Search Results (300)

Obesity is a chronic, relapsing disease and a significant oncological risk factor. The correlation is most pronounced and consistent for endometrial cancer. Conversely, evidence linking obesity to ovarian cancer is less robust and varies by histotype, while the association with cervical cancer is influenced by factors related to screening, diagnosis, treatment, and survival. This review examines obesity, particularly class III (morbid) obesity, in relation to the risk of gynaecological cancer, diagnostic approaches, and management strategies. A structured narrative review of PubMed/MEDLINE, Cochrane Library, Scopus and Web of Science Core Collection was conducted for literature published between January 2000 and December 2025. Eligible evidence included systematic reviews, meta-analyses, cohort and case–control studies, mechanistic studies and clinical guidance relevant to obesity and endometrial, ovarian or cervical cancer. Title/abstract screening and full-text selection were conducted using predefined criteria for conceptual relevance and clinical applicability. Excess adiposity contributes to endometrial carcinogenesis through hormonal dysregulation, insulin resistance and hyperinsulinaemia, adipokine imbalance, chronic inflammation, and oxidative stress. In ovarian cancer, associations are generally weaker but appear more relevant for selected histological subtypes and cumulative adiposity exposure. In cervical cancer, obesity should not be interpreted as replacing HPV-driven pathogenesis; rather, it may affect screening adequacy, treatment selection, perioperative risk, and disease-specific survival in morbidly obese patients. Current evidence does not support morbid obesity as an independent driver of all gynaecological cancers. It supports obesity as a major modifiable risk factor and clinical modifier, particularly for endometrial cancer, and highlights the need for pragmatic risk stratification based on BMI class, adiposity distribution, metabolic comorbidity, functional status and cancer-site-specific pathways. Biomarker evidence remains hypothesis-generating, and obesity-integrated oncological pathways require prospective validation in patients with a BMI ≥ 40 kg/m². Full article

Soft tissue sarcomas (STSs) are rare and heterogeneous mesenchymal malignancies characterized by diverse molecular profiles and immune landscapes. Although immunotherapy has revolutionized the treatment of specific solid tumors, its efficacy in STSs remains limited and variable across histotypes. This review explores the panorama of biomarkers of immunotherapy sensitiveness in STSs, with particular emphasis on tumor-intrinsic features and on tumor microenvironment (TME) signatures. Current evidence highlights low tumor mutational burden, rare microsatellite instability, heterogeneous antigen expression, and epigenetic suppression of antigen presentation as hallmarks of the immune resistance that is characteristic of many STSs. However, growing evidence underlines TME composition as a major determinant of response to different types of immunotherapy. Indeed, the presence of B-cell-rich tertiary lymphoid structures and certain traits of adaptive immune responses are provenly associated with enhanced sensitivity to immunotherapy and enhanced outcomes. We further discuss emerging strategies aimed at enhancing STS immunogenicity, either by increasing intrinsic tumor immunogenicity or remodeling TME composition and functional profile. Collectively, the available data support a paradigm shift from a sarcoma cell-centered approach toward a multi-compartment TME-including strategy, providing a framework for the development of more effective and personalized immunotherapeutic strategies in STS. Full article

Canine cutaneous neoplasms are common and morphologically heterogeneous lesions whose diagnosis relies on integrating gross examination, cytology, and histopathology. This retrospective pilot study assessed the feasibility of a multimodal GPT-based large language model as an assistive, not autonomous, tool for standardized description, differential diagnosis generation, and classification support across this diagnostic workflow. Fifty-one histologically confirmed canine cutaneous tumors were retrospectively selected from the laboratory information system of the Veterinary Pathology Laboratory, University of Naples Federico II. For each case, de-identified gross photographs, digitized cytology, and representative histologic images were provided to the model using templated prompts. Model outputs were independently reviewed by two veterinary pathologists, who reached consensus on descriptive quality and diagnostic concordance with the histologic reference diagnosis. Final diagnostic outputs were classified as correct, partially correct, or incorrect. Strict accuracy was defined as the proportion of fully correct diagnoses, whereas broad accuracy combined correct and partially correct outputs considered diagnostically informative. Overall, the model achieved a strict diagnostic accuracy of 66.7% (34/51; 95% CI: 53.0–78.0) and a broad diagnostic accuracy of 90.2% (46/51; 95% CI: 79.0–95.7). Performance was highest in epithelial tumors and lower in mesenchymal and melanocytic tumors, in which the model more often identified broader diagnostic categories than specific histotypes. These findings suggest that GPT-based systems may support report standardization, descriptive consistency, and morphology-driven reasoning in veterinary pathology. However, reduced entity-level specificity, variable descriptive quality, and the risk of plausible but non-concordant outputs require strict human supervision and further validation before routine implementation. Full article

