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

Yixin Chen, Ziyu Su, Lingbin Meng et al.

Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it will disseminate directly from histopathology remains a fundamental challenge. Although whole-slide images (WSIs) provide rich morphological information, prior computational pathology approaches typically address metastatic status or site prediction as isolated tasks, and do not explicitly model the clinically sequential decision process of metastatic risk assessment followed by downstream site-specific evaluation. To address this research gap, we present a decision-aware, concept-aligned MIL framework, HistoMet, for prognostic metastatic outcome prediction from primary tumor WSIs. Our proposed framework adopts a two-module prediction pipeline in which the likelihood of metastatic progression from the primary tumor is first estimated, followed by conditional prediction of metastatic site for high-risk cases. To guide representation learning and improve clinical interpretability, our framework integrates linguistically defined and data-adaptive metastatic concepts through a pretrained pathology vision-language model. We evaluate HistoMet on a multi-institutional pan-cancer cohort of 6504 patients with metastasis follow-up and site annotations. Under clinically relevant high-sensitivity screening settings (95 percent sensitivity), HistoMet significantly reduces downstream workload while maintaining high metastatic risk recall. Conditional on metastatic cases, HistoMet achieves a macro F1 of 74.6 with a standard deviation of 1.3 and a macro one-vs-rest AUC of 92.1. These results demonstrate that explicitly modeling clinical decision structure enables robust and deployable prognostic prediction of metastatic progression and site tropism directly from primary tumor histopathology.

Chengyun Pan, Lin Zheng, Jishi Wang

The co evolution of tumor cells and microenvironmental matrix components almost determines the series of processes involved in cancer occurrence and progression. However, many anti-cancer treatments are designed around tumor cells, neglecting the supportive role of stromal cells. Cancer-associated fibroblasts (CAFs), as the main stromal cells in tumor microenvironment, are currently considered as a key component promoting tumorigenesis, development, and regulating the transfer of tumor cells to distant locations through secretion of different growth factors, cytokines, chemokines, and the degradation of extracellular matrix. Therefore, the strategy of targeting both cancer cells and CAFs shows great potential in cancer treatment. In hematological malignancies, the role of CAFs in the progression of tumors has gradually been recently tapped. This review describes the role and functional characteristics of CAFs in tumors, mainly concentrates on the potential role of CAFs in the disease progression of hematological malignancies according to recent findings, and emphasizes the importance of CAFs as a key target to overcome tumor progression and improve treatment efficacy.

Dana Narvaez, Federico Waisberg

Yan Zhang, Wenying Li, Shuai Wang et al.

Background Chimeric antigen receptor (CAR)-T cell therapy has demonstrated remarkable success in hematologic malignancies; however, its efficacy in solid tumors remains limited. A major barrier is the immunosuppressive tumor microenvironment (TME), which is characterized by hypoxia and nutrient deprivation, leading to impaired CAR-T cell proliferation, persistence, and cytotoxic function. To address these barriers, we designed a dual-regulatory CAR-T strategy that integrates hypoxia-responsive control with metabolic enhancement to improve therapeutic efficacy in solid tumors.Methods To overcome these barriers, we developed a next-generation CAR-T platform with dual adaptations targeting the metabolic and transcriptional constraints of the TME. Specifically, we engineered hypoxia-responsive regulatory elements derived from VEGF to drive sustained CAR expression under hypoxic conditions. Concurrently, we overexpressed the glutamine transporter SLC38A2 to enhance glutamine uptake and metabolic fitness in nutrient-deprived environments.Results Compared with conventional CAR-T cells, our engineered CAR-T cells exhibited superior antitumor activity under hypoxia and nutrient stress, with enhanced proliferation, elevated memory phenotype, and reduced exhaustion markers. Mechanistically, quantitative PCR demonstrated upregulation of glutamine metabolic and glycolytic pathways, while Seahorse assays confirmed enhanced oxidative phosphorylation and glycolysis. SLC38A2 knockout reversed these enhancements, highlighting its role in sustaining CAR-T metabolic fitness.Conclusion Our findings establish SLC38A2 as a critical metabolic regulator that enhances CAR-T antitumor efficacy, providing a promising strategy to improve the durability and efficacy of CAR-T cell therapies in TME.

