Generative Simulation Benchmarking for precision oncology clinical workflows with inverse simulation verification

Generative Simulation Benchmarking for precision oncology clinical workflows with inverse simulation verification

Introduction: The Clinical Data Gap

During my research into AI-driven clinical decision support systems, I encountered a fundamental problem that changed my approach to medical AI validation. While building a reinforcement learning agent to optimize chemotherapy scheduling, I realized we were benchmarking against historical data that inherently contained the biases and limitations of past clinical decisions. The "ground truth" we were using wasn't a ground truth at all—it was a collection of human decisions made under uncertainty, resource constraints, and incomplete information.

One particular incident stands out. While experimenting with a transformer-based model to predict treatment response in non-small cell lung cancer, I discovered that our validation metrics were excellent—until we tried to deploy the system in a prospective study. The model performed poorly because it had learned patterns from historical treatment protocols that were no longer optimal given newer targeted therapies. This experience led me to question: how do we benchmark AI systems for clinical workflows when the "correct" decisions are often unknown, evolving, and patient-specific?

Through studying simulation theory and generative AI, I realized we needed a paradigm shift. Instead of benchmarking against historical data, we needed to create synthetic, physiologically-plausible patient trajectories that could serve as a controlled testing environment. This insight sparked my exploration into generative simulation benchmarking with inverse verification—a methodology that has since become central to my work in precision oncology AI systems.

Technical Background: From Static Datasets to Dynamic Simulations

The Limitations of Traditional Benchmarking

In my investigation of current oncology AI benchmarks, I found that most rely on static datasets like TCGA (The Cancer Genome Atlas) or institutional EHR data. While valuable, these datasets suffer from several critical limitations:

1. Censored outcomes: Many patients are lost to follow-up
2. Treatment confounding: Patients receive heterogeneous treatments
3. Missing counterfactuals: We only observe one treatment path per patient
4. Temporal sparsity: Clinical measurements are irregularly spaced

While exploring generative adversarial networks for medical data synthesis, I discovered that simple data augmentation wasn't sufficient. We needed causal models that could simulate disease progression, treatment effects, and patient responses in a biologically plausible manner.

Generative Simulation Architecture

Through studying pharmacokinetic-pharmacodynamic (PK-PD) models and combining them with deep generative approaches, I developed a multi-scale simulation framework:

import torch
import torch.nn as nn
import numpy as np
from typing import Dict, Tuple, List

class MultiScaleCancerSimulator(nn.Module):
    """Generative simulator for cancer progression and treatment response"""

    def __init__(self,
                 genetic_dim: int = 100,
                 cellular_dim: int = 50,
                 tissue_dim: int = 20,
                 pkpd_dim: int = 30):
        super().__init__()

        # Genetic mutation dynamics
        self.mutation_encoder = nn.LSTM(genetic_dim, 128, batch_first=True)
        self.mutation_decoder = nn.TransformerDecoder(
            nn.TransformerDecoderLayer(d_model=128, nhead=8),
            num_layers=3
        )

        # Cellular population dynamics (ODE-based)
        self.cellular_ode = nn.ModuleDict({
            'proliferation': nn.Sequential(
                nn.Linear(genetic_dim + cellular_dim, 64),
                nn.ReLU(),
                nn.Linear(64, cellular_dim)
            ),
            'apoptosis': nn.Sequential(
                nn.Linear(genetic_dim + cellular_dim, 64),
                nn.ReLU(),
                nn.Linear(64, cellular_dim)
            )
        })

        # PK-PD response model
        self.pkpd_network = PKPDNetwork(
            drug_dim=10,
            patient_dim=genetic_dim + cellular_dim,
            output_dim=pkpd_dim
        )

        # Tissue-level imaging simulator
        self.tissue_generator = DiffusionModel(
            in_channels=cellular_dim + tissue_dim,
            out_channels=3  # RGB representation
        )

    def forward(self,
                genetic_profile: torch.Tensor,
                treatment_plan: torch.Tensor,
                time_steps: int = 100) -> Dict[str, torch.Tensor]:
        """Generate synthetic patient trajectory"""

        trajectories = {
            'genetic_evolution': [],
            'cell_populations': [],
            'biomarkers': [],
            'imaging': [],
            'toxicity': []
        }

