Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study

OBJECTIVES

We aimed to develop an automated method for measuring the volume of the psoas muscle using CT to aid sarcopenia research efficiently.

METHODS

We used a data set comprising the CT scans of 520 participants who underwent health check-ups at a health promotion centre. We developed a psoas muscle segmentation model using deep learning in a three-step process based on the nnU-Net method. The automated segmentation method was evaluated for accuracy, reliability, and time required for the measurement.

RESULTS

The Dice similarity coefficient was used to compare the manual segmentation with automated segmentation; an average Dice score of 0.927 ± 0.019 was obtained, with no critical outliers. Our automated segmentation system had an average measurement time of 2 min 20 s ± 20 s, which was 48 times shorter than that of the manual measurement method (111 min 6 s ± 25 min 25 s).

CONCLUSION

We have successfully developed an automated segmentation method to measure the psoas muscle volume that ensures consistent and unbiased estimates across a wide range of CT images.

Influence of sarcopenia on surgical efficacy and mortality of percutaneous kyphoplasty in the treatment of older adults with osteoporotic thoracolumbar fracture

BACKGROUND

Sarcopenia is an age-related condition that causes loss of skeletal muscle mass and disability. Sarcopenia is closely related to the prognosis of patients suffering osteoporotic thoraco-lumbar compression fractures (OTLCF). The purpose of this study was to investigate the effect of sarcopenia on the efficacy of percutaneous kyphoplasty (PKP) in the treatment of older adults with OTLCF surgery and postoperative mortality.

METHODS

From February 2016 to June 2019, 101 patients who met the inclusion and exclusion criteria were included in this study. The grip strength of the dominant hand was measured using an electronic grip tester. The diagnostic cutoff value of grip strength for sarcopenia was <27 kg for males and <16 kg for females. The cross-sectional area (cm2) of the musculature at the level of the pedicle of the thoracic 12th vertebra (T12) was measured by chest CT. The skeletal muscle index (SMI) was calculated by dividing the muscle cross-sectional area at the T12 pedicle level by the square of the height. The diagnostic cut-off value of SMI at T12 level is 42.6 cm2/m2 for males and 30.6 cm2/m2 for females. Sarcopenia was diagnosed when the grip strength and SMI values were both lower than the diagnostic cut-off value. All included patients received PKP treatment for OTLCF. The age, gender, operation time, bleeding volume, time to ground, length of hospital stay, visual analog scale (VAS) score before operation and one month after operation, Oswestry Disability Index (ODI) one month after operation and the incidence of refracture within 36 months after operation were compared between the two groups. The survival curves of the two groups were analyzed by Kaplan Meier. Chi-square test was used to compare the differences in survival rates between the two groups at 12, 24, and 36 months after operation. Univariate and multivariate Cox regression analysis compared multivariate factors on OTLCF postoperative mortality.

RESULTS

There was no significant difference in gender, operation time, blood loss and preoperative VAS score between the two groups (χ2 = 1.750, p = 0.186; t = 1.195, p = 0.235; t = -0.582, p = 0.562; t = -1.513, p = 0.133), respectively. The patients in the sarcopenia group were older (t = 3.708, p = 0.000), and had longer postoperative grounding time and hospitalization time (t = 4.360, p = 0.000; t = 6.458, p = 0.000). The VAS scores and ODI scores one month postoperatively were also higher in sarcopenia group (t = 5.900, p = 0.000; t = 7.294, p = 0.000), and there was a statistical difference between the two groups. Interestingly, there was no significant difference in the incidence of spinal refracture within 36 months between the two groups (χ2 = 1.510, p = 0.219). The sarcopenia group had a higher mortality rate at 36 months after operation, and the difference was statistically significant (p = 0.002). Sarcopenia is an independent risk factor for long-term mortality in OTLCF patients received PKP surgery.

CONCLUSIONS

Patients with sarcopenia combined with OTLCF have poor postoperative recovery of limb function and a high risk of death in the long-term (36 months) after surgery. Active and effective intervention for sarcopenia is required during treatment.

Supervised Home-Based Exercise Prehabilitation in Unfit Patients Scheduled for Pancreatic Surgery: Protocol for a Multicenter Feasibility Study

BACKGROUND

Morbidity rates in pancreatic surgery are high, and frail patients with low aerobic capacity are especially at risk of complications and require prophylactic interventions. Previous studies of small patient cohorts receiving intra-abdominal surgery have shown that an exercise prehabilitation program increases aerobic capacity, leading to better treatment outcomes.

