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

Ouissam Al Jarroudi, Ouissam Al Jarroudi, Khalid El Bairi et al.

Non-Gastrointestinal Stromal Tumors (Non-GIST) Soft Tissue Sarcomas (STS) are highly aggressive and challenging diseases with poor prognosis and limited therapeutic options. Molecular profiling is urgently required to gain a deeper understanding of STS pathogenesis and to identify a comprehensive landscape of genomic alterations in order to develop effective targeted therapies. The mitogen-activated protein kinase (MAPK) signaling pathway is a key molecular mechanism involved in sarcoma development. This study aims to conduct a literature review on the involvement of the MAPK cascade in non-GIST STS, with a focus on the role of MAPK inhibitors in the current treatment paradigm for STS. Furthermore, recent data have provided promising preliminary findings regarding the use of new molecular agents targeting the MAPK pathway, either as single therapies or in combination with other drugs. Numerous clinical trials are currently ongoing, and their outcomes are eagerly awaited. Further research is required in both translational and clinical settings to molecularly characterize STS, identify novel causal alterations, accelerate target discovery, and identify potential biomarkers. Moreover, the development of novel nanomaterials provides a promising perspective that may lead to significant advancements in clinical practice.

Jinliang Yu, Mingduo Xie, Yue Wang et al.

Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnostic outcomes. There is an urgent need for objective, accurate, and efficient computational approaches to improve bladder cancer diagnostics. Leveraging recent advancements in deep learning, this study proposes an integrated multi-task deep learning framework specifically designed for bladder cancer diagnosis from cystoscopic images. Our framework includes a robust classification model using EfficientNet-B0 enhanced with Convolutional Block Attention Module (CBAM), an advanced segmentation model based on ResNet34-UNet++ architecture with self-attention mechanisms and attention gating, and molecular subtyping using ConvNeXt-Tiny to classify molecular markers such as HER-2 and Ki-67. Additionally, we introduce a Gradio-based online diagnostic platform integrating all developed models, providing intuitive features including multi-format image uploads, bilingual interfaces, and dynamic threshold adjustments. Extensive experimentation demonstrates the effectiveness of our methods, achieving outstanding accuracy (93.28%), F1-score (82.05%), and AUC (96.41%) for classification tasks, and exceptional segmentation performance indicated by a Dice coefficient of 0.9091. The online platform significantly improved the accuracy, efficiency, and accessibility of clinical bladder cancer diagnostics, enabling practical and user-friendly deployment. The code is publicly available. Our multi-task framework and integrated online tool collectively advance the field of intelligent bladder cancer diagnosis by improving clinical reliability, supporting early tumor detection, and enabling real-time diagnostic feedback. These contributions mark a significant step toward AI-assisted decision-making in urology.

Syed Haider Ali, Asrar Ahmad, Muhammad Ali et al.

Cancer is an abnormal growth with potential to invade locally and metastasize to distant organs. Accurate auto-segmentation of the tumor and surrounding normal tissues is required for radiotherapy treatment plan optimization. Recent AI-based segmentation models are generally trained on large public datasets, which lack the heterogeneity of local patient populations. While these studies advance AI-based medical image segmentation, research on local datasets is necessary to develop and integrate AI tumor segmentation models directly into hospital software for efficient and accurate oncology treatment planning and execution. This study enhances tumor segmentation using computationally efficient hybrid UNet-Transformer models on magnetic resonance imaging (MRI) datasets acquired from a local hospital under strict privacy protection. We developed a robust data pipeline for seamless DICOM extraction and preprocessing, followed by extensive image augmentation to ensure model generalization across diverse clinical settings, resulting in a total dataset of 6080 images for training. Our novel architecture integrates UNet-based convolutional neural networks with a transformer bottleneck and complementary attention modules, including efficient attention, Squeeze-and-Excitation (SE) blocks, Convolutional Block Attention Module (CBAM), and ResNeXt blocks. To accelerate convergence and reduce computational demands, we used a maximum batch size of 8 and initialized the encoder with pretrained ImageNet weights, training the model on dual NVIDIA T4 GPUs via checkpointing to overcome Kaggle's runtime limits. Quantitative evaluation on the local MRI dataset yielded a Dice similarity coefficient of 0.764 and an Intersection over Union (IoU) of 0.736, demonstrating competitive performance despite limited data and underscoring the importance of site-specific model development for clinical deployment.

