Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.

Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.

Serum cytokines and creatinine/cystatin C ratio as prognostic biomarkers in advanced cancer patients treated with anti-PD-1/PD-L1 therapy.

OBJECTIVE: Immune checkpoint inhibitors (ICIs), specifically targeting the programmed cell death protein-1 or its ligand (PD-1/PD-L1), have been extensively used in the treatment of a spectrum of malignancies, although the predictive biomarkers remain to be elucidated. This study aims to investigate the association between baseline circulating levels of cytokines and the creatinine/cystatin C ratio (CCR) with the treatment outcomes of ICIs in patients with advanced cancer. METHODS: The pre-treatment circulating levels of 10 cytokines (PD-L1, CTLA4, CXCL10, LAG3, HGF, CCL2, MIG, GRANB, IL-18, and IL-6) were measured via automated capillary-based immunoassay platform in the serum of 65 advanced cancer patients treated with anti-PD-1/PD-L1-based systemic therapy and 10 healthy volunteers. The levels of cytokines and CCR were quantified and categorized into high and low groups based on the median value. The associations of serum cytokines and CCR with response to treatment, survival, and immune-related adverse events were assessed. RESULTS: Elevated circulating levels of 6 cytokines (PD-L1, CXCL10, HGF, CCL2, MIG, and IL-6) were observed in cancer patients compared with that in healthy volunteers. The correlation coefficients between cytokines, CCR and nutritional risk index were also calculated. In the cancer cohort (N = 65), low circulating HGF (P = 0.023, P = 0.029), low IL-6 (P = 0.002, P < 0.001), and high CCR (P = 0.031, P = 0.008) were associated with significantly improved progression-free survival (PFS) and overall survival (OS). Multi-variable COX analyses adjusted for clinicopathological factors revealed that low HGF, low IL-6, and high CCR were independent favorable prognostic factors for PFS (P = 0.028, P = 0.010, and P = 0.015, respectively) and OS (P = 0.043, P = 0.003, and P = 0.026, respectively). Grade 2 irAEs occurred more frequently in patients with low levels of circulating CCL2 and LAG3. CONCLUSIONS: Pre-treatment circulating levels of serum IL-6, HGF, and CCR may serve as independent predictive and prognostic biomarkers in advanced cancer patients treated with ICIs-based systemic therapy. These findings might help to identify potential patients who would benefit from these therapies.

Mapping the intellectual structure and emerging trends for the application of nanomaterials in gastric cancer: A bibliometric study.

BACKGROUND: Recent reviews have outlined the main nanomaterials used in relation to gastrointestinal tumors and described the basic properties of these materials. However, the research hotspots and trends in the application of nanomaterials in gastric cancer (GC) remain obscure. AIM: To demonstrate the knowledge structure and evolutionary trends of research into the application of nanomaterials in GC. METHODS: Publications related to the application of nanomaterials in GC were retrieved from the Web of Science Core Collection for this systematic review and bibliometric study. VOSviewer and CiteSpace were used for bibliometric and visualization analyses. RESULTS: From 2000 to 2022, the application of nanomaterials in GC developed rapidly. The keyword co-occurrence analysis showed that the related research topics were divided into three clusters: (1) The application of nanomaterials in GC treatment; (2) The application and toxicity of nanomaterials in GC diagnosis; and (3) The effects of nanomaterials on the biological behavior of GC cells. Complexes, silver nanoparticles, and green synthesis are the latest high-frequency keywords that represent promising future research directions. CONCLUSION: The application of nanomaterials in GC diagnosis and treatment and the mechanisms of their effects on GC cells have been major themes in this field over the past 23 years.

Effect of different treatment modalities on ovarian cancer patients with liver metastases: A retrospective cohort study based on SEER.