OBJECTIVE

In this study, we aim to assess the feasibility of a home-based exercise prehabilitation program in unfit patients scheduled for pancreatic surgery on a larger scale.

METHODS

In this multicenter study, adult patients scheduled for elective pancreatic surgery with a preoperative oxygen uptake (VO2) at the ventilatory anaerobic threshold ≤13 mL/kg/min or a VO2 at peak exercise ≤18 mL/kg/min will be recruited. A total of 30 patients will be included in the 4-week, home-based, partly supervised exercise prehabilitation program. The program comprises 25-minute high-intensity interval training on an advanced cycle ergometer 3 times a week. Training intensity will be based on steep ramp test performance (ie, a short-term maximal exercise test on a cycle ergometer), aiming to improve aerobic capacity. Twice a week, patients will perform functional task exercises to improve muscle function and functional mobility. A steep ramp test will be repeated weekly, and training intensity will be adjusted accordingly. Next to assessing the feasibility (participation rate, reasons for nonparticipation, adherence, dropout rate, reasons for dropout, adverse events, and patient and therapist appreciation) of this program, individual patients' responses to prehabilitation on aerobic capacity, functional mobility, body composition, quality of life, and immune system factors will be evaluated.

RESULTS

Recruitment for this study began in January 2022 and is expected to be completed in the summer of 2023.

CONCLUSIONS

Results of this study will provide important clinical and scientific knowledge on the feasibility of a partly supervised home-based exercise prehabilitation program in a vulnerable patient population. This might ease the path to implementing prehabilitation programs in unfit patients undergoing complex abdominal surgery, such as pancreatic surgery.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05496777; https://classic.clinicaltrials.gov/ct2/show/NCT05496777.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/46526.

Sarcopenia, More Than Just Muscle Atrophy: Imaging Methods for the Assessment of Muscle Quantity and Quality

BACKGROUND

Sarcopenia, a progressive reduction of muscle mass and function, is associated with adverse outcomes in the elderly. Sarcopenia and muscle atrophy are not equal processes. Low muscle strength in association with muscle quantity/quality reduction is currently the optimal method for assessing sarcopenia. There is a practical need for indirect measurement of muscle strength using state-of-the-art imaging techniques.

METHODS

The following provides a narrative, broad review of all current imaging techniques for evaluating muscles and identifying sarcopenia, including DEXA, CT, MRI, and high-resolution ultrasound, their main strengths, weaknesses, and possible solutions to problems regarding each technique.

RESULTS AND CONCLUSION

Well-recognized imaging methods for the assessment of muscle mass are explained, including evaluation with DEXA, CT, and MRI muscle quantity assessment, ultrasound evaluation of muscle thickness and CSA, and their correlations with established muscle mass calculation methods. A special focus is on imaging methods for muscle quality evaluation. Several innovative and promising techniques that are still in the research phase but show potential in the assessment of different properties of muscle quality, including MRI DIXON sequences, MRI spectroscopy, Diffusion Tensor Imaging, ultrasound echo intensity, ultrasound elastography, and speed-of-sound ultrasound imaging are briefly mentioned.

KEY POINTS

· Sarcopenia definition includes low muscle strength and low muscle quantity/quality.. · DEXA is a low-radiation method for whole-body composition measurement in a single image.. · CT has established cut-off values for muscle quality/quantity evaluation and sarcopenia diagnosis.. · MRI is the most sophisticated muscle quality assessment method capable of evaluating myosteatosis, myofibrosis, and microstructure.. · Ultrasound can evaluate muscle quality, including tissue architecture, and elasticity with excellent spatial resolution..

CITATION FORMAT

· Vasilevska Nikodinovska V, Ivanoski S, . Sarcopenia, More Than Just Muscle Atrophy: Imaging Methods for the Assessment of Muscle Quantity and Quality. Fortschr Röntgenstr 2023; 195: 777 - 789.

CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19

BACKGROUND

COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19.

RESEARCH QUESTION

Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score?

STUDY DESIGN AND METHODS

This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records.

