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This capability can be integrated into a digital twin framework in which bi-directional data-flow between the physical tumor and the digital tumor facilitate dynamic model re-calibration, uncertainty quantification, and clinical decision-support via recommendation of optimal therapeutic interventions. However, many digital twin frameworks rely on bespoke implementations tailored to each disease site, modeling choice, and algorithmic implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present , a modular and differentiable software framework for initializing, updating, and leveraging patient-specific cancer tumor digital twins.  is publicly available as a Python package, with associated documentation, datasets, and tutorials. Novel contributions include the development of a patient-data structure adaptable to different disease sites, a modular architecture to enable the composition of different data, model, solver, and optimization objects, and CPU or GPU parallelized implementations of forward model solves and gradient computations. We demonstrate the functionality of  via an in silico dataset of high-grade glioma growth and response to radiation therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The  framework enables rapid prototyping and testing of image-guided oncology digital twins. This allows researchers to systematically investigate different models, algorithms, disease sites, or treatment decisions while leveraging robust numerical and computational infrastructure.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03520-2","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:55:32Z","timestamp":1778496932000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["TumorTwin: a Python framework for patient-specific digital twins in oncology"],"prefix":"10.1186","volume":"26","author":[{"given":"Michael G.","family":"Kapteyn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anirban","family":"Chaudhuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto A. B. 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