Search Results (2,214)

Change point models are widely used in medical and epidemiological studies to capture the threshold effects of continuous covariates on health outcomes. These threshold effects represent critical points at which the relationship between biomarkers or risk factors and disease risk shifts, often reflecting underlying biological mechanisms or clinically relevant intervention points. While most existing methods focus on right-censored data, interval censoring is common in large-scale clinical trials and follow-up studies, where the exact event times are not observed but are known to fall within time intervals. In this paper, we propose a semiparametric transformation model with an unknown change point for interval-censored data. The model allows flexible transformation functions, including the proportional hazards and proportional odds models, and it accommodates both main effects and their interactions with the threshold variable. Model parameters are estimated via the EM algorithm, with the change point identified through a profile likelihood approach using grid search. We establish the asymptotic properties of the proposed estimators and evaluate their finite-sample performance through extensive simulations, showing good accuracy and coverage properties. The method is further illustrated through an application to the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial data. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

The objective of the present study was to evaluate the association between liver cirrhosis (LC) and subsequent Heart failure (HF). This retrospective cohort study utilized data from the Disease Analyzer database (IQVIA) and included adults with a first-time diagnosis of LC in 1293 general practices in Germany between January 2005 and December 2023. A comparison cohort without liver diseases was matched to the cirrhosis group using 5:1 propensity score matching. Univariable Cox proportional hazards models were used to assess the association between alcoholic vs. non-alcoholic LC and HF. The final study cohort included 5530 patients with alcoholic LC and 27,650 matched patients without liver disease, as well as 7063 patients with non-alcoholic LC and 35,315 matched patients without liver disease. After up to 10 years of follow-up, HF was diagnosed in 20.9% of patients with alcoholic LC compared to 10.3% of matched cohort, and in 23.0% of patients with non-alcoholic LC, compared to 14.2% in matched cohort. Alcoholic LC (Hazard Ratio (HR): 2.07 (95% CI: 1.85–2.31) and non-alcoholic LC (HR: 1.70; 95% CI: 1.56–1.82) were associated with an increased risk of HF. The association was also stronger in men than in women. LC, both alcoholic and non-alcoholic, is significantly associated with an increased long-term risk of HF. The association is particularly pronounced in patients with alcoholic cirrhosis and in men. To the best of the authors’ knowledge, this is the first real-world evidence for the positive association between LC and subsequent HF from Europe. Full article

Background: The development of brain metastases (BM) is a relatively uncommon but significantly adverse event in the spread of colorectal cancer (CRC). Although management of CRC BM has improved with advances in imaging and systemic therapies, clinical outcomes remain poor. Methods: This retrospective cohort study used the U.S. National Cancer Database to evaluate survival outcomes, treatment patterns, and prognostic factors in CRC patients diagnosed with BM between 2010 and 2020. Patients with isolated brain-only metastases formed the primary analytic cohort, while those with additional extracranial metastases were included for descriptive comparison. Multivariable Cox proportional hazards and logistic regression models were used to assess factors associated with of survival. Proportional hazards assumptions were tested using Schoenfeld residuals. Accelerated failure time models were also employed. Results: From a cohort of 1,040,877 individuals with CRC, 795 had metastatic disease present along with relevant data, of which 296 had isolated BM. Median overall survival (mOS) in BM-only metastatic disease group was 7.82 months (95% CI: 5.82–9.66). The longest survival was observed among patients treated with stereotactic radiosurgery combined with systemic therapy (SRS+Sys), with a median OS of 23.26 months (95% CI: 17.51–41.95) and a 3-year survival rate of 35.8%. In adjusted Cox models, SRS, systemic therapy, and definitive surgery of the primary site were each independently associated with reduced hazard of death. Rectal cancer patients had longer survival than those with colon primaries (mOS: 10.35 vs. 6.08 months). Age, comorbidity burden, and insurance status were not associated with survival in adjusted analyses. Conclusions: SRS+Sys was associated with longer survival compared to other treatment strategies. However, treatment selection is highly dependent on individual clinical factors such as performance status, comorbidities, and disease extent; therefore, these findings must be interpreted with caution Future prospective studies incorporating molecular and biomarker data are warranted to better guide care in this rare and high-risk group. Full article

