Cancer Investigation最新文献

{"title":"Correction.","authors":"","doi":"10.1080/07357907.2025.2537525","DOIUrl":"https://doi.org/10.1080/07357907.2025.2537525","url":null,"abstract":"","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"1-2"},"PeriodicalIF":1.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thyroid Cancer Detection Using Py-SpinalNet: A Pyramid and SpinalNet Approach.","authors":"Murugadoss R, Augustus Devarajan A, Vetriselvi T, Rajanarayanan S","doi":"10.1080/07357907.2025.2543853","DOIUrl":"10.1080/07357907.2025.2543853","url":null,"abstract":"<p><p>Currently, thyroid cancer and thyroid nodules disorders are increasing globally. The diagnosis of these conditions relies on the development of medical technology. Current methods often suffer from the overfitting issue due to a small dataset and a lack of generalizability to diverse clinical settings. Some of the traditional methods encounter challenges with false positive and false negative rates, which affects the performance of the model. To overcome these challenges, a novel module called Pyramid-SpinalNet (Py-SpinalNet) has been developed for thyroid cancer classification. From the given database, the image is pre-processed through the Wiener filter. After this, 3D-UNet is employed for nodule segmentation. In addition, key features are derived through the process of feature extraction. Eventually, the Py-SpinalNet is used for the classification of thyroid cancer. The Py-SpinalNet is developed by merging PyramidNet and SpinalNet. Here, Accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) are the metrics employed for Py-SpinalNet acquired 91.9, 90.9 and 92.8%. The Py-SpinalNet model can accurately detect thyroid cancer at the early stage, thereby minimizing both false-positive and false-negative rates. Thus, it offers a more efficient and reliable classification of thyroid cancer.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"569-593"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified Le-Net Model with Multiple Image Features for Skin Cancer Detection.","authors":"Vinay Kumar Y B, Vimala H S, Shreyas J","doi":"10.1080/07357907.2025.2518400","DOIUrl":"10.1080/07357907.2025.2518400","url":null,"abstract":"<p><p>Computer-based technologies significantly improve melanoma and non-melanoma skin cancer detection by providing non-invasive, cost-effective, and rapid diagnostic solutions. In this context, the study proposes a novel Deep Learning (DL)-based skin cancer detection approach that leverages an advanced segmentation technique called Improved DeepJoint Segmentation (IDJS). This method is designed to enhance the accuracy and precision of the detection process. Initially, the proposed Modified LeNet (MLeNet)-based model applies a Gaussian filter during preprocessing to reduce speckle noise in the input skin images effectively. Following this, the preprocessed images undergo the IDJS segmentation process, which effectively partitions the cancerous regions with high accuracy. Subsequently, three types of features are extracted from the segmented images and they are Multi-Texton Histogram (MTH)-based features, Improved Pyramid Histogram of Oriented Gradient (IPHOG)-based features, and Median Binary Pattern (MBP). These extracted features serve as the input to the MLeNet model for the final skin cancer detection. The datasets used in this work are the HAM10000 dataset and the ISIC 2019 dataset. With a positive metric value of 0.952, the MLeNet model outperforms the traditional models, with LeNet achieving the highest score of 0.932.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"485-514"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Modal Lung Cancer Detection Using Pyramidal Cascade Neuro-Fuzzy Fractional Network.","authors":"Ramachandran A, Michael Mahesh K, Vijayan Panneerselvam, S V J Mani","doi":"10.1080/07357907.2025.2520610","DOIUrl":"10.1080/07357907.2025.2520610","url":null,"abstract":"<p><p>Lung cancer detection (LCD) is a process of identifying an occurrence of lung cancer (LC) or irregularities in the lungs. Early detection of lung cancer is crucial for improving patient survival and enabling effective treatment. Computed Tomography (CT) images and Positron emission tomography (PET) are employed for screening