Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Abdulrahman Alotaibi, Noura Alajmi, Mohammed Alnuhait

Xin Gao, Xin Gao, Linghui Tao et al.

BackgroundDespite advancements in cancer treatment, malignant tumors remain a significant global health challenge. Drawbacks of chemotherapy, such as drug resistance and strong side effects, have prompted the exploration of antibody-drug conjugates (ADCs), which combine targeting capabilities with potent cytotoxins to enhance therapeutic efficacy.MethodsWe searched PubMed, Cochrane, and EMBASE library databases up to June 8, 2025, for eligible randomized controlled trials (RCTs) and extracted relevant data. The primary outcome measures were overall survival (OS) and progression-free survival (PFS), with subgroup analysis and sensitivity analysis conducted to assess the heterogeneity of statistical results.ResultsA total of thirteen RCTs involving 5,927 patients were included. The results indicated that ADCs offered superior OS (HR = 0.67, 95%CI: 0.55-0.81) and PFS (HR = 0.76; 95% CI: 0.66-0.86) compared to chemotherapy drugs. The any adverse event (96.8% vs. 93.6%), grade 3–5 adverse event (51.7% vs. 51.8%) and serious adverse event (25.3% vs. 22.4%) caused to patients were generally similar between ADCs and chemotherapy.ConclusionOur meta-analysis demonstrates that compared to chemotherapy drugs, ADC drugs can prolong both OS and PFS in cancer patients, with no significant difference in adverse reactions.Systematic Review Registrationhttp://www.crd.york.ac.uk/PROSPERO, identifier CRD42024592020.

Lang Xu, Bingna Wang, Chen Wang et al.

Abstract Emerging evidence suggests the importance of basement membrane components in cancer metastasis; however, their specific roles in esophageal carcinoma remain underexplored. To investigate this, we analyzed 152 esophageal cancer and 11 normal esophageal tissue samples, identifying basement membrane-related prognostic signatures through differential gene expression profiling and Least Absolute Shrinkage and Selection Operator regression. A six-gene panel (LAMC2, GPC2, AGRN, ITGA3, LAMA3, and LOXL4) demonstrated robust predictive capacity, which we subsequently integrated with clinical features via nomogram modeling to predict overall survival. Our computational analyses revealed distinct tumor microenvironment immune cell profiles and chemotherapeutic drug sensitivities across risk strata. We performed an immunohistochemical assay to confirm increased tumor tissue expression, thereby reinforcing the clinical relevance of these biomarkers. Experimental validation using KYSE-150 esophageal squamous carcinoma cells demonstrated that while LAMC2 knockdown attenuated cellular migration, AGRN, GPC2, ITGA3, LAMA3, and LOXL4 suppression enhanced migratory capacity. Proliferation assays further revealed increased growth rates upon GPC2, ITGA3, and LAMA3 expression inhibition. Our results established a basement membrane-derived risk model for esophageal carcinoma and revealed the roles of the model genes in tumor progression regulation. This model advances prognostic stratification and provides insights into therapeutic targets.

Cheng Peng, Tengteng Wang, Michelle D. Holmes et al.

Abstract Epidemiologic data, supported by experiments, suggest aspirin may improve survival in breast cancer patients. However, recent trials reported a lack of protection, though the length of intervention was limited. Among 10,705 stages I–III breast cancer patients in the Nurses’ Health Studies (NHS/NHSII), we examined the associations between post-diagnostic aspirin use and long-term breast cancer survival. During up to 34 years of follow-up, regular post-diagnostic aspirin use was associated with a 38% and 28% lower risk of breast cancer-specific and total mortality. Associations were more evident with longer duration of post-diagnostic aspirin use but attenuated with higher stage and older age at diagnosis. Pre-diagnostic long-term aspirin use was associated with the downregulation of tumor proliferation pathways in NHS/NHSII and the aspirin-gene-expression-signature predicted better survival in METABRIC. Our study highlighted the need for trials with longer duration and suggested that aspirin use before diagnosis may alter the tumor-microenvironment towards a less proliferative type.

