ArticleJournal of medical imaging (Bellingham, Wash.)2018
Classification of suspicious lesions on prostate multiparametric MRI using machine learning.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled 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
- mpMRI-Based Risk Estimation to Optimize Prostate Cancer Patient Selection for Active Surveillance.Cancers · 2026Article
- MRI-based patient selection for active surveillance in prostate cancer using U-Found: a generalized deep learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Development and application of an artificial intelligence-based quantitative system for white without pressure in myopic retinopathy.Frontiers in cell and developmental biology · 2026Article
- Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer.Scientific reports · 2024Article
- Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.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
- A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.Frontiers in oncology · 2023Article
- Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study.Scientific reports · 2022Article
- Article
- Current Status of Biparametric MRI in Prostate Cancer Diagnosis: Literature Analysis.Life (Basel, Switzerland) · 2022Review
- Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review.Insights into imaging · 2022Review
- Predictive Value of Delta-Radiomics Texture Features in 0.35 Tesla Magnetic Resonance Setup Images Acquired During Stereotactic Ablative Radiotherapy of Pancreatic Cancer.Frontiers in oncology · 2022Article
- Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization.International journal of molecular sciences · 2021Review
- A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.Journal of magnetic resonance imaging : JMRI · 2021Article
- Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study.European radiology · 2021Article
- Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature.Contrast media & molecular imaging · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a "training dataset" (330 suspected lesions from 204 patients) and a "test dataset" (208 lesions/140 patients). All studies included T2-weighted (T2-W), proton density-weighted, dynamic contrast enhanced, and diffusion-weighted imaging. Analysis of the images was performed using the MIM imaging platform (MIM Software, Cleveland, Ohio). Prostate and peripheral zone contours were manually outlined on the T2-W images. A workflow for rigid fusion of the aforementioned images to T2-W was created in MIM. The suspicious lesion was outlined using the high b-value image. Intensity and texture features were extracted on four imaging modalities and characterized using nine histogram descriptors: 10%, 25%, 50%, 75%, 90%, mean, standard deviation, kurtosis, and skewness (216 features). Three classification methods were used: classification and regression trees (CART), random forests, and adaptive least absolute shrinkage and selection operator (LASSO). In the held out by the organizers test dataset, the areas under the curve (AUCs) were: 0.82 (random forests), 0.76 (CART), and 0.76 (adaptive LASSO). AUC of 0.82 was the fourth-highest score of 71 entries (32 teams) and the highest for feature-based methods.
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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.