and detecting LC. These methods offer full cross-sectional images of the lungs to detect smaller lesions. Several techniques are developed for LCD, but they often fall into uncertainty. Therefore, a Pyramidal Cascade Neuro-Fuzzy Fractional Network (PCNFFN) is introduced for LCD utilizing CT and PET images. Initially, PET and CT images are pre-processed employing a Bilateral filter (BF). Then, lung lobes are segmented from both images utilizing Dual-Attention V-Network (DAV-Net). Thereafter, Black Hole Entropic Fuzzy Clustering (BHEFC) is employed to segment tumor locations from both lung lobe segmented images. Next, features are extracted from tumor location segmented images. Lastly, LCD is performed by PCNFFN. However, PCNFFN is a combination of Deep Pyramidal residual Network (PyramidNet) and Cascade Neuro-Fuzzy Network (NFN) with Fractional Calculus (FC). In addition, PCNFFN achieved an accuracy of about 91.002%, a true negative rate (TNR) of about 90.504% and a true positive rate (TPR) of about 92.571%.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"535-559"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemotherapy and Persistent Depression in Older Mexican Cancer Survivors: Secondary Analysis of the Mexican Health and Aging Study.","authors":"Diego Arriaga-Izabal, Francisco Morales-Lazcano, Adrián Canizalez-Román","doi":"10.1080/07357907.2025.2521693","DOIUrl":"10.1080/07357907.2025.2521693","url":null,"abstract":"<p><strong>Introduction: </strong>Depressive symptoms (DS) are prevalent among cancer survivors and may be exacerbated by chemotherapy. However, longitudinal data on this relationship within the Mexican population are lacking. The current study aimed to analyze the relationship between chemotherapy and the persistence of depressive symptoms over time in cancer survivors.</p><p><strong>Methods: </strong>Retrospective observational study using Mexican Study of Health and Aging (MHAS) data (2012-2021). Participants aged 50+ included chemotherapy patients (n = 30) and healthy controls (n = 6,970). Depressive symptoms were assessed with a modified Center for Epidemiologic Studies Depression Scale. Mann-Whitney U, X² tests, and generalized estimating equations analyzed chemotherapy's impact on depressive symptoms over time.</p><p><strong>Results: </strong>Chemotherapy recipients showed significantly higher depressive symptoms at early follow-ups (2012, 2015, 2018; p < 0.05), with no significant difference by 2021. Adjusted analyses indicated chemotherapy was associated with a more than twofold increase in odds of depression (OR = 2.165; 95% CI: 1.220-3.810). Lower education and comorbidities such as diabetes and hypertension were also independently linked to increased depression risk.</p><p><strong>Conclusions: </strong>Chemotherapy is a significant predictor of persistent depressive symptoms among Mexican cancer survivors aged 50 and above. These findings highlight the critical need for integrated mental health screening and targeted psychosocial care within oncology settings.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"560-568"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Lung Cancer Diagnosis: An Optimization-Driven Deep Learning Approach with CT Imaging.","authors":"Kasetty Lakshminarasimha, A T Priyeshkumar, M Karthikeyan, Rajalaxmi Sakthivel","doi":"10.1080/07357907.2025.2518404","DOIUrl":"10.1080/07357907.2025.2518404","url":null,"abstract":"<p><p>Lung cancer (LC) remains a leading cause of mortality worldwide, affecting individuals across all genders and age groups. Early and accurate diagnosis is critical for effective treatment and improved survival rates. Computed Tomography (CT) imaging is widely used for LC detection and classification. However, manual identification can be time-consuming and error-prone due to the visual similarities among various LC types. Deep learning (DL) has shown significant promise in medical image analysis. Although numerous studies have investigated LC detection using deep learning techniques, the effective extraction of highly correlated features remains a significant challenge, thereby limiting diagnostic accuracy. Furthermore, most existing models encounter substantial computational complexity and find it difficult to efficiently handle