Search Results (439)

Objective: This study aimed to compare the concordance of abbreviated MRI (AB-MRI) and full diagnostic protocol MRI (FDP-MRI) with pathology in assessing tumor extent for surgical planning in patients with newly diagnosed breast cancer. Additionally, we evaluated the performance of AB-MRI and FDP-MRI in detecting additional malignant lesions in the ipsilateral breast. Materials and Methods: A total of 319 patients with 330 index breast cancers were enrolled in the study. Two radiologists independently assessed tumor extent on AB-MRI and FDP-MRI and compared their measurements with the pathological tumor extent. For both MRI protocols, concordance rates and agreement of tumor extent with pathology were analyzed according to histopathologic and molecular subtypes using the chi-square test and ICC. Additional malignant lesions detection rates in the ipsilateral breast were compared between the two MRI protocols. Results: The mean total pathologic tumor extent, including the in situ component and adjacent malignant lesions, was 2.2 cm (standard deviation [SD]: 1.3 cm; range: 0.7–8.5 cm). The concordance rate of tumor extent with pathology between the two MRI protocols (AB- and FDP-MRI) showed no significant difference (reader 1, p = 0.68; reader 2, p = 0.74). The agreement of tumor extent with pathology was not significantly different between the two MRI protocols (AB-MRI and FDP-MRI: K = 0.70, 0.75, p = 0.17 in reader 1; K = 0.65, 0.71, p = 0.15 in reader 2). The detection rate of additional malignant lesions showed no significant difference between AB-MRI and FDP-MRI (p = 0.71 in reader 1, p = 0.89 in reader 2). Conclusions: AB-MRI is comparable to FDP-MRI for assessing tumor extent and detecting additional malignant lesions. Full article

Elexacaftor/tezacaftor/ivacaftor (ETI) is a cystic fibrosis (CF) transmembrane conductance regulator modulator, which has shown efficacy in people with CF (pwCF) carrying the F508del (F) variant, both in homozygosity and heterozygosity with a minimal function (MF) variant. Limited data exist on the effects of ETI in pwCF with advanced lung disease. Our aim was to investigate ETI safety and effectiveness in this patient group in a real-life setting over 2 years. A multicenter observational cohort study was designed to gather real-world information on the effect of ETI treatment on CF patients (aged >12 years, genotype: F/MF mutation) with advanced lung disease as defined by a FEV1 < 40% predicted. Retrospective demographic and clinical data were recorded for the two years preceding and the two years following ETI initiation. The following outcomes were investigated: treatment-associated adverse events (AEs), drug interruptions (temporary or permanent), variations in percent predicted FEV1 (ppFEV1), sweat chloride concentration (SwCl), antibiotic use, body mass index (BMI), and quality of life. A total of 124 (51.6% males) pwCF were treated with ETI over 2 years. The median (IQR) age and ppFEV₁ were 34 (26, 43) years and 34 (29, 41) percentage points, respectively. ETI was discontinued in two pwCF due to lung transplantation, and temporarily interrupted in two because of skin rash, and in three following elevated levels of aminotransferase. Most AEs were mild and short-lasting. In 12.1% pwCF, we registered an increase greater than twice the upper limit of the normal range in alanine aminotransferase, and in 16% we registered an increase in conjugated bilirubin with no increase in aminotransferase. Both increases were recurrent in about half of the subjects. The mean differences (95% CI) for ppFEV₁ and SwCl, assessed as mean values in the pre-ETI and ETI treatment periods, were +11.8 (11.1 to 12.6) and −43.7 (−47.6 to −39.9) mmol/L. A modest increase in ppFEV₁ persisted during the second year of treatment. Number of oral and IV antibiotic cycles/year, as well as hospitalizations/year, decreased significantly from 3.6 to 1.2, from 2.4 to 0.6, and from 2.1 to 0.5 during ETI treatment. A total of 8 of 16 (50%) pwCF were taken off the waiting list for lung transplantation, and significant reductions in the percentages of pwCF using long-term oxygen therapy and non-invasive ventilation were observed. A poor concordance between ppFEV1 and SwCl was found. In only 3/82 (3.7%), subjects with chronic airway infection by Pseudomonas aeruginosa cultures were always negative during ETI treatment. In CF patients with advanced lung disease on ETI treatment, we observed an improvement in a number of clinically significant outcomes over a 2-year study period. However, several additional observations, such as liver dysfunction, variable degrees of lung function improvement, and limited impact on chronic airway infection, underscore the fact that the benefit–risk profile of ETI treatment in cystic fibrosis patients with advanced lung disease has not been fully elucidated and warrants prolonged-term monitoring. Full article

