factor for PFS (HR 1.371 95%CI 1.057-1.777 p = 0.017) and OS (HR 1.352 95%CI 1.003-1.824 p = 0.048), respectively. Subsequent subgroup analyses performed across immunotherapy lines, regimens, PD-1 inhibitor agents, and age groups revealed a poorer prognosis in the high M:L group. Notably, an increase in the value of M:L after treatment significantly increased the risk of poor prognosis.

M:L ≥ 3.76 is associated with poor prognostic outcomes in AGC patients receiving immunotherapy and may be a predictive biomarker of prognosis. This result needs to be confirmed by larger prospective studies.

The present study aimed to assess the efficacy and safety of immune checkpoint inhibitor (ICI)-based therapy in patients with metastatic breast cancer (MBC). Therefore, eligible patients with histologically confirmed MBC, treated with ICI-based therapy, were enrolled. The primary endpoint was progression-free survival (PFS) and the secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS) and safety. A total of 90 patients with MBC, treated with ICI-based therapy, with different treatment lines, were included in the present study. The median age was 50 years (range, 27-76). The predominant tumor subtypes were triple negative (53.3%) and luminal (31.1%) breast cancer. The majority of patients (61.1%) were heavily pretreated (lines of treatment, ≥3). Approximately half of the patients (46.7%) had ≥3 metastatic sites. The overall ORR was 36.7% (33/90 patients), while a DCR of 78.9% (71/90 patients) was also recorded. With a median follow-up of 16.0 months, the median PFS and OS were 4.9 months [95% confidence interval (CI), 3.8-6.1] and 13.9 months (95% CI, 9.5-18.2), respectively. Patients treated with ICIs as first-line therapy exhibited notable improvement, with a median PFS of 11.0 months (95% CI, 6.0-16.0) and a median OS of 24.3 months (95% CI, 11.4-37.2). In addition, the pretreatment blood platelet-to-lymphocyte ratio was an independent risk factor for PFS [hazard ratio (HR)=2.406; 95% CI, 1.325-4.370; P=0.004] and OS (HR=2.376; 95% CI, 1.059-5.328; P=0.036). The most common adverse events were nausea (44.4%), neutropenia (42.0%) and alanine aminotransferase/aspartate aminotransferase elevation (22.2%). Furthermore, three (3.3%) patients developed grade 1/2 immuno-related toxicity and recovered after supportive care. Overall, the present study suggested that the ICI-based therapy exhibited encouraging clinical outcomes with manageable toxicity in patients with MBC in real-world settings, with the most favorable efficacy in first-line treatment.

This study aims to assess the predictive value of certain markers of inflammation in patients with locally advanced or recurrent/metastatic cervical cancer who are undergoing treatment with anti-programmed death 1 (PD-1) therapy. A total of 105 patients with cervical cancer, who received treatment involving immunocheckpoint inhibitors (ICIs), were included in this retrospective study. We collected information on various peripheral blood indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI). To determine the appropriate cutoff values for these inflammatory markers, we performed receiver operating characteristic curve (ROC) analysis. Progression-free survival (PFS) was estimated using the Kaplan-Meier method, and we conducted both univariate and multivariate Cox regression analyses to evaluate the prognostic value of these markers. Out of the 105 patients who received ICI treatment, the median progression-free survival (mPFS) was 19.0 months. We obtained the patients' clinical characteristics, such as age, pathological type, therapy regimen, Figo stage, NLR, PLR, LMR, SII, and PNI from their medical records. The optimal cutoff values for NLR, PLR, LMR, SII, and PNI were determined as 3.76, 218.1, 3.34, 1147.7, 43.75, respectively. In the univariate analysis, age, pathological type, therapy regimen, Figo stage, and LMR were not found to be associated with PFS. However, high NLR(P=0.001), high PLR(P<0.001), high SII(P<0.001), and low PNI (P=0.003)were all associated with shorter PFS. Multivariate analysis indicated that SII (P=0.017) was an independent risk factor for PFS. This study highlights the potential use of SII as a predictor of progression-free survival in cervical cancer patients undergoing immunotherapy.

Immune checkpoint inhibitors (ICIs) have transformed advanced gastric cancer treatment, yet patient responses vary, highlighting the need for effective biomarkers. Common markers, such as programmed cell death ligand-1 (PD-L1), microsatellite instability/mismatch repair (MSI/MMR), tumor mutational burden, tumor-infiltrating lymphocytes, and Epstein-Barr virus, face sampling challenges and high costs. This study seeks practical, minimally invasive biomarkers to enhance patient selection and improve outcomes.