        # Initialize states
        cell_state = self.initialize_cell_population(genetic_profile)

        for t in range(time_steps):
            # Genetic evolution with treatment pressure
            genetic_mutations = self.simulate_mutation_accumulation(
                genetic_profile, treatment_plan[:, t], t
            )

            # Cellular dynamics
            proliferation = self.cellular_ode['proliferation'](
                torch.cat([genetic_mutations, cell_state], dim=-1)
            )
            apoptosis = self.cellular_ode['apoptosis'](
                torch.cat([genetic_mutations, cell_state], dim=-1)
            )

            # Update cell populations
            cell_state = cell_state + proliferation - apoptosis

            # PK-PD response
            drug_response = self.pkpd_network(
                treatment_plan[:, t],
                torch.cat([genetic_mutations, cell_state], dim=-1)
            )

            # Generate synthetic imaging
            synthetic_image = self.tissue_generator(
                torch.cat([cell_state, drug_response], dim=-1)
            )

            # Store trajectory
            trajectories['genetic_evolution'].append(genetic_mutations)
            trajectories['cell_populations'].append(cell_state)
            trajectories['biomarkers'].append(drug_response[:, :10])
            trajectories['imaging'].append(synthetic_image)
            trajectories['toxicity'].append(drug_response[:, 10:])

        return {k: torch.stack(v, dim=1) for k, v in trajectories.items()}

This architecture represents my learning journey in combining mechanistic models with neural networks. Through experimentation, I found that purely data-driven approaches lacked biological plausibility, while purely mechanistic models were too rigid. The hybrid approach proved most effective.

Implementation Details: Inverse Simulation Verification

The Core Innovation

One interesting finding from my experimentation with generative models was that simulation accuracy is difficult to verify. Traditional metrics like likelihood or reconstruction error don't guarantee clinical plausibility. This led me to develop the concept of inverse simulation verification—if we can accurately infer known parameters from generated data, the simulation is likely valid.

class InverseVerificationNetwork(nn.Module):
    """Verify simulation quality by inverting the generative process"""

    def __init__(self, simulator: MultiScaleCancerSimulator):
        super().__init__()
        self.simulator = simulator

        # Inverse models for each component
        self.genetic_inference = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=256, nhead=8),
            num_layers=4
        )

        self.treatment_inference = nn.Sequential(
            nn.Conv1d(in_channels=100, out_channels=64, kernel_size=3),
            nn.ReLU(),
            nn.AdaptiveMaxPool1d(1),
            nn.Flatten(),
            nn.Linear(64, 20)  # Treatment plan reconstruction
        )

        # Adversarial critic for realism assessment
        self.realism_critic = nn.Sequential(
            nn.Linear(300, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 128),
            nn.LeakyReLU(0.2),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )

    def verify_simulation(self,
                         synthetic_trajectory: Dict[str, torch.Tensor],
                         ground_truth_params: Dict[str, torch.Tensor]) -> Dict[str, float]:
        """Compute verification metrics"""

        metrics = {}

        # 1. Parameter reconstruction accuracy
        inferred_genetics = self.infer_genetic_profile(
            synthetic_trajectory['biomarkers'],
            synthetic_trajectory['imaging']
        )

        genetic_accuracy = cosine_similarity(
            inferred_genetics,
            ground_truth_params['genetic_profile']
        )
        metrics['genetic_reconstruction'] = genetic_accuracy.mean().item()

        # 2. Treatment plan inference
        inferred_treatment = self.infer_treatment_plan(
            synthetic_trajectory['cell_populations'],
            synthetic_trajectory['toxicity']
        )

        treatment_accuracy = f1_score(
            ground_truth_params['treatment_plan'].argmax(dim=-1),
            inferred_treatment.argmax(dim=-1)
        )
        metrics['treatment_inference'] = treatment_accuracy

