{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:19:02Z","timestamp":1778271542966,"version":"3.51.4"},"reference-count":49,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"2","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Ordinary differential equation mathematical models of tumor volume dynamics can accurately describe tumor growth and treatment response. Here, we extend such continuous models to also simulate outcomes. We conceptualize post-treatment viable tumor burden distributions across a treatment population and a novel model of tumor regrowth that can simulate population-level recurrence patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>\n                      We use a mathematical model of tumor regrowth dynamics that is attenuated by a minimum viable tumor burden threshold (\u03b5\n                      <jats:sub>V<\/jats:sub>\n                      ) below which the tumor will be cured. Tumor regrowth is simulated until the tumor burden exceeds a detection threshold (\u03c9\n                      <jats:sub>d<\/jats:sub>\n                      ), which allows for the modeling of Kaplan-Meier curves.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>We then explore the effect of the different model parameters and growth laws on the shapes of simulated Kaplan-Meier curves and demonstrate how this model can be used to further our understanding of clinical trial results. We also present qualitative fitting of this model to real-world recurrence data from a clinical trial comparing different radiation therapy protocols in head and neck cancer (RTOG 9003).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>The theoretical framework described in this brief report provides a means to connect models of tumor dynamics to recurrence patterns. We foresee that it will also provide a new methodology for interpreting the shapes of Kaplan-Meier curves and provide insights as to why particular clinical trials failed and guide how to redesign them for success.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00072","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:00:13Z","timestamp":1778270413000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Simulating Cancer Recurrence Patterns From Post-Treatment Viable Tumor Burden Distributions"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4893-4462","authenticated-orcid":true,"given":"Mohammad U.","family":"Zahid","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph D.","family":"Butner","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3174-1656","authenticated-orcid":true,"given":"David M.","family":"Swanson","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9643-1694","authenticated-orcid":true,"suffix":"II","given":"David A.","family":"Hormuth","sequence":"additional","affiliation":[{"name":"Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX"},{"name":"Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9696-6410","authenticated-orcid":true,"given":"Heiko","family":"Enderling","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX"},{"name":"Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"233","reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11538-019-00640-x"},{"key":"e_1_3_7_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2014.11.005"},{"key":"e_1_3_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2023.01.035"},{"key":"e_1_3_7_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-06585-x"},{"key":"e_1_3_7_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-022-00321-4"},{"key":"e_1_3_7_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2023.102200"},{"key":"e_1_3_7_8_2","article-title":"Evolution of breast cancer recurrence risk prediction: A systematic review of statistical and machine learning\u2013based models","author":"El Haji H","unstructured":"El Haji H, Souadka A, Patel BN, et al: Evolution of breast cancer recurrence risk prediction: A systematic review of statistical and machine learning\u2013based models. 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