BACKGROUND: To examine the trends in morbidity and mortality among ovarian cancer patients with liver metastases, and investigate the impact of different treatments on both overall survival (OS) and cancer-specific survival (CSS). METHODS: 2,925 ovarian cancer patients with liver metastases from Surveillance, Epidemiology, and End Results 2010-2019 were included. The primary endpoint was considered as OS and CSS. We conducted trend analysis of the incidence, OS and CSS rates of liver metastases in ovarian cancer. Univariate and multivariate COX proportional risk models were used to investigate the association between different treatment methods and OS, and univariate and multivariate competing risk models were employed to evaluate the impact of treatment methods on CSS. RESULTS: At the end of follow-up, 689 patients remained alive. The OS and CSS rates were 76.44% and 72.99% for all patients, respectively. There was a significant decreasing trend in the incidence [average annual percent change (AAPC) = -2.3, 95% confidence interval (CI): -3.9, -0.7], all-cause mortality (AAPC = -12.8, 95% CI: -15.6, -9.9) and specific mortality (AAPC = -13.0, 95% CI: -16.1, -9.8) rate of liver metastases in ovarian cancer. After adjusting all confounding factor, only receiving surgery was associated with OS [hazard ratio (HR) = 0.39, 95%CI: 0.31-0.48]/CSS (HR = 0.37, 95%CI: 0.30-0.47). Chemotherapy was found to be protective factor for OS (HR = 0.33, 95%CI: 0.30-0.37)/CSS (HR = 0.44, 95%CI: 0.39-0.50) of ovarian cancer patients, while not receiving surgery remained a risk factor. Additionally, the result of subgroup analyses also showed that only receiving surgery and chemotherapy still were significant protective factor of OS and CSS for patients without other distant metastases, with distant metastases to the bone, lung, brain or other organs, with bone metastasis, and with lung metastasis. CONCLUSION: Our research has elucidated a downward trend in morbidity and mortality rates among patients with liver metastases originating from ovarian cancer. Only receiving surgery and chemotherapy as therapies methods confer survival benefits to patients.

Creatinine-to-cystatin C ratio and body composition predict response to PD-1 inhibitors-based combination treatment in metastatic gastric cancer.

Background: Creatinine-to-cystatin C ratio (CCR) and body composition (BC) parameters have emerged as significant prognostic factors in cancer patients. However, the potential effects of CCR in gastric cancer (GC) remains to be elucidated. This multi-center retrospective study explored the predictive and prognostic value of CCR and BC-parameters in patients with metastatic GC receiving PD-1 inhibitors-based combination therapy. Methods: One hundred and thirteen GC patients undergoing PD-1 inhibitors-based combination therapy were enrolled at three academic medical centers from January 2021 to July 2023. A deep-learning platform based on U-Net was developed to automatically segment skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and visceral adipose tissue index (VATI). Patients were divided into two groups based on the median of CCR or the upper tertile of BC-parameters. Logistic and Cox regression analysis were used to determine the effect of CCR and BC-parameters in predicting response rates and survival rates. Results: The CCR was positively correlated with SMI (r=0.43; P<0.001), but not with SATI or VATI (P>0.05). Multivariable logistic analysis identified that both low CCR (OR=0.423, P=0.066 for ORR; OR=0.026, P=0.005 for DCR) and low SATI (OR=0.270, P=0.020 for ORR; OR=0.149, P=0.056 for DCR) were independently associated with worse objective response rate (ORR) and disease control rate (DCR). Patients with low CCR or low SATI had significantly lower 8-month progression-free survival (PFS) rate and 16-month overall survival (OS) rate than those with high CCR (PFS rate, 37.6% vs. 55.1%, P=0.011; OS rate, 19.4% vs. 44.9%, P=0.002) or those with high SATI (PFS rate, 37.2% vs. 53.8%, P=0.035; OS rate, 8.0% vs. 36.0%, P<0.001). Multivariate Cox analysis showed that low CCR (HR=2.395, 95% CI: 1.234-4.648, P=0.010 for PFS rate; HR=2.528, 95% CI: 1.317-4.854, P=0.005 for OS rate) and low SATI (HR=2.188, 95% CI: 1.050-4.560, P=0.037 for PFS rate; HR=2.818, 95% CI: 1.381-5.752, P=0.004 for OS rate) were both independent prognostic factors of poor 8-month PFS rate and 16-month OS rate. A nomogram based on CCR and BC-parameters showed a good performance in predicting the 12- and 16-month OS, with a concordance index of 0.756 (95% CI, 0.722-0.789). Conclusions: Low pre-treatment CCR and SATI were independently associated with lower response rates and worse survival in patients with metastatic GC receiving PD-1 inhibitors-based combination therapy.