Background/Objectives: Accurate prognostication of hepatocellular carcinoma (HCC) remains essential for treatment selection and risk stratification. This study aimed to compare the prognostic performance of individual serum biomarkers and composite scoring models, including GALAD, BALAD, BALAD-2, GAAP, ASAP, the Doylestown algorithm, and aMAP, using data from a biobank-based HCC cohort. Methods: This study enrolled 186 patients with confirmed HCC diagnosed between 2019 and 2024. Serum biomarkers (AFP, AFP-L3%, DCP) and composite models were evaluated for their association with overall survival (OS). Prognostic performance was assessed using time-dependent area under the receiver operating characteristic curve (AUROC) at 1-, 2-, 3-, and 5-year intervals and Harrel’s concordance index (c-index). Subgroup analyses were performed based on treatment intent and liver disease etiology. Results: All three biomarkers and composite models were independently associated with OS in multivariate analyses (all p < 0.05). Among all models, BALAD-2 demonstrated the best overall performance (c-index: 0.737), with the highest AUROCs at 1 year (0.827), 2 years (0.846), 3 years (0.781), and 5 years (0.716). BALAD-2 consistently showed superior discrimination in patients treated with curative or noncurative therapies and in the viral etiology subgroup. In the non-viral etiology subgroup, BALAD-2 remained among the top performers, although the GAAP, ASAP, and Doylestown algorithms showed slightly higher metrics. Conclusions: BALAD-2 demonstrated consistent and robust prognostic performance compared with other biomarker-based and clinical models across different patient subgroups, particularly among those receiving curative therapy and viral etiologies. These findings support its integration into clinical risk stratification and decision-making for HCC management. Full article

Background/Objectives: The burden of nursing care is defined as the relation between the care needs of hospitalized individuals and the time available for nursing staff to perform direct, indirect, and educational care activities. This study aimed to design an instrument to measure the burden of nursing care and to assess its face and content validity. Methods: This methodological study was conducted in three phases: (1) operationalization of the concept, (2) instrument design, and (3) face and content validity assessment. Expert panels using the nominal group technique were employed in phases one and two. In phase three, item evaluations regarding clarity, coherence, and relevance were conducted by experts. Results: Face validity was assessed by six expert researchers, while content validity was evaluated by 55 nurses with graduate-level education. The results demonstrated content validity index (CVI) values ranging from 0.89 to 0.95; Aiken’s V values between 0.84 and 0.94; and Kendall’s W concordance coefficients between 0.54 and 0.73, all statistically significant (p < 0.001). Conclusions: The Care Load Scale, designed to measure the burden of nursing care based on hospitalized patients’ needs, demonstrated strong face and content validity. The instrument shows potential for use in clinical settings to guide nursing care planning, allocate resources effectively, and inform institutional policies. The inclusion of expert judgment and rigorous validation procedures ensures the instrument’s relevance and applicability. This scale represents a significant contribution to nursing research and practice by offering a standardized tool aligned with patient-centered care principles. Full article

Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies. Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients. Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments. Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics. Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies. Full article

