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

Frances Phang, Anna Weiss

Mengchi Zhang, Long Zhang, Tingting Miao et al.

Abstract Objective Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies worldwide with a poor prognosis. PANoptosis is a novel programmed cell death pathway. Presently, the expression pattern and prognosis of PANoptosis in HNSCC remain not fully elucidated. Methods The expression profile and corresponding clinical information were downloaded from The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO). Univariate cox regression and consensus clustering analysis were applied to identify PANoptosis-related molecular subtypes. The subtype specific pathways were identify through gene set variation analysis (GSVA). The differentially expressed genes (DEGs) between subtypes were identified using limma algorithm. Then, a PANoptosis-related signature was constructed using univariate cox regression analysis and lasso analysis based on the DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were exploited to evaluate the prognostic value of signature. In addition, the relationship between signature, immune microenvironment, and drug sensitivity was examined. Results A total of 707 tumor samples and 44 normal samples with corresponding clinical information were included in the study. Two molecular subtypes with distinct overall survival rates, immune cell infiltration and pathways were identified using clustering analysis. Compared to cluster N, cluster A was characterized by a favorable survival outcome and increasing immune cell infiltration. The TIDE analysis result indicated that patients in cluster A may have a better response to immunotherapy. In addition, a novel PANoptosis-related signature was constructed based on the DEGs from two clusters. The patients with high PANoptosis score were corresponding to a poor survival outcome. Cox regression analysis result revealed that the signature score could serve as an independent prognostic factors. Further drug analysis result suggest that patients in high PANoptosis group have more sensitivity to docetaxel drugs. Conclusions The two molecular subtypes and PANoptosis-related signature have the potential to underlying the molecular mechanism and provide reliable marker for the prognosis of HNSCC.

Anwen Lu, Mingxin Liu, Yiping Jiao et al.

Primary liver malignancies are widely recognized as the most heterogeneous and prognostically diverse cancers of the digestive system. Among these, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) emerge as the two principal histological subtypes, demonstrating significantly greater complexity in tissue morphology and cellular architecture than other common tumors. The intricate representation of features in Whole Slide Images (WSIs) encompasses abundant crucial information for liver cancer histological subtyping, regarding hierarchical pyramid structure, tumor microenvironment (TME), and geometric representation. However, recent approaches have not adequately exploited these indispensable effective descriptors, resulting in a limited understanding of histological representation and suboptimal subtyping performance. To mitigate these limitations, ARGUS is proposed to advance histological subtyping in liver cancer by capturing the macro-meso-micro hierarchical information within the TME. Specifically, we first construct a micro-geometry feature to represent fine-grained cell-level pattern via a geometric structure across nuclei, thereby providing a more refined and precise perspective for delineating pathological images. Then, a Hierarchical Field-of-Views (FoVs) Alignment module is designed to model macro- and meso-level hierarchical interactions inherent in WSIs. Finally, the augmented micro-geometry and FoVs features are fused into a joint representation via present Geometry Prior Guided Fusion strategy for modeling holistic phenotype interactions. Extensive experiments on public and private cohorts demonstrate that our ARGUS achieves state-of-the-art (SOTA) performance in histological subtyping of liver cancer, which provide an effective diagnostic tool for primary liver malignancies in clinical practice.

Shuyue Jiang, Wenjing Ma, Shaojun Yu et al.

Biological age, which may be older or younger than chronological age due to factors such as genetic predisposition, environmental exposures, serves as a meaningful biomarker of aging processes and can inform risk stratification, treatment planning, and survivorship care in cancer patients. We propose EpiCAge, a multimodal framework that integrates epigenetic and phenotypic data to improve biological age prediction. Evaluated on eight internal and four external cancer cohorts, EpiCAge consistently outperforms existing epigenetic and phenotypic age clocks. Our analyses show that EpiCAge identifies biologically relevant markers, and its derived age acceleration is significantly associated with mortality risk. These results highlight EpiCAge as a promising multimodal machine learning tool for biological age assessment in oncology.

Dheerendranath Battalapalli, Apoorva Safai, Maria Jaramillo et al.