Naji Naseef Pathoor, Gopal Rajesh Kanna, Pitchaipillai Sankar Ganesh

The integration of ChatGPT in oral pathology diagnosis holds significant promise for enhancing various aspects of oral healthcare delivery. Drawing from research findings, ChatGPT's ability to provide precise answers to clinical questions is highlighted. Additionally, it explores AI's diverse applications in oral pathology, discussing challenges such as occasional inaccuracies and ethical considerations. Implications for education and innovation is also addressed. Overall, it offers valuable insights into ChatGPT integration in oral pathology diagnosis, guiding future research and implementation efforts.

Hao Chen, Jiangjiang Xu, Qiang Zhang et al.

BackgroundOxidative stress plays a significant role in aging and cancer, yet there is currently a lack of research utilizing machine learning models to examine the relationship between oxidative stress and prognosis in elderly non-small cell lung cancer (NSCLC) patients.MethodsThis study included elderly NSCLC patients who underwent radical lung cancer resection from January 2012 to April 2018, exploring the relationship between Oxidative Stress Score (OSS) and prognosis. Machine learning techniques, including Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to develop predictive models for 5-year overall survival (OS).ResultsThe datasets consisted of 1647 patients in the training set, 705 in the internal validation set, and 516 in the external validation set. An OSS was formulated from six systemic oxidative stress biomarkers, such as albumin, total bilirubin, and blood urea nitrogen, among others. Boruta variable importance analysis identified low OSS as a key indicator of poor prognosis. The OSS was subsequently integrated into the DT, RF, and SVM models for training. These models, optimized through hyperparameter tuning on the training set, were then evaluated on the internal and external validation sets. The RF model demonstrated the highest predictive performance, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.794 in the internal validation set, compared to AUCs of 0.711 and 0.760 for the DT and SVM models, respectively. Similarly, in the external validation set, the RF model achieved an AUC of 0.784, outperforming the DT and SVM models, which had AUCs of 0.699 and 0.730, respectively. Calibration plots confirmed the RF model’s superior calibration, followed by the SVM model, with the DT model performing the poorest.ConclusionThe OSS-based clinical prediction model, constructed using machine learning methodologies, effectively predicts the prognosis of elderly NSCLC patients post-radical surgery.

Huahui Yi, Xiaofei Wang, Kang Li et al.

Multimodal learning, integrating histology images and genomics, promises to enhance precision oncology with comprehensive views at microscopic and molecular levels. However, existing methods may not sufficiently model the shared or complementary information for more effective integration. In this study, we introduce a Unified Modeling Enhanced Multimodal Learning (UMEML) framework that employs a hierarchical attention structure to effectively leverage shared and complementary features of both modalities of histology and genomics. Specifically, to mitigate unimodal bias from modality imbalance, we utilize a query-based cross-attention mechanism for prototype clustering in the pathology encoder. Our prototype assignment and modularity strategy are designed to align shared features and minimizes modality gaps. An additional registration mechanism with learnable tokens is introduced to enhance cross-modal feature integration and robustness in multimodal unified modeling. Our experiments demonstrate that our method surpasses previous state-of-the-art approaches in glioma diagnosis and prognosis tasks, underscoring its superiority in precision neuro-Oncology.

Mingchen Li, Jiatan Huang, Jeremy Yeung et al.

Medical Large Language Models (LLMs) have demonstrated impressive performance on a wide variety of medical NLP tasks; however, there still lacks a LLM specifically designed for phenotyping identification and diagnosis in cancer domain. Moreover, these LLMs typically have several billions of parameters, making them computationally expensive for healthcare systems. Thus, in this study, we propose CancerLLM, a model with 7 billion parameters and a Mistral-style architecture, pre-trained on nearly 2.7M clinical notes and over 515K pathology reports covering 17 cancer types, followed by fine-tuning on two cancer-relevant tasks, including cancer phenotypes extraction and cancer diagnosis generation. Our evaluation demonstrated that the CancerLLM achieves state-of-the-art results with F1 score of 91.78% on phenotyping extraction and 86.81% on disganois generation. It outperformed existing LLMs, with an average F1 score improvement of 9.23%. Additionally, the CancerLLM demonstrated its efficiency on time and GPU usage, and robustness comparing with other LLMs. We demonstrated that CancerLLM can potentially provide an effective and robust solution to advance clinical research and practice in cancer domain

