Search Results (219)

Background/Objectives: Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns. Methods: A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters. Results: We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p < 0.001), smoking intensity (p < 0.001), geographic distribution (p < 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer. Conclusions: This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine. Full article

Bone is a preferred site for disseminated tumor cells, yet the molecular mechanisms that prepare the skeletal microenvironment for metastatic colonization are only beginning to be understood. At the heart of this process are extracellular vesicles (EVs), nano-sized, lipid-encapsulated particles secreted by cancer cells and stromal components. This review consolidates current findings that position EVs as key architects of the bone-metastatic niche. We detail the biogenesis of EVs and their organotropic distribution, focusing on how integrin patterns and bone-specific ligands guide vesicle homing to mineralized tissues. We then outline the sequential establishment of the pre-metastatic niche, driven by EV-mediated processes including fibronectin deposition, stromal cell reprogramming, angiogenesis, neurogenesis, metabolic reconfiguration, and immune modulation, specifically, the expansion of myeloid-derived suppressor cells and impaired lymphocyte function. Within the bone microenvironment, tumor-derived EVs carrying microRNAs and proteins shift the balance toward osteoclastogenesis, inhibit osteoblast differentiation, and disrupt osteocyte signaling. These alterations promote osteolytic destruction or aberrant bone formation depending on tumor type. We also highlight cutting-edge imaging modalities and single-EV omics technologies that resolve EV heterogeneity and identify potential biomarkers detectable in plasma and urine. Finally, we explore therapeutic approaches targeting EVs, such as inhibition of nSMase2 or Rab27A, extracorporeal EV clearance, and delivery of engineered, bone-targeted vesicles, while addressing translational challenges and regulatory considerations. This review offers a roadmap for leveraging EV biology in predicting, preventing, and treating skeletal metastases by integrating advances across basic biology, bioengineering, and translational science. Full article

Canine transmissible venereal tumor (CTVT) is a contagious neoplasm with low metastatic potential, primarily affecting free-roaming and stray dogs. Despite its global presence, epidemiological data from some regions remain limited. This study employed a retrospective observational design and analyzed 131 CTVT cases diagnosed via cytology or histopathology at a veterinary teaching hospital in Belo Horizonte, Brazil, aiming to describe the anatomical distribution, treatment outcomes, and epidemiological patterns. Most affected dogs were mixed-breed (70.2%) and female (61.1%), with a median age of 4.5 years. Genital involvement was most common (87.0%), followed by cutaneous (10.7%), nasal (6.1%), and oral (4.6%) tumors. Concurrent tumor locations included genital-cutaneous (5.3%) and oronasal (3.1%). Females had more genital cases, while males were more likely to present cutaneous and nasal CTVT, with 5.2 times greater odds for nasal tumors (OR = 5.2; 95% CI = 1.2–25.9). Purebred dogs also had increased odds of nasal involvement (OR = 8.2; 95% CI = 1.9–40.7). Vincristine chemotherapy was effective, and the number of sessions required for a complete response was not associated with clinical presentation, breed or size. These findings highlight the varied presentations of CTVT and reinforce the need for clinical awareness of non-genital forms. Full article

Introduction: This study investigates the distribution and interaction of three polymorphisms—XRCC1 (rs1799782), CHEK2 (rs17879961), and XPD (rs238406)—in Romanian breast cancer patients, aiming to understand their association with histopathological subtypes, age, and BMI. Materials and Methods: This retrospective study analyzed 36 breast cancer patients from a Romanian clinic (2020–2024) with complete genetic data for XRCC1 (rs1799782), CHEK2 (rs17879961), and XPD (rs238406). The patients had invasive, non-metastatic breast cancer and no history of other cancers. Statistical analysis with Jamovi included descriptive stats, McNemar’s test for genotype associations, and multinomial logistic regression to explore links between variants, age, BMI, and tumor subtypes. Results: McNemar tests showed no significant association between XRCC1 and CHEK2 (p = 0.180), nor between XRCC1 and XPD (p = 0.03) or XPD and CHEK2 (p = 0.049) after applying the Bonferroni correction (α = 0.0167), indicating no statistically significant genetic dependency among these variants. A multinomial logistic regression model found that genetic variants, BMI, and age significantly predicted breast cancer subtypes, particularly CDI TNB. All predictors remained significant in the comparisons of CDI TNB vs. CDI LB/CDI LA. Notably, these associations remained unchanged even after applying the Bonferroni correction (α = 0.0021), confirming the robustness of the findings. Conclusions: This study identifies significant associations between XRCC1, CHEK2, and XPD gene variants and CDI TNB, suggesting a role of DNA repair deficiencies in its pathogenesis. Protective genotypes were under-represented in TNB cases. Limited links with luminal subtypes highlight TNB’s distinct genetic profile. Results support further research on these polymorphisms as markers for TNB risk and precision treatment. Full article