Javad Noorbakhsh, Ali Foroughi pour, Jeffrey Chuang

Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms, in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling, as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.

Abhishek Srivastava, Koushik Biswas, Gorkem Durak et al.

Segmentation of colorectal cancer (CRC) tumors in 3D medical imaging is both complex and clinically critical, providing vital support for effective radiation therapy planning and survival outcome assessment. Recently, 3D volumetric segmentation architectures incorporating long-range sequence modeling mechanisms, such as Transformers and Mamba, have gained attention for their capacity to achieve high accuracy in 3D medical image segmentation. In this work, we evaluate the effectiveness of these global token modeling techniques by pitting them against our proposed MambaOutUNet within the context of our newly introduced colorectal tumor segmentation dataset (CTS-204). Our findings suggest that robust local token interactions can outperform long-range modeling techniques in cases where the region of interest is small and anatomically complex, proposing a potential shift in 3D tumor segmentation research.

Sebastian Johannes Müller, Eya Khadhraoui, Hans Henkes et al.

Abstract Purpose Differentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy. Methods In this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available. Results A total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001). Conclusion The lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

Veronica Murianni, Alessio Signori, Sebastiano Buti et al.

BackgroundImmunotherapies exhibit peculiar cancer response patterns in contrast to chemotherapy and targeted therapy. Some patients experience disease response after initial progression or durable responses after treatment interruption. In clinical practice, immune checkpoint inhibitors may be continued after radiological progression if clinical benefit is observed. As a result, estimating progression-free survival (PFS) based on the first disease progression may not accurately reflect the actual benefit of immunotherapy.MethodsThe Meet-URO 15 study was a multicenter retrospective analysis of 571 pretreated metastatic renal cell carcinoma (mRCC) patients receiving nivolumab. Time to strategy failure (TSF) was defined as the interval from the start of immunotherapy to definitive disease progression or death. This post-hoc analysis compared TSF to PFS and assess the response and survival outcomes between patients treatated beyond progression (TBP) and non-TBP. Moreover, we evaluated the prognostic accuracy of the Meet-URO score versus the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) score based on TSF and PFS.ResultsOverall, 571 mRCC patients were included in the analysis. Median TSF was 8.6 months (95% CI: 7.0 – 10.1), while mPFS was 7.0 months (95% CI: 5.7 – 8.5). TBP patients (N = 93) had significantly longer TSF (16.3 vs 5.5 months; p &lt; 0.001) and overall survival (OS) (34.8 vs 17.9 months; p &lt; 0.001) but similar PFS compared to non-TBP patients. In TBP patients, a median delay of 9.6 months (range: 6.7-16.3) from the first to the definitive disease progression was observed, whereas non-TBP patients had overlapped median TSF and PFS (5.5 months). Moreover, TBP patients had a trend toward a higher overall response rate (33.3% vs 24.3%; p = 0.075) and disease control rate (61.3% vs 55.5%; p = 0.31). Finally, in the whole population the Meet-URO score outperformed the IMDC score in predicting both TSF (c-index: 0.63 vs 0.59) and PFS (0.62 vs 0.59).ConclusionWe found a 2-month difference between mTSF and mPFS in mRCC patients receiving nivolumab. However, TBP patients had better outcomes, including significantly longer TSF and OS than non-TBP patients. The Meet-URO score is a reliable predictor of TSF and PFS.

Kyrie Zhixuan Zhou, Royta Iftakher, Sean P. Mullen et al.

Cancer survivors experience a wide range of impairments arising from cancer or its treatment, such as chemo brain, visual impairments, and physical impairments. These impairments degrade their quality of life and potentially make software use more challenging for them. However, there has been limited research on designing accessible software for cancer survivors. To bridge this research gap, we conducted a formative study including a survey (n=46), semi-structured interviews (n=20), and a diary study (n=10) with cancer survivors. Our results revealed a wide range of impairments experienced by cancer survivors, including chemo brain, neuropathy, and visual impairments. Cancer survivors heavily relied on software for socialization, health purposes, and cancer advocacy, but their impairments made software use more challenging for them. Based on the results, we offer a set of accessibility guidelines that software designers can utilize when creating applications for cancer survivors. Further, we suggest design features for inclusion, such as health resources, socialization tools, and games, tailored to the needs of cancer survivors. This research aims to spotlight cancer survivors' software accessibility challenges and software needs and invite more research in this important yet under-investigated domain.

