ArticleCancers2021
Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 29 citations in OpenAlex.
- Beyond Radiomics Alone: Enhancing Prostate Cancer Classification with ADC Ratio in a Multicenter Benchmarking Study.Diagnostics (Basel, Switzerland) · 2025Article
- Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.Radiology. Imaging cancer · 2025Article
- Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences.Insights into imaging · 2024Article
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study.Discover oncology · 2024Article
- The emerging role of the gut microbiome in cancer cell plasticity and therapeutic resistance.Cancer metastasis reviews · 2024Review
- Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.Insights into imaging · 2024Article
- A deep learning model, NAFNet, predicts adverse pathology and recurrence in prostate cancer using MRIs.NPJ precision oncology · 2023Article
- MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.Cancers · 2023Article
- 3D-Vision-Transformer Stacking Ensemble for Assessing Prostate Cancer Aggressiveness from T2w Images.Bioengineering (Basel, Switzerland) · 2023Article
- Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models.Cancers · 2023Article
- Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness.Scientific reports · 2023Article
- The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Article
- Article
- An Overview of the Role of Multiparametric MRI in the Investigation of Testicular Tumors.Cancers · 2022Review
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.
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.