ArticleJournal of medical imaging (Bellingham, Wash.)2018
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.
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 130 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
130 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies.Frontiers in oncology · 2022Pooled it
- Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.Medical physics · 2020Pooled it
- Phase I/II Study of AXL-Specific Antibody-Drug Conjugate Enapotamab Vedotin in Patients with Advanced Solid Tumors.Cancer research communications · 2025Trial
- MRI-based Radiomic Features for Risk Stratification of Ductal Carcinoma in Situ in a Multicenter Setting (ECOG-ACRIN E4112 Trial).Radiology · 2025Trial
- Arterial Spin Labeling and Dynamic Susceptibility Contrast-enhanced MR Imaging for evaluation of arteriovenous shunting and tumor hypoxia in glioblastoma.Scientific reports · 2019Trial
- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions.International journal of molecular sciences · 2026Review
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- An Unsupervised Brain Extraction Quality Control Approach for Efficient Neuro-Oncology Studies.Journal of imaging informatics in medicine · 2026Article
- Fractal-geometry analysis of pediatric posterior fossa tumors - a preoperative tool for prediction of histopathology.Neurosurgical review · 2026Article
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- Intratumoral preservation of the stem-like malignant cell proportion in glioblastoma, prognostic impact, and its radiomethylomic signatures.Neuro-oncology · 2025Article
- Role of repulsive guidance signaling and GPR180 in pediatric low-grade glioma infiltration.NPJ precision oncology · 2025Article
- Probabilistic template matching for detecting resting-state functional MRI language network in brain tumor patients.Medical physics · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Consistency and grade prediction of intracranial meningiomas based on fractal geometry analysis.Neurosurgical review · 2025Article
- Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.BMC medical imaging · 2025Article
- Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge.Nature communications · 2025Article
- Exploring the associations between features from multi-parametric MR images in Glioblastoma using radiomics.BMC medical imaging · 2025Article
- Targeting the Enhanced Sensitivity of Radiotherapy in Cancer: Mechanisms, Applications, and Challenges.MedComm · 2025Review
70 more citing papers are in PubMed but not listed here.
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Authors and funding
25 authors.
Funding
Abstract
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.
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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.