Timothy Mulvany, Daniel Griffiths-King, Jan Novak et al.

Monitoring of Diffuse Intrinsic Pontine Glioma (DIPG) and Diffuse Midline Glioma (DMG) brain tumors in pediatric patients is key for assessment of treatment response. Response Assessment in Pediatric Neuro-Oncology (RAPNO) guidelines recommend the volumetric measurement of these tumors using MRI. Segmentation challenges, such as the Brain Tumor Segmentation (BraTS) Challenge, promote development of automated approaches which are replicable, generalizable and accurate, to aid in these tasks. The current study presents a novel adaptation of existing nnU-Net approaches for pediatric brain tumor segmentation, submitted to the BraTS-PEDs 2024 challenge. We apply an adapted nnU-Net with hierarchical cascades to the segmentation task of the BraTS-PEDs 2024 challenge. The residual encoder variant of nnU-Net, used as our baseline model, already provides high quality segmentations. We incorporate multiple changes to the implementation of nnU-Net and devise a novel two-stage cascaded nnU-Net to segment the substructures of brain tumors from coarse to fine. Using outputs from the nnU-Net Residual Encoder (trained to segment CC, ED, ET and NET tumor labels from T1w, T1w-CE, T2w and T2-FLAIR MRI), these are passed to two additional models one classifying ET versus NET and a second classifying CC vs ED using cascade learning. We use radiological guidelines to steer which multi parametric MRI (mpMRI) to use in these cascading models. Compared to a default nnU-Net and an ensembled nnU-net as baseline approaches, our novel method provides robust segmentations for the BraTS-PEDs 2024 challenge, achieving mean Dice scores of 0.657, 0.904, 0.703, and 0.967, and HD95 of 76.2, 10.1, 111.0, and 12.3 for the ET, NET, CC and ED, respectively.

Akhila Krishna, Ravi Kant Gupta, Pranav Jeevan et al.

Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients. This paper introduces two gene selection strategies for deep learning-based survival prediction models. The first strategy uses a sparsity-inducing method while the second one uses importance based gene selection for identifying relevant genes. Our overall approach leverages the power of deep learning to model complex biological data structures, while sparsity-inducing methods ensure the selection process focuses on the most informative genes, minimizing noise and redundancy. Through comprehensive experimentation on diverse genomic and survival datasets, we demonstrate that our strategy not only identifies gene signatures with high predictive power for survival outcomes but can also streamlines the process for low-cost genomic profiling. The implications of this research are profound as it offers a scalable and effective tool for advancing personalized medicine and targeted cancer therapies. By pushing the boundaries of gene selection methodologies, our work contributes significantly to the ongoing efforts in cancer genomics, promising improved diagnostic and prognostic capabilities in clinical settings.

Yi Cui, Yao Li, Jayson R. Miedema et al.

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep-learning method to allow for classification, at the slide level, of nevi and melanomas. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors.

Liangrui Pan, Qingchun Liang, Wenwu Zeng et al.

Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images. VERN captures spatial topological features with feature sharing and skip connections to enhance model training. Using 1,546 histopathology slides, we built a large single-cohort STAS lung cancer dataset. VERN achieved an AUC of 0.9215 in internal validation and AUCs of 0.8275 and 0.8829 in frozen and paraffin-embedded test sections, respectively, demonstrating clinical-grade performance. Validated on a single-cohort and three external datasets, VERN showed robust predictive performance and generalizability, providing an open platform (http://plr.20210706.xyz:5000/) to enhance STAS diagnosis efficiency and accuracy.

Peng Du, Hongyi Chen, Li Shen et al.