Salivary gland tumors, although relatively rare, exhibit a wide histological variety. The most modern classifications list over 30 histotypes, both benign and malignant, with widely varying morphological, epidemiological, and clinical characteristics, sometimes even within the same tumor type based on grading. The consequence of these characteristics is that regarding the diagnosis and treatment of salivary gland tumors, there have been, and still are, some certainties, many uncertainties, and some myths not supported by irrefutable studies, but which are cited, repeated, and taken for granted from one article to the next. The purpose of this narrative review is to analyze the most important and controversial opinions regarding the diagnosis and treatment of salivary gland tumors of the head and neck, and, in particular, those of the most frequent and/or problematic histological types, both malignant and benign. To this end, approximately one hundred historical and recent studies on these topics were analyzed. Full article

Epithelial ovarian cancers (EOCs) histotypes are characterized by marked molecular heterogeneity and limited effectiveness of current screening and monitoring strategies. Earlier identification of tumor-associated alterations may support timely intervention, especially in genetically predisposed or early-onset patient populations. While liquid biopsy approaches have primarily focused on circulating DNA, RNA, and proteins, increasing evidence indicates that cancer-associated metabolic reprogramming generates measurable informative signals in peripheral biofluids. This review summarizes recent progress in liquid biopsy-derived metabolomics in EOCs, covering analytical platforms applied to serum, plasma, urine, and ascites. Recurrent metabolic signatures linked to tumor burden, disease stage, treatment response, and clinical outcome are described, and their significance in discriminating malignant and non-malignant conditions is critically discussed. Collectively, these findings suggest that metabolomics may provide complementary functional information alongside genomic and histopathological profiling. Although its clinical implementation still requires further validation and methodological standardization, ongoing advances in analytical technologies and the integration of high-dimensional metabolic data into machine learning-based frameworks may progressively support the identification of early tumor-associated alterations and contribute to more accurate disease stratification and biologically informed clinical management. Full article

Objective: Our objectives were to describe molecular profiling in a real-life cohort of patients with radioiodine-resistant (RAI-R) differentiated or poorly differentiated thyroid cancer (DTC or PDTC) treated with lenvatinib and to focus on factors potentially influencing the quality of tissue samples for molecular analysis, including the impact of storage time, defined as the interval between tissue collection and molecular testing. Design: We retrospectively included all lenvatinib-treated RAI-R DTC or PDTC patients tested with DNA- and/or RNA-based next-generation sequencing (NGS) in our center, also analyzing the results of fluorescence in situ hybridization (FISH) for RET fusions if the sample did not satisfy quality criteria for RNA-based NGS analysis. We investigated differences in terms of histotype, biopsy site, or storage time between adequate and inadequate samples for RNA-based NGS. Results: At least one gene alteration was detected in 50% of the cohort (18 out of 36 patients); RAS and BRAF were the most frequent mutations, while gene fusions accounted for 5.6% of cases. Tissue samples were more frequently adequate for DNA-based NGS compared to RNA-NGS analysis (93.9% vs. 58.3%, p < 0.001). The median storage time was significantly longer in the case of inadequate samples for RNA-based NGS compared with adequate specimens (41.5 vs. 9.5 months, p = 0.016); samples archived for ≥3 years led more frequently to an inadequate result. Conclusions: Advanced RAI-R TC candidates for systemic therapy often harbor gene alterations. An adequate result was less frequently achieved in cases of RNA-based NGS than in DNA-based NGS, especially if the interval between tissue collection and molecular analysis was longer; nevertheless, the limited cohort size precludes definitive conclusions. Full article

