System and Method for Stabilized Stochastic Inference and Risk-Averse Optimization in Physics-Constrained Oncology Digital Twins

Precision oncology increasingly relies on machine learning models to predict patient outcomes under candidate therapeutic regimens. Conventional models produce a single point estimate—e.g., a predicted tumor volume trajectory or a scalar risk score—which fails to capture epistemic uncertainty (model ignorance due to limited training data) versus aleatoric uncertainty (inherent biological stochasticity). Without quantifying these distinct uncertainty sources, clinicians cannot distinguish between a high-confidence prediction and one that merely happens to land on a central value by chance. This limitation is particularly acute in rare cancer types where training data is sparse and model predictions are inherently less reliable.

Monte Carlo (MC) Dropout is a computationally efficient approximation to Bayesian inference: by running multiple forward passes with dropout layers active, one obtains a distribution of predictions that reflects model uncertainty. However, MC Dropout interacts pathologically with Batch Normalization (BN) layers during single-sample inference. BN layers maintain running statistics (mean and variance) computed over mini-batches during training. When the model is switched to training mode (required to activate dropout), BN layers recalculate statistics from the current input—which may be a single sample or a small set of stochastic replications. This causes variance collapse: the BN statistics converge to near-zero variance, producing deterministic or near-deterministic outputs that defeat the purpose of stochastic inference.

Deep Ensembles provide an alternative but require training and storing multiple independent models (typically 5–10), incurring $O(K)$ storage and compute overhead that is prohibitive for real-time clinical decision support systems operating on GPU-constrained hardware.

When stochastic inference is used to compare candidate treatment regimens, independent noise injected into each treatment simulation obscures the true comparative efficacy. Consider a system evaluating $M$ treatments across $N$ stochastic forward passes. If each treatment’s parameters are sampled independently, the variance of the pairwise difference between two treatments equals the sum of their individual variances: $\text{Var}(\Delta_{ij}) = \text{Var}(r_i) + \text{Var}(r_j)$. A correlated sampling strategy where the same stochastic parameter vector is reused across all treatment branches eliminates independent noise, reducing comparison variance by a factor proportional to the correlation coefficient.

Standard optimization selects the treatment with the highest predicted benefit (e.g., maximum tumor volume reduction or minimum risk score). However, some treatments that yield high benefit under nominal conditions also exhibit high fragility—their predicted outcomes are highly sensitive to small perturbations in dose, ODE parameters, or model uncertainty. These “Glass Cannon” treatments are dangerous in clinical practice because the nominal prediction masks catastrophic sensitivity to real-world variability. Existing systems provide no mechanism to detect or gate such treatments.

[0001]For the purposes of this disclosure, the following terms have the meanings set forth below:

Set-Valued Digital Twin

A computational model that generates a distribution of predicted outcomes (rather than a single point estimate) for a patient under each candidate treatment. The distribution is obtained by executing multiple stochastic forward passes through the neural network with active dropout.

Execution Token

A cryptographically signed data structure that authorizes a specific treatment simulation to proceed to the ODE solver for trajectory computation. The token encodes an Information Sufficiency Score (ISS), constrained parameter values, a configuration hash, a policy identifier, a nonce, and a time-to-live (TTL). The ODE solver verifies the token's Ed25519 signature and configuration hash before allocating GPU memory.

Variance Collapse

A failure mode in which Batch Normalization running statistics converge to near-zero variance during single-sample stochastic inference, producing deterministic outputs that eliminate the uncertainty information required for clinical decision-making.

BN Buffer Hash

A SHA-256 cryptographic digest computed over the concatenation of a Batch Normalization layer's running_mean and running_var tensors. The hash is used to verify buffer integrity before and after stochastic inference.

[0002]Referring to FIG. 1, the system comprises a computing platform configured with GPU accelerators (e.g., NVIDIA T4, A10G, or H100). The core architecture includes three primary coupled neural network subsystems:

Foundation VAE (Variational Autoencoder)

A hierarchical, biologically-disentangled VAE that encodes multi-modal omics data (RNA-seq: 2,579 genes; DNA mutation: 500 genes; CNV: 1,886 features; Methylation: 1,000 features) into a structured latent space $z_{\text{full}}$ of $D$ dimensions (e.g., $D = 328$). The latent space is structured into biologically meaningful slices: $z_{\text{prolif}}$ (proliferation, 1d),$z_{\text{pathway}}$ (50 MSigDB Hallmark pathways, 200d),$z_{\text{ctx}}$ (context, 31d),$z_{\text{residual}}$ (16d),$z_{\text{meth}}$ (48d), and$z_{\text{cnv}}$ (32d). The VAE is frozen during downstream training and inference.

Hypernetwork (ConditionedGatingModelV3)

A neural network that maps the latent vector $z_{\text{full}}$ and optional high-dimensional Whole Slide Image (WSI) embeddings (e.g., 1536-dimensional vectors from a UNI2-h foundation model) to the parameters of a tumor dynamics differential equation. The Hypernetwork includes BatchNorm layers for input normalization and Dropout layers for regularization. A conditioned gating mechanism with FiLM (Feature-wise Linear Modulation) fuses omics and histopathology modalities, with learned cancer-type embeddings providing conditioning context.