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims to examine the relationship of ACEs with incident osteoporosis and investigate interactions with polygenic risk score (PRS). Materials and Methods: This study analyzed 124,789 UK Biobank participants initially free of osteoporosis. Cumulative ACE burden (emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse) was ascertained through validated questionnaires. Multivariable-adjusted Cox proportional hazards models assessed osteoporosis risk during a median follow-up of 12.8 years. Moderation analysis examined genetic susceptibility interactions using a standardized PRS incorporating osteoporosis-related SNPs. Results: Among 2474 incident osteoporosis cases, cumulative ACEs showed dose–response associations with osteoporosis risk (adjusted hazard ratio [HR]_{per one-unit increase} = 1.07, 95% confidence interval [CI] 1.04–1.11; high ACEs [≥3 types] vs. none: HR = 1.26, 1.10–1.43). Specifically, emotional neglect (HR = 1.14, 1.04–1.25), emotional abuse (HR = 1.14, 1.03–1.27), physical abuse (HR = 1.17, 1.05–1.30), and sexual abuse (HR = 1.15, 1.01–1.31) demonstrated comparable effect sizes. Sex-stratified analysis revealed stronger associations in women. Joint exposure to high ACEs/high PRS tripled osteoporosis risk (HR = 3.04, 2.46–3.76 vs. low ACEs/low PRS) although G × E interaction was nonsignificant (P-interaction = 0.10). Conclusions: These results suggest that ACEs conferred incremental osteoporosis risk independent of genetic predisposition. These findings support the inclusion of ACE screening in osteoporosis prevention strategies and highlight the need for targeted bone health interventions for youth exposed to ACEs. Full article

Background and Objectives: The Naples prognostic score (NPS), which incorporates inflammatory and nutritional indicators, is increasingly used as a prognostic score for various malignancies. Nonetheless, few studies have specifically evaluated the NPS as a prognostic factor for cervical cancer. This study aimed to assess the value of NPS at diagnosis as a predictor of cancer progression. Materials and Methods: This study included patients diagnosed with cervical cancer at Korea University Anam Hospital from January 2019 to December 2023. Patients with incomplete data or those who were lost to follow-up were excluded. The NPS was calculated based on laboratory results at the time of diagnosis, categorizing patients into the low-NPS group (NPS 0–1) and high-NPS group (NPS ≥ 2). Survival analysis was performed using the Kaplan–Meier method and log-rank test. Univariate and multivariate Cox proportional hazards models were used to identify independent prognostic factors. Results: Out of 178 patients, 98 and 80 were categorized into the low-NPS and high-NPS groups, respectively. Kaplan–Meier survival analysis showed that the high-NPS group had significantly lower disease-free survival (DFS) (p < 0.001) and overall survival (OS) (p = 0.02) rates than the low-NPS group. Multivariate Cox regression analysis identified the NPS as an independent prognostic factor for DFS (adjusted hazard ratio, 1.98; p = 0.017), but not for OS. Conclusions: This study demonstrated that the NPS measured at diagnosis may serve as a useful independent prognostic factor for cancer progression in patients with cervical cancer. Full article

