Volume 100 Issue 10, October 2020

Volume 100 Issue 10

The paper by Roy et al. ( p 1374) shows the use of deep learning for histological assessment of liver biopsies, resulting in accurate quantification of hepatic steatosis. The cover shows visualization of segmented steatosis droplets in masks of distinct colors.

Inside the USCAP Journals

Article

  • Article |

    Polarization-second harmonic microscopy was utilized to investigate whether collagen ultrastructure in thyroid due to four carcinoma types and Graves’ disease could be differentiated in human histopathology samples. Three parameters were extracted, revealing that the degree of linear polarization and χ(2)zzz/χ(2)zxx were effective in differentiating some diseases, while the parameter χ(2)xyz/χ(2)zxx was less effective.

    • Danielle Tokarz
    • , Richard Cisek
    • , Ariana Joseph
    • , Sylvia L. Asa
    • , Brian C. Wilson
    •  & Virginijus Barzda
  • Article | | Open Access

    Proteomic profiling may contribute to the analysis and classification of cancer. The authors applied the digital western blot technique DigiWest with a panel of 102 proteins and phosphoproteins in combination with a machine learning algorithm to classify the tissue origin of five common cancer types in fresh frozen and formalin-fixed paraffin-embedded tissue. DigiWest profiling represents a valuable method for cancer classification, yielding conclusive and decisive data, thus making this approach attractive for routine clinical applications.

    • Teresa Bockmayr
    • , Gerrit Erdmann
    • , Denise Treue
    • , Philipp Jurmeister
    • , Julia Schneider
    • , Anja Arndt
    • , Daniel Heim
    • , Michael Bockmayr
    • , Christoph Sachse
    •  & Frederick Klauschen
  • Article |

    This study aims to classify histopathological images of malignant lymphoma through deep learning. The classifier achieved the high levels of accuracy in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia, which were higher than those of pathologists. Artificial intelligence can potentially support diagnosis of malignant lymphoma.

    • Hiroaki Miyoshi
    • , Kensaku Sato
    • , Yoshinori Kabeya
    • , Sho Yonezawa
    • , Hiroki Nakano
    • , Yusuke Takeuchi
    • , Issei Ozawa
    • , Shoichi Higo
    • , Eriko Yanagida
    • , Kyohei Yamada
    • , Kei Kohno
    • , Takuya Furuta
    • , Hiroko Muta
    • , Mai Takeuchi
    • , Yuya Sasaki
    • , Takuro Yoshimura
    • , Kotaro Matsuda
    • , Reiji Muto
    • , Mayuko Moritsubo
    • , Kanako Inoue
    • , Takaharu Suzuki
    • , Hiroaki Sekinaga
    •  & Koichi Ohshima
  • Article |

    Digital spatial profiling is a new high-plex technology with potential to multiplex hundreds of proteins on a single slide. Here the authors validate the digital aspect of the technology on a control tissue microarray with known amounts of PD-L1 expression to show it has quantitative capacity comparable to quantitative immunofluorescence.

    • Swati Gupta
    • , Jon Zugazagoitia
    • , Sandra Martinez-Morilla
    • , Kit Fuhrman
    •  & David L. Rimm
  • Article |

    Traditional RNA sequencing data may fail to detect the exact cellular changes in tumor cells. With comprehensively single-cell and traditional RNA-seq data, these authors detected and compared differentially expressed genes in lung adenocarcinoma. These results may improve our understanding of cellular and molecular differences between cancerous and non-malignant tissue and provide tumor markers as well as potential therapeutic targets.

    • Zhencong Chen
    • , Mengnan Zhao
    • , Ming Li
    • , Qihai Sui
    • , Yunyi Bian
    • , Jiaqi Liang
    • , Zhengyang Hu
    • , Yuansheng Zheng
    • , Tao Lu
    • , Yiwei Huang
    • , Cheng Zhan
    • , Wei Jiang
    • , Qun Wang
    •  & Lijie Tan
  • Article |

    The authors developed a novel simplified assay for glioblastoma transcriptional classification on formalin-fixed-paraffin-embedded tissue samples. On such dataset, immunohistochemical profiles, based on expression of a restricted panel of gene classifiers, were integrated by machine learning approach to generate a glioblastoma transcriptional signature based on protein quantification that allowed to efficiently assign transcriptional subgroups to an extended cohort. Correlations with both histopathological features and clinical outcome have been also performed.

    • Francesca Orzan
    • , Francesca Pagani
    • , Manuela Cominelli
    • , Luca Triggiani
    • , Stefano Calza
    • , Francesca De Bacco
    • , Daniela Medicina
    • , Piera Balzarini
    • , Pier Paolo Panciani
    • , Roberto Liserre
    • , Michela Buglione
    • , Marco Maria Fontanella
    • , Enzo Medico
    • , Rossella Galli
    • , Claudio Isella
    • , Carla Boccaccio
    •  & Pietro Luigi Poliani
  • Article | | Open Access

    RNA of sufficient quality and quantity can be extracted from formalin-fixed paraffin-embedded (FFPE) samples to obtain comprehensive transcriptome profiling using the 3′ massive analysis of c-DNA ends (MACE) RNA-sequencing technology. Thus, MACE provides an opportunity for utilizing FFPE samples stored in histological archives.

    • Stefaniya Boneva
    • , Anja Schlecht
    • , Daniel Böhringer
    • , Hans Mittelviefhaus
    • , Thomas Reinhard
    • , Hansjürgen Agostini
    • , Claudia Auw-Haedrich
    • , Günther Schlunck
    • , Julian Wolf
    •  & Clemens Lange

Technical Report

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