Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase

Salvi, Massimo; Manini, Claudia; López, Jose I.; Fenoglio, Dario; Molinari, Filippo

doi:10.1016/j.compmedimag.2023.102288

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…This poses a big limitation on the choice of step value, especially for images with different sizes. A more recent approach to address the patch-based reconstruction artifacts is Gaussian reconstruction [7]. This method employs a Gaussian function to smooth the borders of each patch during the aggregation process.…”

Section: Introductionmentioning

confidence: 99%

Parametric Kernels for Artifact Mitigation in Patch-based Image Aggregation using Generative Models

Michielli,

Marzola,

Branciforti

et al. 2023

Preprint

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Rapid advancements have been made in artificial intelligence applications recently, and generative models have prominently emerged as effective tools for domain transfer, image enhancement, and simulation. However, when dealing with large-scale gigapixel images, the use of traditional patch-based image aggregation methods introduces checkerboard or blocking artifacts, which compromises image quality and fidelity. Here we propose a parametric kernel that is specifically designed to target the underlying grid structure to mitigate these artifacts. The proposed parametric kernels are validated using three medical imaging modalities for three different generative model tasks, demonstrating improved visual fidelity and quantitative quality evaluation of the generated patch-aggregated images. The proposed method is versatile and compatible with various generative models, offering a robust framework for artifact reduction that can be seamlessly adjusted by modifying kernel parameters, and they can be directly applied and extended to other imaging modalities that employ large-scale images, such as astronomy and satellite imaging. The findings of this study have significant implications for medical imaging applications: by mitigating aggregation artifacts, our approach enhances the overall quality of medical images synthesized with generative models, which is crucial for accurate clinical assessment and subsequent image analysis. Furthermore, the proposed kernels provide a general formulation that can be extended to unpaired tasks, semantic segmentation, classification networks, and other large field-of-view imaging applications.

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Section: Introductionmentioning

confidence: 99%

Parametric Kernels for Artifact Mitigation in Patch-based Image Aggregation using Generative Models

Michielli,

Marzola,

Branciforti

et al. 2023

Preprint

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SEG-LUS: A novel ultrasound segmentation method for liver and its accessory structures based on muti-head self-attention

Zhang,

Wu,

Zhang

et al. 2024

Computerized Medical Imaging and Graphics

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Prognostic Impact of H19/Cell Adhesion Molecules Circuitry on Prostate Cancer Biopsy

Pecci,

Pierconti,

Carlino

et al. 2024

Biomedicines

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Introduction: Metastatic prostate cancer (PCa) presents a significant challenge in oncology due to its high mortality rate and the absence of effective biomarkers for predicting patient outcomes. Building on previous research that highlighted the critical role of the long noncoding RNA (lncRNA) H19 and cell adhesion molecules in promoting tumor progression under hypoxia and estrogen stimulation, this study aimed to assess the potential of these components as prognostic biomarkers for PCa at the biopsy stage. Methods: This research utilized immunohistochemistry and droplet digital PCR to analyze formalin-fixed paraffin-embedded (FFPE) biopsies, focusing on specific markers within the H19/cell adhesion molecules pathway. Results: A novel multivariate analysis led to a “BioScore”, a composite biomarker score to predict disease progression. This score is based on evaluating five key markers: the expression levels of Hypoxia-Inducible Factor 2 Alpha (HIF-2α), endothelial Nitric Oxide Synthase (eNOS), β4 integrin, E-cadherin transcript (CDH1), and lncRNA H19. The criteria for the “BioScore” involve identifying three out of these five markers, combining elevated levels of HIF-2α, eNOS, β4 integrin, and CDH1 with reduced H19 expression. Conclusions: This finding suggests the possibility of identifying, at the time of biopsy, PCa patients at higher risk of metastasis based on dysregulation in the H19/cell adhesion molecules circuitry. This study provides a valuable opportunity for early intervention in managing PCa, potentially contributing to personalized treatment strategies.

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Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase

Cited by 3 publications

References 47 publications

Parametric Kernels for Artifact Mitigation in Patch-based Image Aggregation using Generative Models

Parametric Kernels for Artifact Mitigation in Patch-based Image Aggregation using Generative Models

SEG-LUS: A novel ultrasound segmentation method for liver and its accessory structures based on muti-head self-attention

Prognostic Impact of H19/Cell Adhesion Molecules Circuitry on Prostate Cancer Biopsy

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