Correction to: Machine learning for rhabdomyosarcoma histopathology

Frankel, Arthur O; Lathara, Melvin; Shaw, Celine Y; Wogmon, Owen; Jackson, Jacob M; Clark, Mattie M; Eshraghi, Navah; Keenen, Stephanie E; Woods, Andrew D; Purohit, Reshma; Ishi, Yukitomo; Moran, Nirupama; Eguchi, Mariko; Ahmed, Farhat Ul Ain; Khan, Sara; Ιωάννου, Μαρία; Perivoliotis, Konstantinos; Пин, Ли; Zhou, Huixia; Alkhaledi, Ahmad; Davis, Elizabeth J.; Galipeau, Danielle; Randall, R. Lor; Woźniak, Agnieszka; Schöffski, Patrick; Lee, Che-Jui; Huang, Paul H.; Jones, Robin L.; Rubin, Brian P.; Darrow, Morgan A.; Srinivasa, Ganapati; Rudzinski, Erin R.; Chen, Sonja; Berlow, Noah; Keller, Charles

doi:10.1038/s41379-022-01098-4

Cited by 3 publications

(1 citation statement)

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“…We have developed an AI-based tool and corresponding online software (metfinder.org) to detect and quantify metastasis burden in sections of liver and brain tissues of mice carrying xenograft, allograft or syngeneic tumor implants. For that, we use a machine learning approach based on the DeepPATH pipeline, which has previously shown reliable results in classifying different types of human tissues 29,46 , and is based on Google’s inception v3 architecture 47 .…”

Section: Discussionmentioning

confidence: 99%

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

Karz,

Coudray,

Bayraktar

et al. 2023

Preprint

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Diagnosis of most diseases relies on expert histopathological evaluation of tissue sections by an experienced pathologist. By using standardized staining techniques and an expanding repertoire of markers, a trained eye is able to recognize disease-specific patterns with high accuracy and determine a diagnosis. As efforts to study mechanisms of metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate effects on tumor burden resulting from specific interventions. However, current methods of quantifying tumor burden are low in either resolution or throughput. Artificial neural networks, which can perform in-depth image analyses of tissue sections, provide an opportunity for automated recognition of consistent histopathological patterns. In order to increase the outflow of data collection from preclinical studies, we trained a deep neural network for quantitative analysis of melanoma tumor content on histopathological sections of murine models. This AI-based algorithm, made freely available to academic labs through a web-interface called MetFinder, promises to become an asset for researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

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Section: Discussionmentioning

confidence: 99%

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

Karz,

Coudray,

Bayraktar

et al. 2023

Preprint

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How engineers and drug developers are working to change childhood cancer’s deadly calculus

Landhuis¹

2022

Nature

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Pediatric Sarcomas: The Next Generation of Molecular Studies

Giannikopoulos

Parham

2022

Cancers

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Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.

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Correction to: Machine learning for rhabdomyosarcoma histopathology

Cited by 3 publications

References 0 publications

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

How engineers and drug developers are working to change childhood cancer’s deadly calculus

Pediatric Sarcomas: The Next Generation of Molecular Studies

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