Background/Objectives: Mucinous adenocarcinoma (MAC) is a rare and clinically problematic subtype of rectal cancer, tending to present at an advanced stage and to respond poorly to neoadjuvant therapy. The consistently worse prognosis than that of not-otherwise-specified adenocarcinoma (NOS-AC) is not fully understood, potentially owing to intrinsically more aggressive biology or specific immune evasion mechanisms. We used the IMMUNOREACT multicentre cohort, with external validation in TCGA, to investigate the clinical and immunological features of rectal MAC in detail. Methods: Two hundred patients with rectal adenocarcinoma (16 MAC, 184 NOS-AC) from the IMMUNOREACT 1 (NCT04915326) and IMMUNOREACT 2 (NCT04917263) prospective cohorts were included. To account for the imbalance in baseline characteristics, propensity score matching (PSM) was performed on age, sex, neoadjuvant treatment and TNM stage. The immune microenvironment was characterised using immunohistochemistry (CD3, CD4, CD8, CD8β, Tbet, FoxP3, PD-L1, MSH6, PMS2, CD80), flow cytometry and NanoString PanCancer IO 360™ transcriptomics of adjacent healthy mucosa. Findings were externally validated against TCGA rectal and colon adenocarcinoma datasets. Results: MAC presented at significantly more advanced stage than NOS-AC across all TNM parameters: higher T stage (p = 0.006), N stage (p < 0.001), M stage (p = 0.039) and overall TNM stage (p < 0.001). In the unmatched cohort, MAC was associated with worse overall survival (HR 2.53; 95% CI 1.03–6.23; p = 0.043) and disease-free survival (HR 2.86; 95% CI 1.25–6.55; p = 0.013), but both differences became non-significant after PSM. MAC patients had higher haemoglobin after adjusting for confounders (mean difference [MD] 1.26 g/dL, 95% CI 0.30–2.31, p = 0.012), consistent with a hypothesis of reduced chronic rectal bleeding as a possible mechanism for late presentation. Transcriptomically, MAC showed suppression of HLA class II antigen presentation genes (HLA-DQA1, HLA-DQB1, HLA-DRB1) and myeloid activation genes (S100A8/A9/A12) in adjacent healthy mucosa. Loss of MMR proteins MSH6 and PMS2 in histologically normal mucosa was significantly more frequent in MAC. These findings were replicated in the TCGA cohort, which also showed lower tumour mutational burden and a distinct mucin-associated transcriptomic profile in MAC. Conclusions: The worse outcomes of rectal MAC appear to be driven largely by late-stage presentation, possibly owing to later diagnosis. MAC nonetheless carries a distinct immune phenotype, detectable even in histologically normal surrounding mucosa, that likely contributes to its treatment resistance. These observations provide a basis for developing histotype-specific approaches to both early detection and treatment in this uncommon but clinically challenging tumour subtype. Full article

Introduction: Lung cancer, the leading cause of cancer-related mortality worldwide, is a heterogeneous malignancy comprising distinct histological and molecular subtypes, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases and adenocarcinoma (ADC) representing the most prevalent histotype. An emerging pathological feature of NSCLC, spread through air spaces (STAS)—defined as the extension of tumor cells into the lung parenchyma beyond the main tumor margin—has been associated with worse disease-free and overall survival and has been proposed as a possible predictor of recurrence to guide surgical extent. Concurrently, recent comprehensive genomic profiling of early-stage NSCLC has highlighted the need to interpret multi-omics data and their relationship with pathological variables, including IASLC histological subtypes, to better personalize treatment strategies. In this context, we investigated the overall distribution of STAS and its association with tumor mutational profiles and IASLC histological subtypes in a large real-world cohort of lung adenocarcinoma patients from the LANTERN project. Materials and Methods: In a prospective, multicenter observational study (March 2023–December 2024), 271 NSCLC patients were enrolled, and clinicopathological, immunohistochemical, and genomic data were collected; comprehensive genomic profiling was performed using the TruSight Oncology 500 assay to analyze 523 cancer-related genes, tumor mutational burden (TMB), and microsatellite instability; and STAS was assessed according to IASLC criteria. Adenocarcinoma accounted for roughly 90% of the cases, with a median age of 69 years and a predominance of stage IV disease (49.5%). STAS was evaluable in 162 cases and was detected in 17.9% of tumors. Results: STAS-positive tumors showed a higher trend towards locally advanced and advanced disease; no differences were observed in sex, age, smoking status, tumor mutational burden, or PD-L1 expression. Additionally, STAS-positive tumors showed a higher association with micropapillary, mucinous, and papillary patterns, whereas the acinar pattern was more frequent in STAS-negative tumors. The most frequently mutated genes were TP53, KRAS, EGFR, and STK11, with no significant differences between groups; ROS1 alterations were absent in STAS-negative tumors but detected more frequently in STAS-positive cases. Conclusions: Overall, these findings indicate that STAS positivity is associated with high-risk histological subtypes and advanced disease, suggesting its importance as a marker of tumor aggressiveness and emphasizing the need for its systematic evaluation in lung adenocarcinoma to better guide surgical planning and patient risk assessment. Full article