Objective: Early diagnosis and treatment of metastatic pericardial disease are crucial to prevent the life-threatening complication of cardiac tamponade. Thin Layer Cytology (TLC), a widely adopted technique in cytology, has gained significant acceptance for most specimens. Our study aimed to assess the utility of TLC in diagnosing metastatic neoplasms and their origins in pericardial effusions, as well as monitoring response to chemotherapy. Methods: We examined 184 pericardial fluids collected by pericardiocentesis and processed using the ThinPrep liquid-based technique. Various immunocytochemical markers were used to determine the site of metastatic neoplasms. We also evaluated the response to therapy in 53 patients with lung and breast cancer. Results: Out of 184 specimens, 113 pericardial fluids were diagnosed as positive for malignancy, while 71 were negative. Twenty-three cases of unknown primary site were included in the total positive cases. Ninety cases positive for malignancy had a known primary site of origin, including 31 lung carcinomas, 22 breast carcinomas, 10 ovarian carcinomas, 6 T-cell lymphomas, 3 urinary bladder carcinomas, 4 renal carcinomas, 5 adenocarcinomas of the colon, 5 prostate carcinomas, 2 parotid adenocarcinomas, and 2 melanomas. Regarding the 53 cases with chemotherapy treatment, the cytologic examination of pericardial fluid showed a remarkable reduction in neoplastic burden after the third dose of cisplatin or thiotepa instilled into the pericardial cavity. ThinPrep provided excellent preservation of cytomorphological features, high cellularity per slide, and a clear background. This comprehensive analysis provides crucial information about the types and distribution of cancerous cells present in the samples. Conclusions: Thin Layer Cytology (TLC) is a valuable diagnostic tool for detecting metastatic pericardial malignancy. It allows the examination of exfoliated cells from the pericardial fluid, providing crucial information for diagnosis, management, and monitoring the acute responsiveness to intrapericardial chemotherapy. Immunocytochemistry (IHC) can identify specific markers for various types of cancer, enabling a more accurate diagnosis and guiding further treatment decisions. Full article

Background/Objectives: Opinions concerning the role of obesity and changes in muscle mass in individuals with malignancies vary. It is believed that decreased fat tissue leads to a higher complication rate, while decreased muscle mass results in a poorer oncologic outcome. This study aimed to evaluate the impact of fat distribution and skeletal muscle mass on postoperative morbidity and long-term oncological outcomes in patients with non-metastatic colon cancer. Methods: Between 2012 and 2018, a total of 129 patients with stage I-III colon cancer were evaluated. Abdominal CT scans were used to assess muscle mass and fat tissue (subcutaneous and visceral). Differences in postoperative morbidity and long-term oncologic outcome were analyzed and compared. Results: No significant differences occurred concerning complication rate or anastomotic leakage. Individuals with altered body composition parameters had a shorter length of hospital stay (p = 0.046) and an increased duration of surgery (p = 0.029). In patients with an ASA III score, altered fat tissue distribution was associated with improvements in both overall and disease-free survival (p = 0.031 and p = 0.027, respectively) but also resulted in longer hospital stay. Conclusions: Changes in body composition parameters lead to alterations in economic factors as well as changes in oncologic survival, especially in patients with higher ASA scores. No differences in morbidity were observed. Full article

