ArticleCancers2018
Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer.
Article in Cancers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05024162 (Can Magnetic Resonance Imaging of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour), which is not on this map. Cited by 64 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Can Magnetic Resonance Imaging of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour (Can MRI-PREDICT)
Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.Clinical proteomics · 2025Article
- Can PSMA PET detect intratumour heterogeneity in histological PSMA expression of primary prostate cancer? Analysis of [European journal of nuclear medicine and molecular imaging · 2025Article
- A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach.Journal of medical radiation sciences · 2025Article
- Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.Insights into imaging · 2025Article
- Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models.Frontiers in oncology · 2025Article
- Evaluation of a PSA and transrectal prostate ultrasound video-based machine learning model as a tool for prostate cancer diagnosis.Frontiers in oncology · 2025Article
- Development and Validation of an MRI-Based Radiomics Nomogram to Predict the Prognosis of De Novo Oligometastatic Prostate Cancer Patients.Cancer medicine · 2024Article
- A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients.Biomedicines · 2024Article
- A dynamic online nomogram predicting prostate cancer short-term prognosis based onAbdominal radiology (New York) · 2024Article
- Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging.International journal of molecular sciences · 2024Article
- Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer onNuclear medicine and molecular imaging · 2024Article
- A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study.Theranostics · 2024Article
- The grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography.Communications medicine · 2023Article
- T1-weighted images-based radiomics for structural lesions evaluation in patients with suspected axial spondyloarthritis.La Radiologia medica · 2023Article
- MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.Cancers · 2023Article
- T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.Cancers · 2023Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNovel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study's purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients.
methodsA retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive's (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman's rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS.
resultsFive JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (
conclusionThis retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.
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