{"doi":"10.1038/nrclinonc.2014.134","title":"Quantitative multimodality imaging in cancer research and therapy","abstract":null,"journal":"Nature Reviews Clinical Oncology","year":2014,"id":686974,"datarank":0.7266280629687888,"base_score":4.844187086458591,"endowment":4.844187086458591,"self_citation_contribution":0.7266280629687888,"citation_network_contribution":0.0,"self_endowment_contribution":0.7266280629687888,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":126,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":536218,"name":"Richard G. Abramson","orcid":"0000-0002-1200-0281","position":1,"is_corresponding":false},{"id":247455,"name":"C. Chad Quarles","orcid":"0000-0002-1731-0940","position":2,"is_corresponding":false},{"id":582369,"name":"Thomas E. Yankeelov","orcid":"0000-0002-7022-0565","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Quantitative multimodality imaging in cancer research and therapy","abstract":"Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.","is_dataset_classified":null,"base_score":4.844187086458591,"endowment":4.844187086458591,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"25113842","pmcid":"PMC4909117","openalex_id":"https://openalex.org/W2030677921","authors":[],"funders":[{"funder_name":"NCI NIH HHS","grant_id":"R01 CA158079","title":null},{"funder_name":"NCI NIH HHS","grant_id":"U01 CA174706","title":null},{"funder_name":"NCI NIH HHS","grant_id":"R25 CA092043","title":null},{"funder_name":"NCI NIH HHS","grant_id":"P30 CA068485","title":null},{"funder_name":"NCI NIH HHS","grant_id":"R01 CA138599","title":null},{"funder_name":"NCI NIH HHS","grant_id":"U01 CA142565","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01CA158079-05","title":"MRI Assessment of Tumor Perfusion, Permeability and Cellularity"},{"funder_name":"National Institutes of Health","grant_id":"5U01CA142565-05","title":"PET-MRI for Assessing Treatment Response in Breast Cancer Clinical Trials"},{"funder_name":"National Institutes of Health","grant_id":"3P30CA068485-23S2","title":"Cancer Center Support Grant"},{"funder_name":"National Institutes of Health","grant_id":"5R01CA138599-06","title":"Evaluation and Validation of Imaging Biomarkers of Tumor Response to Treatment"},{"funder_name":"National Institutes of Health","grant_id":"5R25CA092043-03","title":"Multidisciplinary Research Training in Cancer Imaging"},{"funder_name":"National Institutes of Health","grant_id":"5U01CA174706-03","title":"Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer"}],"total_grants":12,"fwci":6.2421,"citation_percentile":0.96769182,"influential_citations":0,"citation_trend":[{"year":2015,"count":9},{"year":2016,"count":12},{"year":2017,"count":5},{"year":2018,"count":13},{"year":2019,"count":14},{"year":2020,"count":15},{"year":2021,"count":12},{"year":2022,"count":14},{"year":2023,"count":12},{"year":2024,"count":6},{"year":2025,"count":10},{"year":2026,"count":4}],"oa_status":"closed","license":"Springer TDM","oa_locations":[{"url":"https://www.nature.com/articles/nrclinonc.2014.134","host_type":"publisher"},{"url":"https://www.nature.com/articles/nrclinonc.2014.134.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1038/nrclinonc.2014.134","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/25113842","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4909117","host_type":"repository"},{"url":"https://europepmc.org/articles/pmc4909117?pdf=render","host_type":""},{"url":"https://dx.doi.org/10.1038/nrclinonc.2014.134","host_type":""}],"fields_of_study":["Medical Imaging Techniques and Applications","Radiomics and Machine Learning in Medical Imaging","MRI in cancer diagnosis","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":["Humans","Image Processing, Computer-Assisted","Neoplasms","Tomography, X-Ray Computed","Tomography, Emission-Computed, Single-Photon","Biomedical Research","Positron-Emission Tomography","Translational Research, Biomedical","Multimodal Imaging"],"keywords":["Multimodality","Cancer imaging","Positron emission tomography","Functional imaging","Medical physics","Molecular imaging","Medical imaging","Medicine","Cancer","Computer science","Radiology","In vivo","Tomography, Emission-Computed, Single-Photon","Translational Research, Biomedical","Biomedical Research","Neoplasms","Positron-Emission Tomography","Image Processing, Computer-Assisted","Humans","Tomography, X-Ray Computed","Multimodal Imaging"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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