Background: Primary central nervous system lymphoma (PCNSL) is a rare extranodal non-Hodgkin’s lymphoma that occurs in the central nervous system. Although sensitive to chemotherapy, 35–60% of PCNSL patients still relapse within 2 years after the initial treatment. High-dose methotrexate (HD-MTX) rechallenge is generally used in recurrent PCNSL, especially for patients who have achieved a response after initial methotrexate (MTX) treatment. However, the overall remission rate (ORR) of HD-MTX rechallenge is about 70–80%. Additionally, the side effects of HD-MTX treatment endanger the health of patients and affect their quality of life. Methods: This is a retrospective study of patients with first relapse PCNSL at Huashan Hospital, Fudan University between January 2000 and November 2020. By comparing the clinical characteristics and radiological manifestations of first relapsed PCNSL patients with remission and non-remission after receiving HD-MTX rechallenge, we screened out the key factors associated with HD-MTX rechallenge treatment response, to provide some help for the selection of salvage treatment strategies for patients with recurrent PCNSL. Additionally, patients with remission after HD-MTX rechallenge were followed up to identify the factors related to progression-free survival of the second time (PFS2) (time from the first relapse to second relapse/last follow-up). The Kruskal–Wallis and Pearson chi-square tests were performed to examine the univariate association. Further, multivariable logistic regression analysis was used to study the simultaneous effect of different variables. Results: A total of 207 patients were enrolled in the study based on the inclusion criteria, including 114 patients in the remission group (RG) and 81 patients in the non-remission group (nRG), and 12 patients were judged as having a stable disease. In Kruskal–Wallis and Pearson chi-square tests, progression-free survival rates for first time (PFS1) and whether the initial treatment was combined with consolidated whole brain radiotherapy (WBRT) were related to the response to HD-MTX rechallenge treatment, which was further validated in regression analysis. Further, after univariate analysis and regression analysis, KPS was related to PFS2. Conclusions: For PCNSL patients in their first relapse, HD-MTX rechallenge may be an effective salvage treatment. PFS1 and whether initial treatment was combined with consolidation WBRT were associated with HD-MTX rechallenge treatment response. In addition, patients with higher KPS at the time of the first relapse had a longer PFS2 after HD-MTX rechallenge treatment.

Dietrich Kong, Ke Wang, Qiu-Ning Zhang et al.

MicroRNAs play an indispensable role in numerous biological processes ranging from organismic development to tumor progression.In oncology,these microRNAs constitute a fundamental regulation role in the pathology of cancer that provides the basis for probing into the influences on clinical features through transcriptome data. Previous work focused on machine learning (ML) for searching biomarkers in different cancer databases, but the functions of these biomarkers are fully not clear. Taking lung cancer as a prototype case of study. Through integrating clinical information into the transcripts expression data, we systematically analyzed the effect of microRNA on diagnostic and prognostic factors at deteriorative lung adenocarcinoma (LUAD). After dimension reduction, unsupervised hierarchical clustering was used to find the diagnostic factors which represent the unique expression patterns of microRNA at various patient's stages. In addition, we developed a classification framework, Light Gradient Boosting Machine (LightGBM) and SHAPley Additive explanation (SHAP) algorithm, to screen out the prognostic factors. Enrichment analyses show that the diagnostic and prognostic factors are not only enriched in cancer-related athways, but also involved in many vital cellular signaling transduction and immune responses. These key microRNAs also impact the survival risk of LUAD patients at all (or a specific) stage(s) and some of them target some important Transcription Factors (TF).The key finding is that five microRNAs (hsa-mir-196b, hsa-mir-31, hsa-mir-891a, hsa-mir-34c, and hsa-mir-653) can then serve as not only potential diagnostic factors but also prognostic tools in the monitoring of lung cancer.

Sicheng Zhou, Rui Zhang, Anne Blaes et al.

