Bosnian Journal of Basic Medical Sciences
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A novel deep learning framework for automatic scoring of PD-L1 expression in non-small cell lung cancer
Full text linkA critical predictive marker for anti-PD-1/PD-L1 therapy is programmed death-ligand 1 (PD-L1) expression, assessed by immunohistochemistry (IHC). This paper explores a novel automated framework using deep learning to accurately evaluate PD-L1 expression from whole slide images (WSIs) of non-small cell lung cancer (NSCLC), aiming to improve the precision and consistency of Tumor Proportion Score (TPS) evaluation, which is essential for determining patient eligibility for immunotherapy. Automating TPS evaluation can enhance accuracy and consistency while reducing pathologists\u27 workload. The proposed automated framework encompasses three stages: identifying tumor patches, segmenting tumor areas, and detecting cell nuclei within these areas, followed by estimating the TPS based on the ratio of positively stained to total viable tumor cells. This study utilized a Reference Medicine (Phoenix, Arizona) dataset containing 66 NSCLC tissue samples, adopting a hybrid human-machine approach for annotating extensive WSIs. Patches of size 1000x1000 pixels were generated to train classification models such as EfficientNet, Inception, and Vision Transformer models. Additionally, segmentation performance was evaluated across various UNet and DeepLabV3 architectures, and the pre-trained StarDist model was employed for nuclei detection, replacing traditional watershed techniques. PD-L1 expression was categorized into three levels based on TPS: negative expression (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). The Vision Transformer-based model excelled in classification, achieving an F1-score of 97.54%, while the modified DeepLabV3+ model led in segmentation, attaining a Dice Similarity Coefficient of 83.47%. The TPS predicted by the framework closely correlated with the pathologist\u27s TPS at 0.9635, and the framework\u27s three-level classification F1-score was 93.89%. The proposed deep learning framework for automatically evaluating the TPS of PD-L1 expression in NSCLC demonstrated promising performance. This framework presents a potential tool that could produce clinically significant results more efficiently and cost-effectively
Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study
Full text linkImmune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer (NSCLC), underscoring the unmet need for noninvasive biomarkers to predict early therapeutic responses and improve survival outcomes. To address this, we developed a CT-based deep learning model integrated with the systemic immune-inflammatory-nutritional index (SIINI) for early prediction of ICI response. In a retrospective multicenter study of 265 patients treated with ICIs (incorporating chest CT and laboratory data), the cohort was divided into training (70%), internal validation (30%), and external validation sets. The combined model—leveraging DenseNet121-derived deep radiomic features alongside SIINI—achieved strong predictive performance, with AUCs of 0.865 (95% CI: 0.7709–0.9595) in the internal validation cohort and 0.823 (95% CI: 0.6627–0.9827) in the external validation cohort. Gradient-weighted class activation mapping (Grad-CAM) highlighted key CT regions contributing to model predictions, enhancing interpretability for clinical application. These findings highlight the potential of integrating deep learning with inflammatory biomarkers to support personalized ICI therapy in unresectable NSCLC. Future directions include incorporating multi-omics biomarkers, expanding multicenter validation, and increasing sample sizes to further improve predictive accuracy and facilitate clinical translation
Predicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005–2020
Full text linkOveractive bladder (OAB), a prevalent condition characterized by urgency and nocturia, imposes significant burdens on both quality of life and healthcare systems. Emerging evidence implicates systemic inflammation in OAB pathogenesis; however, the role of complete blood count (CBC)-derived inflammatory biomarkers remains underexplored. This cross-sectional study analyzed data from 35,394 participants in the National Health and Nutrition Examination Survey (NHANES, 2005–2020) to evaluate associations between CBC-derived biomarkers—such as the Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Neutrophil-to-Lymphocyte Ratio (NLR)—and OAB (defined by an OAB Symptom Score ≥3). Multivariable