Meicen Liu, Dengmin Huang, Yuanli Liu

Abstract Objective To develop a comprehensive understanding of financial toxicity (FT) among patients with lung cancer in China and the major factors affecting FT. Methods Drawing from a national cross‐sectional survey, which used the validated comprehensive score for financial toxicity (COST) questionnaire, we estimated the prevalence and degree of FT. Patient coping actions were investigated. Pearson's chi tests and multinomial logistic regression were used to evaluate the predictors of FT in patients with lung cancer. Results The median score of FT was 20 (scored on a range of 0–44, with lower scores indicating more severe toxicity). Altogether, 77% of the sample patients had FT (COST <26), 54.5% had mild FT (COST 14–25), and 22.5% had moderate and severe FT (COST 0–13). Living in the less‐developed western region of China, being male, having a lower educational level, lower annual family income, and advanced stage or worse self‐reported health status were significantly related to higher FT than their counterparts (p < 0.05). Patients with higher FT tended to have a lower level of medical compliance, a higher risk of incurring debts, and reduced living expenditures relative to those with lower FT. Conclusion Despite China's remarkable progress in the past two decades with regard to Universal Healthcare Coverage, FT still presents a serious challenge for patients with lung cancer. Keen attention must be paid to reducing the disproportionate high financial risks of patients with low socioeconomic status.

Yun-Chen Chang, Gen-Min Lin, Tzuhui Angie Tseng et al.

Background and Aim: The objective was to decrease patient menopausal symptoms, sleep disturbance, and body image using a nonpharmacological therapy for cultivating key healthy lifestyle habits in patients with breast cancer. Materials and Methods: The participants were 26 women with breast cancer who had recently received structured mindfulness-based stress reduction (MBSR) training in a clinical trial. Focus groups and interviews were conducted, during which the participants were asked semistructured, open-ended questions regarding the experiences of MBSR. Results: The participants indicated that MBSR helped them to alleviate hot flashes and night sweats, and improve sleep quality and be more at ease with the external aspect of their body. On the other hand, during MBSR intervention in a group manner, the participants felt more psychological support and an outlet for sharing negative emotional experiences. Conclusion: This study identified the short-term benefits associated with group-based MBSR for women with breast cancer. In addition, our research identified the difficulties of intervention measures and coping methods. The study described the benefits of MBSR for patients with breast cancer. The findings of this study will help nursing staff identify the main coping menopausal symptoms and control negative mental health.

Yanfen Cui, Hui Liu, Zhaosong Wang et al.

Abstract Background Fructose is a very common sugar found in natural foods, while current studies demonstrate that high fructose intake is significantly associated with increased risk of multiple cancers and more aggressive tumor behavior, but the relevant mechanisms are not fully understood. Methods Tumor-grafting experiments and in vitro angiogenesis assays were conducted to detect the effect of fructose and the conditioned medium of fructose-cultured tumor cells on biological function of vascular endothelial cells (VECs) and angiogenesis. 448 colorectal cancer specimens were utilized to analyze the relationship between Glut5 expression levels in VECs and tumor cells and microvascular density (MVD). Results We found that fructose can be metabolized by VECs and activate the Akt and Src signaling pathways, thereby enhancing the proliferation, migration, and tube-forming abilities of VECs and thereby promoting angiogenesis. Moreover, fructose can also improve the expression of vascular endothelial growth factor (VEGF) by upregulating the production of reactive oxygen species (ROS) in colorectal cancer cells, thus indirectly enhancing the biological function of VECs. Furthermore, this pro-angiogenic effect of fructose metabolism has also been well validated in clinical colorectal cancer tissues and mouse models. Fructose contributes to angiogenesis in mouse subcutaneous tumor grafts, and MVD is positively correlated with Glut5 expression levels of both endothelial cells and tumor cells of human colorectal cancer specimens. Conclusions These findings establish the direct role and mechanism by which fructose promotes tumor progression through increased angiogenesis, and provide reliable evidence for a better understanding of tumor metabolic reprogramming.

Yasuyuki Kawamoto

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu et al.

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Thomas R Fleming, Lisa V Hampson, Bharani Bharani-Dharan et al.

Indolent cancers are characterized by long overall survival (OS) times. Therefore, powering a clinical trial to provide definitive assessment of the effects of an experimental intervention on OS in a reasonable timeframe is generally infeasible. Instead, the primary outcome in many pivotal trials is an intermediate clinical response such as progression-free survival (PFS). In several recently reported pivotal trials of interventions for indolent cancers that yielded promising results on an intermediate outcome, however, more mature data or post-approval trials showed concerning OS trends. These problematic results have prompted a keen interest in quantitative approaches for monitoring OS that can support regulatory decision-making related to the risk of an unacceptably large detrimental effect on OS. For example, the US Food and Drug Administration, the American Association for Cancer Research, and the American Statistical Association recently organized a one-day multi-stakeholder workshop entitled 'Overall Survival in Oncology Clinical Trials'. In this paper, we propose OS monitoring guidelines tailored for the setting of indolent cancers. Our pragmatic approach is modeled, in part, on the monitoring guidelines the FDA has used in cardiovascular safety trials conducted in Type 2 Diabetes Mellitus. We illustrate proposals through application to several examples informed by actual case studies.

