ArticleCancer research2016
PET and MRI: Is the Whole Greater than the Sum of Its Parts?
Article in Cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 21 citations in OpenAlex.
- Integration of multi-omics approaches in exploring intra-tumoral heterogeneity.Cancer cell international · 2025Review
- Precision medicine in the prediction of metachronous liver metastasis in rectal cancer: Applications and challenges.World journal of gastrointestinal oncology · 2025Article
- Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.Nature biomedical engineering · 2023Article
- Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients.EJNMMI physics · 2022Article
- Molecular Imaging of the Tumor Microenvironment Reveals the Relationship between Tumor Oxygenation, Glucose Uptake, and Glycolysis in Pancreatic Ductal Adenocarcinoma.Cancer research · 2020Article
- Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.Cancer research · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
- Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues.Journal of the Royal Society, Interface · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Over the past decades, imaging in oncology has been undergoing a "quiet" revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ Important advances along this path have been the increasing power of MRI, which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, PET imaging allows quantitative assessment of the spatial localization of positron-emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules. Recent years have witnessed another technological advance, wherein these two powerful modalities have been physically merged into combined PET/MRI systems, appropriate for both preclinical or clinical imaging. As with all new enabling technologies driven by engineering physics, the full extent of potential applications is rarely known at the outset. In the work of Schmitz and colleagues, the authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both preclinical and clinical data. With combined PET and MRI and sophisticated machine-learning tools, they have been able identify multiple coexisting regions ("habitats") within living tumors and, in some cases, have been able to assign these habitats to known histologies. This work addresses an issue of fundamental importance to both cancer biology and cancer care. As with most new paradigm-shifting applications, it is not the last word on the subject and introduces a number of new avenues of investigation to pursue. Cancer Res; 76(21); 6163-6. ©2016 AACR.
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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.