Ovarian cancer (OC) comprises multiple histotypes with distinct mechanisms, molecular features, and clinical behavior. We used Mendelian randomization (MR) to map histotype-stratified metabolic pathways and connect them to drug targets, establishing a translatable target–metabolic node–histotype risk chain. We built a multi-stage MR framework using Integrative Epidemiology Unit (IEU) OpenGWAS summary statistics. After screening 1400 plasma metabolites against overall ovarian cancer in UK Biobank and Ovarian Cancer Association Consortium (OCAC) with KEGG enrichment, we traced a prespecified amino acid/energy–nitrogen axis using histotype-stratified univariable MR and pathway-restricted multivariable MR. We then performed cis drug-target MR for PPARG, DPP4, ABCC8/KCNJ11, and SLC5A2, integrated triangulation, colocalization, and mediation analyses, and experimentally interrogated the prioritized PPARG/ABCC8-KCNJ11–lactate–invasive mucinous ovarian cancer (IMOC) triangle. Screening nominated 55 and 72 metabolites in UK Biobank and OCAC, respectively (IVW p < 0.05), highlighting amino-acid nitrogen and central-carbon metabolism. Univariable Mendelian randomization (UVMR) showed marked heterogeneity: alanine increased low-grade serous ovarian cancer (LGSOC) risk, glutamate was protective for endometrioid OC, and lactate-related traits most consistently implicated the low-grade/borderline serous lineage. In multivariable Mendelian randomization (MVMR), tryptophan and lactate levels emerged as independent risk nodes for serous low-grade plus low malignant potential (LG + LMP). Drug-target MR prioritized PPARG as protective (OR = 0.18) and ABCC8/KCNJ11 as risk-increasing (OR = 7.50) for IMOC, with opposite target → lactate effects supporting a directionally symmetric target–lactate–IMOC triangle. Experimental perturbation in mucinous ovarian cancer models produced concordant reciprocal changes in lactate and malignant phenotypes, extending this triangle biologically. This integrative MR framework delineates histotype-specific metabolic drivers and links them to actionable targets, providing a roadmap from genetic prioritization to mechanistic and translational validation. Full article