Background and Objectives: Patients with hip fractures have a high mortality rate, highlighting the need for a reliable prognostic tool. Although the prognostic nutritional index (PNI) is a well-established predictor in patients with cancer, its utility has not been thoroughly investigated in patients with hip fractures. Therefore, this study aims to evaluate the association between PNI and mortality in patients undergoing hip fracture surgery. Materials and Methods: A retrospective review was conducted on all patients aged ≥65 years who underwent surgery for hip fracture between January 2014 and February 2018. Quartile stratification was chosen because no universally accepted clinical cut-off exists for PNI; this approach enables comparison of equally sized groups and exploration of potential non-linear risk patterns. The primary endpoints were 1-year and overall mortality in older adults undergoing hip fracture surgery. Multivariable Cox proportional-hazards models adjusted for age, sex, ASA class and comorbidities. Results: A total of 815 patients were analyzed. One-year and overall mortality rates were highest in the Q1 group (26.6%, 14.2%, 6.9%, 6.4% [p < 0.001] and 56.7%, 36.3%, 27.0%, 15.2% [p < 0.001], respectively). In Cox regression analysis, a lower preoperative PNI was significantly associated with an increased risk of overall mortality (Q1: HR 3.25, 95% confidence interval [CI] 2.11–5.01, p < 0.001; Q2: HR 1.85, 95% CI 1.19–2.86, p = 0.006; Q3: HR 1.52, 95% CI 0.97–2.38, p = 0.065; Q4 as reference), indicating a stepwise, dose–response increase in mortality risk as PNI decreases. Conclusions: The findings demonstrate that a lower preoperative PNI is significantly associated with higher 1-year and overall mortality in older adults undergoing hip fracture surgery. Although further prospective validation is needed, preoperative PNI may help predict mortality in frail patients undergoing hip fracture surgery and identify those who could benefit from nutritional assessment and optimization before surgery. Full article

Background/Objectives: Artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis may assist in objective and reproducible risk stratification. However, the prognostic utility of serial ECGs, particularly the follow-up ECG prior to discharge, has not been extensively studied. This study aimed to evaluate whether dynamic changes in AI-predicted ECG risk scores could enhance prediction of post-discharge outcomes. Methods: This retrospective cohort study included 11,508 ED visits from a single medical center where patients underwent two ECGs and were directly discharged. We stratified the mortality risk of patients as low risk, medium risk, and high risk based on the first and follow-up ECG prior to discharge using AI-enabled ECG models. The Area Under the Curve (AUC) was calculated for the predictive performance of the two ECGs. Kaplan–Meier (KM) curves were used for 90-day mortality analysis, and the Cox proportional hazards model was utilized to compare the risk of death across categories. Results: The AI-enabled ECG risk prediction model, based on the initial and follow-up ECGs prior to discharge, indicated risk transitions among different groups. The AUC for mortality risk was 78.6% for the first ECG and 83.3% for the follow-up ECG. KM curves revealed a significant increase in 90-day mortality for patients transitioning from low to medium/high risk upon discharge (Hazard Ratio: 6.01; Confidence Interval: 1.70–21.27). Conclusions: AI-enabled ECGs obtained prior to discharge provide superior mortality risk stratification for ED patients compared to initial ECGs. Patients classified as medium- or high-risk at discharge require careful consideration, whereas those at low risk can generally be discharged safely. Although AI-ECG alone does not replace comprehensive risk assessment, it offers a practical tool to support clinical judgment, particularly in the dynamic ED environment, by aiding safer discharge decisions. Full article

Background and Objectives: General-purpose multimodal large language models (LLMs) are increasingly used for medical image interpretation despite lacking clinical validation. This study evaluates the diagnostic reliability of ChatGPT-4o and Claude 2 in photographic assessment of adolescent idiopathic scoliosis (AIS) against radiological standards. This study examines two critical questions: whether families can derive reliable preliminary assessments from LLMs through analysis of clinical photographs and whether LLMs exhibit cognitive fidelity in their visuospatial reasoning capabilities for AIS assessment. Materials and Methods: A prospective diagnostic accuracy study (STARD-compliant) analyzed 97 adolescents (74 with AIS and 23 with postural asymmetry). Standardized clinical photographs (nine views/patient) were assessed by two LLMs and two orthopedic residents against reference radiological measurements. Primary outcomes included diagnostic accuracy (sensitivity/specificity), Cobb angle concordance (Lin’s CCC), inter-rater reliability (Cohen’s κ), and measurement agreement (Bland–Altman LoA). Results: The LLMs exhibited hazardous diagnostic inaccuracy: ChatGPT misclassified all non-AIS cases (specificity 0% [95% CI: 0.0–14.8]), while Claude 2 generated 78.3% false positives. Systematic measurement errors exceeded clinical tolerance: ChatGPT overestimated thoracic curves by +10.74° (LoA: −21.45° to +42.92°), exceeding tolerance by >800%. Both LLMs showed inverse biomechanical concordance in thoracolumbar curves (CCC ≤ −0.106). Inter-rater reliability fell below random chance (ChatGPT κ = −0.039). Universal proportional bias (slopes ≈ −1.0) caused severe curve underestimation (e.g., 10–15° error for 50° deformities). Human evaluators demonstrated superior bias control (0.3–2.8° vs. 2.6–10.7°) but suboptimal specificity (21.7–26.1%) and hazardous lumbar concordance (CCC: −0.123). Conclusions: General-purpose LLMs demonstrate clinically unacceptable inaccuracy in photographic AIS assessment, contraindicating clinical deployment. Catastrophic false positives, systematic measurement errors exceeding tolerance by 480–1074%, and inverse diagnostic concordance necessitate urgent regulatory safeguards under frameworks like the EU AI Act. Neither LLMs nor photographic human assessment achieve reliability thresholds for standalone screening, mandating domain-specific algorithm development and integration of 3D modalities. Full article