Objectives: Revision total hip arthroplasty (rTHA) is a complex surgery with variable functional outcomes that often differ between the surgeon’s perception and the patient’s experience. Therefore, the aim of this study is, first, to evaluate functional outcomes based on the reason for revision, type of revision, acetabular defect, and number of prior revision surgeries; and second, to compare outcomes from both the surgeon’s and the patient’s perspectives to determine whether or not there is agreement between them. Materials and Methods: An observational study was conducted on patients who underwent rTHA at a tertiary-level center from January 2013 to December 2018, with a median follow-up of 41 months. A total of 149 procedures were performed during this period. The variables analyzed included the indication for revision surgery, type of revision, presence of acetabular defect, and number of previous revision surgeries. The surgeon’s perspective was assessed using the Harris Hip Score (HHS), while the patient’s perspective was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Short Form-12 Health Survey (SF-12). Results: Analysis of the variables from both surgeon’s and patient’s perspectives showed statistically significant differences regarding the indication for revision and the SF-12 component, with patients undergoing revision for infection or dislocation reporting worse functional outcomes. Although the remaining variables did not reach statistical significance, the surgeon perceived worse outcomes in patients revised for infection and in those who underwent revision of both components (acetabular and femoral). Conversely, patients reported poorer functional outcomes when operated on for infection or dislocation, when both components were revised, and when they had undergone more than one revision surgery. Additionally, a statistically significant trend was observed showing worse outcomes with increasing anesthetic risk. Linear regression analysis between the surgeon’s evaluation and the patient-reported outcome measures showed a statistically significant association, indicating that higher surgeon scores correlated with fewer symptoms and better hip function as reported by patients. Conclusion: There was concordance between the surgeon’s evaluation, measured by the Harris Hip Score (HHS), and the patient’s perception of health status through PROMs, specifically the SF-12 and WOMAC questionnaires. Although overall results were satisfactory regardless of the reason for the revision, type of revision, defect grade, or number of revisions; outcomes were slightly worse in patients revised for dislocation or infection, those undergoing revisions of both components, and in cases involving multiple revision surgeries. Full article

Background: Multifocal (MF) and multicentric (MC) breast cancers, defined by the presence of multiple synchronous tumor foci within the same breast, present important diagnostic, therapeutic, and prognostic challenges. Historically considered a contraindication for breast-conserving therapy (BCT), advances in imaging, surgical techniques, and adjuvant therapy have reshaped management strategies. Methods: A narrative literature review was conducted through PubMed, Web of Science, and Scopus, prioritizing ISI-indexed articles published within the last 10–15 years. More than 55 relevant studies, including systematic reviews, meta-analyses, and large cohorts, were analyzed to evaluate epidemiology, pathological features, imaging modalities, treatment outcomes, and prognosis of MF/MC breast cancers. Results: The reported incidence of MF/MC breast cancers ranges from 10% to 24%, increasing when MRI or whole-organ pathology is applied. MRI can detect otherwise occult additional foci in up to 30% of patients, improving staging accuracy but raising concerns of overdiagnosis. MF/MC presentation is strongly associated with lobular histology, younger age at diagnosis, and higher rates of axillary involvement—nodal positivity is observed in up to 45% of MF/MC cases versus 28% in unifocal tumors. Pathological analyses demonstrate frequent clonal origin of MF lesions, whereas MC lesions may represent independent primaries, occasionally with receptor heterogeneity that alters systemic therapy selection. From a prognostic perspective, older series suggested shorter breast cancer-specific survival (e.g., median 154 vs. 204 months for MF/MC vs. unifocal disease), and higher local recurrence with BCT. However, contemporary analyses, including a 2022 meta-analysis of 15,703 patients, demonstrated no significant difference in overall or disease-free survival once adjusted for tumor size and nodal status. Local recurrence remains slightly higher with BCT in MF/MC (5.6% vs. 4.2%), but outcomes are equivalent to mastectomy when radiotherapy is appropriately delivered. Five-year survival in early-stage MF/MC exceeds 90% with guideline-concordant multimodal therapy. Conclusions: MF/MC breast cancers represent a biologically heterogeneous entity. Optimal outcomes rely on precise imaging, complete excision, tailored systemic therapy, and multidisciplinary management, with increasing acceptance of breast conservation in selected patients. Full article