This study retrospectively investigated the impact of stereotactic radiosurgery (SRS) normal tissue objective (NTO) and multileaf collimator (MLC) width on radiation dose distribution in patients with brain metastasis treated using HyperArc. In total, 21 patients who underwent SRS using the HyperArc of the TrueBeam linear accelerator from November 2022 to June 2024 were included. All patients received radiotherapy with HA_SH planned with SRS NTO and HD MLC. HyperArc(HA_AH) combined with the auto NTO and HD MLC and HyperArc(HA_AM) with auto NTO and millennium MLC were generated and compared. Monitor units (MU), conformity index (CI), radical dose homogeneity index (rDHI), moderate DHI (mDHI), and gradient index (GI) were evaluated as target factors, and V₂(Gy), V₁₀(Gy), V₁₂(Gy), V₁₈(Gy), V₁₀(cc), and V₁₂(cc) were evaluated as normal brain factors. Dosimetric comparisons were performed between HA_SH, HA_AH, and HA_AM and between target and normal brain tissues. Between HA_SH and HA_AH, average MU was 7206 and 5798, respectively; the difference was significant (p < 0.001). The MU of HA_AM was 5835. Among HA_SH, HA_AH, and HA_AM, CI and mDHI were not significantly different, but there were significant differences in rDHI, GI, and normal brain tissues. When treating a single lesion using HyperArc, SRS NTO influences MU and GI, and the MLC width influences rDHI and GI. In HyperArc for single metastatic brain lesions, SRS NTO and MLC width have a significant effect on the radiation dose delivered to the target and normal brain tissues. Full article

Background: Chondrosarcoma is the second most common bone tumor in adults with an average incidence of 0.1–0.3 individuals per 100,000 per year. These tumors are often resistant to chemotherapy and radiation, and surgical excision is a mainstay of current treatment. However, survival in the setting of metastatic disease is still poor, and research is needed to identify prognostic biomarkers and potential therapeutic targets. Several studies have examined the role of IDH mutations in chondrosarcoma, but the results vary widely. The goal of this analysis was to aggregate individual patient data from these studies and conduct a high-powered analysis of the impact of IDH mutations on survival outcomes in chondrosarcoma. Methods: Chondrosarcoma studies that included data on the IDH mutation status of tumors were queried, and the individual datasets reporting patient and tumor variables were extracted. The data from these studies were added to the internal dataset from the authors’ home institution. Two-sample tests for equality of proportions were used to assess the distribution of sample characteristics between groups. Univariate Kaplan–Meier (KM) curves and multivariate Cox Proportional Hazards (CPH) models were used to assess the relationship between tumor IDH mutations and five and ten-year patient overall survival (OS). Results: The final cohort included 1152 patients sourced from 21 studies and the authors’ internal dataset. IDH mutations were more common in higher grade tumors and were more likely to be found in individuals over 60 years old. Patients with IDH mutant tumors had shorter five-year OS in univariate KM analysis when analyzing all chondrosarcomas combined. However, multivariate CPH models accounting for age and tumor grade, found that the effect of IDH mutation was isolated to patients with dedifferentiated tumors only. Patients with IDH mutant dedifferentiated tumors displayed significantly shorter five-year OS (HR: 1.99, p = 0.02) relative to patients with IDH wild-type (WT) dedifferentiated tumors. The primary predictor of five-year OS in the conventional chondrosarcoma cohort was tumor grade, regardless of IDH mutation status (HR: 2.72, p < 0.005). Discussion: IDH mutations are relatively common in cartilaginous neoplasms (including benign tumors), with the literature reporting rates as high as 50% in chondrosarcomas. Prior studies have investigated the link between IDH1/2 mutation status, tumor grade and overall survival, with mixed results on the effect of IDH mutation on survival. Vuong et al. performed a meta-analysis in 2021 and found that IDH mutation was associated with older patient age, larger tumor size, higher tumor grade, and increased risk of death compared to WT tumors. Our analysis, which builds on the Vuong et al. study, indicates that IDH status itself is not independently predictive of overall survival in conventional chondrosarcoma, however, it does correlate with survival in dedifferentiated tumors. Further analysis is needed to investigate the potential correlation of IDH mutations in higher grade tumors and patients of older age. Full article