Yanju Wu, Shu Meng, Haoqi Zhao et al.

Abstract Objective This research explores the prognostic value of DHRS9 in ovarian carcinoma and elucidates its regulatory mechanisms. Methods Bioinformatic analyses were applied to clarify the association between DHRS9 expression level and clinical survival outcomes in ovarian cancer patients. Functional assays were conducted to evaluate cell growth, migration, and invasion. Apoptosis was quantified via flow cytometry. The expression of Dehydrogenase/Reductase Member 9 (DHRS9) and sequestosome 1 (SQSTM1) at both mRNA and protein levels was analyzed via quantitative real-time polymerase chain reaction (qRT-PCR) and western blot assays. Mass spectrometry identified SQSTM1 as a putative downstream effector of DHRS9, and their interaction was validated by co-immunoprecipitation (Co-IP). The in vivo effects of DHRS9 knockdown were examined in a subcutaneous xenograft tumor model of nude mice. Results Bioinformatic analysis showed that elevated DHRS9 expression correlated with reduced overall survival in ovarian cancer patients. Silencing DHRS9 attenuated cell growth, migration, and invasion, whereas promoting apoptotic activity. In contrast, DHRS9 overexpression enhanced oncogenic behaviors and suppressed apoptosis. Mass spectrometry and Co-IP analyses confirmed SQSTM1 as an interacting partner of DHRS9, and knockdown of DHRS9 decreased SQSTM1 protein levels in vivo and in vitro, while its overexpression increased SQSTM1 levels. Moreover, functional studies demonstrated that SQSTM1 knockdown reduced ovarian cell growth, migration, and invasion. Xenograft experiments further demonstrated that DHRS9 knockdown resulted in decreased tumor volume. Conclusion DHRS9 promotes ovarian cancer proliferation, migration, and invasion, and inhibits apoptosis through its interaction with SQSTM1. These findings indicate that DHRS9 may serve as a potential prognostic indicator and therapeutic candidate in ovarian cancer.

Chen Li, Yang Lu, Yihao Liu et al.

Background Sialic acid-binding immunoglobulin-like lectins (SIGLECs) are widely expressed on immune cell surfaces, play an important role in maintaining immune homeostasis and regulating inflammatory responses, and are increasingly emerging as potential targets for tumor immunotherapy. However, the expression profile and crucial role of SIGLEC11 in gastric cancer (GC) remain unclear. This study aimed to elucidate the prognostic relevance of SIGLEC11 expression and its role in the immune microenvironment in patients with GC.Methods SIGLEC11 expression profile was analyzed using bioinformatics, immunohistochemistry, and immunofluorescence staining. Flow cytometry, mouse tumor models, patient-derived tumor organoid models, and RNA sequencing were used to explore the potential functions with the underlying mechanisms of SIGLEC11 in a coculture system of macrophages and GC cells.Results We demonstrated that SIGLEC11 was predominantly expressed in normal tissues. However, tumor-infiltrating SIGLEC11+ cells in the high SIGLEC11 expression subgroups showed poor overall survival, which was associated with the expression of an immunosuppressive regulator. Our results showed that SIGLEC11 was predominantly expressed in monocytes and macrophages and selectively upregulated in tumor-associated macrophages. Furthermore, SIGLEC11 promoted macrophage M2 polarization via AKT–mTOR signaling. In addition, SIGLEC11+ macrophages accelerate GC progression.Conclusions The abundance of SIGLEC11+ M2-like macrophage-infiltrating tumors may serve as a biomarker for identifying immunosuppressive subtypes of GC. Thus, the potential role of SIGLEC11+ M2 macrophages as therapeutic targets warrants further investigation.

Cansu Turker Saricoban, Ayse Yavuz, Yagmur Arslan et al.

