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Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

ABSTRACT: Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.

SUBMITTER: Pavliha D

PROVIDER: S-EPMC3732275 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

Pavliha Denis D Mušič Maja M MM Serša Gregor G Miklavčič Damijan D

PloS one 20130802 8

Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geomet ...[more]

PMID: 23936315

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Project description:BACKGROUND: Electrochemotherapy treats tumors by combining specific chemotherapeutic drugs with an intracellular target and electric pulses, which increases drug uptake into the tumor cells. Electrochemotherapy has been successfully used for treatment of easily accessible superficial tumor nodules. In this paper, we present the first case of deep-seated tumor electrochemotherapy based on numerical treatment planning. METHODS: The aim of our study was to treat a melanoma metastasis in the thigh of a patient. Treatment planning for electrode positioning and electrical pulse parameters was performed for two different electrode configurations: one with four and another with five long needle electrodes. During the procedure, the four electrode treatment plan was adopted and the patient was treated accordingly by electrochemotherapy with bleomycin. The response to treatment was clinically and radiographically evaluated. Due to a partial response of the treated tumor, the metastasis was surgically removed after 2 months and pathological analysis was performed. RESULTS: A partial response of the tumor to electrochemotherapy was obtained. Histologically, the metastasis showed partial necrosis due to electrochemotherapy, estimated to represent 40-50% of the tumor. Based on the data obtained, we re-evaluated the electrical treatment parameters in order to correlate the treatment plan with the clinical response. Electrode positions in the numerical model were updated according to the actual positions during treatment. We compared the maximum value of the measured electric current with the current predicted by the model and good agreement was obtained. Finally, tumor coverage with an electric field above the reversible threshold was recalculated and determined to be approximately 94%. Therefore, according to the calculations, a small volume of tumor cells remained viable after electrochemotherapy, and these were sufficient for tumor regrowth. CONCLUSIONS: In this, the first reported clinical case, deep-seated melanoma metastasis in the thigh of the patient was treated by electrochemotherapy, according to a treatment plan obtained by numerical modeling and optimization. Although only a partial response was obtained, the presented work demonstrates that treatment of deep-seated tumor nodules by electrochemotherapy is feasible and sets the ground for numerical treatment planning-based electrochemotherapy. TRIAL REGISTRATION: EudraCT:2008-008290-54.

Project description:BackgroundPulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach.MethodsThe proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation.ResultsFor the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm.ConclusionsThis study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Dataset Information

Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

Publications

Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

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