Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm

Logu, Francesco De; Ugolini, Filippo; Maio, Vincenza; Simi, Sara; Cossu, Antonio; Massi, Daniela; Nassini, Romina; Laurino, Marco

doi:10.3389/fonc.2020.01559

Cited by 45 publications

(45 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Conventionally, pathologists observe the spatial architecture of the TIME in formalin-fixed and paraffin-embedded (FFPE) or fresh-frozen samples under a microscope ( 42 – 44 ). However, manual observation of slices is laborious and may result in considerable interobserver discrepancies ( 45 , 46 ). The introduction of deep learning methods based on whole slide images (WSIs) can automate and standardize the process.…”

Section: Emerging Technologies Used To Characterize the Spatial Architecture Of The Timementioning

confidence: 99%

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

et al. 2021

View full text Add to dashboard Cite

Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.

show abstract

Section: Emerging Technologies Used To Characterize the Spatial Architecture Of The Timementioning

confidence: 99%

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Refs. [10,11]). Besides the proposed clinical and dermoscopic studies, two comparative approaches using histopathological WSIs met the inclusion criteria of this systematic review (see Table 3) [31,32].…”

Section: Automated Skin Cancer Classification Of Histopathological Wsimentioning

confidence: 99%

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Haggenmüller

Maron

Hekler

et al. 2021

European Journal of Cancer

180

113

View full text Add to dashboard Cite

show abstract

“…AI technologies are often employed for helping in detection of cancer [130][131][132][133][134][135][136] potentially reducing the healthcare costs due to misdiagnosis and aiding the transition towards novel precision medicine protocols. Also, AI can be used to characterize cancer by describing tumor gene mutation status [14,24,103,133,137] or infiltration of nearby structures [138,139].…”

Section: Cancer Diagnosis and Characterizationmentioning

confidence: 99%

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Avanzo

Porzio²,

Lorenzon

et al. 2021

Physica Medica

View full text Add to dashboard Cite

To perform a systematic review on the research on the application of artificial intelligence (AI) to imaging published in Italy and identify its fields of application, methods and results. Materials and Methods: A Pubmed search was conducted using terms Artificial Intelligence, Machine Learning, Deep learning, imaging, and Italy as affiliation, excluding reviews and papers outside time interval 2015-2020. In a second phase, participants of the working group AI4MP on Artificial Intelligence of the Italian Association of Physics in Medicine (AIFM) searched for papers on AI in imaging. Results: The Pubmed search produced 794 results. 168 studies were selected, of which 122 were from Pubmed search and 46 from the working group. The most used imaging modality was MRI (44%) followed by CT(12%) ad radiography/mammography (11%). The most common clinical indication were neurological diseases (29%) and diagnosis of cancer (25%). Classification was the most common task for AI (57%) followed by segmentation (16%). 65% of studies used machine learning and 35% used deep learning. We observed a rapid increase of research in Italy on artificial intelligence in the last 5 years, peaking at 155% from 2018 to 2019.

show abstract

Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm

Cited by 45 publications

References 26 publications

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

Contact Info

Product

Resources

About