        # 3. Realism score (adversarial)
        realism_input = torch.cat([
            synthetic_trajectory['biomarkers'].flatten(start_dim=1),
            synthetic_trajectory['toxicity'].flatten(start_dim=1)
        ], dim=-1)

        realism_score = self.realism_critic(realism_input)
        metrics['realism_score'] = realism_score.mean().item()

        # 4. Causal consistency check
        causal_consistency = self.check_causal_consistency(synthetic_trajectory)
        metrics['causal_consistency'] = causal_consistency

        return metrics

    def check_causal_consistency(self, trajectory: Dict) -> float:
        """Verify that simulated trajectories obey known causal relationships"""

        # Example: Check that toxicity increases with certain drug combinations
        toxicity = trajectory['toxicity']
        treatment_indicators = trajectory.get('treatment_indicators', None)

        if treatment_indicators is not None:
            # Known causal relationship: Drug A + Drug B → Increased hepatic toxicity
            drug_a_mask = treatment_indicators[:, :, 0] > 0.5
            drug_b_mask = treatment_indicators[:, :, 1] > 0.5
            combination_mask = drug_a_mask & drug_b_mask

            hepatic_toxicity = toxicity[:, :, 2]  # Hepatic toxicity index

            # Compute average toxicity increase
            baseline_toxicity = hepatic_toxicity[~combination_mask].mean()
            combination_toxicity = hepatic_toxicity[combination_mask].mean()

            return (combination_toxicity > baseline_toxicity).float().item()

        return 0.5  # Neutral score if no treatment indicators available

Through my exploration of causal inference methods, I realized that verification must go beyond statistical similarity to include causal and mechanistic plausibility. The inverse verification approach ensures that our simulations aren't just statistically similar to real data, but actually encode the correct causal relationships.

Benchmarking Framework Implementation

During my investigation of clinical AI evaluation, I developed a comprehensive benchmarking framework that uses generative simulations:

class GenerativeSimulationBenchmark:
    """Benchmark clinical AI systems using generative simulations"""

    def __init__(self,
                 num_synthetic_patients: int = 1000,
                 pathology_types: List[str] = None,
                 difficulty_levels: List[str] = ['easy', 'medium', 'hard']):

        self.simulator = MultiScaleCancerSimulator()
        self.verifier = InverseVerificationNetwork(self.simulator)

        # Generate benchmark dataset
        self.benchmark_data = self.generate_benchmark_dataset(
            num_synthetic_patients,
            pathology_types,
            difficulty_levels
        )

        # Define evaluation metrics
        self.metrics = {
            'treatment_recommendation': {
                'survival_benefit': self.compute_survival_benefit,
                'toxicity_avoidance': self.compute_toxicity_avoidance,
                'qaly_gain': self.compute_qaly_gain
            },
            'diagnostic_accuracy': {
                'auc_roc': self.compute_auc_roc,
                'early_detection': self.compute_early_detection_score
            },
            'prognostic_calibration': {
                'calibration_error': self.compute_calibration_error,
                'discrimination': self.compute_c_index
            }
        }

    def evaluate_model(self,
                      model: nn.Module,
                      task: str = 'treatment_recommendation') -> Dict[str, float]:
        """Evaluate a clinical AI model on synthetic benchmark"""

        results = {}

        for difficulty in self.benchmark_data['difficulty_levels']:
            difficulty_data = self.benchmark_data[difficulty]

            # Run model predictions
            with torch.no_grad():
                predictions = model(difficulty_data['patient_profiles'])

            # Compute ground truth outcomes from simulation
            ground_truth = self.simulate_counterfactuals(
                difficulty_data['patient_profiles'],
                predictions if task == 'treatment_recommendation' else None
            )

            # Compute metrics
            for metric_name, metric_fn in self.metrics[task].items():
                score = metric_fn(predictions, ground_truth)
                results[f'{difficulty}_{metric_name}'] = score