Objectives: Extrarenal Wilms tumor (ERWT) is an exceedingly rare neoplasm, particularly when located in the uterine cervix. Methods: We report the case of a 26-year-old nulliparous female who presented with a large cervical mass and underwent clinical, radiological, and pathological evaluation. Results: Initial biopsy revealed biphasic morphology with atypical epithelial and stromal components, while the absence of a blastemal component posed a diagnostic challenge. Subsequent excision demonstrated classic triphasic morphology, confirming the diagnosis of ERWT. The patient underwent fertility-preserving surgery followed by adjuvant chemotherapy and remains disease-free at one-year follow-up. Conclusions: This case highlights the importance of considering ERWT in the differential diagnosis of cervical tumors and demonstrates that standard renal Wilms tumor treatment protocols can be effectively adapted to extrarenal locations.

Xing-Li Yang, Li Lin, Sha-Sha He et al.

Abstract Objectives This study aimed to investigate the incidence of post radiation nasopharyngeal necrosis (PRNN) in primary NPC after intensity modulated radiation therapy (IMRT) and identify the predictors of risk. Methods Data of 5798 NPC patients who received IMRT-based treatment between April 2009 and December 2015 were retrospectively reviewed. PRNN was diagnosed by MRI or nasopharyngoscopy. Dosimetric factors were selected by the least absolute shrinkage and selection operator logistic regression and applied to Cox proportional hazards modeling with clinical predictors. Results Among the 5798 patients, 53 developed PRNN—an incidence rate of 0.89%. Age > 55 years, diabetes, LDH > 170 U/L, and tumor volume of nasopharynx > 60.5 cm3,were independently associated with risk of PRNN(all p < 0.05. Dosimetric analysis showed that D0.5cc EQD2 of 80.20 Gy might be the dose constraint for nasopharynx (sensitivity = 62.3%, 33 out of 53; specificity = 84.2%, 4897 out of 5925). Besides, the RTOG dose constraints of V110% (V77.0) should be less than 0.2% in case of increasing risk of PRNN(HR = 2.28, 95% CI: 1.26–4.41, p = 0. 01). Conclusion Nasopharyngeal necrosis is rare after primary IMRT. The independent risk factors for this rare complication include age > 55 years, diabetes mellitus, LDH > 170 U/L, tumor volume of nasopharynx > 60.5 cm3, D0.5cc EQD2 > 80.20 Gy, and V77.0 < 0.2% to the planning treatment volume of nasopharynx. Keypoints High radiation dose may lead to devastating nasopharyngeal necrosis after primary IMRT. Real world analysis will provide valuable information for prevention. Findings The aged, diabetes mellitus, large tumor volume, D0.5cc EQD2 > 80.20 Gy and V77.0 < 0.2% to planning treatment volume increased the risk of nasopharyngeal necrosis. Clinical relevance This real-world study provided valuable information for prevention of PRNN. Compared with RTOG protocol, D0.5cc EQD2 > 80.20 Gy is a reliable evidence-based new complement to dose constraint, especially for T3-4 disease, who received high prescribe dose in China.

Ruben T. Lucassen, Marjanna Romers, Chiel F. Ebbelaar et al.

Spitz tumors are diagnostically challenging due to overlap in atypical histological features with conventional melanomas. We investigated to what extent AI models, using histological and/or clinical features, can: (1) distinguish Spitz tumors from conventional melanomas; (2) predict the underlying genetic aberration of Spitz tumors; and (3) predict the diagnostic category of Spitz tumors. The AI models were developed and validated using a dataset of 393 Spitz tumors and 379 conventional melanomas. Predictive performance was measured using the AUROC and the accuracy. The performance of the AI models was compared with that of four experienced pathologists in a reader study. Moreover, a simulation experiment was conducted to investigate the impact of implementing AI-based recommendations for ancillary diagnostic testing on the workflow of the pathology department. The best AI model based on UNI features reached an AUROC of 0.95 and an accuracy of 0.86 in differentiating Spitz tumors from conventional melanomas. The genetic aberration was predicted with an accuracy of 0.55 compared to 0.25 for randomly guessing. The diagnostic category was predicted with an accuracy of 0.51, where random chance-level accuracy equaled 0.33. On all three tasks, the AI models performed better than the four pathologists, although differences were not statistically significant for most individual comparisons. Based on the simulation experiment, implementing AI-based recommendations for ancillary diagnostic testing could reduce material costs, turnaround times, and examinations. In conclusion, the AI models achieved a strong predictive performance in distinguishing between Spitz tumors and conventional melanomas. On the more challenging tasks of predicting the genetic aberration and the diagnostic category of Spitz tumors, the AI models performed better than random chance.

