Patient-Specific Electric Field Simulation In Spinal Metastasis Electrochemotherapy

Summary

This protocol describes a complete, reproducible workflow for patient-specific electric-field simulation and validation of electrochemotherapy (ECT) for spinal metastasis. The workflow integrates multimodal imaging, semi-automatic and manual segmentation, tissue conductivity modeling, linear finite-element electric-field simulation, and experimental validation via post-procedure MRI-based necrosis overlap analysis.

Abstract

Electrochemotherapy (ECT) combines the administration of cytotoxic agents with high-voltage electric pulses that transiently permeabilize tumor cell membranes and enhance intracellular drug uptake. This minimally invasive, nonthermal technique is particularly suitable for tumors located near critical structures, where surgery, radiotherapy, or percutaneous thermal ablation may be limited. In the spine, ECT can provide pain relief, neural decompression, and local tumor control while preserving neural structures. However, its implementation remains challenging due to complex vertebral anatomy, limited understanding of electric field distribution, the absence of dedicated planning tools, and the risk of neural injury. This protocol describes a reproducible workflow for patient-specific electric field simulation in spinal ECT. Multimodal imaging combining CT and MRI enables reconstruction of tumoral, vertebral, neural, and soft-tissue anatomy using semi-automatic and manual segmentations performed within the open-source 3D Slicer platform. Tissue conductivities are assigned according to the IT’IS database, and linear finite-element simulations (constant conductivity) are performed with AI4DEEP, a dedicated 3D Slicer module, to compute 3D electric field maps across multiple isodose thresholds. Follow-up contrast-enhanced MRI is used for validation through Dice similarity coefficients comparing simulated isoelectric field volumes with post-ECT necrotic tumoral areas. Qualitative comparison of simulated electric field maps, follow-up MRI, and clinical outcomes was performed by expert interventional radiologists to evaluate the ability of the software to predict undertreated and overtreated regions. Nine ECT procedures were processed to assess the workflow. The highest concordance between simulated electric field and post-ECT necrosis was observed in the 160–200 V/cm range in this specific clinical and numerical setting. The workflow also identified regions of insufficient or excessive treatment, consistent with clinical and imaging follow-up. The described workflow lays the groundwork for reproducible ECT planning, supporting optimized electrode placement and parameter adjustment to improve safety and efficacy in complex spinal ECT procedures.

Introduction

Spinal metastatic epiduritis is frequent because the spine is the predominant site of skeletal metastases, accounting for up to 50% of all bone localizations^1,2. Clinical presentation typically includes severe pain with marked impairment of quality of life, followed by rapidly progressive neurological deficits that may end in paraplegia or tetraplegia, depending on the level of involvement^3,4. Radiotherapy is the standard of care for metastatic epidural spinal cord compression, while decompressive and stabilizing surgery benefits selected patients with adequate performance status and expected survival^5,6. Stereotactic ablative radiotherapy can improve local control in selected cases but is limited by spinal cord tolerance and planning complexity⁷. Local recurrence or progression after radiotherapy is frequent, and re-irradiation is limited by cumulative dose constraints, often resulting in therapeutic dead-ends for patients with persistent pain or progressive neurological compromise^6,7. In this context, a local nonthermal technique capable of achieving pain relief, decompression, and tumor control near critical neural structures is needed.

Electrochemotherapy (ECT) combines the administration of cytotoxic agents, most commonly bleomycin, with short high-voltage electric pulses that transiently increase plasma membrane permeability and enhance intracellular drug uptake^8,9. ECT is minimally invasive, nonthermal, and relatively tumor-selective, and has shown encouraging results for cutaneous, subcutaneous, and deep-seated tumors located close to critical structures^10,11. A recent clinical series reported an MRI objective response rate of 77% at 1 month and 66.5% at 3 months after percutaneous spinal ECT for radiotherapy-resistant epidural spinal cord compression. Pain also decreased markedly, with the median Numeric Rating Scale pain score decreasing from 7 at baseline to 1 at 1 month. Irreversible neurological deficits still occurred in a subset of patients¹². These events highlight the central role of electric field distribution: small changes in electrode geometry or tissue properties can markedly alter the treated volume, exposing patients to both undertreatment and overtreatment^13,14. Numerical studies suggest that patient-specific modeling can optimize electrode placement and improve coverage of vertebral tumors while limiting exposure of neural structures^15,16.