the high-dimensional nature of CT images. This study introduces an optimized CBAM-EfficientNet model to enhance feature extraction and improve LC classification. EfficientNet is utilized to reduce computational complexity, while the Convolutional Block Attention Module (CBAM) emphasizes essential spatial and channel features. Additionally, optimization algorithms including Gray Wolf Optimization (GWO), Whale Optimization (WO), and the Bat Algorithm (BA) are applied to fine-tune hyperparameters and boost predictive accuracy. The proposed model, integrated with different optimization strategies, is evaluated on two benchmark datasets. The GWO-based CBAM-EfficientNet achieves outstanding classification accuracies of 99.81% and 99.25% on the Lung-PET-CT-Dx and LIDC-IDRI datasets, respectively. Following GWO, the BA-based CBAM-EfficientNet achieves 99.44% and 98.75% accuracy on the same datasets. Comparative analysis highlights the superiority of the proposed model over existing approaches, demonstrating strong potential for reliable and automated LC diagnosis. Its lightweight architecture also supports real-time implementation, offering valuable assistance to radiologists in high-demand clinical environments.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"515-534"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tandem Dual CAR-T Cells Targeting HER2 and Mesothelin Enhance anti-Tumor Effects in Pancreatic Cancer.","authors":"Fengting Jiang, Mei Zheng, Yahong Ding, Feifei Xiong, Xueying Liu, Xu Zhou, Zihou Yan, Jian Luo","doi":"10.1080/07357907.2025.2548603","DOIUrl":"10.1080/07357907.2025.2548603","url":null,"abstract":"<p><p>The therapeutic application of T cells engineered to express chimeric antigen receptors (CARs) is hindered by the risk of antigen escape in single-target CAR constructs, particularly in the treatment of solid tumors. Pancreatic cancer cells frequently overexpress tumor-associated antigens, such as human epidermal growth factor receptor 2 (HER2) and Mesothelin (Meso). In this study, we therefore investigated the therapeutic effect of tandem dual CAR-T cells co-targeting Her2 and Meso <i>versus</i> single-targeted CAR-T cells in pancreatic cancer models. We constructed a dual CAR by fusing a HER2-binding single-chain variable fragment (ScFv) with a Meso-binding ScFv. The expression levels of CARs and the anti-tumor efficacy of CAR-T cells were systematically compared <i>via in vitro</i> and <i>in vivo</i> experiments. In HER2/Meso co-expressing pancreatic cancer cell lines (AsPC-1 and SW-1990), dual CAR-T cells exhibited superior antitumor activity, accompanied by increased secretion of anti-tumor cytokines (IL-2 and IFN-γ), compare to HER2-specific or Meso-specific single-target CAR-T cells. In a xenograft mouse model, dual CAR-T cells significantly reduced tumor volume and prolonged mouse survival relative to single-target CAR-T cells. Collectively, our findings demonstrated that dual CAR-T cells enhance antitumor cytotoxicity, supporting their potential as a promising therapeutic strategy for Pancreatic Cancer and other solid tumors.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"594-607"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Serum Irisin Levels in Cancer Patients and Healthy Individuals: A Systematic Review and Meta-Analysis of Diagnostic Evidence.","authors":"Rahul Mohandas, Supriya Kheur, Subhashree Mohapatra","doi":"10.1080/07357907.2025.2533267","DOIUrl":"10.1080/07357907.2025.2533267","url":null,"abstract":"<p><strong>Background: </strong>Irisin influences key cancer-related processes such as cell proliferation, apoptosis, angiogenesis, and metastasis. This review aimed to evaluate serum irisin's potential in cancer diagnosis, prognosis, and treatment monitoring.</p><p><strong>Methods: </strong>Databases including Scopus, PubMed, Cochrane, and others were searched. Studies comparing serum irisin in cancer patients and healthy individuals were assessed using Joanna Briggs Institute criteria.</p><p><strong>Results: </strong>Nine studies showed significantly lower irisin levels in cancer patients (SMD: -1.16, <i>p</i> < 0.0001), especially in hepatocellular and bladder cancers.</p><p><strong>Conclusion: </strong>Reduced serum irisin may serve as a diagnostic cancer biomarker, though its utility varies by cancer type and requires further research.