This multicenter retrospective study analyzed 617 patients with advanced gastric or gastroesophageal junction cancer treated with programmed cell death protein-1 (PD-1)/PD-L1 inhibitors from January 2019 to March 2023. Clinical data and peripheral blood marker data were collected before and after treatment. The primary endpoints were overall survival (OS) and progression-free survival (PFS); the secondary endpoints included the objective response rate (ORR) and disease control rate (DCR). Least absolute shrinkage and selection operator (LASSO)-Cox and LASSO logistic regression analyses identified independent factors for OS, PFS, and ORR. Predictive nomograms were validated using receiver operating characteristic (ROC) curves, areas under the curve (AUCs), C-indices, and calibration curves, with clinical utility assessed via decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

OS-related factors included treatment line, T stage, ascites, pretreatment indirect bilirubin (pre-IBIL), posttreatment CA125, CA199, CA724, and the PLR. PFS-related factors included treatment lines, T stage, metastatic sites, pre-IBIL, posttreatment globulin (GLOB), CA125, and CA199 changes. ORR-related factors included treatment line, T stage, N stage, liver metastasis, pretreatment red cell distribution width-to-platelet ratio (RPR), CA125, and CA724 changes. The nomograms showed strong predictive performance and clinical utility.

Early treatment, lower T stage, the absence of ascites, and lower pre-IBIL, post-CA125, CA199, CA724, and PLR correlate with better OS. Factors for improved PFS include early treatment, lower T stage, fewer metastatic sites, and lower pre-IBIL, post-GLOB, and post-CA125 levels. Nomogram models can help identify patients who may benefit from immunotherapy, providing valuable clinical guidance.

To explore peripheral blood indicators that may serve as early indicators for multidrug-resistant bacteria (MDR) infections in this demographic, with the goal of providing reference suggestions for the clinical prevention of MDR infections in elderly inpatients.

Clinical data of patients were divided into the MDR-infected group (n = 488) and the MDR-uninfected group (n = 233) according to the results of drug sensitivity experiments, risk factors for MDR infection, and peripheral blood indicators related to MDR infections were analyzed using univariate and multivariate logistic regression in conjunction with the construction of a Chi-squared automatic interaction detector (CHAID) decision tree model, considering statistical significance at p < .05.

Of 721 patients, 488 multidrug-resistant strains were identified. Among them, with Staphylococcus spp. the most prevalent in 148 strains. The most frequent detection of MDR occurred in puncture fluid samples (167 cases). Univariate and multivariate regression analyses revealed that prolonged hospitalization, use of antibiotics preadmission, duration of antibiotics, invasive procedures or recent surgery, and coexisting lung disease were independent risk factors for contracting MDR. Subsequent analysis comparing the aforementioned influences with peripheral blood cells revealed associations between the number of antibiotic treatment days and increased neutrophil-to-lymphocyte ratio (NLR), platelet count-to-lymphocyte ratio (PLR), neutrophils, decreased lymphocytes, and increased eosinophils; preadmission antibiotic use correlated with increased PLR, NLR, neutrophils, and decreased lymphocytes; and invasive manipulation or surgery correlated with increased PLR and NLR.

Elevated NLR, PLR, neutrophils, lowered lymphocytes, and eosinophils may serve as early indicators of MDR infections in elderly hospitalized patients.

Lung cancer is one of the most common contributors to cancer-related deaths worldwide. This study aimed to develop a new blood index on the basis of the patient's systemic inflammation and nutritional status, which can be used to predict the prognosis of patients with non-small cell lung cancer (NSCLC).

Pre-treatment blood markers were analyzed in 556 NSCLC patients from 2010 to 2019. A least absolute shrinkage and selection operator (LASSO) method was used to select indicators to establish a new integrated biomarker (PNAGR). Kaplan-Meier survival curves were used to assess the prognostic impact of platelet-to-lymphocyte ratio (PLR), albumin (ALB), and the PNAGR. The prognostic value was verified using univariate and multivariate Cox analyses.

We used four biomarkers including PLR, ALB, 1/albumin-to-globulin ratio (1/AGR), and neutrophil/albumin-to-globulin ratio (N/AGR) were used to screen for the PNAGR using LASSO. Patients with high PNAGR demonstrated lower overall survival (OS) compared to those with low PNAGR. In both univariate and multivariate analyses, PNAGR was revealed as an independent prognostic factor for OS. The predictive power of PNAGR [area under the curve (AUC): 0.753] was higher than that of the metrics alone.

PNAGR is a novel and effective clinical prognostic tool with good clinical predictive value for NSCLC patients.

The prognostic relevance of the platelet-to-lymphocyte ratio (PLR) in gastric cancer (GC) patients undergoing immune checkpoint inhibitor (ICI) treatment remains unclear. This meta-analysis aimed to determine the prognostic impact of PLR in this specific patient cohort.

We searched the PubMed, Cochrane Library, CNKI, and EMBASE databases, including literature published up to September 2023, to investigate the prognostic implications of PLR in patients with gastric cancer undergoing immune checkpoint inhibitor therapy. Outcome measures encompassed overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rates (DCR).