Several research planning frameworks have been proposed for electroporation therapies, but routine clinical implementation remains limited by meshing/parameter requirements and computation time^{17,18,19,20,21}. Recent work by Sutter and Poignard supports a peri-procedural, imaging-driven simulation workflow compatible with clinical constraints and shows that incomplete electric-field isodose coverage accurately correlates with local failure after IRE^22,23. This protocol builds on the same simulation framework.

The aim of this article is therefore to provide a reproducible, patient-specific workflow for electric field modeling and validation in spinal ECT, based on real clinical imaging datasets and delivered electrode configurations. In its current form, this workflow is best suited for pre-procedural planning to optimize electrode positioning, as segmentation and model preparation times remain a limitation for routine intra-procedural adjustment. It is intended as a methodological framework rather than a hypothesis-driven efficacy study and is designed for interventional oncology and spine teams already performing or planning percutaneous CT-guided ECT in epidural metastases and other anatomically complex settings.

Protocol

This retrospective study was conducted in accordance with the institutional ethics committee (IRB 2025-566) and complied with applicable national regulations. All imaging data were anonymized prior to analysis, and the requirement for informed consent was waived because of the retrospective design and use of de-identified data. The equipment and the software used are listed in the Table of Materials.

1. Data import and scene preparation

1. Import all anonymized DICOM series into 3D Slicer (available at https://download.slicer.org/) and assign explicit names to each volume: “MRIp” (pre-procedural MRI), “CTi” (initial intra-procedural planning CT), “CTa” (intra-procedural CT with needles in place), and “MRIs” (post-procedural MRI).
2. Set “CTi” as the reference volume for all subsequent segmentations and registrations.
3. Create a new segmentation node in 3D Slicer and link it to “CTi” as the “source volume”.
4. Save the Slicer scene as a baseline project file to allow recovery and future modifications.
   NOTE: Voxel size standardization is not required and can be omitted if native image resolution is adequate.

2. Anatomical segmentation on initial CT (CTi)

1. Open the Segment Editor module and create a segment named “Cortical bone”. Use the “Threshold” tool on “CTi” to isolate high-density cortical bone, then refine the segment manually using Scissors or Paint tools if needed.
2. Create a segment named “Fat”. Use the Threshold tool to select low-density fat, for example, in the perirenal or subcutaneous space.
3. Create a segment named “Intervertebral disc”. Use the 3D Paint tool on axial, sagittal, and coronal views, and check Paint outside existing segments, ensuring that painting is restricted to empty voxels to avoid overwriting existing segments.
4. Create a segment named “Cancellous bone” for vertebral cancellous bone. Use the 3D Paint tool to fill the internal trabecular compartments, and check Paint outside existing segments as described above.
5. Create a segment named “Spinal cord”. Use the 3D paint tool to manually contour the cord on axial slices from above to below the treated levels.
6. Create a segment named “Cerebro-spinal fluid”. Use the 3D paint tool to fill the cerebrospinal fluid space surrounding the spinal cord within the thecal sac, also checking the Paint outside existing segments.
7. Create additional segments for cement, coils, or lungs if present. Use the Threshold tool to identify hyperdense cement or coils and hypodense lung parenchyma, followed by manual correction as needed.
8. Save the updated Slicer scene.
   NOTE: Background voxels not explicitly assigned to a segment are later treated as “muscle-like” tissue in AI4DEEP (default conductivity).

3. MRI fusion and tumor segmentation

1. Import the pre-procedural MRI volume “MRIp” into the Slicer scene.
2. Use the Transforms module to perform a rigid registration of “MRIp” onto “CTi”, using vertebral osseous landmarks at the treated level. Registration is guided by four predefined landmarks: the tip of the spinous process and the tip of one transverse process on the axial plane, and the tip of the spinous process and the anterior cortical margin of the vertebral body on the sagittal plane. Because the posterior vertebral cortex is often altered by lytic disease, it should not be used as the primary landmark.
3. Apply the transform to “MRIp” and visually assess co-registration in axial and sagittal planes. The target registration error (TRE) is defined as the mean in-plane distance, in millimeters, between the corresponding CT and MRI landmarks at the predefined axial and sagittal reference points. Accept the registration only if both visual alignment and TRE are less than 2.5 mm; otherwise, repeat the registration.
4. Create a new segment named “Tumor” and use the Paint tool on “MRIp” (or on “CTi” when the epiduritis is well seen) to manually delineate epidural and vertebral tumor involvement at baseline.
5. Apply optional smoothing to the “Tumor” segment to obtain a coherent 3D volume without gaps.
6. Save the updated Slicer scene.
   NOTE: One may pause the protocol here and resume later from this step.