A significant challenge in solid tumors is reliably distinguishing confounding pathologies from malignant neoplasms on routine imaging. While radiomics methods seek surrogate markers of lesion heterogeneity on CT/MRI, many aggregate features across the region of interest (ROI) and miss complex spatial relationships among varying intensity compositions. We present a new Graph-Radiomic Learning (GrRAiL) descriptor for characterizing intralesional heterogeneity (ILH) on clinical MRI scans. GrRAiL (1) identifies clusters of sub-regions using per-voxel radiomic measurements, then (2) computes graph-theoretic metrics to quantify spatial associations among clusters. The resulting weighted graphs encode higher-order spatial relationships within the ROI, aiming to reliably capture ILH and disambiguate confounding pathologies from malignancy. To assess efficacy and clinical feasibility, GrRAiL was evaluated in n=947 subjects spanning three use cases: differentiating tumor recurrence from radiation effects in glioblastoma (GBM; n=106) and brain metastasis (n=233), and stratifying pancreatic intraductal papillary mucinous neoplasms (IPMNs) into no+low vs high risk (n=608). In a multi-institutional setting, GrRAiL consistently outperformed state-of-the-art baselines - Graph Neural Networks (GNNs), textural radiomics, and intensity-graph analysis. In GBM, cross-validation (CV) and test accuracies for recurrence vs pseudo-progression were 89% and 78% with >10% test-accuracy gains over comparators. In brain metastasis, CV and test accuracies for recurrence vs radiation necrosis were 84% and 74% (>13% improvement). For IPMN risk stratification, CV and test accuracies were 84% and 75%, showing >10% improvement.

Tran-Der Tan, Lun-Wei Chiou

Background: Hematopoietic stem cell transplantation (HCT) is a curative treatment for various hematologic malignancies and some benign hematologic diseases. However, in addition to chronic graft-versus-host disease, second primary malignancy is also a long-term adverse effect. Materials and Methods: We retrospectively collected long-term follow-up data of 380 patients who had undergone transplantation (autologous in 184 with 126 long-term survivors and allogeneic in 196 patients with 100 long-term survivors) between 2001 and 2021 and analyzed the incidence and types of second primary malignancy. Results: Twelve patients had second primary malignancy, including five with head-and-neck squamous cell carcinoma (SCC), three with myelodysplastic syndrome/acute myeloid leukemia (MDS/AML), one with acute lymphoblastic leukemia (ALL), one with esophageal SCC, one with breast cancer, and one with papillary thyroid cancer. Of eight patients who underwent allogeneic hematopoietic stem cell transplants, five had head and neck, one had esophageal, one had breast, and one had papillary thyroid cancer. Of four patients who underwent autologous transplants, three had MDS/AML, and one had ALL. The cumulative incidence of second malignancy was 6% at 10 years and 16% at 19 years, and the postautologous and postallogeneic transplant rates were 5% versus 7% at 10 years and 15% versus 17% at 19 years. Conclusion: The occurrence of a second malignancy after HCT is a crucial issue of concern, and an early diagnosis is essential for posttransplant patients.

Vaseem M. Khatri, Mariella A. Mestres-Villanueva, Sreenija Yarlagadda et al.

Abstract Trastuzumab-deruxtecan (T-DXd) has demonstrated intracranial efficacy; however, safety and efficacy data remains limited with stereotactic radiosurgery (SRS). A multi-institutional review was performed with HER2+ or HER2-low metastatic breast cancer treated with T-DXd and SRS for active brain metastases. We identified 215 lesions treated over 48 SRS courses in 34 patients. Median follow up from T-DXd initiation was 13.9 months. The cumulative incidence of symptomatic radiation necrosis at 24 months per lesion was 2.1% and per patient 11%. The 12-month LC was 97%. HER2-low was associated with worse distant intracranial control (DIC) (adjusted HR 2.5, 95% CI 1.1–5.6, p = 0.03) and worse systemic progression free survival (PFS) (HR 4.1, 95% CI 1.6–10.7, p = 0.004). Concurrent SRS and T-DXd has excellent local control, without an increased risk of radiation necrosis. HER2-low disease is associated with worse systemic PFS and DIC with T-DXd compared to HER2+.

