ReviewCancer2018
Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.
Review in Cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 5 of them syntheses that pooled it.
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
144 citing papers in PubMed, 5 syntheses or guidelines pooled it, 203 citations in OpenAlex.
- Application advances of habitat imaging based on CT or PET/CT: a systematic review.La Radiologia medica · 2026Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Visualising spatial heterogeneity in glioblastoma using imaging habitats.Frontiers in oncology · 2022Pooled it
- Promises of artificial intelligence in neuroradiology: a systematic technographic review.Neuroradiology · 2020Pooled it
- Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning.European radiology · 2022Trial
- A clinical-radiomics model based on MRI sub-regions of gluteus maximus for recurrence prediction in high-grade serous ovarian cancer.Abdominal radiology (New York) · 2026Article
- Multidimensional deep learning for grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma.Insights into imaging · 2026Article
- Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.Radiation oncology (London, England) · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- Heterogeneity Analyzed by CT-Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
- Preoperative assessment of intratumor heterogeneity using intravoxel incoherent motion MRI for survival prediction in high-grade gliomas: a feasibility study.BMC medical imaging · 2026Article
- MRI-Based DeltaHabitat Radiomic Model Predicts Pathological Complete Response in Oral Cavity Cancer Treated With Neoadjuvant Chemoimmunotherapy.Cancer medicine · 2026Article
- Predictions of Oncotype DXBioengineering (Basel, Switzerland) · 2026Article
- Habitat analysis and other AI technologies for Ki-67 prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2026Article
- Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.BMC medical imaging · 2026Article
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict HER2-negative breast cancer subtypes: a multicenter study.BMC medical imaging · 2026Article
- CT radiomics of adenocarcinoma of the esophagogastric junction: machine learning-based prediction of perineural invasion status.World journal of surgical oncology · 2026Article
- CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms.Insights into imaging · 2026Article
84 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.
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.