Search Results (775)

Background/Objectives: Prostate cancer is the second most common cause of cancer-related mortality among the male population worldwide. It is among the leading reasons for the increasing number of years of life lost, working years of life lost, and gross domestic product (GDP) loss in Bulgaria. The primary objective of this study is to evaluate the burden of prostate cancer in Bulgaria, including calculating years of life lost (YLL), years of working life lost (YWLL), and the associated indirect costs. Methods: An observational time-series study was conducted using official national data from the National Statistical Institute (NSI), the INFOSTAT database, and the National Social Security Institute. The study covered the period 2008–2023 and included all registered male deaths attributed to malignant neoplasm of the prostate (ICD-10: C61). YLL, YWLL, and indirect costs were calculated using the human capital approach. Due to restricted access to age-specific mortality files, additional mortality records were obtained through formal data requests to NSI. Results: Prostate cancer led to 127,457 YLL and 6345 YWLL, with productivity losses reaching €88.2 million. Mortality showed an overall increasing trend up to 2020, while YWLL declined due to deaths shifting to older age groups. Conclusions: Despite the advancements in prostate cancer diagnosis and treatment, our findings demonstrate a negative trend regarding YLL, YWLL, and indirect costs associated with the disease, in contrast to other European countries. Strengthening early screening, reducing diagnostic delays, and improving national cancer registry capacity are critical to mitigating future health and economic losses. Full article

Background/Objectives: Liver cancer is among the most frequent poor-prognosis malignancies worldwide, with currently insufficient effective treatment. The two-stage microwave hyperthermia using magnetic nanoparticles is a modern technique designed to specifically target tumor tissues and facilitate chemotherapy activation, with promising results from fundamental studies across various tumor types. The method consists of a first irradiation, performed before nano-assemblies administration. This is intended to sensitize the tumor by inducing a hyperthermic effect, leading to increasing blood supply, enhancing endothelial damage/permeation and inflammatory activation, with the final goal of improving the diffusion/retention of nano-assemblies in the tumor. Subsequently, the second microwave irradiation follows the injection in the hepatic artery and diffusion in the tumor of the activated nano-assemblies, to further determine a strong, but localized and focalized hyperthermic action. Nano-magnetic assemblies for hyperthermia accomplish the proposed chemo-thermal delivery, i.e., act per se on the tumor and also destabilize co-administered assemblies of nanoparticles loaded with chemotherapeutics, which would be consequently released locally in the most efficient way. This article aims to demonstrate the efficacy of this therapeutic approach in a rat liver model and its potential applicability in patients with liver tumors. Methods: Adult male Wistar rats were used to obtain liver samples, which were divided into three groups, each receiving a different hyperthermia protocol in terms of temperature (41–45 °C), duration, and co-administration of nanoparticles. Results: The most suitable exposure temperature for rat liver appears to be 42 °C, resulting in vacuolar degeneration lesions at the focal level. The effects of thermal conditioning do not appear to be homogeneous in the tested liver, and the controlling environment and methodology should be improved in the near future. The level of hepatic inflammation, as indicated by elevated interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) levels, appears negligible under the current hyperthermia protocol. Conclusions: Two-stage microwave hyperthermia using magnetic nanoparticles is a promising therapeutic modality for liver cancer, with promising results from animal studies opening the way for further research in humans. Full article

Background: There is an increasing number of synchronous tumor diagnoses, mainly due to new investigative techniques and diagnostic guidelines. While renal and colonic malignancies are common, synchronous cases remain rare. They are usually diagnosed during the staging work-up performed for the primary cancer. Case Presentation: We share our experience with two cases of synchronous colon adenocarcinoma and renal cell carcinoma. The surgical intervention was performed simultaneously and laparoscopically, with good results and prognosis. Reviewing the literature, we found few studies reporting these synchronous tumors, which reflects their low incidence. Renal tumors are often identified during imaging studies performed for staging colonic tumors, and performing surgical treatment during the same operation is widely accepted. We performed a search of the literature to identify similar cases and to look for associations that can lead to synchronous colonic and renal malignancies. We also wanted to highlight the potential for therapeutic management as a single step, thereby avoiding a second surgical procedure. Conclusions: Synchronous renal and colonic malignancies are rare and are generally sporadic. Due to their rarity, there are no established guidelines, and management can be challenging. Presently, the treatment needs to be individualized based on discussions from the tumor board. Full article

