Nanomedicine in Clinical Photodynamic Therapy for the Treatment of Brain Tumors

Biomedicines, Год журнала: 2022, Номер 10(1), С. 96 - 96

Опубликована: Янв. 3, 2022

The current treatment for malignant brain tumors includes surgical resection, radiotherapy, and chemotherapy. Nevertheless, the survival rate patients with glioblastoma multiforme (GBM) a high grade of malignancy is less than one year. From clinical point view, effective GBM limited by several challenges. First, anatomical complexity influences extent resection because fine balance must be struck between maximal removal tissue minimal risk. Second, central nervous system has distinct microenvironment that protected blood-brain barrier, restricting systemically delivered drugs from accessing brain. Additionally, characterized intra-tumor inter-tumor heterogeneity at cellular histological levels. This peculiarity GBM-constituent tissues induces different responses to therapeutic agents, leading failure targeted therapies. Unlike photodynamic therapy (PDT) can treat micro-invasive areas while protecting sensitive regions. PDT involves photoactivation photosensitizers (PSs) are selectively incorporated into tumor cells. Photo-irradiation activates PS transfer energy, resulting in production reactive oxygen species induce cell death. Clinical outcomes PDT-treated advanced terms nanomedicine. review discusses applications nanomedicine GBM.

Язык: Английский

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Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июнь 30, 2023

ABSTRACT Purpose Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms tumors are rare, due limited data availability, have yet demonstrate translation. Methods We leveraged two datasets from a national brain consortium (n=184) cancer center (n=100) develop, externally validate, clinically benchmark deep learning neural networks low-grade glioma (pLGG) segmentation using novel in-domain, stepwise transfer approach. The best model [via Dice similarity coefficient (DSC)] was validated subject randomized, blinded by three expert clinicians wherein assessed acceptability of expert- AI-generated segmentations via 10-point Likert scales Turing tests. Results AI utilized (median DSC: 0.877 [IQR 0.715-0.914]) versus baseline DSC 0.812 0.559-0.888]; p <0.05). On external testing (n=60), the yielded accuracy comparable inter-expert agreement 0.834 0.726-0.901] vs. 0.861 0.795-0.905], =0.13). benchmarking (n=100 scans, 300 3 experts), experts rated higher on average compared other rating: 9 7-9]) 7 7-9], <0.05 each). Additionally, had significantly ( <0.05) overall (80.2% 65.4%). Experts correctly predicted origins in an 26.0% cases. Conclusions Stepwise enabled expert-level, automated auto-segmentation measurement with high level acceptability. This approach may development translation imaging scenarios. Summary Authors proposed develop validate whose performance were par neuroradiologists radiation oncologists. Key Points There available train tumors, adult-centric models generalize poorly setting. demonstrated gains (Dice score: methodologies human validation. testing, received score rating Transfer-Encoder expert: 80.2% 65.4%) tests showed uniformly low ability experts’ identify as human-generated (mean accuracy: 26%).

Язык: Английский

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A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations

Physics and Imaging in Radiation Oncology, Год журнала: 2023, Номер 28, С. 100500 - 100500

Опубликована: Окт. 1, 2023

Язык: Английский

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Least square-support vector machine based brain tumor classification system with multi model texture features

Frontiers in Applied Mathematics and Statistics, Год журнала: 2023, Номер 9

Опубликована: Дек. 6, 2023

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through analysis MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on capabilities Least Squares Support Vector Machines (LS-SVM) in tandem rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted T1-weighted MR underwent meticulous evaluation a substantial dataset encompassing 139 cases, consisting 119 cases aberrant 20 normal The outcomes we achieved are nothing short extraordinary. LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance exceptional accuracy rate 98.97%. This represents 3.97% improvement over alternative methods, accompanied by notable 2.48% enhancement Sensitivity 10% increase Specificity. These results conclusively surpass performance traditional classifiers such as (SVM), Radial Basis Function (RBF), Artificial Neural Networks (ANN) terms classification accuracy. outstanding our model realm tumor diagnosis signifies leap forward field, holding promise delivering more precise dependable tools for radiologists healthcare professionals their pivotal role identifying using imaging techniques.

Язык: Английский

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Dual view deep learning for enhanced breast cancer screening using mammography

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 15, 2024

Abstract Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where cure is delayed or not possible. To address this issue, mammography-based screening widely accepted as an effective technique for early detection. However, interpretation mammography images requires experienced radiologists breast imaging, resource that limited Ethiopia. In research, we have developed model assist mass abnormalities and prioritizing patients. Our approach combines ensemble EfficientNet-based classifiers with YOLOv5, suspicious detection method, identify abnormalities. The inclusion YOLOv5 crucial providing explanations classifier predictions improving sensitivity, particularly when fails detect further enhance process, also incorporated abnormality model. achieves F1-score 0.87 sensitivity 0.82. With addition detection, increases 0.89, albeit expense slightly lower 0.79.

Язык: Английский

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