Aneesh Rangnekar, Nishant Nadkarni, Jue Jiang et al.

Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution (ID) datasets. However, reliable performance on ID datasets does not guarantee robust generalization on out-of-distribution (OOD) datasets. Importantly, once deployed for clinical use, it is impractical to have `ground truth' delineations to assess ongoing performance drifts, especially when images fall into the OOD category due to different imaging protocols. Hence, we introduced a comprehensive set of computationally fast metrics to evaluate the performance of multiple foundation models (Swin UNETR, SimMIM, iBOT, SMIT) trained with self-supervised learning (SSL). All models were fine-tuned on identical datasets for lung tumor segmentation from computed tomography (CT) scans. The evaluation was performed on two public lung cancer datasets (LRAD: n = 140, 5Rater: n = 21) with different image acquisitions and tumor stages compared to training data (n = 317 public resource with stage III-IV lung cancers) and a public non-cancer dataset containing volumetric CT scans of patients with pulmonary embolism (n = 120). All models produced similarly accurate tumor segmentation on the lung cancer testing datasets. SMIT produced the highest F1-score (LRAD: 0.60, 5Rater: 0.64) and lowest entropy (LRAD: 0.06, 5Rater: 0.12), indicating higher tumor detection rate and confident segmentations. In the OOD dataset, SMIT misdetected the least number of tumors, marked by a median volume occupancy of 5.67 cc compared to the best method SimMIM of 9.97 cc. Our analysis shows that additional metrics such as entropy and volume occupancy may help better understand model performance on mixed domain datasets.

Zhifan Jiang, Daniel Capellán-Martín, Abhijeet Parida et al.

Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmarking opportunity, including various types of brain tumors in both adult and pediatric populations, such as pediatric brain tumors (PED), meningiomas (MEN-RT) and brain metastases (MET), among others. Compared to previous editions, BraTS 2024 has implemented changes to substantially increase clinical relevance, such as refined tumor regions for evaluation. We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models. Additionally, we introduce innovative, adaptive pre- and post-processing techniques that employ MRI-based radiomic analyses to differentiate tumor subtypes. Given the heterogeneous nature of the tumors present in the BraTS datasets, this approach enhances the precision and generalizability of segmentation models. On the final testing sets, our method achieved mean lesion-wise Dice similarity coefficients of 0.926, 0.801, and 0.688 for the whole tumor in PED, MEN-RT, and MET, respectively. These results demonstrate the effectiveness of our approach in improving segmentation performance and generalizability for various brain tumor types. The source code of our implementation is available at https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids. Additionally, an open-source web-application is accessible at https://segmenter.hope4kids.io/ which uses the docker container aparida12/brats-peds-2024:v20240913 .

Daniel Capellán-Martín, Zhifan Jiang, Abhijeet Parida et al.

Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively.

Jerónimo Fotinós, María Paula Marks, Lucas Barberis et al.

In previous theoretical research, we inferred that cancer stem cells (CSCs), the cells that presumably drive tumor growth and resistance to conventional cancer treatments, are not uniformly distributed in the bulk of a tumorsphere. To confirm this theoretical prediction, we cultivated tumorspheres enriched in CSCs, and performed immunofluorecent detection of the stemness marker SOX2 using a confocal microscope. In this article, we present a method developed to process the images that reconstruct the amount and location of the CSCs in the tumorspheres. Its advantage is the use of a statistical criterion to classify the cells in stem and differentiated instead of setting an arbitrary threshold. From the analysis of the results of the methods using graph theory and computational modeling, we concluded that the distribution of Cancer Stem Cells in an experimental tumorsphere is non-homogeneous. This method is independent of the tumorsphere assay being useful for analyzing images in which several different kinds of cells are stained with different markers.