Background: Lung cancer screening using low-dose computed tomography (LDCT) demands not only early pulmonary nodule detection but also accurate estimation of malignancy risk. This remains challenging due to subtle nodule appearances, the large number of CT slices per scan, and variability in radiological interpretation. The objective of this study is to develop a unified computer-aided detection and diagnosis framework that improves both nodule localization and malignancy assessment while maintaining clinical reliability. Methods: We propose Seg-CADe-CADx, a dual-stage deep learning framework that integrates segmentation-guided detection and malignancy classification. In the first stage, a segmentation-guided detector with a lightweight 2.5D refinement head is employed to enhance nodule localization accuracy, particularly for small nodules with diameters of 6 mm or less. In the second stage, a hybrid 3D DenseNet–Swin Transformer classifier is used for malignancy prediction, incorporating probability calibration to improve the reliability of risk estimates. Results: The proposed framework was evaluated on established public benchmarks. On the LUNA16 dataset, the system achieved a competitive performance metric (CPM) of 0.944 for nodule detection. On the LIDC-IDRI dataset, the malignancy classification module achieved a ROC-AUC of 0.988, a PR-AUC of 0.947, and a specificity of 97.8% at 95% sensitivity. Calibration analysis further demonstrated strong agreement between predicted probabilities and true malignancy likelihoods, with an expected calibration error of 0.209 and a Brier score of 0.083. Conclusions: The results demonstrate that hybrid segmentation-guided CNN–Transformer architectures can effectively improve both diagnostic accuracy and clinical reliability in lung cancer screening. By combining precise nodule localization with calibrated malignancy risk estimation, the proposed framework offers a promising tool for supporting radiologists in LDCT-based lung cancer assessment. Full article

Background/Objectives: Small-cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid proliferation, early metastasis, and near-universal relapse after initial therapy. While chemo-immunotherapy modestly improves first-line outcomes, survival after progression remains poor and highlights the urgent need for biomarker-directed strategies. Methods: A comprehensive literature search was conducted using major medical databases looking at key relevant studies on SCLC antibody studies. All authors reviewed the literature, assessed study quality, and interpreted the results from each study. Results: Recent advances in antibody–drug conjugates (ADCs) and T-cell engagers (TCEs) have transformed therapeutic development by targeting antigens selectively expressed on SCLC cells, enabling more precise and potentially durable tumor control. DLL3 has emerged as the most clinically relevant target to date, with the bispecific TCE tarlatamab demonstrating meaningful and durable response, manageable cytokine-release toxicity, and ultimately achieving accelerated FDA approval for previously treated extensive-stage SCLC. Concurrently, DLL3-directed ADCs have shown variable efficacy, underscoring the importance of payload selection, linker chemistry, and antigen density. Beyond DLL3, next-generation ADCs targeting TROP2, B7-H3, and SEZ6 have reported encouraging early-phase activity, including response rates exceeding those of existing second-line cytotoxic options, though myelosuppression, interstitial lung disease, and hepatic toxicity remain key considerations. Conclusions: Collectively, these emerging immunotherapies illustrate a shift toward antigen-specific targeting in a disease historically defined by limited therapeutic innovation. Continued optimization of antigen selection, payload and linker engineering, and biomarker-driven trial design will be critical for translating early promise into durable clinical benefit and reshaping the treatment landscape for SCLC. Full article

