Evidence map›Paper›PMID 38563856›Full record

ArticleJournal of neuro-oncology2024

Comparative analysis of the spatial distribution of brain metastases across several primary cancers using machine learning and deep learning models.

Saeedeh Mahmoodifar, Dhiraj J Pangal, Josh Neman, Gabriel Zada, Jeremy Mason, Bodour Salhia, Tehila Kaisman-Elbaz, Selcuk Peker, Yavuz Samanci, Andréanne Hamel and 6 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Journal of neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Saeedeh MahmoodifarDepartment of Physics & Astronomy, University of Southern California, Los Angeles, CA, 90089, USA.
Dhiraj J PangalDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Josh NemanDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Gabriel ZadaDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Jeremy MasonCatherine & Joseph Aresty Department of Urology, Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Bodour SalhiaDepartment of Translational Genomics Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Tehila Kaisman-ElbazRose Ella Burkhardt Brain Tumor & Neuro-Oncology Center, Neurological Institute, The Cleveland Clinic, Cleveland, OH, 44195, USA.
Selcuk PekerDepartment of Neurosurgery, Koc University School of Medicine, Istanbul, Turkey.
Yavuz SamanciDepartment of Neurosurgery, Koc University School of Medicine, Istanbul, Turkey.
Andréanne HamelDepartment of Neurosurgery, Université de Sherbrooke, Centre de recherche du CHUS, QC, Canada.
David MathieuDepartment of Neurosurgery, Université de Sherbrooke, Centre de recherche du CHUS, QC, Canada.
Manjul TripathiDepartment of Neurosurgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Jason SheehanDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, 22903, USA.
Stylianos PikisDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, 22903, USA.
Georgios MantziarisDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, 22903, USA.
Paul K NewtonDepartment of Aerospace & Mechanical Engineering, Mathematics, Quantitative & Computational Biology, and Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, 90089, USA. newton@usc.edu.

Funding

NIH HHS USC Norris Comprehensive Cancer Center Pilot Award
6 · The paper itself

Abstract

objectiveBrain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable.

methodsTo test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume.

resultsOur findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature.

conclusionsIn summary, our results support accurate multiclass ML classification regarding brain metastases distribution.

Indexed as

Brain NeoplasmsDeep LearningMachine LearningAlgorithmsFemaleHumansMaleMiddle AgedNeoplasmsBrain metastasesDeep learning modelsPan cancer analysisPrincipal components

Identifiers

PMID38563856

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.