Background/Objectives: The COVID-19 pandemic disrupted global cancer care. This study compared gastric cancer surgical outcomes before and during the pandemic in Turkey. We also aimed to analyze the impact of the pandemic and factors on survival and mortality in gastric cancer patients. Materials and Methods: This retrospective, multicenter cohort study included 324 patients from three tertiary centers in Turkey who underwent gastric cancer surgery between January 2018 and December 2022. Patients were stratified into Pre-COVID-19 (n = 150) and COVID-19 Era (n = 174) groups. Comprehensive demographic, surgical, pathological, and survival data were analyzed. To identify factors independently associated with postoperative mortality, a multivariable logistic regression model was applied. For evaluating predictors of long-term survival, multivariable Cox proportional hazards regression analysis was conducted. Results: The median time from diagnosis to surgery was comparable between groups, while the time from surgery to pathology report was significantly prolonged during the pandemic (p = 0.012). Laparoscopic surgery (p = 0.040) and near-total gastrectomy (p = 0.025) were more frequently performed in the Pre-COVID-19 group. Although survival rates between groups were similar (p = 0.964), follow-up duration was significantly shorter in the COVID-19 Era (p < 0.001). Comparison between survivor and non-survivor groups showed that several variables were significantly associated with mortality, including larger tumor size (p < 0.001), greater number of metastatic lymph nodes (p < 0.001), elevated preoperative CEA (p = 0.001), CA 19-9 (p < 0.001), poor tumor differentiation (p = 0.002), signet ring cell histology (p = 0.003), lymphovascular invasion (p < 0.001), and perineural invasion (p < 0.001). Multivariable logistic regression identified total gastrectomy (OR: 2.14), T4 tumor stage (OR: 2.93), N3 nodal status (OR: 2.87), and lymphovascular invasion (OR: 2.87) as independent predictors of postoperative mortality. Cox regression analysis revealed that combined tumor location (HR: 1.73), total gastrectomy (HR: 1.56), lymphovascular invasion (HR: 2.63), T4 tumor stage (HR: 1.93), N3 nodal status (HR: 1.71), and distant metastasis (HR: 1.74) were independently associated with decreased overall survival. Conclusions: Although gastric cancer surgery continued during the COVID-19 pandemic, some delays in pathology reporting were observed; however, these did not significantly affect the timing of adjuvant therapy or patient outcomes. Importantly, pandemic timing was not identified as an independent risk factor for mortality in multivariable logistic regression analysis, nor for survival in multivariable Cox regression analysis. Instead, tumor burden and aggressiveness—specifically advanced stage, lymphovascular invasion, and total gastrectomy—remained the primary independent determinants of poor prognosis. While pandemic-related workflow delays occurred, institutional adaptability preserved oncologic outcomes. Full article

