{"doi":"10.1002/psp4.12499","title":"A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics","abstract":"<jats:p>Progression‐free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum‐resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.</jats:p>","journal":"CPT: Pharmacometrics &amp; Systems Pharmacology","year":2020,"id":19929,"datarank":0.7132817113437745,"base_score":2.639057329615259,"endowment":2.639057329615259,"self_citation_contribution":0.3958585994422889,"citation_network_contribution":0.3174231119014856,"self_endowment_contribution":0.3958585994422889,"citer_contribution":0.3174231119014856,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":13,"citers_with_citation_signal":10,"citers_with_endowment":10,"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":133441,"name":"Nina Wang","orcid":null,"position":1,"is_corresponding":false},{"id":133442,"name":"Matts Kågedal","orcid":null,"position":2,"is_corresponding":false},{"id":133440,"name":"Jiajie Yu","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.639057329615259,"endowment":2.639057329615259,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"32036626","pmcid":"PMC7080535","openalex_id":"https://openalex.org/W3005414578","authors":[],"funders":[{"funder_name":"Roche","grant_id":"","title":null},{"funder_name":"Genentech","grant_id":"","title":null}],"total_grants":2,"fwci":1.1818,"citation_percentile":0.78521766,"influential_citations":3,"citation_trend":[{"year":2020,"count":1},{"year":2022,"count":1},{"year":2023,"count":4},{"year":2024,"count":6},{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp4.12499","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp4.12499","host_type":"GOLD"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/psp4.12499","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12499","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/psp4.12499","host_type":"publisher"},{"url":"https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12499","host_type":"publisher"},{"url":"https://doi.org/10.1002/psp4.12499","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/32036626","host_type":"repository"},{"url":"https://doaj.org/article/bb192ad3c4384059a862f04a9b70eecf","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7080535","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC7080535","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC7080535?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Ovarian cancer diagnosis and treatment","Mathematical Biology Tumor Growth","Cancer Genomics and Diagnostics","Medicine","Biomarkers, Tumor","CA-125 Antigen","Decision Making","Disease Progression","Doxorubicin","Drug Resistance, Neoplasm","Female","Humans","Membrane Proteins","Models, Theoretical","Organoplatinum Compounds","Ovarian Neoplasms","Platinum","Predictive Value of Tests","Progression-Free Survival","Response Evaluation Criteria in Solid Tumors","Sodium-Phosphate Cotransporter Proteins, Type IIb","Software","Topoisomerase II Inhibitors","Treatment Outcome","Tumor Burden"],"mesh_terms":["Progression-Free Survival","Decision Making","Doxorubicin","Female","Humans","Membrane Proteins","Models, Theoretical","Organoplatinum Compounds","Ovarian Neoplasms","Platinum","Predictive Value of Tests","Software","Biomarkers, Tumor","Treatment Outcome","CA-125 Antigen","Disease Progression","Drug Resistance, Neoplasm","Tumor Burden","Sodium-Phosphate Cotransporter Proteins, Type IIb","Topoisomerase II Inhibitors","Response Evaluation Criteria in Solid Tumors"],"keywords":["Tumor progression","Progression-free survival","Lesion","Oncology","Hazard ratio","Target lesion","Internal medicine","Solid tumor","Clinical endpoint","Medicine","Response Evaluation Criteria in Solid Tumors","Clinical trial","Cancer","Overall survival","Pathology","Confidence interval","Phases of clinical research"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and 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