ArticleMedical physics2014
Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.
Article in Medical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled 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.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interval brain imaging for adults with cerebral glioma.The Cochrane database of systematic reviews · 2019Pooled it
- Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.Scientific reports · 2016Trial
- Accelerating MRI With Longitudinally-Informed Latent Posterior Sampling.Magnetic resonance in medicine · 2026Article
- Enhanced Brain Tumor Classification with Convolutional Neural Networks.Advances in experimental medicine and biology · 2026Article
- Article
- Treatment evaluation by volumetric segmentation in pediatric optic pathway glioma: evaluation of the effect of bevacizumab on intra-tumor components.Journal of neuro-oncology · 2024Article
- Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.JAMA network open · 2022Article
- Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation.Frontiers in neuroscience · 2019Article
- Surveillance magnetic resonance imaging for isolated optic pathway gliomas: is gadolinium necessary?Pediatric radiology · 2018Article
- Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.International journal of computer assisted radiology and surgery · 2018Article
- Expertise Affects Inter-Observer Agreement at Peripheral Locations within a Brain Tumor.Frontiers in psychology · 2017Article
- Intra-rater variability in low-grade glioma segmentation.Journal of neuro-oncology · 2017Article
- Patterns of relapse and growth kinetics of surgery- and radiation-refractory meningiomas.Journal of neuro-oncology · 2015Article
- Diffuse low-grade glioma: What is the optimal linear measure to assess tumor growth?Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeTracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans.
methodsThe authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a "gold standard" was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution.
resultsCompared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard.
conclusionsThe authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.