Background and Objectives: Sarcomas are rare malignancies of mesenchymal origin comprising less than 1% of all adult solid tumors, exhibiting marked histological heterogeneity and variable clinical behavior. Data from Eastern European tertiary oncology centers remain scarce. This study characterized the clinicopathological features, treatment modalities, and survival outcomes of patients with sarcomas of the extremities and trunk treated at the “Prof. Dr. Alexandru Trestioreanu” Institute of Oncology from Bucharest over a ten-year period. Materials and Methods: We conducted a retrospective analysis of 164 patients diagnosed with sarcomas of the extremities and trunk between 2010 and 2020 at “Prof. Dr. Alexandru Trestioreanu” Institute of Oncology. Variables included age, sex, tumor localization, histological subtype, immunohistochemical profile, treatment modalities, recurrence, metastatic spread, and overall survival (OS). Kaplan–Meier curves estimated survival; log-rank tests were applied for subgroup comparisons. Results: The cohort comprised 82 males and 82 females (50.0% each), with a mean age of 48.8 ± 18.3 years. The lower limb was the most frequent site (n = 96, 58.5%), particularly the thigh/femur (34.1%). The most common subtypes were undifferentiated pleomorphic sarcoma (14.6%), osteosarcoma (12.2%), and fibrosarcoma (11.0%). Surgery was performed in 75.6%, chemotherapy in 80.5%, and radiotherapy in 59.8%. Local recurrence occurred in 35.4% and distant metastases in 41.5%. The median OS was 96.0 months (vital status known for 160/164 patients; 90 deceased, 70 alive; OS duration available in 126 patients). Metastatic disease was associated with shorter observed survival in descriptive Kaplan–Meier analysis (log-rank p < 0.001); this comparison is exploratory given the time-dependent nature of the variable. Survival ranged from 11.5 months (leiomyosarcoma) to 162.5 months (dermatofibrosarcoma protuberans) by histotype. Conclusions: This study provides clinically relevant epidemiological and survival data from Romania. The findings illustrate real-world heterogeneity of sarcoma presentations and outcomes at an Eastern European tertiary center and highlight the need for improved diagnostic standardization, prospective data collection, and integration within specialized sarcoma networks. Full article

Background/Objectives: Primary spinal sarcomas, encompassing both bone and soft tissue histotypes, demand individualized reconstruction due to heterogeneous tumor biology, anatomic complexity, and host environments compromised by radiation, systemic therapy, or prior surgery. This narrative review reframes post-resection spinal reconstruction through a precision-medicine lens. Methods: A structured literature review was performed using PubMed and Scopus, targeting articles published between 2000 and 2026. Searches encompassed spinal sarcoma reconstruction, radiation and fusion, biologic reconstruction, and emerging technologies. Results: Tumor grade, radiation exposure, and systemic therapy timing emerge as multiplicative determinants of reconstructive environment quality, with drug-class-specific perioperative effects warranting stratified management. Vascularized bone grafts achieve reliable fusion in compromised hosts where avascular constructs fail. A precision-oriented reconstructive ladder is proposed as a conceptual, hypothesis-generating framework to guide strategy selection. Hybrid PSI-VBG constructs may further expand reconstructive possibilities. The evidence base remains largely composed of small, retrospective series. Conclusions: Individualized strategies anchored in tumor biology and host environment are the cornerstone of durable spinal sarcoma reconstructions. The proposed framework requires prospective, multi-institutional validation. Standardized outcome definitions, prospective registries, and histotype-stratified analyses are needed to advance the field. Full article

Early diagnosis of ovarian cancer remains one of the most important unmet needs in gynecologic oncology because survival is strongly stage-dependent and most patients still present with disseminated disease. Conventional non-invasive tools, particularly CA-125, transvaginal ultrasound, and composite triage algorithms, remain clinically useful but are limited by suboptimal sensitivity for stage I disease and by reduced specificity in premenopausal women and in benign inflammatory or endometriosis-associated conditions. Circulating tumor DNA (ctDNA) has therefore emerged as a candidate biomarker capable of extending liquid biopsy beyond conventional serology. In ovarian cancer, however, ctDNA implementation is constrained by low tumor shedding in early-stage disease, marked biologic heterogeneity across histotypes, clonal hematopoiesis-related background noise, and major pre-analytical and analytical sources of variability. This narrative review, informed by structured searches of PubMed, Scopus, and Web of Science, examines the evolving evidence for ctDNA mutations, methylation-based assays, multi-omic platforms, and machine-learning models across three distinct clinical contexts: population screening, preoperative triage of adnexal masses, and post-treatment assessment of molecular residual disease. We also discuss positive predictive value, false-positive harms, health-economic implications, standardization initiatives, and ongoing prospective studies. Overall, current evidence suggests that the most plausible near-term role for liquid biopsy in ovarian cancer is not as a universal stand-alone screening test, but as an integrated component of risk stratification and disease-monitoring frameworks that combine molecular signals with clinicopathologic and imaging data. Full article

