Machine Learning Improves Diagnosis of CNS Cancers.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

3 citing papers in PubMed.

1. Artificial intelligence in neuro-oncology.Frontiers in neuroscience · 2023
   Review
2. Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach.Cancers · 2022
   Article
3. Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques.Frontiers in oncology · 2021
   Article

Corrections and comments

• Comment on
  DNA methylation-based classification of central nervous system tumours.2018

Authors and funding

0 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

Indexed as