ReviewNeuro-oncology advances
A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients.
Review in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed, 10 citations in OpenAlex.
- Neutrophile-to-lymphocyte ratio predicts survival in patients aged 80 years and older undergoing radiotherapy: a single-center experience.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- TensorFlow-based MobileNetV2 U-Net tumor segmentation and multiparametric MRI radiomics for predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma.Therapeutic advances in medical oncology · 2026Article
- Review
- A review of the current status and progress in difficult airway assessment research.European journal of medical research · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPretreatment assessments for glioblastoma (GBM) patients, especially elderly or frail patients, are critical for treatment planning. However, genetic profiling with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the
methodsPublic datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma (TCGA-GBM), and the Chinese Glioma Genome Atlas (CGGA) were used for training and testing of the ANNs. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. A ratio comparing the surface area of tumor borders versus the total volume (SvV) was derived from the DICOM-SEG conversions of segmented tumors. The edges of these borders were detected using the canny edge detector. Packages including Keras, Pytorch, and TensorFlow were tested to build the ANNs. A 4-layered ANN (8-8-8-2) with a binary output was built with optimal performance after extensive testing.
resultsThe 4-layered deep learning ANN can identify a GBM patient's overall survival (OS) cohort with 80%-85% accuracy. The ANN requires 4 inputs, including
conclusionQuantifiable image features can significantly improve the ability of ANNs to identify a GBM patients' survival cohort. Features such as clinical measures, genetic data, and image data, can be integrated into a single ANN for GBM pretreatment evaluation.
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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.