        # Compute overall verification score
        verification_score = self.verifier.verify_simulation(
            ground_truth['trajectories'],
            difficulty_data['ground_truth_params']
        )

        results['simulation_verification_score'] = np.mean(
            list(verification_score.values())
        )

        return results

    def generate_counterfactual_scenarios(self,
                                         patient_profile: torch.Tensor,
                                         treatment_options: List[torch.Tensor]) -> Dict:
        """Generate what-if scenarios for different treatment choices"""

        scenarios = {}

        for i, treatment in enumerate(treatment_options):
            # Simulate trajectory with this treatment
            trajectory = self.simulator(
                patient_profile.unsqueeze(0),
                treatment.unsqueeze(0)
            )

            # Compute outcomes
            survival = self.compute_survival(trajectory)
            toxicity = self.compute_toxicity_burden(trajectory)
            quality_of_life = self.compute_quality_of_life(trajectory)

            scenarios[f'treatment_{i}'] = {
                'trajectory': trajectory,
                'survival': survival,
                'toxicity': toxicity,
                'quality_of_life': quality_of_life,
                'qaly': survival * quality_of_life  # Quality-adjusted life years
            }

        return scenarios

One realization from building this framework was that traditional metrics like accuracy or AUC are insufficient for clinical AI. We need multi-dimensional metrics that capture survival benefit, toxicity burden, quality of life, and economic considerations—all of which can be precisely measured in simulations but are often unobservable in real-world data.

Real-World Applications: Precision Oncology Workflows

Clinical Decision Support Systems

In my experimentation with deploying AI systems in clinical settings, I found that generative simulation benchmarking helped address several critical challenges:

1. Personalized Treatment Optimization: By simulating thousands of counterfactual scenarios for each patient, we can identify optimal treatment sequences that balance efficacy and toxicity.

class TreatmentOptimizer:
    """Optimize treatment plans using generative simulation"""

    def optimize_treatment(self,
                          patient_data: Dict,
                          candidate_treatments: List[Dict],
                          optimization_horizon: int = 12) -> Dict:

        # Initialize reinforcement learning environment
        env = OncologySimulationEnvironment(
            patient_data=patient_data,
            simulator=self.simulator,
            horizon=optimization_horizon
        )

        # Define reward function incorporating multiple objectives
        def reward_function(trajectory: Dict) -> float:
            survival = self.compute_survival(trajectory)
            toxicity = self.compute_toxicity_burden(trajectory)
            cost = self.compute_treatment_cost(trajectory)

            # Multi-objective reward with weights
            return (0.5 * survival +
                    0.3 * (1 - toxicity) +
                    0.2 * (1 - min(cost / 100000, 1)))

        # Use PPO for policy optimization
        policy = self.train_ppo_policy(
            env=env,
            reward_fn=reward_function,
            num_epochs=1000
        )

        # Generate optimal treatment plan
        optimal_plan = policy.generate_plan(patient_data)

        # Validate with inverse verification
        verification = self.verifier.verify_simulation(
            optimal_plan['simulated_trajectory'],
            {'genetic_profile': patient_data['genetics']}
        )

        return {
            'treatment_plan': optimal_plan,
            'expected_outcomes': self.compute_expected_outcomes(optimal_plan),
            'verification_scores': verification,
            'alternative_scenarios': self.generate_alternative_scenarios(optimal_plan)
        }

1. Clinical Trial Simulation: During my research into accelerating drug development, I discovered that generative simulations can predict trial outcomes and optimize trial design.

python
class ClinicalTrialSimulator:
    """Simulate clinical trials using generative patient populations"""

    def simulate_trial(self,
                      trial_design: Dict,
                      num_virtual_patients: int = 1000,
                      num_simulations: int = 100) -> TrialResults:

        results = []

        for sim in range(num_simulations):
            # Generate virtual patient cohort
            cohort = self.generate_virtual_cohort(
                num_patients=num_virtual_patients,
                inclusion_criteria=trial_design['inclusion_criteria'],
                exclusion_criteria=trial_design['exclusion_criteria']
            )

            # Randomize to treatment arms
            randomized_cohort = self.randomize_cohort(
                cohort,
                trial_design['arms']
            )

            # Simulate trial outcomes
            trial_outcomes = []
            for arm_name, arm_patients in randomized_cohort.items():
                arm_results = self.simulate_arm(
                    arm_patients,
                    trial_design['treatment_regimens'][arm_name