4. CT with needle fusion and electrode definition in AI4DEEP

1. Import the intra-procedural CT with needles, “CTa”, into 3D Slicer.
2. Use the Transforms module to perform a rigid registration of “CTa” onto “CTi” using the same vertebral bony landmarks as above.
   NOTE: In this workflow, “CTi” and “CTa” are acquired under general anesthesia without patient mobilization between the initial scan and electrode placement, such that CT-to-CT alignment is usually already close to optimal and requires no manual adjustment. TRE is therefore not applicable. With bone window settings, the metallic artifacts generated by the electrodes do not hinder visualization of the vertebral landmarks used for registration.
3. Launch the AI4DEEP module and create one virtual needle for each electrode, starting from the active electrode tip.
4. Verify in axial, coronal, and sagittal views that each “Electrode_n” matches accurately with a single physical active tip.
   NOTE: The quality of overall anatomical structure and electrode segmentation, as well as correspondence with the physical electrodes, is externally validated by an expert interventional radiologist who was not involved in the segmentation process. If deemed unsatisfactory, the segmentation is revised accordingly.
5. Save the updated scene containing anatomical structures, tumor segmentation, and electrode geometry.
   NOTE: The exact active tip length is specified later in AI4DEEP during the ECT parameter configuration and does not need to be encoded at this stage.

5. Conductivity assignment in AI4DEEP

1. In the AI4DEEP interface, assign each anatomical segment to a corresponding tissue class with predefined electrical conductivity (for example: tumor, cortical bone, cancellous bone, cerebrospinal fluid, epidural fat, intervertebral disc, spinal cord, cement, lung) as shown in Table 1.
2. Set the default background tissue to “muscle” conductivity so that all non-segmented voxels are treated as muscle.
3. Confirm in the AI4DEEP summary panel that each segment is mapped to the expected tissue type and conductivity, then save the configuration.
   NOTE: Conductivity values are derived from the IT’IS database and used as fixed (linear) conductivities for all simulations²⁴.

6. Pulse and needle characteristics

1. In AI4DEEP, specify the active tip length for each electrode (for example, 20 mm, 30 mm, or 40 mm) according to the clinical procedure. Electrode diameter was 1.8 mm.
2. Enter the clinically applied electric field amplitude in V/cm (for example, 500 V/cm, 600 V/cm, or 1000 V/cm).
3. Define the electrode pairs that were activated during the treatment.
4. Let AI4DEEP compute the applied voltage for each electrode pair based on the inter-electrode distance, ensuring the requested voltage-to-distance ratio in V/cm is respected.
5. Set the number of pulses (8) and the pulse duration (100 µs) according to the clinical protocol.
6. Save the AI4DEEP configuration.
   NOTE: Voltage is entered as a voltage-to-distance ratio in V/cm; AI4DEEP automatically converts this value into an absolute voltage for each electrode pair.

7. Electric field simulation

1. In AI4DEEP, start the electrostatic field computation using the configured anatomy, conductivities, and electrode parameters.
2. Allow the software to automatically generate the geometrical configuration and solve the linear electrostatic potential using high-order unfitted finite difference methods on the medical image²⁵.
   NOTE: On a typical workstation (for example, Windows 11, 16 GB RAM), processing takes approximately 5 min per case.
3. Save the simulation output and the updated Slicer scene.
   NOTE: One may pause the protocol here after simulation and resume later for isodose analysis and validation.

8. Isodose visualization and tumor coverage

1. In AI4DEEP, select the option to generate electric field isosurfaces (isodose volumes) from the simulated field.
2. Select a range of isodose thresholds (for example, 50 V/cm, 100 V/cm, 120 V/cm, 140 V/cm, 160 V/cm, 180 V/cm, 200 V/cm, 220 V/cm, 240 V/cm, 260 V/cm, 300 V/cm, 400 V/cm, 500, and 600 V/cm) and generate the corresponding 3D isodose maps.
3. Display individual isodose maps in 3D view to observe the spatial field distribution, typically ranging from pale yellow at low field amplitudes to red at high field amplitudes as shown in Figure 1.
4. For each isodose, record the percentage of tumor coverage values automatically computed by AI4DEEP based on the overlap between the isodose volume and the “Tumor” segmentation.
5. Save all isodose segments or models and the tumor coverage values for subsequent analysis.