Hongxu Yang, Edina Timko, Levente Lippenszky et al.

Synthetic tumors in medical images offer controllable characteristics that facilitate the training of machine learning models, leading to an improved segmentation performance. However, the existing methods of tumor synthesis yield suboptimal performances when tumor occupies a large spatial volume, such as breast tumor segmentation in MRI with a large field-of-view (FOV), while commonly used tumor generation methods are based on small patches. In this paper, we propose a 3D medical diffusion model, called SynBT, to generate high-quality breast tumor (BT) in contrast-enhanced MRI images. The proposed model consists of a patch-to-volume autoencoder, which is able to compress the high-resolution MRIs into compact latent space, while preserving the resolution of volumes with large FOV. Using the obtained latent space feature vector, a mask-conditioned diffusion model is used to synthesize breast tumors within selected regions of breast tissue, resulting in realistic tumor appearances. We evaluated the proposed method for a tumor segmentation task, which demonstrated the proposed high-quality tumor synthesis method can facilitate the common segmentation models with performance improvement of 2-3% Dice Score on a large public dataset, and therefore provides benefits for tumor segmentation in MRI images.

Izabel Valdez, Paramahansa Pramanik

This study explores how obesity and race jointly influence the development and prognosis of Luminal subtypes of breast cancer, with a focus on distinguishing Luminal A from the more aggressive Luminal B tumors. Drawing on large-scale epidemiological data and employing statistical approaches such as logistic regression and mediation analysis, the research examines biological factors like estrogen metabolism, adipokines, and chronic inflammation alongside social determinants including healthcare access, socioeconomic status, and cultural attitudes toward body weight. The findings reveal that both obesity and racial background are significant predictors of risk for Luminal B breast cancers. The study highlights the need for a dual approach that combines medical treatment with targeted social interventions aimed at reducing disparities. These insights can improve individualized risk assessments, guide tailored screening programs, and support policies that address the heightened cancer burden experienced by marginalized communities.

Nuo Tong, Changhao Liu, Zizhao Tang et al.

Although the combined treatment of surgery, radiotherapy, chemotherapy, and emerging target therapy has significantly improved the outcomes of patients with head and neck cancer, distant metastasis remains the leading cause of treatment failure. In this study, we propose a deep learning-based multimodal framework integrating CT imaging, radiomics, and clinical data to predict metastasis risk in HNSCC. A total of 1497 patients were retrospectively analyzed. Tumor and organ masks were generated from pretreatment CT scans, from which a 3D Swin Transformer extracted deep imaging features, while 1562 radiomics features were reduced to 36 via correlation filtering and random forest selection. Clinical data (age, sex, smoking, and alcohol status) were encoded and fused with imaging features, and the multimodal representation was fed into a fully connected network for prediction. Five-fold cross-validation was used to assess performance via AUC, accuracy, sensitivity, and specificity. The multimodal model outperformed all single-modality baselines. The deep learning module alone achieved an AUC of 0.715, whereas multimodal fusion significantly improved performance (AUC = 0.803, ACC = 0.752, SEN = 0.730, SPE = 0.758). Stratified analyses confirmed good generalizability across tumor subtypes. Ablation experiments demonstrated complementary contributions from each modality, and the 3D Swin Transformer provided more robust representations than conventional architectures. This multimodal deep learning model enables accurate, non-invasive metastasis prediction in HNSCC and shows strong potential for individualized treatment planning.

Ruah Alyamany, Ahmed Alnughmush, Hazzaa Alzahrani et al.

