Evidence map›Paper›PMID 39738168›Full record

ArticleScientific reports2024

Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI.

Vincent Andrearczyk, Luis Schiappacasse, Daniel Abler, Marek Wodzinski, Andreas Hottinger, Matthieu Raccaud, Jean Bourhis, John O Prior, Vincent Dunet, Adrien Depeurnge

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Vincent AndrearczykInstitute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Luis SchiappacasseDepartment of Radiation Oncology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Bugnon 46, 1011, Lausanne, Switzerland. luis.schiappacasse@chuv.ch.
Daniel AblerInstitute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Marek WodzinskiInstitute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Andreas HottingerDepartment of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
Matthieu RaccaudDepartment of Medical Radiology, Service of Diagnostic and Interventional Radiology, Neuroradiology Unit, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
Jean BourhisDepartment of Radiation Oncology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Bugnon 46, 1011, Lausanne, Switzerland.
John O PriorDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
Vincent DunetDepartment of Medical Radiology, Service of Diagnostic and Interventional Radiology, Neuroradiology Unit, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
Adrien DepeurngeInstitute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.

Funding

Hasler Stiftung 21042Swiss Cancer Research Foundation KFS-5549-02-2022-R
6 · The paper itself

Abstract

Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs. It focuses on several important aspects: identifying and segmenting new lesions for screening and treatment planning, re-segmenting lesions in successive images using prior lesion locations as an additional input channel, and performing multi-component segmentation to distinguish between enhancing tissue, edema, and necrosis. The key component of the proposed approach is to propagate the lesion mask from the previous time point to improve the detection performance, which we refer to as "re-segmentation". The retrospective data includes 518 metastases in 184 contrast-enhanced T1-weighted MRIs originating from 49 patients (63% male, 37% female). 131 time-points (36 patients, 418 BMs) are used for cross-validation, the remaining 53 time-points (13 patients, 100 BMs) are used for testing. The lesions were manually delineated with label 1: enhancing lesion, label 2: edema, and label 3: necrosis. One-tailed t-tests are used to compare model performance including multiple segmentation and detection metrics. Significance is considered as p < 0.05. A Dice Similarity Coefficient (DSC) of 0.79 and

Indexed as

Brain NeoplasmsMagnetic Resonance ImagingAdultAgedAlgorithmsFemaleHumansImage Processing, Computer-AssistedLongitudinal StudiesMaleMiddle AgedRetrospective StudiesBrain metastasesDeep learningMagnetic resonance imagingSegmentation

Identifiers

PMID39738168
PMCPMC11686181

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Registered trials

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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.