Theranostic Digital Twins

• Theranostic digital twins are dynamic patient-specific models that integrate multimodal data to simulate disease states and treatment responses in real time.
• They employ advanced mathematical methods like reaction–diffusion equations and PK/PD frameworks to personalize and optimize therapeutic interventions.
• Robust validation and uncertainty quantification frameworks ensure accurate calibration and clinical relevance in oncological and radiopharmaceutical contexts.

Theranostic digital twins are patient-specific digital twins that couple diagnostics, prognosis, and therapeutics in a closed loop: they assimilate multimodal patient data to characterize disease state, simulate disease and treatment response under alternative interventions, and are iteratively updated so that model outputs can influence subsequent measurement and therapy. In the current literature, the most explicit formulations occur in oncology and radiopharmaceutical therapy, where imaging, mechanistic disease models, dosimetry, radiobiology, and decision support are organized into a single “data-to-decisions” framework (Ryhiner et al., 4 Nov 2025, Kapteyn et al., 1 May 2025, Zhang, 22 Jun 2026).

1. Definition and conceptual boundaries

A digital twin in medicine is commonly defined as a dynamic, data-driven virtual counterpart of a physical system, continuously updated through real-time or repeated data streams, capable of bidirectional interaction, and embedded in a closed-loop cyber–physical system (Zhang et al., 24 Nov 2025). In a feedback-centric formulation, the physical state $S_t$, observations $o_t$, virtual state $\hat S_t$, update operator $U$, and decision operator $D$ satisfy

$$\hat S_t = U(\hat S_{t-1}, o_t), \qquad a_t = D(\hat S_t),$$

with the closed loop written as

$$S_{t+1} = F\bigl(S_t, D\bigl(U(\hat S_{t-1}, o_t)\bigr)\bigr).$$

This formalization makes diagnosis and therapy two halves of the same governed feedback system rather than separate software functions (Zhang, 22 Jun 2026).

Within this broader class, a theranostic digital twin in radiopharmaceutical therapy is defined as a mathematical and computational model of both pharmacokinetics and pharmacodynamics of a radiopharmaceutical in a specific patient. It is a virtual replica of the patient–tumor–therapy system that ingests personalized data, simulates biodistribution and dose deposition, maps absorbed dose to biological effect, and is iteratively updated with clinical response data to refine treatment (Ryhiner et al., 4 Nov 2025). More generally, the literature uses “theranostic digital twin” for a patient-specific twin that characterizes disease state and risk, simulates and optimizes individualized interventions, and adapts as new data arrive (Zhang et al., 24 Nov 2025).

This definition excludes several weaker constructs. Reduced or non-personalized models, population-only PK models, dashboards, anatomical avatars, and data mirrors may be useful, but they do not satisfy the defining requirement of patient-specific calibration plus bidirectional feedback into real-world decisions (Ryhiner et al., 4 Nov 2025, Zhang, 22 Jun 2026).

2. Mathematical substrate

Theranostic digital twins are built on executable disease-and-treatment models with explicit state, parameter, and control structure. At the most abstract level, the twin is treated as a dynamical system

$\dot{x}(t) = f\big(x(t), u(t), \theta\big),$

where $x(t)$ is physiological state, $u(t)$ therapeutic control, and $o_t$0 patient-specific parameters; therapy design is then posed as

$o_t$1

subject to the personalized dynamics (Zhang et al., 24 Nov 2025).

In image-guided oncology, one prominent substrate is the reaction–diffusion family. TumorTwin uses a 3D reaction–diffusion PDE for tumor cell density with explicit treatment terms for radiotherapy and chemotherapy, together with discrete radiotherapy events governed by a linear–quadratic survival factor. After finite-difference discretization on the MRI voxel grid, the model becomes a high-dimensional ODE system in voxel-wise cellularity, with treatment schedule, dose, and pharmacodynamic parameters entering as explicit inputs (Kapteyn et al., 1 May 2025). This makes the same model usable for diagnosis and prognosis, calibration from imaging, and prospective therapy-scenario exploration.

