Computational Pathology

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  Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

  Computational modeling has emerged as a promising and cost-effective alternative method for screening potentially endocrine active compounds. This study applies classic machine learning algorithms and deep learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERβ) activity.

  • Heather L. Ciallella
  • Daniel P. Russo
  • Hao Zhu

  Article10 Aug 2020 Laboratory Investigation
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  A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas

  In this paper, the authors describe the development and validation of a novel image signature-based radiomics model. A total of 655 glioma patients were enrolled to build this model which is shown to be an effective tool to achieve multilayer preoperative diagnosis and prognostic stratification of gliomas.

  • Huigao Luo
  • Qiyuan Zhuang
  • Zhifeng Shi

  Article22 Aug 2020 Laboratory Investigation
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  Characterization of pathological thyroid tissue using polarization-sensitive second harmonic generation microscopy

  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
  • Virginijus Barzda

  Article31 Jul 2020 Laboratory Investigation
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  Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis

  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
  • Frederick Klauschen

  ArticleOpen Access29 Jun 2020 Laboratory Investigation
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  Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma

  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
  • Koichi Ohshima

  Article29 May 2020 Laboratory Investigation
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  Digital quantitative assessment of PD-L1 using digital spatial profiling

  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
  • David L. Rimm

  Article6 Apr 2020 Laboratory Investigation
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  Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray

  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
  • Lijie Tan

  Article23 Apr 2020 Laboratory Investigation
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  A simplified integrated molecular and immunohistochemistry-based algorithm allows high accuracy prediction of glioblastoma transcriptional subtypes

  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
  • on behalf of the Neuro-Oncology group of Spedali Civili of Brescia

  Article13 May 2020 Laboratory Investigation
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  3′ MACE RNA-sequencing allows for transcriptome profiling in human tissue samples after long-term storage

  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
  • Clemens Lange

  ArticleOpen Access28 May 2020 Laboratory Investigation
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  Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer

  We propose a statistical framework named WTSPP to generate prognostic gene signatures. Using ovarian cancer and lung adenocarcinoma as examples, they provide evidence that our prognostic signatures overperform previous reported signatures, capture prognostic features not explained by clinical variables and expose biologically relevant prognostic pathways.

  • Evelien Schaafsma
  • Yanding Zhao
  • Chao Cheng

  Technical Report6 Mar 2020 Laboratory Investigation
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  Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies

  Accurate quantification of steatosis in liver biopsies is a key step in the treatment of patients with fatty liver diseases. To assist pathologists for such analysis tasks, we develop a novel deep learning-based framework to segment overlapped steatosis droplets in whole slide liver biopsy images. Quantitative measurements of steatosis at both pixel and object-level present strong correlation with clinical data, suggesting its potential for clinical decision support.

  • Mousumi Roy
  • Fusheng Wang
  • Jun Kong

  Technical Report13 Jul 2020 Laboratory Investigation