Beyond the Frontline: A Triple-Line Approach of Thoracic Surgeons in Lung Cancer Management—State of the Art

Bottet, Benjamin; Piton, Nicolas; Selim, Jean; Sarsam, Matthieu; Guisier, Florian; Baste, Jean-Marc

doi:10.3390/cancers15164039

Cited by 2 publications

(1 citation statement)

References 136 publications

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“…Our work addresses this very gap by proposing an innovative method that combines the strengths of multiple existing models. The overarching goal is to arm clinicians with sophisticated tools and methods, facilitating more effective screening, diagnosis, therapeutic planning, image-guided interventions, evaluation of treatment outcomes, and patient monitoring across a myriad of significant medical conditions [2]. Zooming into the specific context of brain tumors, these are characterized by the aberrant proliferation of cells within the cerebral environment [3].…”

Section: Introductionmentioning

confidence: 99%

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Rahman,

Zhang,

Bhutto

2023

Symmetry

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The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the complex interplay between the deep learning network for MRI brain tumor imaging and the harmonious compound of both local and global feature information, which can throw off the balance in segmentation accuracy. Addressing this asymmetry becomes essential for precise diagnosis. In answer to this challenge, we introduce a balanced, end-to-end solution for brain tumor segmentation, incorporating modifications that mirror the U-Net architecture, ensuring a harmonious flow of information. Beginning with symmetric enhancement of the visual quality of MRI brain images, we then apply a symmetrical residual structure. By replacing the convolutional modules in both the encoder and decoder sections with deep residual modules, we establish a balance that counters the vanishing gradient problem commonly faced when the network depth increases. Following this, a symmetrical threefold attention block is integrated. This addition ensures a balanced fusion of local and global image features, fine-tuning the network to symmetrically discern and learn essential image characteristics. This harmonious integration remarkably amplifies the network’s precision in segmenting MRI brain tumors. We further validate the equilibrium achieved by our proposed model using three brain tumor segmentation datasets and four metrics and by juxtaposing our model against 21 traditional and learning-based counterparts. The results confirm that our balanced approach significantly elevates performance in the segmentation of MRI brain tumor images without an asymmetrical increase in computational time.

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Section: Introductionmentioning

confidence: 99%

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Rahman,

Zhang,

Bhutto

2023

Symmetry

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Empowering lung cancer treatment: Harnessing the potential of natural phytoconstituent‐loaded nanoparticles

Siddiquee,

Bhaskaran,

Nathani

et al. 2024

Phytotherapy Research

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Lung cancer, the second leading cause of cancer‐related deaths, accounts for a substantial portion, representing 18.4% of all cancer fatalities. Despite advances in treatment modalities such as chemotherapy, surgery, and immunotherapy, significant challenges persist, including chemoresistance, non‐specific targeting, and adverse effects. Consequently, there is an urgent need for innovative therapeutic approaches to overcome these limitations. Natural compounds, particularly phytoconstituents, have emerged as promising candidates due to their potent anticancer properties and relatively low incidence of adverse effects compared to conventional treatments. However, inherent challenges such as poor solubility, rapid metabolism, and enzymatic degradation hinder their clinical utility. To address these obstacles, researchers have increasingly turned to nanotechnology‐based drug delivery systems (DDS). Nanocarriers offer several advantages, including enhanced drug stability, prolonged circulation time, and targeted delivery to tumor sites, thereby minimizing off‐target effects. By encapsulating phytoconstituents within nanocarriers, researchers aim to optimize their bioavailability and therapeutic efficacy while reducing systemic toxicity. Moreover, the integration of nanotechnology with phytoconstituents allows for a nuanced understanding of the intricate molecular pathways involved in lung cancer pathogenesis. This integrated approach holds promise for modulating key cellular processes implicated in tumor growth and progression. Additionally, by leveraging the synergistic effects of phytoconstituents and nanocarriers, researchers seek to develop tailored therapeutic strategies that maximize efficacy while minimizing adverse effects. In conclusion, the integration of phytoconstituents with nanocarriers represents a promising avenue for advancing lung cancer treatment. This synergistic approach has the potential to revolutionize current therapeutic paradigms by offering targeted, efficient, and minimally toxic interventions. Continued research in this field holds the promise of improving patient outcomes and addressing unmet clinical needs in lung cancer management.

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Beyond the Frontline: A Triple-Line Approach of Thoracic Surgeons in Lung Cancer Management—State of the Art

Cited by 2 publications

References 136 publications

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Empowering lung cancer treatment: Harnessing the potential of natural phytoconstituent‐loaded nanoparticles

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