Metagenomic next-generation sequencing (mNGS) is increasingly employed for the diagnosis of lower respiratory tract infections (LRTIs). However, the relative diagnostic performance of long-read versus short-read sequencing platforms remains incompletely defined. For this systematic review, a search was conducted in PubMed, Embase, Scopus, Web of Science, and Google Scholar to identify studies directly comparing long-read (e.g., Oxford Nanopore, PacBio) and short-read (e.g., Illumina, Ion Torrent, BGISEQ) metagenomic sequencing for the diagnosis of LRTI. Eligible studies reported diagnostic accuracy or comparative performance between platforms. Risk of bias was evaluated using the QUADAS-2 tool. Thirteen studies met inclusion criteria. Reported platforms included Illumina, Oxford Nanopore, PacBio, Ion Torrent, and BGISEQ-500. A total of 13 studies met inclusion criteria. Across studies reporting sensitivity, average sensitivity was similar for Illumina (71.8%) and Nanopore (71.9%). Specificity varied substantially, ranging from 42.9 to 95% for Illumina and 28.6 to 100% for Nanopore. Concordance between platforms ranged from 56 to 100%. Illumina consistently produced superior genome coverage (approaching 100% in most reports) and higher per-base accuracy, whereas Nanopore demonstrated faster turnaround times (<24 h), greater flexibility in pathogen detection, and superior sensitivity for Mycobacterium species. Risk of bias was frequently high or unclear, particularly in patient selection (6 studies), index test interpretation (5), and flow and timing (4), limiting the robustness of pooled estimates. Long-read and short-read mNGS platforms exhibit comparable strengths in the diagnosis of LRTIs. Illumina remains optimal for applications requiring maximal accuracy and genome coverage, whereas Nanopore offers rapid, versatile pathogen detection, particularly for difficult-to-detect organisms such as Mycobacterium. However, there are certain limitations of the review, including a lack of comparable outcomes reported in all studies; therefore, further research is warranted to address this. Full article

Maritime Autonomous Surface Ships (MASSs) raise safety and regulatory challenges that extend beyond technical reliability. This study builds on a published system-theoretic process analysis (STPA) of degraded operations that identified 92 loss scenarios. These scenarios were reformulated into a two-round Delphi survey with 20 experts from academic, industry, seafaring, and regulatory backgrounds. Panelists rated each scenario on severity, likelihood, and detectability. To avoid rank reversal, common in the Risk Priority Number, an adjusted index was applied. Initial concordance was low (Kendall’s W = 0.07), reflecting diverse perspectives. After feedback, Round 2 reached substantial agreement (W = 0.693, χ² = 3265.42, df = 91, p < 0.001) and produced a stable Top 10. High-priority items involved propulsion and machinery, communication links, sensing, integrated control, and human–machine interaction. These risks are further exacerbated by oceanographic conditions, such as strong currents, wave-induced motions, and biofouling, which can impair propulsion efficiency and sensor accuracy. This highlights the importance of environmental resilience in MASS safety. These clusters were translated into five action bundles that addressed fallback procedures, link assurance, sensor fusion, control chain verification, and alarm governance. The findings show that Remote Operator competence and oversight are central to MASS safety. At the same time, MASSs rely on artificial intelligence systems that can fail in degraded states, for example, through reduced explainability in decision making, vulnerabilities in sensor fusion, or adversarial conditions such as fog-obscured cameras. Recognizing these AI-specific challenges highlights the need for both human oversight and resilient algorithmic design. They support explicit inclusion of Remote Operators in the STCW convention, along with watchkeeping and fatigue rules for Remote Operation Centers. This study provides a consensus-based baseline for regulatory debate, while future work should extend these insights through quantitative system modeling. Full article