In nuclear oncology, the mechanistic core is usually a physiologically based pharmacokinetic model coupled to dosimetry and radiobiology. The PK block is a multi-organ, multi-compartment ODE system for activity concentrations $o_t$2 in blood, organs, tumor, and subcellular compartments; the dosimetry block converts time–activity curves into absorbed dose rates $o_t$3 via kernel methods, MIRD S-values, or Monte Carlo; and the PD block maps time-resolved dose rate to survival, regression, toxicity, immune effects, or hypoxia-modified response through models such as LQ with Lea–Catcheside correction, linear-only formalisms, or MEDRAS-type mechanistic DNA-damage models (Ryhiner et al., 4 Nov 2025). In this setting, the full time course of dose rate is retained rather than collapsing everything to total absorbed dose.

Other theranostic twins use additional mechanistic layers. Immuno-oncology work places digital twins on top of ODE-based quantitative systems pharmacology models of tumor–immune–drug dynamics, including checkpoint inhibition, cytokines, trafficking, and metastatic lesions, with the aim of monitoring and optimizing treatment for individual patients as new measurements arrive (Wang et al., 2024). Radioembolization twins use incompressible Navier–Stokes flow, Lagrangian microsphere transport, and sometimes fluid–structure interaction or CFD–DEM to connect vascular imaging to microsphere deposition and absorbed dose distributions (Panneerselvam et al., 30 Aug 2025).

Across these domains, the common mathematical property is explicit intervention encoding: dose, fractionation, injection activity, catheter location, drug schedule, or device setting enters the dynamical system as a controllable variable. That property is what turns a predictive digital model into a theranostic one.

3. Data assimilation, personalization, and uncertainty

Theranostic digital twins are data-intensive systems. In radiopharmaceutical therapy, the input layer may include PET/CT and SPECT/CT for lesion localization, uptake, and pharmacokinetics; CT or MRI for anatomy and organ volumes; histology for microarchitecture and cell geometry; genomics or ctDNA for DNA-repair and radiosensitivity markers; and clinical covariates such as renal function, blood counts, and prior therapies (Ryhiner et al., 4 Nov 2025). Broader human digital twin reviews extend this multimodal inventory to EHR, imaging, wearables, patient-generated data, and environmental data, emphasizing continuous updating and predictive simulation (Pan et al., 18 Aug 2025).

In image-based oncology, personalization often begins by converting imaging into model state variables. TumorTwin uses an ADC-to-cellularity relation to map diffusion MRI into voxel-wise tumor cell density $o_t$4, which serves both as initial condition and as observation vector for calibration. Patient history is encoded in a structured PatientData object with visits, timestamps, imaging modalities, segmentations, and treatment timelines, including RT fractions and CT schedules (Kapteyn et al., 1 May 2025). This creates a persistent patient representation from which the tumor model is instantiated and updated.

Calibration then proceeds as an inverse problem. Deterministic approaches minimize a data-misfit objective over patient-specific parameters using gradient-based optimization, automatic differentiation, adjoint methods, or Levenberg–Marquardt. Bayesian approaches place priors on uncertain parameters, assimilate sparse imaging data through the likelihood, and return posterior distributions that quantify uncertainty in proliferation, diffusion, radiosensitivity, or other latent traits (Kapteyn et al., 1 May 2025, Chaudhuri et al., 2023). In predictive glioma digital twins, this posterior is propagated through the mechanistic model to obtain posterior predictive distributions for tumor volume, total tumor cellularity, Dice coefficient, or concordance measures, enabling risk-aware decision support under sparse and noisy measurements (Pash et al., 13 May 2025).