Chemotherapy-induced cytopenia (CIC) is characterized by neutropenia, anemia, and thrombocytopenia, which are common and serious complications in cancer treatment. These conditions affect approximately 60% of patients undergoing chemotherapy and can significantly impact quality of life, treatment continuity, and overall survival. The use of growth factors, including granulocyte colony-stimulating factors (GCSFs), erythropoietin-stimulating agents (ESAs), and thrombopoietin receptor agonists (TPO-RAs), has emerged as a promising strategy for managing CIC. However, the use of these growth factors must be approached with caution. This review provides an overview of the mechanisms, efficacy, and safety of growth factors in the management of CIC. Additionally, we discuss predictive markers for treatment response, potential risks, and highlight areas for future research.

Rikako Ebisuda, Naoki Furuya, Takeo Inoue et al.

BackgroundImmune check point inhibitors (ICIs) are standard treatment for patients with non-small cell lung cancer (NSCLC). Nearly a decade has passed since nivolumab was approved by the FDA for NSCLC patients. However, long-term outcomes and clinical features remain unclear for individual cases. Pseudo-progression is a well-known paradoxical radiological response pattern under ICI treatment which occurs when tumor index lesions regress after apparent initial progression. We herein report a unique case of NSCLC with repeating pseudo-progression in late phase treated with nivolumab monotherapy for 8.5 years.Case presentationA 56-year-old male diagnosed with Non-sq NSCLC clinical stage IVA, at the left upper lobe primary lesion. The primary lesion was PD-L1 negative with no oncogenic driver mutations. He had multiple pulmonary metastases and a left adrenal gland metastasis, and subsequently, received nivolumab as third-line therapy. After initiation of nivolumab, the lung lesion and adrenal metastasis shrank rapidly; however, the patient experienced three late-phase pseudo-progressions in the mediastinal lymph node (LN). This patient is still receiving nivolumab with no symptoms and PS 0. Acquired resistance should be judged carefully in patients with LN-only oligo-progression to avoid unnecessary local therapies and the misjudgment of treatment.

Jing Rong, Tongtong Liu, Xiujuan Yin et al.

Abstract Background Camptothecin (CPT) is one of the frequently used small chemotherapy drugs for treating hepatocellular carcinoma (HCC), but its clinical application is limited due to severe toxicities and acquired resistance. Combined chemo-gene therapy has been reported to be an effective strategy for counteracting drug resistance while sensitizing cancer cells to cytotoxic agents. Thus, we hypothesized that combining CPT with miR-145 could synergistically suppress tumor proliferation and enhance anti-tumor activity. Methods Lactobionic acid (LA) modified lipid nanoparticles (LNPs) were developed to co-deliver CPT and miR-145 into asialoglycoprotein receptors-expressing HCC in vitro and in vivo. We evaluated the synergetic antitumor effect of miR-145 and CPT using CCK8, Western blotting, apoptosis and wound scratch assay in vitro, and the mechanisms underlying the synergetic antitumor effects were further investigated. Tumor inhibitory efficacy, safety evaluation and MRI-visible ability were assessed using diethylnitrosamine (DEN) + CCl4-induced HCC mouse model. Results The LA modification improved the targeting delivery of cargos to HCC cells and tissues. The LA-CMGL-mediated co-delivery of miR-145 and CPT is more effective on tumor inhibitory than LA-CPT-L or LA-miR-145-L treatment alone, both in vitro and in vivo, with almost no side effects during the treatment period. Mechanistically, miR-145 likely induces apoptosis by targeting SUMO-specific peptidase 1 (SENP1)-mediated hexokinase (HK2) SUMOylation and glycolysis pathways and, in turn, sensitizing the cancer cells to CPT. In vitro and in vivo tests confirmed that the loaded Gd-DOTA served as an effective T1-weighted contrast agent for noninvasive tumor detection as well as real-time monitoring of drug delivery and biodistribution. Conclusions The LA-CMGL-mediated co-delivery of miR-145 and CPT displays a synergistic therapy against HCC. The novel MRI-visible, actively targeted chemo-gene co-delivery system for HCC therapy provides a scientific basis and a useful idea for the development of HCC treatment strategies in the future.