logistic regression, threshold analysis, and machine learning models (Random Forest [RF], Extreme Gradient Boosting) were employed, adjusting for sociodemographic, lifestyle, and clinical covariates. Elevated levels of SII, SIRI, NLR, Monocyte-to-Lymphocyte Ratio (MLR), and Neutrophil-MLR (NMLR) were significantly associated with increased OAB risk (all P < 0.05), with adjusted odds ratios for the highest quartiles ranging from 1.21 (SII; 95% CI: 1.10–1.34) to 1.31 (NMLR; 1.19–1.44). Nonlinear associations were observed, with inflection points (e.g., NLR = 1.071, MLR = 0.174) marking abrupt increases in risk. RF models showed strong predictive performance (area under the curve = 0.89 for training; 0.76 for testing), identifying SII and SIRI as key predictors. Subgroup analyses demonstrated consistent associations across most demographic groups, with the exception of hyperlipidemia, which modified the effects of SIRI, NLR, and NMLR. These findings highlight the role of systemic inflammation in OAB and suggest that CBC-derived biomarkers could serve as cost-effective tools for risk stratification. The integration of epidemiological analysis and machine learning enhances our understanding of OAB’s inflammatory underpinnings, although longitudinal studies are needed to establish causal relationships and therapeutic implications
Molecular classification and fertility-sparing outcomes in endometrial cancer and atypical endometrial hyperplasia
Full text linkMolecular classification has emerged as a critical tool for guiding personalized treatment in endometrial cancer (EC) and atypical endometrial hyperplasia (AEH). This retrospective study aimed to assess the impact of molecular classification on fertility-sparing treatment outcomes in patients diagnosed with EC and AEH who underwent fertility preservation therapy between 2006 and 2021. Patients were categorized into four molecular subtypes using immunohistochemistry (IHC) and Sanger sequencing, based on the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE): POLE-ultramutated, mismatch repair (MMR) deficient (MMRd), p53 abnormal (p53abn), and p53 wild-type (p53wt). All patients were evaluated for oncological prognosis and fertility outcomes, with a total of 103 patients included in the analysis. Recurrence rates exhibited significant differences among the molecular classifications, with the lowest recurrence rate observed in the p53wt subtype (19.7%), followed by MMRd (30.4%), POLE-ultramutated (66.7%), and p53abn (71.4%) subtypes. Multivariate Cox regression analysis indicated that the p53abn subtype was a significant risk factor for recurrence following conservation therapy when compared to the p53wt subtype. Additionally, there was a notable disparity in standard surgical treatment due to treatment failure, with operation rates of 7.5%, 19.2%, 66.7%, and 57.1% for the p53wt, MMRd, POLE-ultramutated, and p53abn subtypes, respectively. Regarding fertility outcomes, the p53wt group demonstrated the highest pregnancy rate after achieving a complete response compared to the other subtypes; however, no significant differences were observed in overall pregnancy outcomes. The ProMisE molecular classification holds significant prognostic value for patients with EC and AEH undergoing fertility-sparing treatment. Among the molecular subtypes, p53wt appears to be the most favorable for fertility-preserving interventions. This study provides essential insights into reproductive outcomes for this patient population
TUDCA combined with Syndopa protects the midbrain and gut from MPTP toxicity in a Parkinson’s disease mouse model: Immunohistochemical evidence
Full text linkNeuro-inflammation plays a significant role in the neurodegenerative processes associated with Parkinson\u27s disease (PD). A hallmark of PD is the degeneration of dopaminergic neurons within the nigrostriatal pathway. The standard treatment for PD is Syndopa (a combination of levodopa and carbidopa). However, while Syndopa alleviates symptoms, it is also associated with numerous side effects in patients. Research has demonstrated the protective effects of Tauroursodeoxycholic acid (TUDCA) in mitigating the neuropathological consequences of PD in several preclinical studies. Nonetheless, further investigation is necessary to delineate the role of TUDCA in PD therapeutics. Although the efficacy of TUDCA monotherapy in PD has been explored, there is a lack of preclinical research examining the additive effects of TUDCA in conjunction with Syndopa therapy. In this study, we utilized an MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model