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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...

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u-LINNDA: A protocol for user-optimized lymphoma identification through neural network detection aid.

Maximilian Fischer1, Miriam Cindy Maurer2, Robin Peretzke3

  • 1Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany; Forschungscampus M(2)OLIE, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.

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Summary

This study introduces u-LINNDA, a novel convolutional neural network (CNN) algorithm for diagnosing primary central nervous system lymphoma (PCNSL) from brain tumor biopsies. The tool aids in accurate histopathological identification for targeted treatment strategies.

Keywords:
BioinformaticsCancerComputer sciencesHealth SciencesNeuroscience

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Area of Science:

  • Neuro-oncology
  • Computational pathology
  • Artificial intelligence in medicine

Background:

  • Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are aggressive brain tumors requiring accurate diagnosis.
  • Histopathological identification is crucial for targeted neurosurgical and oncological treatment.
  • Current diagnostic methods can be time-consuming and require specialized expertise.

Purpose of the Study:

  • To present a protocol for diagnosing PCNSL using a convolutional neural network (CNN)-based algorithm.
  • To detail the installation, data preparation, and prediction steps for the u-LINNDA algorithm.
  • To provide a method for inspecting the u-LINNDA report for tumor identity prediction.

Main Methods:

  • Development and implementation of the u-LINNDA (user-optimized lymphoma identification through neural network detection aid) algorithm.
  • Description of data preparation and preprocessing pipelines for histopathological images.
  • Application of the CNN algorithm for predicting tumor entities and identity.

Main Results:

  • The study outlines a reproducible protocol for utilizing the u-LINNDA algorithm.
  • The CNN-based approach facilitates the identification of PCNSL.
  • The u-LINNDA report provides insights into tumor identity prediction.

Conclusions:

  • The u-LINNDA algorithm offers a promising tool for the accurate and efficient diagnosis of PCNSL.
  • This CNN-based approach can aid pathologists in identifying brain tumor entities.
  • The protocol facilitates the integration of AI into routine neuropathological workflows for improved patient care.