Background/Objectives: Patients with stage IV gastric cancer who develop chronic intestinal failure require home parenteral nutrition (HPN). This study aimed to evaluate the prognostic relevance of nutritional and immune–inflammatory biomarkers and to construct an individualised survival prediction model using machine learning techniques. Methods: A secondary analysis was performed on a cohort of 410 patients with TNM stage IV gastric adenocarcinoma who initiated HPN between 2015 and 2023. Nutritional and inflammatory indices, including the Controlling Nutritional Status (CONUT) score and lymphocyte-to-monocyte ratio (LMR), were assessed. Independent prognostic factors were identified using Cox proportional hazards models. A Random Survival Forest (RSF) model was constructed to estimate survival probabilities and quantify variable importance. Results: Both the CONUT score and LMR were independently associated with overall survival. In multivariate analysis, higher CONUT scores were linked to increased mortality risk (HR = 1.656, 95% CI: 1.306–2.101, p < 0.001), whereas higher LMR values were protective (HR = 0.632, 95% CI: 0.514–0.777, p < 0.001). The RSF model demonstrated strong predictive accuracy (C-index: 0.985–0.986) and effectively stratified patients by survival risk. The CONUT score exerted the greatest prognostic influence, with the LMR providing additional discriminatory value. A gradual decline in survival probability was observed with an increasing CONUT score and a decreasing LMR. Conclusions: The application of machine learning to immune–nutritional data offers a robust tool for predicting survival in patients with advanced gastric cancer requiring HPN. This approach may enhance risk stratification, support individualised clinical decision-making regarding nutritional interventions, and inform treatment intensity adjustment. Full article

Background/Objectives: Biliary tract cancers (BTCs) are aggressive malignancies with a poor prognosis. Surgery remains the only curative option, yet recurrence rates are high, and the role of adjuvant chemotherapy remains debated. This study aims to evaluate the impact of adjuvant chemotherapy and prognostic factors on survival outcomes in resected BTCs. Methods: We conducted a retrospective multicenter study analyzing patients diagnosed with intrahepatic (iCCA) and extrahepatic cholangiocarcinoma (eCCA) or gallbladder cancer (GBC) who underwent curative-intent surgical resection between 1999 and 2023. Demographic, clinicopathological, and treatment data were collected from institutional databases. Survival outcomes were assessed using Kaplan–Meier analysis, and prognostic factors were identified through Cox proportional hazards regression. Results: A total of 155 patients were included, with a median follow-up of 84.6 months. The cohort comprised 38.7% iCCA, 31.6% eCCA, and 29.7% GBC. R0 resection was achieved in 77.4% of cases, while lymph node involvement was present in 39.4%. Median overall survival (OS) significantly varied by stage (p < 0.001), ranging from >60 months for stage I to ~12 months for stage IVA. Eastern Cooperative Oncology Group (ECOG) performance status (PS) emerged as the strongest independent prognostic factor for OS (p < 0.001). Adjuvant chemotherapy, administered to 49.0% of patients, did not significantly improve OS in the overall cohort (p = 0.899). However, subgroup analyses suggested potential benefits in iCCA and eCCA but not in GBC. High CA19-9 levels and vascular invasion were associated with poorer survival outcomes. Conclusions: This study highlights the prognostic significance of ECOG PS, resection margin status, lymph node involvement, and CA19-9 levels in resected BTCs. The lack of a clear survival benefit from adjuvant chemotherapy underscores the need for improved therapeutic strategies. Future research should focus on refining risk stratification models and identifying more effective adjuvant treatments to enhance long-term survival outcomes in patients with BTC. Full article