Xiaoyi Liu, Zhuoyue Wang

Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially save many lives and facilitate better decision-making at the early stages of the disease. Within our paper, four different types of MRI-based images have been collected from the database: glioma tumor, no tumor, pituitary tumor, and meningioma tumor. Our study focuses on making predictions for brain tumor classification. Five models, including four pre-trained models (MobileNet, EfficientNet-B0, ResNet-18, and VGG16) and one new model, MobileNet-BT, have been proposed for this study.

Salma Hassan, Hamad Al Hammadi, Ibrahim Mohammed et al.

The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In this paper, we introduce an innovative integration of multi-modal data, synthesizing fused medical imaging (CT and PET scans) with clinical health records and genomic data. This unique fusion methodology leverages advanced machine learning models, notably MedClip and BEiT, for sophisticated image feature extraction, setting a new standard in computational oncology. Our research surpasses existing approaches, as evidenced by a substantial enhancement in NSCLC detection and classification precision. The results showcase notable improvements across key performance metrics, including accuracy, precision, recall, and F1-score. Specifically, our leading multi-modal classifier model records an impressive accuracy of 94.04%. We believe that our approach has the potential to transform NSCLC diagnostics, facilitating earlier detection and more effective treatment planning and, ultimately, leading to superior patient outcomes in lung cancer care.

Chia-Hsuan Chang, Mary M. Lucas, Grace Lu-Yao et al.

Cancer stage classification is important for making treatment and care management plans for oncology patients. Information on staging is often included in unstructured form in clinical, pathology, radiology and other free-text reports in the electronic health record system, requiring extensive work to parse and obtain. To facilitate the extraction of this information, previous NLP approaches rely on labeled training datasets, which are labor-intensive to prepare. In this study, we demonstrate that without any labeled training data, open-source clinical large language models (LLMs) can extract pathologic tumor-node-metastasis (pTNM) staging information from real-world pathology reports. Our experiments compare LLMs and a BERT-based model fine-tuned using the labeled data. Our findings suggest that while LLMs still exhibit subpar performance in Tumor (T) classification, with the appropriate adoption of prompting strategies, they can achieve comparable performance on Metastasis (M) classification and improved performance on Node (N) classification.

Danyi Huang, Ziang Liu, Yizhou Li

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation techniques often face several challenges in performing liver segmentation: lack of precision, slow processing speed, and computational burden. These shortcomings limit the efficiency of surgical planning and execution. In this work, the model initially describes in detail a new image enhancement algorithm that enhances the key features of an image by adaptively adjusting the contrast and brightness of the image. Then, a deep learning-based segmentation network was introduced, which was specially trained on the enhanced images to optimize the detection accuracy of tumor regions. In addition, multi-scale analysis techniques have been incorporated into the study, allowing the model to analyze images at different resolutions to capture more nuanced tumor features. In the presentation of the experimental results, the study used the 3Dircadb dataset to test the effectiveness of the proposed method. The experimental results show that compared with the traditional image segmentation method, the new method using image enhancement technology has significantly improved the accuracy and recall rate of tumor identification.

Pralaypati Ta, Bhumika Gupta, Arihant Jain et al.

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatically interactable, easy-to-update structured model with minimal human intervention. The existing automated algorithms have minimal scope and cannot handle the varying complexity of the knowledge content in the CPGs for different cancer types. This work proposes an improved automated knowledge modeling algorithm to create knowledge models from the National Comprehensive Cancer Network (NCCN) CPGs in Oncology for different cancer types. The proposed algorithm has been evaluated with NCCN CPGs for four different cancer types. We also proposed an algorithm to compare the knowledge models for different versions of a guideline to discover the specific changes introduced in the treatment protocol of a new version. We created a question-answering (Q&A) framework with the guideline knowledge models as the augmented knowledge base to study our ability to query the knowledge models. We compiled a set of 32 question-answer pairs derived from two reliable data sources for the treatment of Non-Small Cell Lung Cancer (NSCLC) to evaluate the Q&A framework. The framework was evaluated against the question-answer pairs from one data source, and it can generate the answers with 54.5% accuracy from the treatment algorithm and 81.8% accuracy from the discussion part of the NCCN NSCLC guideline knowledge model.