P. Kalaiselvi, S. Anusuya

In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The anticipated model is assessed on a Computed Tomography (CT) scan dataset containing both benign and malignant liver tumors. The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. Additionally, advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors. The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction. It can assist radiologists in their diagnosis and treatment planning. The proposed system achieved a high accuracy of 95.5% in predicting liver tumors, outperforming other state-of-the-art methods.

Mengjie Chen

The dysregulation of transcripts is characterized as one of the main mechanisms in tumor pathogenesis. The recent discovery developed a new hypothesis, competitive endogenous RNAs (ceRNAs), which could regulate other RNA transcripts via competing for their shared miRNAs. The interaction of elements in ceRNAs network was involved in a large range of biological reactions and facilitate to cancer progression. In this study, we performed a comprehensive investigation on the regulatory mechanisms and functional roles of ceRNAs in prostate cancer (PCa) and constructed a ceRNAs network which could possess potential value in patient prognosis and be evaluated as therapeutic targets for PCa.

Christof A. Bertram, Taryn A. Donovan, Alexander Bartel

Increased proliferation is a key driver of tumorigenesis, and quantification of mitotic activity is a standard task for prognostication. The goal of this systematic review is scholarly analysis of all available references on mitotic activity in feline tumors, and to provide an overview of the measuring methods and prognostic value. A systematic literature search in PubMed and Scopus and a manual search in Google Scholar was conducted. All articles on feline tumors that correlated mitotic activity with patient outcome were identified. Data analysis revealed that of the eligible 42 articles, the mitotic count (MC, mitotic figures per tumor area) was evaluated in 39 instances and the mitotic index (MI, mitotic figures per tumor cells) in three instances. The risk of bias was considered high for most studies (26/42, 62%) based on small study populations, insufficient details of the MC/MI methods, and lack of statistical measures for diagnostic accuracy or effect on outcome. The MC/MI methods varied markedly between studies. A significant association of the MC with survival was determined in 21/29 (72%) studies, while one study found an inverse effect. There were three tumor types with at least four studies and a prognostic association was found in 5/6 studies on mast cell tumors, 5/5 on mammary tumors and 3/4 on soft tissue sarcomas. The MI was shown to correlate with survival by two research groups, however a comparison to the MC was not conducted. An updated systematic review will be needed with of new literature for different tumor types.

Chenghai Zhang, Binghong Wu, Hong Yang et al.

Abstract Background Paclitaxel plus S-1(PTXS) has shown definite efficacy for advanced gastric cancer. However, the efficacy and safety of this regimen in neoadjuvant setting for locally advanced gastric cancer (LAGC) are unclear. This study aimed to compare the efficacy of neoadjuvant chemotherapy (NAC) PTXS and oxaliplatin plus S-1 (SOX) regime for patients with LAGC. Methods A total of 103 patients with LAGC (cT3/4NanyM0/x) who were treated with three cycles of neoadjuvant SOX regimen (n = 77) or PTXS regimen (n = 26) between 2011 and 2017 were enrolled in this study. NAC-related clinical response, pathological response, postoperative complication, and overall survival were analyzed between the groups. Results The baseline data did not differ significantly between both groups. After NAC, the disease control rate of the SOX group (94.8%) was comparable with that of the PTXS group (92.3%) (p = 0.641). Twenty-three cases (29.9%) in the SOX group and 10 cases (38.5%) in the PTX group got the descending stage with no statistical difference (p = 0.417). No significant differences were observed in the overall pathological response rate and the overall postoperative complication rate between the two groups (p > 0.05). There were also no differences between groups in terms of 5-year overall and disease-free survival (p > 0.05). Conclusions The validity of NAC PTXS was not inferior to that of SOX regimen for locally advanced gastric cancer in terms of treatment response and overall survival. PTXS regimen could be expected to be ideal neoadjuvant chemotherapy for patients with LAGC and should be adopted for the test arm of a large randomized controlled trial.

Jaqueline Vaz Vanini, Leonardo Kenji Sakaue Koyama, Leandro Luongo de Matos et al.