Uveal melanoma (UVM) is a rare cancer that represents the second most common melanoma (after the cutaneous) and the most common primary intraocular malignancy in adults. Despite recent advances in the understanding of UVM pathogenesis, its prognosis remains unchanged, with half of patients dying because of liver metastasis. Erythropoietin-producing human hepatocellular receptors (EPHs) constitute the largest known family of tyrosine receptors, and, along with their ligands, EFNs, regulate key physiological processes and are implicated in cancer pathogenesis. In this study, we used open-access web bioinformatics platforms to explore and analyze big datasets provided by The Cancer Genome Atlas (TCGA) UVM cohort of patients. We profiled the genomic alterations present in a subset of UVM patients, highlighting a likely pathogenic deep deletion of EPHA7. Survival analysis showed that overexpression levels of EPHA4, EPHA5, EPHA8, EPHB2, and EFNB2 are significantly associated with poor overall survival. Additionally, high expression levels of EPHA4, EPHA5, EPHA7, EPHA8, EPHB2, EFNA2, and EFNB2 correlate with reduced progression-free interval and disease-free survival. Finally, we identified the EPHs (EPHA2, EPHA4, EPHA8, and EPHB4) and EFNs (EFNA1, EFNA3, EFNA4, and EFNB2) that are significantly overexpressed in the aggressive epithelioid histological subtype and revealed that the majority of EPHs/EFNs are overexpressed in metastatic disease. In conclusion, our results highlight that a subset of EPHs and EFNs may be associated with worse clinical outcomes (EPHA4, EPHA5, EPHA7, EPHA8, EPHB2, EFNA2, and EFNB2), and an aggressive histological subtype (EPHA2, EPHA4, EPHA8, EPHB4, EFNA1, EFNA3, EFNA4, and EFNB2). The potential correlation of these genes with clinicopathological parameters of UVM need to be evaluated and validated with bioinformatic and experimental approaches in well-characterized cohorts of UVM patients. Full article

Background and Clinical Significance: Anaplastic thyroid cancer (ATC) is a rare and aggressive malignancy with a poor prognosis, where median survival typically ranges from 4 to 10 months. Advances in genetic profiling, particularly the identification of BRAF mutations, offer new opportunities for targeted therapy. Case Presentation: This case report details the journey of a woman in her late 50s diagnosed with symptomatic ATC. Initial immunohistochemistry (IHC) testing for BRAF mutations returned negative results, leaving the patient with limited treatment options and prompting her to pursue medical assistance in dying (MAiD). However, next-generation sequencing (NGS) confirmed a ^V600EBRAF mutation, and a basis for targeted therapy. The patient began treatment with dabrafenib-trametinib, followed by pembrolizumab as second-line therapy, ultimately extending her life by nearly seven months. Conclusions: This case underscores the importance of rapid and comprehensive diagnostic approaches, particularly the higher sensitivity of NGS over IHC for detecting BRAF mutations. The complexities of accessing newer therapies in Canada’s single-payer healthcare system are also emphasized. The utilization of newer rapid diagnostic technologies can have a direct impact on directing treatment for ATC and other aggressive malignancies. Full article

The thyroid cancer incidence has been continuously rising over the last decades. Recently, intelligent cancer detection software are gaining popularity, due to their high diagnostic accuracy and subsequent direct benefits in avoiding unnecessary surgical interventions. This study introduces a novel hybrid computer-aided diagnosis (CAD) system that combines convolutional neural networks (CNNs) and molecular data analysis to achieve comprehensive and reliable thyroid cancer diagnostics. The system consists of two key modules: The first is a CNN-based model leveraging transfer learning, processes ultrasound images to classify patients as either “healthy” or “with a thyroid nodule.” In cases where a nodule is detected, the second module utilizes molecular data to predict the malignancy risk, providing a probability score for clinical decision support. Different image augmentation techniques (traditional ones as well as novels) were carried out to enhance the robustness of the system. The combination of two independent modules makes it possible to use them decoupled, while used together they provide a powerful, in-depth diagnosis of thyroid cancer. The proposed system demonstrates strong performance: the ultrasound-based CNN module achieves an accuracy of 93.65%, with a sensitivity of 100% and a specificity of 69.23%. For the gene analysis component, the model achieves a training mean squared error (MSE) of 4.24 × 10⁻⁵ and a testing MSE 6.31 × 10⁻³. These results underscore the system’s competitive performance with existing thyroid cancer detection CAD systems in both diagnostic performance and the depth of insights provided, supporting clinicians in making informed, reliable decisions in thyroid cancer management. Full article

Background/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 ± 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications. Full article