Global research and emerging trends in autophagy in lung cancer: a bibliometric and visualized study from 2013 to 2022.

Purpose: To highlight the knowledge structure and evolutionary trends in research on autophagy in lung cancer. Methods: Research publications on autophagy in lung cancer were retrieved from the Web of Science Core Collection database. VOSviewer and CiteSpace data analysis software were used for the bibliometric and visualization analysis of countries, institutions, authors, journals, and keywords related to this field. Results: From 2013 to 2022, research on autophagy in lung cancer developed rapidly, showing rising trends in annual publications and citations. China (1,986 papers; 48,913 citations), Shandong University (77 publications; 1,460 citations), and Wei Zhang (20 publications; 342 citations) were the most productive and influential country, institution, and author, respectively. The journal with the most publications and citations on autophagy in lung cancer was the International Journal of Molecular Sciences (93 publications; 3,948 citations). An analysis of keyword co-occurrence showed that related research topics were divided into five clusters: 1) Mechanisms influencing autophagy in lung cancer and the role of autophagy in lung cancer; 2) Effect of autophagy on the biological behavior of lung cancer; 3) Regulatory mechanisms of 2 cell death processes: autophagy and apoptosis in lung cancer cells; 4) Role of autophagy in lung cancer treatment and drug resistance; and 5) Role of autophagy-related genes in the occurrence and development of lung cancer. Cell proliferation, migration, epithelial-mesenchymal transition, and tumor microenvironment were the latest high-frequency keywords that represented promising future research directions. Conclusion: This is the first comprehensive study describing the knowledge structure and emerging frontiers of research on autophagy in lung cancer from 2013 to 2022 by means of a bibliometric analysis. The study points to promising future research directions focusing on in-depth autophagy mechanisms, clinical applications, and potential therapeutic strategies, providing a valuable reference for researchers in the field. Systematic Review Registration: [https://systematicreview.gov/], identifier [registration number].

Glutathione S-transferase M1 null genotype meta-analysis on gastric cancer risk.

BACKGROUND: Glutathione S-transferases (GSTs) have proved to be involved in the detoxifying several carcinogens and may play an important role in carcinogenesis of cancer. Previous studies on the association between Glutathione S-transferase M1 (GSTM1) polymorphism and gastric cancer (GC) risk reported inconclusive results. To get a precise result, we conducted this present meta-analysis through pooling all eligible studies. METHODS: A comprehensive databases of Pubmed, Embase, Web of Science, and the Chinese Biomedical Database (CBM) were searched for case-control studies investigating the association between GSTM1 null genotype and GC risk. Odds ratios (OR) and 95% confidence intervals (95% CI) were used to assess this possible association. A χ2-based Q-test was used to examine the heterogeneity assumption. Begg's and Egger's test were used to examine the potential publication bias. The leave-one-out sensitivity analysis was conducted to determine whether our assumptions or decisions have a major effect on the results of present work. Statistical analyses were performed with the software program STATA 12.0. RESULTS: A total of 47 eligible case-control studies were identified, including 6,678 cases and 12,912 controls. Our analyses suggested that GSTM1 null genotype was significantly associated with increased risk of GC (OR=1.186, 95% CI=1.057-1.329, Pheterogenetiy=0.000, P=0.004). Significant association was also found in Asians (OR=1.269, 95% CI=1.106-1.455, Pheterogenetiy=0.002, P=0.001). However, GSTM1 null genotype was not contributed to GC risk in Caucasians (OR=1.115, 95% CI=0.937-1.326, Pheterogenetiy=0.000, P=0.222). In the subgroup analysis stratified by sources of controls, significant association was detected in hospital-based studies (OR=1.355, 95% CI=1.179-1.557, Pheterogenetiy=0.001, P=0.000), while there was no significant association detected in population-based studies (OR=1.017, 95% CI=0.862-1.200, Pheterogenetiy=0.000, P=0.840). CONCLUSION: This meta-analysis showed the evidence that GSTM1 null genotype contributed to the development of GC. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1644180505119533.