Background and Objectives: KRAS genes are among the most prominent oncogenes that trigger tumor formation in colorectal cancer (CRC) and serve as predictive biomarkers for resistance to anti-EGFR therapies in metastatic colorectal cancer (mCRC) patients. However, the prevalence and mutation spectrum of the KRAS gene family in mCRC patients in Turkey have not been sufficiently analyzed. This study investigates the frequency and distribution of mutations in the KRAS gene family across different regions of Turkey and examines gender-related variations. Materials and Methods: This multicenter observational study included 2458 histologically confirmed mCRC patients collected from 52 centers across Turkey. In a central laboratory, KRAS mutations in codons 12 and 13 were analyzed using polymerase chain reaction (PCR). Statistical analyses were performed using chi-square tests and Monte Carlo simulations, with a significance threshold set at p < 0.05. Results: Depending on the region, KRAS mutations were detected in 45% of patients, ranging from 39.6% to 47.5%. The mutation rate was significantly higher in female patients (48.8%) compared to male patients (42.6%) (p = 0.002). Codon 12 mutations were more frequent than codon 13 mutations. G12D, G12V, and G13D mutations accounted for 80% of all detected mutations. The G12V mutation was prevalent in female patients (p = 0.007). Based on region, mutation diversity was similar, and no statistically significant difference was found (p > 0.05). Conclusions: This large-scale, multicenter study provides the most comprehensive dataset of KRAS mutations in mCRC patients in Turkey. This study revealed regional trends, as well as gender differences. The findings highlight the importance of routine KRAS genotyping in guiding personalized treatment strategies, especially regarding candidate selection for anti-EGFR therapies. Further research is required to elucidate the prognostic and therapeutic implications of specific KRAS mutations. Full article

Background/Objectives: Bone metastasis is a frequent and life-threatening event in advanced cancers, affecting up to 70–85% of prostate cancer patients. Understanding the cellular and molecular mechanisms underlying bone metastasis is essential for developing targeted therapies. This study aimed to systematically characterize the heterogeneity and microenvironmental adaptation of prostate cancer bone metastases using single-cell transcriptomics. Methods: We integrated the largest single-cell transcriptome dataset to date, encompassing 124 samples from primary prostate tumors, various bone metastatic sites, and non-malignant tissues (e.g., benign prostatic hyperplasia, normal bone marrow). After quality control, 602,497 high-quality single-cell transcriptomes were analyzed. We employed unsupervised clustering, gene expression profiling, mutation analysis, and metabolic pathway reconstruction to characterize cancer cell subtypes and tumor microenvironmental remodeling. Results: Cancer epithelial cells dominated the tumor microenvironment but exhibited pronounced heterogeneity, posing challenges for conventional clustering methods. By integrating genetic and metabolic features, we revealed key evolutionary trajectories of epithelial cancer cells during metastasis. Notably, we identified a novel epithelial subpopulation, NEndoCs, characterized by unique differentiation patterns and distinct spatial distribution across metastatic niches. We also observed significant metabolic reprogramming and recurrent mutations linked to prostate-to-bone microenvironmental transitions. Conclusions: This study comprehensively elucidates the mutation patterns, metabolic reprogramming, and microenvironment adaptation mechanisms of bone metastasis in prostate cancer, providing key molecular targets and clinical strategies for the precise treatment of bone metastatic prostate cancer. Full article