Objectives: Non-cystic fibrosis bronchiectasis (BE) is a chronic airway disease with increasing prevalence, reduced quality of life, and increased mortality. Inhaled corticosteroids (ICS) are used in BE despite limited evidence of effect on lung function parameters. ICS may increase the risk of Staphylococcus aureus (S. aureus) infections in patients with BE, but this is unexplored. We examined the association between ICS use prior to BE diagnosis at different doses and the risk of S. aureus isolation in patients with BE. Methods: We conducted a national register-based cohort study including Danish patients with a BE diagnosis code between 2001 and 2018 with a 1-year follow-up time from the date of diagnosis. ICS exposure was categorized based on accumulated prescriptions redeemed 365 days before BE diagnosis and divided into none, low, moderate, or high use based on clinically relevant doses. A cause-specific Cox proportional hazards regression model was used to estimate the risk of S. aureus isolation. A sensitivity analysis, an inverse probability of treatment weighted model (IPTW), was performed. Results: A total of 5093 patients were included in this study. S. aureus was isolated in 156 patients (3.1%). High-dose ICS was associated with an increased risk of S. aureus isolation, HR 3.81 (95% CI 2.51; 5.79). No association for low or moderate use was found, low-dose HR 1.22 (95% CI 0.77; 1.93), and moderate-dose HR 1.24 (95% CI 0.72; 2.16). IPTW analysis yielded similar results. Conclusions: High-dose ICS use in patients with BE was associated with an increased risk of S. aureus isolation. ICS should be used cautiously in patients with BE. Full article

Background/Objectives: Despite the numerous health benefits of exclusive breastfeeding (EBF) for the infant and the mother, EBF rates in Lao People’s Democratic Republic, Lao PDR, remain at 40%. We investigate how barriers to EBF were overcome by a social transfer intervention. Methods: Data from the Social Transfers for Exclusive Breastfeeding ongoing randomised controlled trial (RCT) (n = 298) in Vientiane, Lao PDR, was used. Mothers in the RCT were randomised equally into a control group, an unconditional transfer group and a conditional transfer group and followed up to six months (n = 280). We used logistic and Cox proportional hazards regression models to investigate the association of predictor variables with EBF at six months and the risk of EBF cessation in each of the three groups. Results: Greater breastfeeding self-efficacy increased the odds of EBF at six months in both intervention groups (unconditional transfer OR 1.39 [95% CI 1.09, 1.87, p = 0.02] and conditional transfer OR 1.26 [1.01, 1.61, p = 0.05]) and reduced the risk of EBF cessation (unconditional transfer HR 0.87 [0.77, 0.98, p = 0.02]). Maternal secondary and tertiary education in the intervention groups showed higher odds of EBF at six months and lower EBF cessation risk. Participants in the intervention group that intended to exclusively breastfeed in pregnancy showed a reduced EBF cessation risk in both intervention groups. Conclusions: Social transfers helped overcome the exclusive breastfeeding barrier of a higher education level and working status and improved EBF outcomes for mothers who intended to exclusively breastfeed and wanted the pregnancy. Breastfeeding self-efficacy positively influences EBF outcome, regardless of whether the mother received a social transfer or not. Full article

Background: Prostate cancer is a common malignancy among men worldwide, with various histopathologic features that influence its progression and prognosis. One such feature is perineural invasion (PNI), which has been associated with aggressive disease. In this retrospective study, we analyzed genomic alterations associated with PNI in patients who underwent radical prostatectomy. Methods: A total of 421 prostate cancer patients who underwent radical prostatectomy without neoadjuvant therapy were identified from The Cancer Genome Atlas. PNI was present in 378 patients (89.8%) and absent in 43 (10.2%). Differentially expressed genes were identified, and mRNA expression levels of key genes were analyzed. The prognostic significance of these genes was evaluated using log-rank tests and Cox proportional hazards models to estimate hazard ratios and 95% confidence intervals. Results: Levels of COL9A3, ASPN, ESR1, MUC1, PIP, SFRP4, KRT19, CLDN1, and COMP were significantly higher in the tumor tissues of patients in the PNI group compared to those in the non-PNI group (q < 0.05), and RYR2, MME, and AZGP1 expression levels were significantly higher in the non-PNI group (q < 0.05). A high mRNA expression level of AZGP1 was associated with longer disease-free survival, whereas high mRNA expressions of ASPN, COMP, RYR2, and SFRP4 were associated with shorter disease-free survival. Conclusions: Prostate cancer patients with genomic alterations associated with PNI may face a higher risk of disease progression after prostatectomy, highlighting the need for further prospective studies to validate these findings. Full article