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

Hepatocellular carcinoma (HCC) ranks among the most prevalent malignancies globally. Although treatment strategies have improved, the prognosis for patients with advanced HCC remains unfavorable. Tumor-associated macrophages (TAMs) play a dual role, exhibiting both anti-tumor and pro-tumor functions. In this study, we analyzed single-cell RNA sequencing data from 10 HCC tumor cores and 8 adjacent non-tumor liver tissues available in the dataset GSE149614. Using dimensionality reduction and clustering approaches, we identified six major cell types and nine distinct TAM subtypes. We employed Monocle2 for cell trajectory analysis, hdWGCNA for co-expression network analysis, and CellChat to investigate functional communication between TAMs and other components of the tumor microenvironment. Furthermore, we estimated TAM abundance in TCGA-LIHC samples using CIBERSORT and observed that the relative proportions of specific TAM subtypes were significantly correlated with patient survival. To identify TAM-related genes influencing patient outcomes, we developed a high-dimensional, gene-based transformer survival model. This model achieved superior concordance index (C-index) values across multiple datasets, including TCGA-LIHC, OEP000321, and GSE14520, outperforming other methods. Our results emphasize the heterogeneity of tumor-associated macrophages in hepatocellular carcinoma and highlight the practicality of our deep learning framework in survival analysis. Full article

Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are graded per the World Health Organization (WHO) using mitotic count and the Ki-67 index. There is an ongoing debate regarding the concordance between these parameters and their ability to predict metastatic disease. Objective: The objective is to assess concordance between the mitotic count and the Ki-67 index in grading GEP-NETs and to determine which parameter more accurately relates to metastatic disease and local tumor behavior. Methods: We conducted a single-center retrospective cohort study of adults with GEP-NETs managed between January 2006 and February 2024. Tumors were staged according to the TNM system. Grading followed WHO criteria using mitotic count and the Ki-67 index; when discordant, the higher grade was assigned. Results: Concordance between mitotic count- and Ki-67-based grading was 76.5% (78/102) with Cohen’s κ = 0.36, indicating fair-to-moderate agreement. More tumors were classified as G1 by mitotic count (86.3%) than by the Ki-67 index (68.6%). Neither mitotic count nor the Ki-67 index (numerical values or grades) showed a significant association with metastatic disease (all p > 0.05). Mitotic count (as a numerical continuous values) correlated with tumor invasion (T1 vs. T3, p = 0.035; T1 vs. T4, p = 0.036), whereas the Ki-67 index did not (p = 0.11). Tumor size was the strongest predictor of metastases (lymph-node p = 0.028; distant p < 0.001; any p < 0.001). Conclusions: Mitotic count and the Ki-67 index show only 76.5% concordance. Neither marker predicted metastatic disease in this cohort, while tumor size was the most robust predictor. These findings support giving greater weight to tumor size within prognostic algorithms while recognizing the limitations of proliferation-based grading for predicting metastasis. Full article

Background/Objectives: Growing evidence supports the use of a single trough concentration, rather than both a peak and trough, to estimate the 24 h area under the curve (AUC₂₄) of vancomycin using Bayesian software (InsightRx^® Ver.1.71). However, patients with body mass index (BMI) ≥ 40 kg/m² are underrepresented in validation studies. Studies in patients with obesity have produced mixed results, potentially because of different population models used. Methods: This single-center, retrospective study evaluated adult inpatients with BMI ≥ 40 kg/m². Steady-state AUC₂₄ estimates generated by Bayesian software using both two-concentration and one-concentration inputs were compared. Agreement was defined as a percent difference within ±20%. Subgroup analyses were conducted for patients with defined peak and trough concentrations and for comparisons between two Bayesian population models (Carreno vs. Hughes). Linear regression assessed covariates associated with percent difference. Results: Among 82 encounters, 97.5% of one-concentration estimates based on the smaller concentration were within ±20% of the two-concentration AUC_24,SS (mean difference: 2.9%, 95% CI: 0.14 to 3.8%). Similar agreement was observed using the larger concentration (97.5%, mean difference: −3.1%, 95% CI: −4.7 to −0.1.5%). Subgroup analysis for encounters with true peak/trough levels (n = 22) also showed 100% agreement within ±20%. The percent difference did not correlate with BMI or other covariates. Comparison of Hughes vs. Carreno models showed larger variability (only 59.1% within ±20%). Conclusions: In patients with BMI ≥ 40 kg/m², Bayesian AUC_24,SS estimation using a single vancomycin concentration is feasible. Greater caution is warranted in the setting of acute kidney injury, poor model fit, or targeting AUC at the extremes of the therapeutic range. The population model used to generate the Bayesian AUC estimate has a much greater influence than the number of concentrations analyzed. Furthermore, measuring two concentrations does not ensure concordance between models. Full article