Talaporfin sodium (mono-L-aspartyl chlorin e6; NPe6), a second-generation photosensitizer, is clinically used in photodynamic therapy (PDT). It accumulates preferentially in tumors and exhibits deep tissue penetration, rapid systemic clearance, and minimal photosensitivity. However, treatment of deep-seated malignancies remains challenging. Here, we demonstrate that talaporfin sodium undergoes physicochemical reactions with X-rays to generate reactive oxygen species, a mechanism analogous to that of 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX in radiodynamic therapy (RDT). To evaluate its therapeutic efficacy, we employed a pancreatic cancer xenograft model using MIA PaCa-2 cells in mice. Talaporfin sodium was administered intravenously 2 h before X-ray exposure, followed by fractionated X-ray irradiation (3 Gy daily for 3 consecutive days). Talaporfin-mediated RDT significantly inhibited tumor growth compared with radiation therapy alone. Furthermore, an exploratory RNA-seq analysis of xenografts revealed transcriptional signatures of stress and immune activation, suggesting that talaporfin-mediated RDT enhances oxidative and immunogenic responses within the tumor microenvironment. These findings highlight the potential of talaporfin sodium as a clinically translatable radiosensitizer for RDT, offering a promising strategy for the treatment of deep-seated cancers such as pancreatic carcinoma. Full article

Cutaneous lymphomas are a heterogeneous group of extranodal non-Hodgkin lymphomas with distinct clinical and biological features, broadly classified into cutaneous T-cell lymphomas (CTCL) and cutaneous B-cell lymphomas (CBCL). With improved survival due to early detection and therapeutic advances, the emergence of second primary malignancies (SPMs) has become a clinical concern. SPMs, defined as new, distinct malignant neoplasms arising synchronously or metachronously with the index cancer, can significantly impair prognosis and quality of life. In this narrative review, we meticulously examine the current literature, to synthesize evidence on SPMs’ incidence and risk factors in patients with primary cutaneous lymphomas. Evidence from population-based and institutional studies consistently demonstrates elevated risks of hematologic and solid tumors in CTCL. By contrast, data on CBCL remain limited, though recent population-based analyses suggest increased risks of certain hematologic malignancies and solid tumors. We further propose development mechanisms for SPMs, including treatment-related mutagenesis, shared genetic susceptibilities, chronic antigenic stimulation, and immune dysregulation. Lastly, we highlight the clinical implications of these findings, underscoring the need for vigilant surveillance, patient education, and tailored screening strategies. Future research should prioritize large-scale, prospective, and molecularly integrated studies to refine risk stratification and guide personalized survivorship care of this vulnerable population. Full article

Background/Objectives: Breast cancer is one of the most common malignancies in women worldwide. Early diagnosis and accurate classification in breast cancer detection are among the most critical factors determining treatment success and patient survival. In this study, a deep learning-based model was developed that can classify benign, malignant, and normal breast tissues from ultrasound images with high accuracy and achieve better results than the methods commonly used in the literature. Methods: The proposed model was trained on a dataset of breast ultrasound images, and its classification performance was evaluated. The model is designed to effectively learn both local textural features and global contextual relationships by combining Squeeze-and-Excitation (SE) blocks, which emphasize channel-level feature importance, and Convolutional Block Attention Module (CBAM) attention mechanisms, which focus on spatial information, with the MHA structure. The model’s performance is compared with three commonly used convolutional neural networks (CNNs) and three Vision Transformer (ViT) architectures. Results: The developed model achieved an accuracy rate of 96.03% in experimental analyses, outperforming both the six compared models and similar studies in the literature. Additionally, the proposed model was tested on a second dataset consisting of histopathological images and achieved an average accuracy of 99.55%. The results demonstrate that the model can effectively learn meaningful spatial and contextual information from ultrasound data and distinguish different tissue types with high accuracy. Conclusions: This study demonstrates the potential of deep learning-based approaches in breast ultrasound-based computer-aided diagnostic systems, providing a reliable, fast, and accurate decision support tool for early diagnosis. The results obtained with the proposed model suggest that it can significantly contribute to patient management by improving diagnostic accuracy in clinical applications. Full article