of PD to evaluate the potential therapeutic benefits of TUDCA monotherapy and the combined effects of TUDCA and Syndopa therapy, compared to standard Syndopa treatment. We conducted immunohistochemical (IHC) assessments of α-synuclein expression in the gut and substantia nigra pars compacta (SNpc), as well as tyrosine hydroxylase and NF-kB expression in the striatum and SNpc regions, to investigate the efficacy of the test drugs. The immunohistochemical findings indicate that both TUDCA monotherapy and the combination therapy of TUDCA and Syndopa significantly reduced MPTP-induced alterations in the expression levels of α-synuclein, tyrosine hydroxylase, and NFκB in the striatum and SNpc regions. Additionally, the MPTP-induced changes in α-synuclein expression in the gut were notably reversed by these treatments. Collectively, these results suggest that incorporating TUDCA with Syndopa may represent a promising therapeutic strategy to address the pathophysiological challenges associated with PD
Development of a novel clinical prediction model for sepsis related mortality by combining NEWS, PIRO and lactate
Full text linkPrognostic assessment plays a crucial role in guiding therapeutic decision-making for patients with sepsis, particularly in intensive care settings. This study aimed to develop a multivariable model to predict 28-day mortality among intensive care unit (ICU) patients with sepsis by integrating serum lactate levels, the National Early Warning Score (NEWS), and the Predisposition, Infection, Response, and Organ Dysfunction (PIRO) score. Demographic information, clinical characteristics, and laboratory findings routinely collected at ICU admission were used to calculate the NEWS and PIRO scores for each patient. Patients were categorized as survivors or non-survivors based on their outcome. Both logistic regression and Cox proportional hazards models were applied for mortality prediction analysis. The final analysis included 205 patients diagnosed with sepsis (mean age: 73.6 ± 13.2 years; 53.2% male), of whom 109 died during hospitalization. Logistic regression analysis revealed that lactate, NEWS, and PIRO scores were independently associated with 28-day mortality. Combining lactate levels with NEWS and PIRO significantly enhanced mortality prediction, with the greatest accuracy observed when all three parameters were integrated. Pairwise analyses demonstrated that adding lactate to the base model significantly improved predictive accuracy (DBA: −0.103, p = 0.003), and incorporating lactate into a model already including NEWS further enhanced its predictive value (DBA: −0.042, p = 0.037). In conclusion, serum lactate measured at initial ICU admission provides valuable prognostic information for predicting 28-day mortality in sepsis patients. Furthermore, combining lactate levels with NEWS and PIRO scores substantially enhances the accuracy of mortality prediction in these patients
Prognostic impact of pan-immune inflammation value in small-cell lung cancer treated with chemoradiotherapy and prophylactic cranial irradiation
Full text linkDetermining prognosis is crucial for treatment selection, especially for prophylactic cranial irradiation (PCI), in patients with limited-stage small cell lung cancer (LS-SCLC). This study evaluates the prognostic value of the pan-immune inflammation value (PIV) in patients with LS-SCLC. We included patients who underwent thoracic chemoradiotherapy (TRT) and PCI at our clinic between July 2012 and April 2024. PIV was calculated as (neutrophil count × platelet count × monocyte count) / lymphocyte count. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal pre-treatment PIV cut-off to divide patients into two groups. Survival outcomes between these groups were compared using Kaplan-Meier analysis and log-rank tests. Multivariate analyses were conducted using Cox regression. Fifty-nine patients were included in the study. The optimal PIV cut-off was identified as 911 (AUC: 0.60, Sensitivity: 0.31, Specificity: 0.94, J-index: 0.26). Patients were grouped based on PIV levels: low (<911) and high (≥911). Lower PIV levels were significantly associated with improved overall survival (OS) (39 months vs. 10 months, p < 0.001) and intracranial progression-free survival (ICPFS) (not reached vs. 15 months, p < 0.001). The independent prognostic value of PIV was confirmed in multivariate analyses for both OS (p < 0.001) and ICPFS (p < 0.001). These findings suggest that pre-treatment PIV is an independent prognostic marker in LS-SCLC patients undergoing TRT and PCI.