Nine studies from seven articles comprising 948 eligible patients were selected. The results revealed a significant correlation between elevated PLR and poorer OS and progression-free survival (PFS) (OS: HR 1.67, 95% CI 1.39-2.00, p < 0.001; PFS: HR 1.51, 95% CI 1.29-1.76, p < 0.001). Subgroup analyses were performed to validate the robustness of the results. Moreover, a meta-analysis of four studies investigating the correlation between the PLR in gastric cancer (GC) patients and the objective response rate/disease control rate (ORR/DCR), showed no significant association between the PLR and ORR/DCR (ORR: RR = 1.01, p = 0.960; DCR: RR = 0.96, p = 0.319).

This meta-analysis indicates that elevated PLR in GC patients undergoing ICI treatment is significantly linked to worse OS and PFS. Therefore, PLR can serve as a prognostic indicator of post-treatment outcomes in patients with GC receiving ICIs. Further prospective studies are required to assess the reliability of these findings.

https://inplasy.com/, identifier INPLASY2023120103.

Exploring factors associated with the outcome of patients with aneurysmal subarachnoid hemorrhage (aSAH) has become a hot focus in research. We sought to investigate the associations of inflammatory markers and blood cell count in cerebrospinal fluid with the outcome of aSAH patients.

We carried a retrospective study including 200 patients with aSAH and surgeries. The associations of neutrophil, lymphocyte, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune inflammation index (SII), system inflammation response index (SIRI), and blood cell count in cerebrospinal fluid on the 1st and 7th postoperative days with the outcome of aSAH patients were investigated by univariate analysis and multivariate logistic regression model.

According to the modified Rankin scale (mRS) score, there were 147 patients with good outcome and 53 patients with poor outcome. The neutrophil, NLR, SIRI, and SII levels on the seventh postoperative day in patients with poor outcome were all significantly higher than patients with good outcome, P < 0.05. The multivariate logistic regression model including inflammatory markers and blood cell counts in cerebrospinal fluid on the 1st postoperative day confirmed that red blood cell count in cerebrospinal fluid (≥177 × 109/L; OR: 7.227, 95% CI: 1.160-45.050, P = 0.034) was possibly associated with poor outcome of aSAH patients, surgical duration (≥169 min), Fisher grade (III-IV), hypertension, and infections were also possibly associated with the poor outcome. The model including inflammatory markers and blood cell counts in cerebrospinal fluid on the 7th postoperative day confirmed that red blood cell count in cerebrospinal fluid (≥54 × 109/L; OR: 39.787, 95% CI: 6.799-232.836, P < 0.001) and neutrophil-lymphocyte ratio (≥8.16; OR: 6.362, 95% CI: 1.424-28.428, P = 0.015) were all possibly associated with poor outcome of aSAH patients. The NLR (r = 0.297, P = 0.007) and SIRI (r = 0.325, P = 0.003) levels were all correlated with the count of red blood cells in cerebrospinal fluid.

Higher neutrophil-lymphocyte ratio and higher red blood cell count in cerebrospinal fluid were all possibly associated with poor outcome of patients with aneurysmal subarachnoid hemorrhage. However, we need a larger sample study.

The inflammatory response is involved in the progression of aneurysmal subarachnoid hemorrhage (aSAH). We sought to investigate the relationships of inflammatory indicators including blood cell counts and the ratios of different blood cells counts with the prognosis of aSAH patients. We performed a retrospective study including 140 patients with aSAH and aneurysm surgeries. The relationships of neutrophils, lymphocytes, monocytes, platelets, systemic immune inflammation index (SII), system inflammation response index (SIRI), neutrophil-lymphocyte ratio and platelet-lymphocyte ratio with prognosis were investigated by univariable analysis and multivariable logistic regression model. The patient with Modified Rankin Scale (mRS) score＜3 was defined as having a good prognosis, while with mRS score ≥3 was defined as having a poor prognosis. Among 140 patients included, there were 108 cases with good prognosis and 32 cases with poor prognosis after follow-up. On the 3rd postoperative day, the neutrophils counts, SIRI level and SII level in cases with poor prognosis were significantly higher than cases with good prognosis, P < .05. After adjusting for baseline differences in Hunt-Hess grade, Glasgow Coma Scale score, combination with intraventricular hemorrhage and maximum diameter of aneurysm, the levels of SIRI (odds ratio = 3.968, 95% CI: 1.432-10.992, P = .008) and SII (odds ratio = 3.313, 95% CI: 1.029-10.665, P = .045) on the 3rd postoperative day could predict poor prognosis. SII and SIRI on the 3rd postoperative day could independently predict the poor prognosis in aSAH. However, the cutoff values for predicting prognosis needs to be validated in larger-sample studies.