Introduction: Bone invasion is an important prognostic factor in oral squamous cell carcinoma, leading to a lower survival rate and the use of aggressive treatment approaches. Epithelial-mesenchymal transition (EMT) is possibly involved in this process, because it is often related to mechanisms of cell motility and invasiveness. This study examined whether a panel of epithelial-mesenchymal markers are present in cases of oral squamous cell carcinoma with bone invasion and whether these proteins have any relationship with patients’ clinical-pathological parameters and prognostic factors. Methods: Immunohistochemical analysis of E-cadherin, twist, vimentin, TGFβ1, and periostin was performed in paraffin-embedded samples of 62 oral squamous cell carcinoma cases. Results: The analysis revealed that most cases (66%) presented with a dominant tumor infiltrative pattern in bone tissue, associated with lower survival rates, when compared with cases with a dominant erosive invasion pattern (P = 0.048). Twenty-seven cases (43%) expressed markers that were compatible with total or partial EMT at the tumor-bone interface. There was no association between evidence of total or partial EMT and other demographic or prognostic features. E-cadherin-positive cases were associated with tobacco smoking (P = 0.022); vimentin-positive cases correlated with tumors under 4 cm (P = 0.043). Twistexpression was observed in tumors with a dominant infiltrative pattern (P = 0.041) and was associated with the absence of periostin (P = 0.031). Conclusion: We observed evidence of total or partial EMT in oral squamous cell carcinoma bone invasion. The transcription factor twist appears to be involved in bone invasion and disease progression.

Eiman O.R Omar, Naik Haussein, Amal Ahmed

Background: Carcinoma of unknown primary origin (CUP) comprises various malignancies classified by detection of tissue-specific genes through immunohistochemistry (IHC). We aimed to explore the role of available immunohistochemical markers in diagnosing and classifying malignant neoplasms of unknown primary origin.Methods: A cross-sectional study included 141 patients diagnosed histologically as CUP and referred to the Histopathology and Immunohistochemistry Department, Khartoum Oncology Hospital, from 2012 to 2017. Hematoxylin and Eosin (H&E) and immune stained slides used in the workup were reviewed and classified into the main histologic types of CUP. Data were -analyzed by SPSS. Results: Out of 4436 cases, CUP represents (3.2%). The age group (60-69) years have the highest percentage (20.13%), with male predominance (51.77%). Lymph nodes represent (41.84%) followed by the liver (12.77%), spine (3.55%), and lungs (2.13%). Adenocarcinoma (75.89%) was the most common subtype, followed by undifferentiated neoplasm (14.18%), squamous cell carcinoma (7.09%), and carcinoma with neuroendocrine differentiation (2.84%). In 70 cases (49.6%) of the study cases, the primary site was determined, (17.7%) were given an only differential diagnosis, and in (32.6%) the origin remains unknown. Conclusions: CUP cases during the study period are infrequent (3.2%), and the primary origin was determined in nearly half of patients by the available immune markers. CUP’s common histological types were adenocarcinoma, undifferentiated neoplasm, squamous cell carcinoma, and carcinoma with neuroendocrine differentiation. The most common presenting sites were lymph node, liver, spine, and lungs.

Barbara J. Gitlitz, MD, Silvia Novello, MD, PhD, Tiziana Vavalà, MD et al.

Introduction: Lung adenocarcinomas in young patients (<40 y) are more likely to harbor targetable genomic alterations. This study aimed to determine whether the prevalence of targetable alterations is greater in young adults with lung carcinoma than in the overall lung cancer population. To reach this rare patient population, a web-based platform was used to recruit and enroll patients remotely. Methods: In this prospective study, patients less than 40 years old at the time of primary lung cancer diagnosis with confirmed lung carcinoma were recruited from four global sites and remotely by means of a website. Genotyping data were collected, if available, or obtained by means of next-generation sequencing using the FoundationOne platform. The prevalence of targetable alterations was quantified across patients with advanced adenocarcinoma. Results: Overall, 133 patients across five continents were included, 41% of whom enrolled online. The mean (SD) age at diagnosis was 34 (5.2) years; 79% had stage IV disease at diagnosis. Among patients with adenocarcinoma (n = 115), 112 entered the study with previous genomic testing results and 86 (77%) had targetable alterations in EGFR, ALK, ROS1, MET, ERBB2, or RET. Among those without targetable alterations, 14 received further testing and a targetable alteration was identified in eight (57%). Conclusions: This study revealed the feasibility of using a web-based platform to recruit young patients with lung cancer and revealed that 94 of 112 (84%) with adenocarcinoma at any stage had targetable genomic alterations. Among patients with stage IV adenocarcinoma, 85% had a targetable alteration, which is higher than historical expectations for the general population.