</p><p><strong>Prospero registration no: </strong>CRD420250650210.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"436-452"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144648637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomarker Potential of DNA Repair Genes <i>XRCC1, XRCC3</i> and <i>RAD51</i> Polymorphisms in Ovarian Cancer Patients.","authors":"Yanni A, Maria Afzal, Sidra Usman, Ayesha Akram, Hanan Nasir, Muhammad Umar, Anisa Iftikhar, Kashif Bashir","doi":"10.1080/07357907.2025.2524560","DOIUrl":"10.1080/07357907.2025.2524560","url":null,"abstract":"<p><p>Ovarian cancer remains one of the most lethal gynecological malignancies, with a high mortality rate primarily due to late-stage diagnosis. Genetic predispositions play a significant role in its development, alongside environmental and lifestyle factors. The main objective of the study was to check the association of <i>XRCC1, XRCC3,</i> and <i>RAD51</i> gene polymorphism with ovarian cancer. In the present 300 ovarian cancer patients and 300 healthy controls blood samples collected. The results showed that the heterozygous (GA) genotype of rs25487 SNP shows significant correlation with ovarian cancer with decreased risk of disease (OR = 0.39; 95% CI = 0.17-0.88; <i>p</i> < 0.02), whereas the homozygous variant (AA) genotype of the same SNP exhibits a non-significant relation with ovarian cancer. The combined genotype model of this SNP indicated a highly significant association with increased risk of ovarian cancer by twofold (OR = 2.10;95% CI = 1.22-3.64; <i>p</i> < 0.007). In case of rs861539 heterozygous (CT) showed significant association by increasing the risk of disease almost threefold (OR = 2.73; 95% CI 1.68-4.41; <i>p</i> < 0.0001). while the mutant (TT) of the same SNP showed again significant association but with decreased risk of ovarian cancer (OR = 0.27; 95% CI 0.16-0.47; <i>p</i> < 0.0001). The genotype distribution of the <i>RAD51</i> gene's SNP (rs1801320) shows that heterozygous (GC) individuals exhibit a significant correlation and increased risk of ovarian cancer by twofold (OR = 2.81;95% CI = 1.72-4.60; <i>p</i> ≤ 0.0001). Conversely, the mutant (CC) of rs1801320 exhibits a significant correlation with a decrease in the risk of ovarian cancer (OR = 0.32; 95% CI = 0.19-0.55; <i>p</i> < 0.0001). In conclusion, the study's findings suggest that a higher chance of ovarian cancer is related to the gene <i>XRCC1, XRCC3,</i> and <i>RAD51</i> polymorphisms. In this study, SNPs were analyzed for their potential role as biomarkers for the diagnosis of ovarian cancer.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"399-411"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy and Safety of Current Treatment Regimens for Acute Myeloid Leukemia in Elderly Patients.","authors":"Zinaida Stupakova, Oksana Karnabeda, Konstiantyn Isaiev, Ulyana Melnyk","doi":"10.1080/07357907.2025.2533279","DOIUrl":"10.1080/07357907.2025.2533279","url":null,"abstract":"<p><p>The purpose of this study was to evaluate the efficacy and safety of Azacitidine, alone or in combination with Venetoclax, in the treatment of newly diagnosed acute myeloid leukemia in patients with significant comorbidities who are ineligible for intensive chemotherapy. Most patients in the cohort had high-risk acute myeloid leukemia based on clinical and cytogenetic characteristics and required low-intensity therapeutic regimens, such as Azacitidine with or without Venetoclax, due to their age and comorbidities. It was shown that 77% of patients treated with hypomethylating agents with the addition of Venetoclax achieved complete remission. In addition, the clinical case of a 61-year-old patient with severe comorbidity status is described in the end of the article. The diagnosis included a transient ischemic attack in the context of an unruptured cerebral aneurysm, for which the patient was not a candidate for thrombolytic therapy, alongside other conditions requiring comprehensive treatment. A limitation of this study was a small sample of patients with acute myeloid leukemia.</p>","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"453-466"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}