Frailty and survival of patients with renal cell carcinoma: A meta-analysis
Full text linkFrailty is a multidimensional syndrome reflecting decreased physiological reserve and increased vulnerability to stressors, which may adversely affect cancer prognosis. However, its impact on survival outcomes in patients with renal cell carcinoma (RCC) remains unclear. This meta-analysis aimed to evaluate the association between frailty and survival in RCC patients. A systematic search of PubMed, Embase, and Web of Science was conducted for longitudinal studies assessing frailty in adults with RCC. Studies using validated frailty assessment tools and reporting overall survival (OS) and/or progression-free survival (PFS) were included. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using random-effects models. Subgroup and sensitivity analyses were performed to explore heterogeneity. Eight cohort studies involving 15,989 RCC patients were included. Frailty was associated with significantly poorer OS (HR = 1.79, 95% CI: 1.45–2.20; I² = 30%) and PFS (HR = 2.17, 95% CI: 1.54–3.04; I² = 0%). The association between frailty and OS remained robust across sensitivity analyses by excluding one study at a time and was consistent across subgroups stratified by cancer stage, treatment modality, patient age, frailty assessment method, follow-up duration, and analytic model (all p values for subgroup differences > 0.05). Subtype-specific data according to the histologic type of RCC were unavailable, which limits detailed prognostic interpretation. No significant publication bias was detected. Frailty may be significantly associated with poorer survival outcomes in patients with RCC. Incorporating frailty assessment into routine clinical evaluation may aid in prognostication and individualized treatment planning for this patient population
Decitabine suppresses tumor growth by activating mouse mammary tumor virus and interferon-β pathways
Full text linkDecitabine (DAC), a DNA methyltransferase inhibitor (DNMTi), is clinically effective in hematological malignancies such as myelodysplastic syndrome and acute myeloid leukemia, but its precise antineoplastic mechanisms remain incompletely understood. Beyond promoter demethylation, DAC is known to activate endogenous retroviruses (ERVs) and trigger type I interferon (IFN-I) responses, a phenomenon known as viral mimicry. The aim of this study was to investigate the roles of the mouse mammary tumor virus (MMTV) and interferon-β (IFN-β) in DAC-mediated tumor suppression. We employed two murine tumor models—4T1 mammary carcinoma and MC38 colon adenocarcinoma—in syngeneic immunocompetent mice, immunodeficient nude mice, and in vitro cultures. RNA and protein expression were assessed by quantitative PCR and immunoblotting, while functional contributions of MMTV and IFN-β were tested using short hairpin RNA (shRNA) knockdowns. DAC treatment suppressed tumor growth and pulmonary metastasis in vivo and inhibited cancer cell proliferation in vitro. It induced transcription of MMTV and expression of IFN-β, with a strong negative correlation between MMTV Env protein levels and tumor mass. Knockdown of either MMTV or IFN-β conferred resistance to DAC, confirming their functional roles. Reciprocal regulation was observed: MMTV knockdown reduced IFN-β expression, while IFN-β knockdown increased MMTV Env accumulation. Furthermore, DAC upregulated interferon regulatory factor 7 (IRF7), but this effect declined during prolonged treatment, suggesting a temporally restricted therapeutic window. In conclusion, our findings provide in vivo support for the viral mimicry hypothesis and demonstrate that MMTV and IFN-β contribute to DAC-mediated tumor suppression. The observed IRF7 downregulation and potential induction of immune checkpoints highlight the importance of therapeutic strategies combining DNMTis with immune checkpoint blockade to sustain antineoplastic efficacy.
Circulating microRNAs in prostate cancer — Non-invasive biomarkers for diagnosis, prognosis and therapy: A review
Full text linkProstate cancer (PC) is a common malignancy driven by interacting genetic, environmental, and lifestyle factors, including hereditary mutations (BRCA1/2, HPC1, AR variants), premalignant lesions [proliferative inflammatory atrophy (PIA), prostatic intraepithelial neoplasia (PIN)], and Western dietary patterns. This narrative review aims to synthesize evidence on the role of microRNAs (miRNAs) in PC pathogenesis and clinical management across diagnosis, prognosis, therapy, and recurrence prediction. We searched PubMed/MEDLINE (2004–present) using predefined terms, screened reference lists, excluded outdated records, and prioritized biomarker studies with AUC ≥ 0.85. Current diagnostic pathways—digital rectal examination, prostate-specific antigen (PSA) testing, multiparametric MRI, and Gleason-based International Society of Urological Pathology (ISUP) grading—are complemented by molecular tools (4Kscore, PHI, SelectMDx, TMPRSS2–ERG, PCA3, ConfirmMDx). MiRNAs, key post-transcriptional regulators, contribute to PC via dysregulated biogenesis and modulation of androgen receptor signaling within an inflamed, remodeled tumor microenvironment. Circulating and exosomal miRNAs (notably miR-21, miR-375, and miR-182-5p) exhibit greater specificity and stability than PSA, enabling non-invasive diagnosis, risk stratification, treatment monitoring, and recurrence prediction. Therapeutic approaches—antagomirs, sponges, miRNA masks, and CRISPR editing—show preclinical promise, while chemical modifications [peptide nucleic acids (PNAs), locked nucleic acids (LNAs), C2′ modifications] improve stability and delivery but remain limited by biodistribution, tissue penetration, off-target effects, and immunogenicity. In conclusion, standardized workflows and multicenter validation, integrated with clinical and imaging data, are essential to translate miRNA-based tools into precision PC management