Uncertainty quantification is not optional in this literature. Verification, validation, and uncertainty quantification are treated as central credibility requirements for PBPK-based theranostic twins, with explicit attention to goodness-of-fit, model selection, sensitivity analysis, external prediction evaluation, uncertainty propagation, structural identifiability, and practical identifiability (Zaid et al., 25 Sep 2025). In radiopharmaceutical therapy, this is especially important because measurement uncertainty, sparse time points, segmentation error, and parameter non-identifiability can materially affect absorbed-dose predictions and therefore treatment selection.

4. Architecture and workflow

A recurrent systems pattern appears across implementations. TumorTwin organizes the twin into four modules: a data module centered on PatientData, a model module built around TumorGrowthModel3D and concrete subclasses such as ReactionDiffusion3D, a TorchDiffEqSolver for time integration and discrete treatment events, and an optimizer module using PyTorch optimizers and custom Levenberg–Marquardt. The end-to-end workflow is: prepare MRI and treatment inputs; create the patient object; initialize the model from the first visit; calibrate the model to early visits; predict future trajectories and alternative therapies; then iterate as new MRI visits arrive (Kapteyn et al., 1 May 2025).

The same logical structure appears in radiopharmaceutical theranostics. The chapter on computational nuclear oncology describes a pipeline with patient-specific inputs; a PK/PBPK layer; a dosimetry layer producing time-dependent dose rates at voxel, organ, or cell-compartment level; a PD/radiobiology layer producing survival, tumor regression, toxicity, immune modulation, or hypoxia effects; and a decision/optimization layer that simulates radionuclides, injected activities, fractionation schedules, and combinations with EBRT, PARP inhibitors, or immunotherapy. After each cycle, new imaging and response data are used to re-estimate model parameters and update the twin (Ryhiner et al., 4 Nov 2025).

Radioembolization work shows a parallel but multiphysics variant. The envisioned workflow is: use CBCT to segment hepatic arteries; run CFD or a physics-informed surrogate to simulate blood flow, microsphere transport, and dose for candidate catheter positions and injected activities; compare predicted dose to physician-defined tumor targets and organ-at-risk limits; iterate over configurations; and use post-treatment imaging to validate and, if needed, plan retreatment (Panneerselvam et al., 30 Aug 2025). Imaging-based reviews across organ systems describe the same progression from acquisition, preprocessing, segmentation, registration, and 3D reconstruction to model personalization, validation, and longitudinal updating (Zhao et al., 2024).

What distinguishes these workflows from conventional simulation pipelines is persistence. The twin is not constructed once and discarded; it is maintained as a patient-linked computational object that can be recalibrated, queried under counterfactual interventions, and embedded into recurrent clinical decision cycles.

5. Clinical domains and demonstrated results

The most developed theranostic twin demonstrations are in oncology. TumorTwin shows an in silico high-grade glioma digital twin using a reaction–diffusion model with radiotherapy and temozolomide, calibrated to the first five imaging visits. In that synthetic HGG example, the unknown parameters are $o_t$5, $o_t$6, $o_t$7, and $o_t$8, the initial guess is 20% of ground truth, and calibration yields relative errors of $o_t$9–$\hat S_t$0 in parameters together with good agreement in tumor cell count time series and spatial maps. A synthetic triple-negative breast cancer case based on I-SPY2 MRI anatomy shows the same architecture can be ported to a different site and treatment configuration (Kapteyn et al., 1 May 2025).

Risk-aware radiotherapy twins in glioma push the theranostic function further by optimizing treatment under uncertainty. In an in silico cohort of 100 patients with high-grade glioma, a Bayesian-calibrated digital twin was used to generate personalized radiotherapy schedules that trade off tumor control and toxicity through a multi-objective optimization under uncertainty. For the same total radiation dose as standard of care, the personalized regimens lead to a median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as standard of care, the digital twin yields optimal options with a median reduction in radiation dose by 16.7% (10 Gy) compared to the standard 60 Gy total dose (Chaudhuri et al., 2023).