Endometriosis is a chronic estrogen-dependent disorder affecting approximately 10% of women of reproductive age. Increasing epidemiological and molecular evidence indicates that it may represent a precursor condition for a subset of ovarian malignancies collectively defined as endometriosis-associated ovarian cancer (EAOC), predominantly endometrioid and clear cell carcinomas. Malignant transformation is driven by the interplay between chronic inflammation, oxidative stress, and local hyperestrogenism within the endometriotic microenvironment. Recurrent hemorrhage and persistent immune activation further promote genomic instability and clonal expansion. Shared somatic mutations have been identified in both atypical endometriosis and adjacent carcinomas, supporting a model of stepwise tumorigenesis. Dysregulation of signaling pathways and epigenetic mechanisms, including microRNA alterations, further contribute to tumor development. Although the absolute risk of malignant transformation remains low, women with ovarian endometriosis and deep infiltrating disease show an increased risk of ovarian cancer. EAOC is frequently diagnosed at earlier stages and generally demonstrates a more favorable prognosis than high-grade serous carcinoma, although clear cell histotypes may exhibit chemoresistance and distinct molecular vulnerabilities. This review summarizes current evidence on the pathogenesis, molecular mechanisms, and clinical implications of EAOC, highlighting future strategies for risk stratification and personalized surveillance. Full article

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need by developing and validating a deep learning-based diagnostic support tool designed to enhance the objectivity and reproducibility of this classification. In this work, we address a key challenge in computational pathology—the tendency of attention mechanisms to overfit by concentrating on limited features—by systematically evaluating a direct regularization method within multiple instance learning (MIL) models. The models were trained and validated using 10-fold cross-validation on a public training set of 538 whole-slide images and further tested on an independent public dataset for the more challenging task of molecular subtype classification. We utilized features from a foundational model pre-trained on histopathology data to represent tissue morphology. Our findings demonstrate that directly regularizing the attention mechanism with a stochastic approach provides a statistically significant improvement in accuracy and generalization, highlighting its power as a robust technique to mitigate overfitting for this clinical task. In direct contrast to the reported variability in manual assessment, our final model achieved high consistency and accuracy, with a balanced accuracy of 0.854 and a Cohen’s Kappa of 0.791. The model also demonstrated strong generalization on the molecular classification task. Its attention mechanism provides visual heatmaps for pathologist review, fostering interpretability and trust. We have developed a highly accurate and generalizable artificial intelligence tool that directly addresses the challenge of interobserver variability in ovarian cancer classification. Its performance highlights the potential for artificial intelligence to serve as a decision support system, standardizing histopathological assessment. Full article

Introduction and Background: High-grade serous ovarian carcinoma (HGSOC) is notorious for its poor prognosis owing to its inherent biological aggressiveness and development of chemoresistance. The mechanistic target of rapamycin (mTOR) pathway is dysregulated in 55% of epithelial ovarian cancers, representing an appealing therapeutic target. To date, the clinical trials of mTOR inhibitors have shown modest response. In this study, we investigated the mTOR pathway in a clinical cohort of primary, chemo-naive, high-grade ovarian cancer samples, along with its regulatory post-transcriptional miRNA regulation. Methodology: We performed differential gene expression analysis on 100 HGSOC patients from TCGA and 80 healthy controls (i.e., normal ovarian tissue) from GTEx. The differentially expressed genes (DEGs) were overlaid onto the KEGG mTOR signalling pathway, followed by functional enrichment analysis. Next, we conducted differential miRNA expression analysis on the same cohort and identified regulatory miRNA–mTOR gene pairs involved in cancer pathogenesis. Finally, we constructed an interaction network and identified key hub genes and miRNAs with potential prognostic significance. Results: We identified 95 mTOR pathway genes that were significantly differentially expressed, involving upstream regulators, core components, and downstream effectors. Functional pathway analysis revealed a prominent shift toward mTORC1 activation, accompanied by paradoxical activation of autophagy. The let-7 miRNA family was identified as a key regulator of the mTOR pathway, potentially facilitating disease progression. RICTOR downregulation, a key component of the mTORC2 complex, appears to play a critical role in this histotype. In addition, FNIP1, a tumour suppressor gene implicated in mTOR dysregulation, was found to correlate with survival outcomes. Conclusions: We propose a model of dual activation of mTORC1 and autophagy in HGSOC as the metabolic rewiring enabling cancer progression under nutrient and cellular stress. Full article