ArticleJournal of magnetic resonance imaging : JMRI2015
Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.
Article in Journal of magnetic resonance imaging : JMRI, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 31 citations in OpenAlex.
- Trial
- Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.Journal of magnetic resonance imaging : JMRI · 2017Trial
- Non-invasive diagnosis of wheat stripe rust progression using hyperspectral reflectance.Frontiers in plant science · 2024Article
- Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
- Prostate Region-Wise Imaging Biomarker Profiles for Risk Stratification and Biochemical Recurrence Prediction.Cancers · 2023Article
- Quantitative Nuclear Histomorphometry Predicts Molecular Subtype and Clinical Outcome in Medulloblastomas: Preliminary Findings.Journal of pathology informatics · 2022Article
- Article
- Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.Magnetic resonance in medicine · 2020Article
- [Prediction of gene mutation in lung cancer based on deep learning and histomorphology analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.Journal of magnetic resonance imaging : JMRI · 2018Article
- Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.BMC cancer · 2018Article
- Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2018Review
- Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.Journal of magnetic resonance imaging : JMRI · 2018Article
- Article
- Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies.Scientific reports · 2017Article
- An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.Medical physics · 2017Article
- Computational imaging reveals shape differences between normal and malignant prostates on MRI.Scientific reports · 2017Article
- Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.Radiation oncology (London, England) · 2016Article
- Prostate Cancer Detection with Multiparametric Magnetic Resonance Imaging: Prostate Imaging Reporting and Data System Version 1 versus Version 2.Chinese medical journal · 2016Article
- Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.Journal of medical imaging (Bellingham, Wash.) · 2016Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). MATERIALS AND
methodsPreoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization.
resultsClassifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively.
conclusionPCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI.
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