Background: Secondary pathogenic/likely pathogenic germline variants (P/LPGVs) identified on solid tumor genomic profiling (TGP) are a commonly encountered clinical issue. A proportion of oncology patients that undergo TGP will have a secondary P/LPGV identified that may not have been otherwise discovered based on clinical and family history criteria for hereditary cancer syndrome screening. The confirmation of P/LPGVs on germline sequencing has potential treatment implications for patients. Methods: The study design was a retrospective review for secondary data analysis. The inclusion criteria for this study were adult patients with solid tumor malignancy who underwent TGP and germline sequencing. The objective of this study is to evaluate the concordance rate of secondary P/LPGVs on TGP of adult patients with solid tumor malignancy at Hoag Presbyterian Hospital and Tower Health-Reading Hospital. The second and third aims are to analyze if the confirmed P/LPGVs are concordant with the patient’s tumor type and to analyze the variant allele frequencies (VAFs) of the identified secondary P/LPGVs on the tumor genomic profiling. Results: The data included 75 patients who underwent both TGP and germline sequencing, with a median age of 62.5 years. The most represented genes with P/LPGVs in the combined data included BRCA1 and BRCA2, both with 14, and MSH2, with 9. The overall germline concordance rate for the combined population was 64.1%, with 59 out of 92 P/LPGVs identified on both germline and somatic tumor testing. Conclusions: The overall germline concordance rate of 64% for the combined population is in accordance with the reported literature. Possible reasons for the variability in rates could be related to reporting guidelines for secondary germline variants, which can vary by company, and differences between somatic and germline variant curation. The study of P/LPGVs in populations from community cancer centers has the potential to increase the data of underrepresented minority groups regarding this important clinical issue and help expand understanding of hereditary cancer syndrome phenotypes. Full article

Objectives: Breast cancer is one of the most common malignant tumors among women worldwide, and accurate assessment of axillary lymph node metastasis (ALNM) is crucial for determining treatment strategies. Compared to conventional ultrasound, contrast-enhanced ultrasound (CEUS) can observe blood perfusion and microcirculation changes in primary breast tumors, making it a more ideal diagnostic method for ALNM. Methods: To address the issues that CEUS video sequences require a high level of diagnostic experience from clinicians, and the process is time-consuming and labor-intensive, making it challenging to generate large datasets for deep learning models, we proposed a method for predicting breast cancer ALNM that combines pre-trained fine-tuning with contrastive learning. First, within a text-video contrastive learning framework, we fine-tuned pre-trained weights from a large general dataset using a small-scale proprietary dataset. Second, during the fine-tuning phase, we employed random prompt optimization to specifically adjust the text encoder according to the characteristics of breast CEUS videos, and optimized the extracted text and video representations through an adaptive fine-tuning optimizer to better fit the current data distribution. Results: Experimental results demonstrated that our method achieved a sensitivity of 0.792 and a specificity of 0.8. Conclusions: The study demonstrates that the proposed method effectively leverages CEUS to aid in ALNM diagnosis, highlighting its potential to improve the accuracy of early breast cancer screening and to facilitate the development of more personalized treatment plans for patients. Full article

Colorectal cancer (CRC) is the second most common global malignancy with high mortality, and timely early polyp detection is critical to halt its progression. Yet, polyp image segmentation—an essential tool—faces challenges: blurred edges, small sizes, and artifacts from intestinal folds, bubbles, and mucus. To address these, we proposed a novel segmentation model with multi-scale feature extraction. Its encoder uses Multiscale Attention-based Pyramid Vision Transformer v2 (PVTv2) for hierarchical features (lower-stage modules expand receptive field), while the decoder adopts a Parallel Multi-level Aggregation structure, plus multi-branch and improved reverse attention modules. Ablation experiments validated key modules. Compared to nine state-of-the-art networks across five benchmarks, the model showed superiority: optimal mDice/mIoU on polyp datasets, 0.2% higher mDice than MEGANet on Kvasir-SEG, and outperformance over UHA-Net and CSCA-U-Net on CVC-ClinicDB. Full article