Mahin Mohammadi, Saman Jamshidi

Brain tumors pose a serious health threat due to their rapid growth and potential for metastasis. While medical imaging has advanced significantly, accurately identifying and characterizing these tumors remains a challenge. This study addresses this challenge by leveraging the innovative TrAdaBoost methodology to enhance the Brain Tumor Segmentation (BraTS2020) dataset, aiming to improve the efficiency and accuracy of brain tumor classification. Our approach combines state-of-the-art deep learning algorithms, including the Vision Transformer (ViT), Capsule Neural Network (CapsNet), and convolutional neural networks (CNNs) such as ResNet-152 and VGG16. By integrating these models within a multi-classifier framework, we harness the strengths of each approach to achieve more robust and reliable tumor classification. A novel decision template is employed to synergistically combine outputs from different algorithms, further enhancing classification accuracy. To augment the training process, we incorporate a secondary dataset, "Brain Tumor MRI Dataset," as a source domain, providing additional data for model training and improving generalization capabilities. Our findings demonstrate a high accuracy rate in classifying tumor versus non-tumor images, signifying the effectiveness of our approach in the medical imaging domain. This study highlights the potential of advanced machine learning techniques to contribute significantly to the early and accurate diagnosis of brain tumors, ultimately improving patient outcomes.

Shrikant B. Mali

Shared decision making (SDM) has been presented as an ethical framework for cancer care decision making. It is, however, difficult to apply and lacks practicality. SDM is a partnership between physicians and patients that combines personal values and preferences with the most up-to-date medical knowledge. It has the ability to reduce choice conflicts, foster value congruence, and boost patient participation. However, little study has been conducted on the attitudes of patients and surgeons towards SDM in surgical decision-making. Patients and surgeons favoured the SDM in general, but none of the trials looked at decision preferences in an emergency situation. There is a need to broaden research into new and demanding therapeutic contexts.

Xusheng Ai, Melissa C Smith, Frank Alex Feltus

Abstract The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA‐seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA‐seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio‐informatics. Finally, we propose potential directions for future research.

T. A. Bogush, P. D. Potselueva, A. M. Scherbakov et al.

Background. Estrogen receptors beta (ERβ) are an important biological regulator and target of antiestrogens, however, unlike estrogen receptors alpha (ERɑ), their significance in the prognosis and treatment of breast cancer remains unclear. Purpose. Evaluation of the ERβ prognostic value in the comparative assessment of frequency and level of the marker expression in groups with good and poor prognosis by Ki-67 proliferative index score in breast cancer. Methods. ERβ expression level (% of cells expressing the marker) in 68 breast tissue samples was quantified by immunofluorescence and flow cytometry. Primary antibodies to ERβ (clone 14C8, ab288) and secondary antibodies conjugated with DyLight650 (ab98729) were used. In the same samples, the Ki-67 expression level was assessed by the immunohistochemical method. Results. The ERβ and Ki-67 were detected in 100% breast tissue samples with high heterogeneity of the markers’ expression in different patients. Statistical analysis of good and poor prognosis in accordance with the Ki-67 proliferative index score (Ki-67≤20% and Ki-67>20%) showed the prognostic value of the ERβ expression level of 50%. There was no association between the Ki-67 and ERβ expression levels in the same tumor sample (Spearman's rank correlation coefficient R=–0,16; P>0,05). At the same time, high expression of ERβ≥50% was 2,3 times more frequently detected in the good vs poor prognostic group by Ki-67 — 41% vs 18%, P=0,02. Conclusion. The ERβ expression level ≥50% in the tumor can be considered as a factor of good prognosis of breast cancer.

Guanghua Li, Zhihao Zhou, Zhixiong Wang et al.