Background/Objectives: Worldwide, colon cancer is a major cause of cancer-related mortality, with an increasing incidence influenced by genetic, environmental, and lifestyle factors. Despite advances in diagnosis and personalized treatments, challenges remain in improving patient prognosis, particularly in metastatic colorectal cancer (mCRC). Bevacizumab (BEV), a monoclonal antibody, is widely used in colorectal cancer treatment. This study aimed to analyze adverse events associated with BEV compared with other therapies based on data from the EudraVigilance (EV) database. Methods: A descriptive and disproportionality analysis was conducted on signals reported in the EV database related to BEV. The study included comparisons with other antineoplastic treatments, such as chemotherapy, targeted therapy, and immunotherapy. Patient demographics, severity of adverse drug reactions (ADRs), and distribution patterns were analyzed to assess the safety profile of BEV in colorectal cancer treatment. Results: The majority of the signals for BEV were from patients aged 18–64 years (39.42%) and 65–85 years (34.08%). Hypertension, thromboembolism, proteinuria, and gastrointestinal disorders have been the most frequently reported. Serious ADRs, including gastrointestinal perforations, hemorrhage, and arterial thromboembolism, were observed in 93.74% of Individual Case Safety Reports. BEV was associated with a higher likelihood of vascular and endocrine disorders compared with chemotherapy and other targeted therapies. Immunotherapy was linked to increased immunological ADRs, while BEV demonstrated fewer immune-related toxicities. Conclusions: Continuous monitoring is necessary to optimize patient management, particularly in elderly patients or those with cardiovascular comorbidities. Understanding BEV’s safety profile allows for better personalization of treatment strategies, minimizing risks while enhancing therapeutic outcomes. Full article

The rapid growth of diverse $\text{-}\mathrm{omics}$ datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene analysis and pathway enrichment for BRCA patients. The analysis compared 302 tumor samples to 113 normal samples across 60,660 genes. The highly upregulated gene COL10A1, promoting breast cancer progression and poor prognosis, and the consistently downregulated gene CDG300LG, linked to brain metastatic cancer, were identified. Pathway enrichment analysis revealed similarities in cellular matrix organization pathways across subtypes, with notable differences in functions like cell proliferation regulation and endocytosis by host cells. GO Semantic Similarity analysis quantified gene relationships in each subtype, identifying potential biomarkers like MATN2, similar to COL10A1. These insights suggest deeper relationships within clusters and highlight personalized treatment potential based on subtypes. Full article

Background: Intensity modulated radiotherapy with helical tomotherapy (IMRT-HT) is used in the breast cancer (BC) treatment for years now to obtain homogeneous dose distribution in the treated volumes and reduce the doses to organs at risk. The purpose of this study was to evaluate our experience in terms of local control, overall survival, progression free survival and adverse events in BC patients treated with IMRT-HT with long term follow-up. Methods: This study is a retrospective data analysis of patients irradiated with IMRT-HT. Overall survival (OS) and progression free survival (PFS) curves were plotted with Kaplan-Meier method. We also analyzed the OS and PFS data by molecular subgroups of the population. Long-term toxicities including skin, cardiac and pulmonary complications were also evaluated. Multivariant logistic regression analysis was performed to determine the independent predictors of the side effects. Results: Between 2009 and 2015, a total of 194 breasts in 179 women with nonmetastatic breast cancer were treated. Most of the tumors were grade III and N+. With a median follow-up of 10 years, we observed 9 local recurrences, 2 loco-regional recurrences, and 29 patients experienced metastatic disease. Only 18 patients are dear, of them 7 cases with breast cancer death. At 10 years, the Local recurrence free survival was 95.3% [95%CI: 92.1–98.5], the loco-regional relapse free survival was 94.5% [91.1–98.1]. The metastases free survival was 82.9% [76.9–89.3]. The progression free survival was 79.9 [73.6–86.7]. The cancer specific survival was 94.3%, and the overall survival 88% [82.8–93.5]. At long term, there were no cardiac, lung, thyroid, digestive radio induced toxicities. A small number of patients experienced grade I or II fibrosis. Conclusions: IMRT-HT could be safely used for adjuvant breast cancer irradiation in patients with complex anatomy. IMRT-HT provides favourable long-term prognosis, while late toxicity is acceptable. Full article