RESULTS

Data from 578 patients were analyzed (64.6% men; mean age, 67.7 ± 13.5 years; 18.2% 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; P = .002) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; P = .013). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; 95% CI, 0.96-1.00; P = .038).

INTERPRETATION

CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment

Background

Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition.

Methods

A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95).

Results

The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result.

Conclusions

This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies

By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

INTRODUCTION

Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice.

MATERIALS AND METHODS

A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale.

RESULTS

Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating.

CONCLUSION

A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.

Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area

Background

Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.

Methods

This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).

Results

Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.

Conclusion

Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.

Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction

Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin’s Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland−Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65−0.87). Bland−Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland−Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

PURPOSE OF THE REVIEW

We aim to review the methods, current research evidence, and future directions in body composition analysis (BCA) with CT imaging.

RECENT FINDINGS

CT images can be used to evaluate muscle tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) compartments. Manual and semiautomatic segmentation methods are still the gold standards. The segmentation of skeletal muscle tissue and VAT and SAT compartments is most often performed at the level of the 3rd lumbar vertebra. A decreased amount of CT-determined skeletal muscle mass is a marker of impaired survival in many patient populations, including patients with most types of cancer, some surgical patients, and those admitted to the intensive care unit (ICU). Patients with increased VAT are more susceptible to impaired survival / worse outcomes; however, those patients who are critically ill or admitted to the ICU or who will undergo surgery appear to be exceptions. The independent significance of SAT is less well established. Recently, the roles of the CT-determined decrease of muscle mass and increased VAT area and epicardial adipose tissue (EAT) volume have been shown to predict a more debilitating course of illness in patients suffering from severe acute respiratory syndrome coronavirus 2 (COVID-19) infection.

SUMMARY

The field of CT-based body composition analysis is rapidly evolving and shows great potential for clinical implementation.

Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

BACKGROUND

Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume.

METHODS

All patients who have undergone radical cystectomy for urinary bladder cancer 2011-2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985).

RESULTS

Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07-2.44; p = 0.022). Logistic regression did not show any association with high-grade complications.

CONCLUSION

The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.

3D-Printed Replica and Porcine Explants for Pre-Clinical Optimization of Endoscopic Tumor Treatment by Magnetic Targeting

Simple Summary

Animal models are often needed in cancer research but some research questions may be answered with other models, e.g., 3D replicas of patient-specific data, as these mirror the anatomy in more detail. We, therefore, developed a simple eight-step process to fabricate a 3D replica from computer tomography (CT) data using solely open access software and described the method in detail. For evaluation, we performed experiments regarding endoscopic tumor treatment with magnetic nanoparticles by magnetic hyperthermia and local drug release. For this, the magnetic nanoparticles need to be accumulated at the tumor site via a magnetic field trap. Using the developed eight-step process, we printed a replica of a locally advanced pancreatic cancer and used it to find the best position for the magnetic field trap. In addition, we described a method to hold these magnetic field traps stably in place. The results are highly important for the development of endoscopic tumor treatment with magnetic nanoparticles as the handling and the stable positioning of the magnetic field trap at the stomach wall in close proximity to the pancreatic tumor could be defined and practiced. Finally, the detailed description of the workflow and use of open access software allows for a wide range of possible uses.

Abstract

Background: Animal models have limitations in cancer research, especially regarding anatomy-specific questions. An example is the exact endoscopic placement of magnetic field traps for the targeting of therapeutic nanoparticles. Three-dimensional-printed human replicas may be used to overcome these pitfalls. Methods: We developed a transparent method to fabricate a patient-specific replica, allowing for a broad scope of application. As an example, we then additively manufactured the relevant organs of a patient with locally advanced pancreatic ductal adenocarcinoma. We performed experimental design investigations for a magnetic field trap and explored the best fixation methods on an explanted porcine stomach wall. Results: We describe in detail the eight-step development of a 3D replica from CT data. To guide further users in their decisions, a morphologic box was created. Endoscopies were performed on the replica and the resulting magnetic field was investigated. The best fixation method to hold the magnetic field traps stably in place was the fixation of loops at the stomach wall with endoscopic single-use clips. Conclusions: Using only open access software, the developed method may be used for a variety of cancer-related research questions. A detailed description of the workflow allows one to produce a 3D replica for research or training purposes at low costs.