Abstract Background Epstein–Barr virus (EBV)-associated gastric cancer (EBVaGC) was a unique molecular subtype of gastric cancer (GC). However, the clinicopathological characteristics and prognostic role of EBV infection remains unclear. We aimed to evaluate the clinicopathological features of EBVaGC and its role on prognosis. Methods EBV-encoded RNA (EBER) in situ hybridization method was used to evaluate the EBV status in GC. The serum tumor markers AFP, CEA, CA19-9 and CA125 of patients were detected before treatment. HER2 expression and microsatellite instability (MSI) status was evaluated according to established criteria. The relationship between EBV infection and clinicopathological factors as well as its role on prognosis were investigated. Results 420 patients were enrolled in the study and of 53 patients (12.62%) were identified as EBVaGC. EBVaGC was more common in males (p = 0.001) and related to early T stage (p = 0.045), early TNM stage (p = 0.001) and lower level of serum CEA (p = 0.039). No association could be found between EBV infection and HER2 expression, MSI status and other factors (p all > 0.05). Kaplan–Meier analysis revealed that both the overall survival and disease-free survival of EBVaGC patients were similar to that of EBV-negative GC (EBVnGC) patients (p = 0.309 and p = 0.264, respectively). Conclusion EBVaGC was more common in males and in patients with the early T stage and TNM stage as well as patients with lower serum CEA level. Difference in overall survival and disease-free survival between EBVaGC and EBVnGC patients cannot be detected.

Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson et al.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Abu Kaisar Mohammad Masum, Nusrat Badhon, S. M. Saiful Islam Badhon et al.

Abnormal growth of cells in the brain and its surrounding tissues is known as a brain tumor. There are two types, one is benign (non-cancerous) and another is malignant (cancerous) which may cause death. The radiologists' ability to diagnose malignancies is greatly aided by magnetic resonance imaging (MRI). Brain cancer diagnosis has been considerably expedited by the field of computer-assisted diagnostics, especially in machine learning and deep learning. In our study, we categorize three different kinds of brain tumors using four transfer learning techniques. Our models were tested on a benchmark dataset of $3064$ MRI pictures representing three different forms of brain cancer. Notably, ResNet-50 outperformed other models with a remarkable accuracy of $99.06\%$. We stress the significance of a balanced dataset for improving accuracy without the use of augmentation methods. Additionally, we experimentally demonstrate our method and compare with other classification algorithms on the CE-MRI dataset using evaluations like F1-score, AUC, precision and recall.

Sameer Quazi, Javid Malik

The risk of lung cancer continues to elevate for both smokers and never-smokers. With the increasing morbidities and mortalities related to lung cancer, there is much interest on establishing other confounding factors that lead to lung cancer, other than smoking which is the most common cause. Some of the environmental factors have been identified as potential lung cancer causes. Therefore, the aim of this systematic review is to assess the relationship of environmental factors and lung cancer incidences by investigating various carcinogenic risks exposures that predispose an individual to lung cancer. The objective of this systematic review is thus to assess the evidence of relationship between environmental carcinogens and lung cancer incidence by systematically reviewing relevant studies. A standard criterion for the review methodology was formulated to guide the review process and data extraction. Online databases like PubMed, MEDLINE, Scopus (EMBASE), Google Scholar, Web of Science, and CINAHL were systematically searched for articles published between 2000 and 2021 that explored potential environmental carcinogens that were believed to expose occupational workers and individuals within the environment with lung cancer risks. 25 studies were eligible based on the selection criteria, and were finally included in the systematic review among which four were case-control studies, seven were cohorts, five was prospective, four were previous systematic reviews and four were systematic analysis. Chemical exposures like pesticides were analyzed for their carcinogenesis. Air pollution was also discussed with particulate and coal being the core of evidence of association with lung cancer. Second hand smoke, Asbestos, metal compounds like copper, PVC dust particles and ionizing radiations also provided evidence of environmental carcinogenesis associating to lung cancer cases.

Georgii Panshin

Multiple primary malignancies in one person are considered to be a well-established phenomenon with a registered prevalence ranging from 0.73 to 11.7%. However, the synchronous category of these neoplasms, which is detected either simultaneously or within 6 months of the diagnosis of the first primary malignant tumor, is considered less common. At the same time, numerous risk factors contribute to the development of this clinical condition, such as genetic predisposition, immunosuppression, smoking, chemotherapy and ionizing radiation, however, the exact pathophysiology of this process remains completely unclear to date. In this brief literature review, an attempt is made to assess the probability of a combination of primary breast cancer with the simultaneous development of primary brain tumors.