Radiopharmaceutical therapy is the second major application domain. A PSMA-targeted PBPK-plus-ML framework generates 640 realistically varied virtual patients and 15,360 TACs, then predicts AUC, Dose, BED, and EQD2 in tumor and five organs from diagnostic PET TAC features. In that virtual theranostic trial, Cu-64-based imaging yields the most robust predictions, with dose prediction MAPE as low as 8% for tumors and 10–20% for different organs, while F-18 shows volume-dependent trends and Ga-68 exhibits higher variability (Abdollahi et al., 23 Oct 2025). More conceptually, PBPK-centered theranostic twins have been proposed for PSMA-targeted $\hat S_t$1Lu therapy, PARP inhibitor combinations, alpha–beta combinations, and adaptive cycle-by-cycle dose planning in radiopharmaceutical therapy (Ryhiner et al., 4 Nov 2025).

Localized liver radioembolization extends the same idea to catheter-based intervention. There, patient-specific digital twins aim to optimize catheter position, injection rate, and injected activity by simulating hepatic blood flow, microsphere routing, and resulting dose distributions using CFD and increasingly PINNs, PI-GANs, diffusion models, or transformer-based surrogates (Panneerselvam et al., 30 Aug 2025).

These examples indicate that theranostic digital twins are not limited to prognosis. They already function as scenario generators, dose planners, regimen optimizers, and protocol design tools.

6. Validation, misconceptions, and open problems

A recurrent misconception is that a digital twin is simply a realistic simulation, anatomical avatar, dashboard, or data mirror. The recent feedback-centered literature explicitly rejects that view: without bidirectional feedback that changes measurement, intervention, operations, or governance in the physical world, the system is not a digital twin in the strict sense (Zhang, 22 Jun 2026). This distinction matters acutely for theranostics, because recommendation without recurrent model updating is closer to open-loop decision support than to a true twin.

Validation and credibility remain major constraints. PBPK-based theranostic twins are described as promising but not yet mature for routine, fully model-driven clinical decision-making, and their adoption is tied to fit-for-purpose VVUQ frameworks, external validation, accepted error tolerances, and community standards analogous to existing model-credibility practices in other domains (Zaid et al., 25 Sep 2025). In oncology, current implementations still rely heavily on synthetic data, and some predictive failures are directly attributable to model mismatch, sparse imaging, confounding between edema and infiltrative tumor, or fixed treatment-effect parameters that are not yet patient-specific (Kapteyn et al., 1 May 2025, Pash et al., 13 May 2025).

Technical and organizational barriers are equally prominent. The literature repeatedly cites data heterogeneity, absence of standardized formats for dynamic imaging and radiobiology, computational burden of voxel-level Monte Carlo or large-scale PDE solves, difficulty of robust calibration from sparse time points, and the need for fast GPU- or edge-enabled computation to meet clinical turnaround times (Ryhiner et al., 4 Nov 2025, Panneerselvam et al., 30 Aug 2025). Broader human digital twin reviews add privacy, fairness, bias, interpretability, lifecycle-aware regulation, and workflow integration as deployment-critical issues (Pan et al., 18 Aug 2025, Zhang et al., 24 Nov 2025).

Future directions are comparatively consistent across fields. Proposed extensions include Bayesian personalization and sequential updating, optimization under uncertainty, robust or risk-sensitive treatment planning, explicit optimal control, multimodal integration of PET, CT, MRI, histology, omics, and wearables, physics-informed surrogates for expensive forward models, virtual trials based on cohorts of digital patients, explainable AI, federated learning, and multi-organ or multiscale twin compositions (Kapteyn et al., 1 May 2025, Ryhiner et al., 4 Nov 2025, Pan et al., 18 Aug 2025). This suggests that the long-term trajectory of theranostic digital twins is toward learning health systems in which patient-specific models are continuously recalibrated, clinically governed, uncertainty-aware, and directly actionable.