9. Post-procedural MRI selection, import, and necrosis segmentation

1. Follow-up MRIs were performed at 6 weeks after ECT and every 2 months thereafter. The MRI showing the largest necrotic volume or the best radiological response was selected for segmentation. This choice was made to account for the heterogeneous and delayed time to response after ECT, which may vary according to tumor histology and proliferation rate.
2. Import the post-treatment contrast-enhanced T1 fat-suppressed axial sequence centered on the treated levels “MRIs” into 3D Slicer.
3. Perform rigid registration of “MRIs” onto “CTi” using the same predefined vertebral landmarks as those used for pre-procedural MRI registration, and apply the transform. Registration accuracy is documented by calculating the TRE from these same reference landmarks with the same ≤2.5 mm threshold.
4. Create a new segment named “Tumor necrosis” and manually delineate the non-enhancing necrotic portion of the tumor on the post-ECT MRI, slice by slice.
5. When multiple follow-up MRIs are available, select the examination demonstrating the largest necrotic volume or best radiological response and use this dataset for segmentation.
6. Apply optional smoothing to “Tumor necrosis” to obtain a coherent, contiguous volume.
7. In cases of complete radiological response after ECT, the pre-treatment “Tumor” segment may be duplicated and renamed “Tumor necrosis” to reduce manual segmentation time. In 3D Slicer, use the Segmentations module, select “Copy/move segments”, duplicate the “Tumor” segment within the same segmentation node, rename it “Tumor necrosis”, and visually confirm its adequacy on the post-ECT MRI.
8. Perform external validation of necrosis segmentation by an interventional radiologist not involved in segmentation.
9. Save the Slicer scene with the necrosis segment.
   NOTE: One may pause the protocol here and resume later for quantitative comparison.

10. Quantitative comparison between simulated field and necrosis (Dice analysis)

1. Open the Segment Comparison module in 3D Slicer.
2. Select Tumor necrosis as the reference segment and one isodose volume (for example, the 200 V/cm isodose) as the comparison segment.
3. Compute the Dice similarity coefficient between “Tumor necrosis” and the selected isodose volume and record the value. All Dice similarity coefficients were calculated on full 3D segment volumes rather than on a slice-by-slice (2D) basis.
4. Repeat the analysis across all relevant isodose levels of interest to obtain Dice coefficients across the full range of thresholds.
5. Identify the isodose yielding the highest Dice coefficient (“Best Iso”) and the corresponding maximum Dice value (“Best Dice”).
6. Export Dice coefficients, Best Iso, Best Dice, and tumor coverage values to a spreadsheet or statistical software for further analysis.
7. Save the final Slicer scene and all exported quantitative results.

Results

The workflow was successfully applied to nine electrochemotherapy (ECT) procedures performed for spinal metastatic epiduritis. A patient-specific predictive electric field model was generated for all cases, confirming that the full protocol is feasible and reproducible across different anatomical configurations. Rigid registration between CTi and MRIp, and between CTi and MRIs, was achieved in all cases, with mean TREs of 1.8 mm ± 0.6 mm and 1.9 mm ± 0.7 mm, respectively. Linear finite-element simulations produced a cont...

Discussion

This protocol provides a reproducible workflow for patient-specific electric field simulation in spinal ECT. Several methodological steps are critical for obtaining accurate and clinically interpretable predictions. Precise tumor segmentation is essential, as epidural disease is often poorly visualized on non-contrast intra-procedural CT. Fusion of pre-procedural and post-procedural MRI is therefore key for accurate delineation of epidural and vertebral involvement. In practice, image fusion may rely on manual rigid regi...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
3D Slicer	3D Slicer (open-source)	N/A	Version 5.6.2.
AI4DEEP module (3D Slicer extension)	AI4DEEP	N/A	Electric-field simulation module used within 3D Slicer.
Cliniporator VITAE	IGEA	IG0012A	Pulse generator used for electrochemotherapy
Hybrid angio-CT suite (Alphenix 4D CT + Aquilion ONE)	Canon Medical Systems	TSX-305A	Integrated angiography system (Alphenix) and CT scanner (Aquilion ONE) in a single room; used for intraprocedural CT imaging and procedural guidance.
Straight needle electrodes "VGD"	IGEA	IG0E726	Active length: 20 mm / 30 mm / 40 mm (select according to target size and anatomy).
Workstation	Dell	N/A	Dell workstation, intelVPro ISM, Windows 11 - 16 GB RAM; used for image processing and simulations.