Neural ODE / Trajectory Emulator

A differentiable simulator that integrates a Lotka-Volterra tumor dynamics equation parameterized by the Hypernetwork outputs. The governing equation is: $dN_k/dt = \rho_k N_k (1 - \Sigma N / K_{\text{cap}}) - \beta_{k,d} \cdot D_d(t) \cdot N_k - \omega \cdot E(t) \cdot N_k$, where $\rho$ is growth rate, $\beta$ is drug sensitivity, and $\omega$ is immune kill rate. A learned trajectory emulator ($R^2 = 0.997$ vs. full ODE) provides ~100x speedup for production inference.

[0003]Referring to FIG. 2, the system implements a layer-wise mode override protocol to decouple BatchNorm statistics from Dropout stochastic sampling during inference. The protocol is defined by the following algorithm:

This protocol ensures that BN running statistics remain exactly as computed during training, preventing variance collapse while still enabling stochastic parameter sampling via active dropout. The cryptographic hash provides a tamper-evident seal on BN buffer contents.

[0004]The Hypernetwork generates raw unbounded activations ($z_\rho, z_\beta, z_\omega$) which are projected into biologically admissible ranges via differentiable activation functions:

Without these constraints, unconstrained parameters can cause the ODE solver to encounter stiff dynamics, requiring adaptive step-size algorithms to take an excessive number of function evaluations. Experimental validation demonstrates the effect:

The constrained model achieves 100% physics compliance with zero violations across all patients, while also achieving superior predictive performance (+2.6pp C-index) due to the regularization effect of biologically meaningful parameter bounds.

[0005]Referring to FIG. 3, the system addresses ranking instability in counterfactual treatment comparison by using correlated stochastic parameter vectors. The protocol is defined as follows:

Because $r_{i,n}$ and $r_{j,n}$ share the same stochastic parameter vector $\theta_n$, the variance of the pairwise difference $\Delta_{ij}$ is reduced relative to independent sampling:

The covariance term is positive and large due to shared parameters, yielding substantially lower comparison variance and more stable treatment rankings across MC samples.

[0006]The system aggregates pairwise treatment advantages across $N$ MC samples using Conditional Value-at-Risk (CVaR) to produce a risk-averse treatment ranking.

CVaR-Borda ranking is more robust than mean-optimal ranking: experimental validation shows 8.3% discordance between CVaR and mean rankings, with CVaR penalizing high-variance treatments (e.g., targeted therapy loses 267 recommendations) while maintaining equivalent discrimination (CVaR C-index = mean C-index = 0.870).

[0007]Referring to FIG. 4, the system implements a circuit breaker that prevents high-benefit / high-fragility (“Glass Cannon”) treatments from proceeding to the ODE solver.

Experimental validation of Glass Cannon detection:

[0008]The complete system was validated on a multi-center dataset comprising 9,393 patients across 33 cancer types with 1,000 MC dropout forward passes per patient.

Set-Valued Inference Performance:

Risk-Averse Treatment Optimization:

Quadrant Analysis (Benefit vs. Fragility):

These results demonstrate that the inventive system (i) preserves discriminative power under stochastic inference, (ii) significantly improves patient outcomes through risk-averse treatment selection, and (iii) correctly identifies and gates dangerous Glass Cannon treatments that would otherwise be recommended by deterministic optimizers.

[0009]In an embodiment directed to preclinical data translation, the system includes a Domain Separation Network (DSN) that transforms cell-line or Patient-Derived Xenograft (PDX) data into the human latent space before stochastic inference. The DSN comprises a shared encoder that extracts domain-invariant tumor biology, a private encoder that captures species-specific stroma signal (discarded at inference), and a Gradient Reversal Layer (GRL) with a domain discriminator. The DSN output ($z_{\text{shared}}$ + imputed $z_{\text{meth}}$ + imputed $z_{\text{cnv}}$) feeds into a DSN-specific Hypernetwork (Path B) for set-valued inference with all the stabilized inference and risk-averse optimization methods described herein.

When processing PDX data through the stochastic inference pipeline, the system sets the immune kill rate parameter $\omega = 0$ for trajectory computation because PDX models (athymic/NSG mice) lack a human immune system. The immune parameter $\omega$ remains active for human patient data processed through the primary Hypernetwork path (Path A).

[0010]Whole Slide Image (WSI) embeddings enter the Hypernetwork via a late gated fusion mechanism—not through the VAE encoder. A WSI bottleneck projects 1536-dimensional UNI2-h embeddings to a compressed representation, which is combined with the omics projection via a conditioned gating mechanism. The gate learns modality-specific weights per cancer type, enabling the system to rely more heavily on histopathology for cancers where morphological features are predictive (e.g., BRCA: omics 77%, WSI 23%) while maintaining omics-dominant predictions for cancers where molecular profiles are more informative.

[0011]The system computes a Policy Stability Score for each patient, defined as the frequency of the most commonly recommended treatment across all $N$ MC dropout forward passes:

Stability scores near 1.0 indicate high confidence in the treatment recommendation; scores near $1/M$ indicate the model is uniformly uncertain. The system classifies patients into triage tiers based on stability: STABLE (> 0.6), CONTESTED (0.3 – 0.6), and CHAOTIC (< 0.3). Patients in the CHAOTIC tier are flagged for specialist review with an evidence-completion recommendation indicating which additional data modality would most reduce treatment ranking uncertainty.