Background/Objectives: Colorectal cancer (CRC) holds the third and second position among cancers affecting men and women, respectively. Frequently, the first-line treatment for metastatic CRC consists of the intravenous administration of 5-fluorouracil and leucovorin in combination with oxaliplatin or irinotecan. Physiologically-based pharmacokinetic models (PBPK) aim to mechanistically incorporate body physiology and drug physicochemical attributes, enabling the description of both systemic and organ drug exposure based on the treatment specificities. This bottom-up approach represents an opportunity to personalize treatment and minimize the therapeutic risk/benefit ratio through the understanding of drug distribution within colorectal tissue. This project has the goal of characterizing the systemic and tissue exposure of four anti-cancer drugs in humans using a PBPK platform fed with data from the literature. Methods: A literature search was performed to collect clinical data on systemic concentration versus time profiles. Physicochemical features were obtained from the literature, as well as parameters associated with distribution, metabolism, and excretion. The PBPK models were built using PK-Sim^®. Results: The data from 51 clinical studies were extracted and combined in one single dataset. The PBPK models successfully described the exposure vs. time profiles with respect to both, with both the typical tendency and dispersion shown by the data. The percentage of observations falling within the two-fold error bounds ranged between 94 and 100%. The colon/plasma AUC_inf ratios were similar for 5-FU, oxaliplatin, and leucovorin, but it was significantly higher for irinotecan. Conclusions: The PBPK models support tailored treatment approaches by linking in vitro studies to organ exposure. These models serve as the initial step towards incorporating a dedicated tumor compartment, which will further account for the variability in tumor microenvironment characteristics to improve therapeutic strategies. Full article

Background and Objectives: This study aimed to assess the diagnostic accuracy and prognostic significance of staging laparoscopy (SL) compared to computed tomography (CT) and positron emission tomography–computed tomography (PET-CT) in gastric cancer staging. We evaluated the ability of SL to detect occult peritoneal metastases and influence of SL on survival outcomes across cancer stages and treatment approaches. Materials and Methods: In this retrospective cohort study, 95 patients with gastric cancer underwent preoperative assessment using CT, PET-CT, and SL between 2018 and 2024. Diagnostic performance metrics were calculated for SL, CT, and PET/CT across the local, locally advanced, and metastatic stages. Survival outcomes were analyzed using Kaplan–Meier curves, and comparisons were made using log-rank tests. A multivariable Cox proportional hazards model incorporating interaction terms was used to determine the independent prognostic factors affecting survival, focusing on SL findings and cytology results. Results: The cohort comprised 75 males (78.9%) and 20 females (21.1%), with a mean age of 57.4 ± 10.1 years. The tumor location distribution was predominant in the corpus (31.1%) and cardia. Tumor staging revealed that 48.1% were classified as T3 and 28.8% as T4, respectively. Diagnostic accuracy analysis showed that SL outperformed CT and PET-CT in detecting peritoneal metastasis across all stages. Specifically, SL demonstrated a sensitivity and specificity of 85% and 95% for local disease, 92% and 80% for locally advanced disease, and 95% and 99% for metastatic disease, significantly exceeding those of CT and PET-CT. Patients with SL findings had a median overall survival (OS) of 30 months compared with 20 months for those assessed only with CT and PET-CT (p < 0.05). The stage-specific median OS for SL patients was 40 months in the local, 25 months in the locally advanced (p < 0.05), and 15 months in the metastatic disease (p < 0.01) groups, indicating significant survival benefits. Multivariable Cox regression identified SL findings as an independent factor associated with reduced mortality risk (HR: 0.70, 95% CI: 0.50–0.90, p < 0.01), while positive cytology findings predicted poorer survival (HR: 1.80, 95% CI: 1.25–2.60, p < 0.01). Interaction terms revealed that SL yielded greater survival benefits in patients with metastatic disease (hazard ratio [HR]: 0.60, p < 0.01) and those undergoing systemic therapy (HR: 0.75, p = 0.04). Conclusions: SL provides superior diagnostic accuracy and prognostic information for advanced gastric cancer staging compared with CT and PET-CT. By accurately detecting peritoneal metastasis, SL aids in optimizing treatment plans, particularly in advanced stages, thus potentially improving patient outcomes. Full article