Cardiotoxicity induced by the breast cancer treatments (i.e., chemotherapy, targeted therapy and radiation therapy) is a significant problem for breast cancer patients. The cardiotoxicity risk for breast cancer patients receiving different treatments remains unclear. We developed and evaluated risk predictive models for cardiotoxicity in breast cancer patients using EHR data. The AUC scores to predict the CHF, CAD, CM and MI are 0.846, 0.857, 0.858 and 0.804 respectively. After adjusting for baseline differences in cardiovascular health, patients who received chemotherapy or targeted therapy appeared to have higher risk of cardiotoxicity than patients who received radiation therapy. Due to differences in baseline cardiac health across the different breast cancer treatment groups, caution is recommended in interpreting the cardiotoxic effect of these treatments.

David Jozef Hresko, Marek Kurej, Jakub Gazda et al.

The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we combine these two networks, and further boost the performance by mixup augmentation.

Simone Mocellin

Fengxia Wu, Cai Chen, Fulai Peng

Background: The prevalence of Helicobacter pylori infection (HPI) is still high around the world, which induces gastric diseases, such as gastric cancer (GC). The epidemiological investigation showed that there was an association between HPI and asthma (AST). Coptidis rhizoma (CR) has been reported as an herbal medicine with anti-inflammatory and anti-bacterial effects.Purpose: The present study was aimed to investigate the protective mechanism of HPI on AST and its adverse effects on the development of GC. Coptis chinensis was used to neutralize the damage of HPI in GC and to hopefully intensify certain protective pathways for AST.Method: The information about HPI was obtained from the public database Comparative Toxicogenomics Database (CTD). The related targets in AST and GC were obtained from the public database GeneCards. The ingredients of CR were obtained from the public database Traditional Chinese Medicine Systems Pharmacology (TCMSP). The network pharmacology including gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and molecular docking were utilized. Protein–protein interaction was constructed to analyze the functional link of target genes. The molecular docking was employed to study the potential effects of active ingredients from CR on key target genes.Result: The top 10 key targets of HPI for AST were CXCL9, CX3CL1, CCL20, CCL4, PF4, CCL27, C5AR1, PPBP, KNG1, and ADORA1. The GO biological process involved mainly leukocyte migration, which responded to bacterium. The (R)-canadine and quercetin were selected from C. chinensis, which were employed to explore if they inhibited the HPI synchronously and protect against AST. The targets of (R)-canadine were SLC6A4 and OPRM1. For ingredient quercetin, the targets were AKR1B1 and VCAM1.Conclusion: CXCL9 and VCAM1 were the common targets of AST and HPI, which might be one of the imported targets of HPI for AST. Quercetin could be an effective ingredient to suppress HPI and help prevent AST.

Masoud Mohammadnezhad, Mondha Kengganpanich

Introduction Smoking as a public health challenge is globally considered the main risk factor of many non-communicable diseases (NCDs). Knowing factors contributing to smoking commencement and cessation is the necessary step to develop prevention strategies to combat this issue. To date, no study has been conducted in Fiji, therefore this study aimed to explore the reasons adult smokers initiate smoking and cessation in Fiji. Methods A qualitative study was conducted among 35 current smokers who were interviewed between 1 May and 31 July 2020 in Suva, Fiji. Three health centers were chosen randomly to collect data and purposive sampling was applied to reach study participants. A semi-structured, open-ended questionnaire was used to guide the interviews. The content of in-depth interviews was transcribed and data were analyzed using content and thematic analysis. Results The results of this study showed that most of the participants were male (57%), I-taukei (77%), single (54%), had attained tertiary education level (69%), were of Christian religion (77%), and unemployed (63%). Two main themes were identified including: ‘factors affecting smoking initiation’ and ‘factors affecting smoking cessation’. ‘Peer pressure’, ‘smoking myth’, ‘smoking as a fun’, ‘unpleasant event in life’ and ‘smoking establishes friendships’ were factors affecting initiation of smoking; while ‘knowledge on smoking harms’, ‘financial constraints’, ‘desire to improve health’, ‘constant request from family members’, ‘desire to save time’, ‘religious factors’ and ‘cultural factors’, were factors affecting smoking cessation among smokers. Conclusions This study highlights the main factors affecting smoking among adult smokers in Fiji. Considering these factors in future health planning will help policy makers and decision makers to develop tailored interventions to combat this health issue.