Background and Objectives: The prognostic immune and nutritional index (PINI), derived from serum albumin levels and absolute monocyte counts, has demonstrated prognostic value in gastrointestinal cancers. However, its role in non-small cell lung cancer (NSCLC) remains unclear. This study assessed the prognostic utility of the PINI for overall survival (OS) in patients with stage I–IIIA NSCLC undergoing curative-intent resection. Methods: This was a retrospective cohort study that included 522 patients. Cox proportional hazards models were used to evaluate the association between PINI and OS along with clinical and hematologic variables. Model performance was assessed using the concordance index (C-index), integrated area under the curve (iAUC), continuous net reclassification improvement (cNRI), integrated discrimination improvement (IDI), nomogram construction, and calibration curves. Results: In the multivariate analysis, the PINI remained an independent predictor of OS, along with age, American Society of Anesthesiologists physical status, stage, pleural invasion, and the modified Shine–Lal index. The full model (FM), incorporating all these variables, outperformed the baseline model (BM) that was based solely on stage (C-index: 0.841 vs. 0.692; iAUC: 0.804 vs. 0.663; both p < 0.001). Compared with the intermediate model (IM), which included all FM variables except the PINI, the FM demonstrated modest but statistically significant improvements (C-index: 0.841 vs. 0.820, p = 0.012; iAUC: 0.804 vs. 0.793, p = 0.001). At 3- and 5-year time points, the FM still yielded superior risk reclassification over the BM and IM, as indicated by improvements in IDI and cNRI. A nomogram based on the FM showed good calibration with the observed survival outcomes. Conclusions: The PINI is an independent and clinically meaningful prognostic biomarker in patients with stage I–IIIA NSCLC undergoing curative surgery. Incorporating the PINI into the BM or IM improved risk discrimination and reclassification, supporting its potential use in personalized prognostic assessment. However, external validation is warranted. Full article

Background: Cancer staging, guided by anatomical and clinicopathologic factors, is essential for determining treatment strategies and patient prognosis. The current gold standard for prostate cancer is the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) Staging System 9th Version (2024). This system incorporates five prognostic variables: tumor (T), spread to lymph nodes (N), metastasis (M), prostate-specific antigen (PSA) levels (P), and Grade Group/Gleason score (G). While effective, further refinement of prognostic systems may improve prediction of patient outcomes and support more individualized treatment. Methods: We applied the Ensemble Algorithm for Clustering Cancer Data (EACCD), an unsupervised machine learning approach. EACCD involves three steps: calculating initial dissimilarities, performing ensemble learning, and conducting hierarchical clustering. We first developed an EACCD model using the five AJCC variables (T, N, M, P, G). The model was then expanded to include two additional factors, age (A) and race (R). Prostate cancer patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute. Results: The EACCD algorithm effectively stratified patients into distinct prognostic groups, each with well-separated survival curves. The five-variable model achieved a concordance index (C-index) of 0.8293 (95% CI: 0.8245–0.8341), while the seven-variable model, including age and race, improved performance to 0.8504 (95% CI: 0.8461–0.8547). Both outperformed the AJCC TNM system, which had a C-index of 0.7676 (95% CI: 0.7622–0.7731). Conclusions: EACCD provides a refined prognostic framework for primary localized prostate cancer, demonstrating superior accuracy over the AJCC staging system. With further validation in independent cohorts, EACCD could enhance risk stratification and support precision oncology. Full article