Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine

Current Oncology, Journal Year: 2023, Volume and Issue: 30(3), P. 2673 - 2701

Published: Feb. 22, 2023

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages diagnostic and therapeutic process. Although histological investigation will remain difficult to replace, in near future radiomic approach allow a complementary, repeatable non-invasive characterization lesion, assisting oncologists neurosurgeons selecting best option correct molecular target chemotherapy. AI-driven tools are already playing an important role surgical planning, delimiting extent lesion (segmentation) its relationships with structures, thus allowing precision surgery as radical reasonably acceptable preserve quality life. Finally, AI-assisted prediction complications, recurrences response, suggesting most appropriate follow-up. Looking future, AI-powered promise integrate biochemical clinical data stratify risk direct patients personalized screening protocols.

Language: Английский

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SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2023, Volume and Issue: 76(2), P. 2201 - 2216

Published: Jan. 1, 2023

Breast cancer is a major public health concern that affects women worldwide. It leading cause of cancer-related deaths among women, and early detection crucial for successful treatment. Unfortunately, breast can often go undetected until it has reached advanced stages, making more difficult to treat. Therefore, there pressing need accurate efficient diagnostic tools detect at an stage. The proposed approach utilizes SqueezeNet with fire modules complex bypass extract informative features from mammography images. extracted are then utilized train support vector machine (SVM) image classification. SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, accuracy 94.10% sensitivity 94.30%. A 10-fold cross-validation was performed ensure the robustness mean standard deviation various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all indicators, indicating its superior performance. demonstrates effectiveness diagnosis using makes tool diagnosis. may have significant implications reducing mortality rates.

Language: Английский

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Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome

La radiologia medica, Journal Year: 2023, Volume and Issue: 128(11), P. 1347 - 1371

Published: Oct. 6, 2023

Language: Английский

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Machine Learning for Biomedical Applications

Bioengineering, Journal Year: 2024, Volume and Issue: 11(8), P. 790 - 790

Published: Aug. 5, 2024

Machine learning (ML) is a field of artificial intelligence that uses algorithms capable extracting knowledge directly from data could support decisions in multiple fields engineering [...].

Language: Английский

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Innovative Diagnostic Approaches for Predicting Knee Cartilage Degeneration in Osteoarthritis Patients: A Radiomics-Based Study

Information Systems Frontiers, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Language: Английский

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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(2), P. 38 - 38

Published: Jan. 26, 2025

Breast cancer, as of 2022, is the most prevalent type cancer in women. density-a measure non-fatty tissue breast-is a strong risk factor for breast that can be estimated from mammograms. The importance studying density twofold. First, high lowering mammogram sensitivity, dense mask tumors. Second, higher associated with an increased making accurate assessments vital. This paper presents comprehensive review mammographic estimation literature, emphasis on machine-learning-based approaches. approaches reviewed classified visual, software-, machine learning-, and segmentation-based. Machine learning methods further broken down into two categories: traditional deep commonly utilized models are support vector machines (SVMs) convolutional neural networks (CNNs), classification accuracies ranging 76.70% to 98.75%. Major limitations current works include subjectivity cost-inefficiency. Future work focus addressing these limitations, potentially through use unsupervised segmentation state-of-the-art such transformers. By future research pave way more reliable methods, ultimately improving early detection diagnosis.

Language: Английский

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A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

La radiologia medica, Journal Year: 2024, Volume and Issue: 129(6), P. 864 - 878

Published: May 17, 2024

Language: Английский

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A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10315 - 10315

Published: Nov. 9, 2024

Breast cancer is among the most prevalent cancers in female population globally. Therefore, screening campaigns as well approaches to identify patients at risk are particularly important for early detection of suspect lesions. This study aims propose a workflow automatic classification based on one relevant factors breast cancer, which represented by density. The proposed methodology takes advantage features automatically extracted from mammographic images, digital mammography represents major tool women. Textural were parenchyma through radiomics approach, and they used train different machine learning algorithms neural network models classify density according standard Imaging Reporting Data System (BI-RADS) guidelines. Both binary multiclass tasks have been carried out compared terms performance metrics. Preliminary results show interesting accuracy (93.55% task 82.14% task), promising current literature. As relies straightforward computationally efficient algorithms, it could serve basis fast-track protocol mammograms reduce radiologists’ workload.

Language: Английский

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A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions

Journal of Personalized Medicine, Journal Year: 2023, Volume and Issue: 13(7), P. 1104 - 1104

Published: July 7, 2023

Despite mammography (MG) being among the most widespread techniques in breast cancer screening, tumour detection and classification remain challenging tasks due to high morphological variability of lesions. The extraction radiomics features has proved be a promising approach MG. However, can suffer from dependency on factors such as acquisition protocol, segmentation accuracy, feature engineering methods, which prevent implementation robust clinically reliable workflow In this study, robustness is investigated function lesion MG images public database. A statistical analysis carried out assess score introduced based significance tests performed. obtained results indicate that observable not only abnormality type (calcification masses), but also categories (first-order second-order), image view (craniocaudal medial lateral oblique), lesions (benign malignant). Furthermore, through proposed approach, it possible identify those characteristics with higher discriminative power between benign malignant lower segmentation, thus suggesting appropriate choice used inputs automated algorithms.

Language: Английский

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A general framework for the assessment of scatter correction techniques in digital mammography

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 89, P. 105802 - 105802

Published: Dec. 5, 2023

Scattered radiation negatively impacts radiographic imaging, with particular regard to mammography. In clinical practice, anti-scatter grids are exploited for this purpose; however, may also degrade the image quality, since they remove part of useful primary consequent increase dose be administered patient. A suitable digital scatter correction method could tackle limits imposed by such a great impact on diagnosis. The main contribution study is development general framework assessment techniques in To aim, formation process both and scattered described basis systems-theory approach. Through simulation radiological process, reference model obtained used as ground truth compare intensities images applying deconvolution-based scattering technique. Then, an experimental case breast phantom carried out assess using different Point Spread Functions (PSFs) (Gaussian Hyperbolic) varying parameters values. central issue was identification spatially variant PSF radiation. results demonstrate that proposed approach enables comparison kernels employed correction; particular, our procedure shows rather low relative errors ([−0.5;0.5]) PSFs tested Gaussian ones more sensitive variations their parameters.

Language: Английский

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