Detection of cancer-associated cachexia in lung cancer patients using whole-body [18F]FDG-PET/CT imaging: A multi-centre study.

BACKGROUND: Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non-imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole-body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC. METHODS: This multi-centre study included 345 LCP who underwent WB [18F]FDG-PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into 'No CAC' (WLGS-0/1 at baseline prior treatment and at first follow-up: N = 158, 51F/107M), 'Dev CAC' (WLGS-0/1 at baseline and WLGS-3/4 at follow-up: N = 90, 34F/56M), and 'CAC' (WLGS-3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake () and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [18F]FDG-PET/CT images. Imaging and non-imaging parameters from laboratory tests were compared statistically. A machine-learning (ML) model was then trained to classify LCP as 'No CAC', 'Dev CAC', and 'CAC' based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient. RESULTS: The three CAC categories displayed multi-organ differences in . In all target organs, was higher in the 'CAC' cohort compared with 'No CAC' (P < 0.01), except for liver and kidneys, where in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in of pancreas (+4%), skeletal-muscle (+7%), subcutaneous adipose-tissue (+11%), and visceral adipose-tissue (+15%). In 'CAC' patients, a strong negative Spearman correlation (ρ = -0.8) was identified between and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'. CONCLUSIONS: WB [18F]FDG-PET/CT imaging reveals groupwise differences in the multi-organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi-centre study has been initiated to address the limitations of the present retrospective analysis.

Is Automatic Tumor Segmentation on Whole-Body 18F-FDG PET Images a Clinical Reality?

The integration of automated whole-body tumor segmentation using 18F-FDG PET/CT images represents a pivotal shift in oncologic diagnostics, enhancing the precision and efficiency of tumor burden assessment. This editorial examines the transition toward automation, propelled by advancements in artificial intelligence, notably through deep learning techniques. We highlight the current availability of commercial tools and the academic efforts that have set the stage for these developments. Further, we comment on the challenges of data diversity, validation needs, and regulatory barriers. The role of metabolic tumor volume and total lesion glycolysis as vital metrics in cancer management underscores the significance of this evaluation. Despite promising progress, we call for increased collaboration across academia, clinical users, and industry to better realize the clinical benefits of automated segmentation, thus helping to streamline workflows and improve patient outcomes in oncology.

Tirzepatide modulates the regulation of adipocyte nutrient metabolism through long-acting activation of the GIP receptor.

Tirzepatide, a glucose-dependent insulinotropic polypeptide/glucagon-like peptide 1 receptor (GIPR/GLP-1R) agonist, has, in clinical trials, demonstrated greater reductions in glucose, body weight, and triglyceride levels compared with selective GLP-1R agonists in people with type 2 diabetes (T2D). However, cellular mechanisms by which GIPR agonism may contribute to these improved efficacy outcomes have not been fully defined. Using human adipocyte and mouse models, we investigated how long-acting GIPR agonists regulate fasted and fed adipocyte functions. In functional assays, GIPR agonism enhanced insulin signaling, augmented glucose uptake, and increased the conversion of glucose to glycerol in a cooperative manner with insulin; however, in the absence of insulin, GIPR agonists increased lipolysis. In diet-induced obese mice treated with a long-acting GIPR agonist, circulating triglyceride levels were reduced during oral lipid challenge, and lipoprotein-derived fatty acid uptake into adipose tissue was increased. Our findings support a model for long-acting GIPR agonists to modulate both fasted and fed adipose tissue function differentially by cooperating with insulin to augment glucose and lipid clearance in the fed state while enhancing lipid release when insulin levels are reduced in the fasted state.

Carboxyl-terminal sequences in APOA5 are important for suppressing ANGPTL3/8 activity.

Apolipoprotein AV (APOA5) lowers plasma triglyceride (TG) levels by binding to the angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its capacity to inhibit lipoprotein lipase (LPL) catalytic activity and its ability to detach LPL from binding sites within capillaries. However, the sequences in APOA5 that are required for suppressing ANGPTL3/8 activity have never been defined. A clue to the identity of those sequences was the presence of severe hypertriglyceridemia in two patients harboring an APOA5 mutation that truncates APOA5 by 35 residues ("APOA5Δ35"). We found that wild-type (WT) human APOA5, but not APOA5Δ35, suppressed ANGPTL3/8's ability to inhibit LPL catalytic activity. To pursue that finding, we prepared a mutant mouse APOA5 protein lacking 40 C-terminal amino acids ("APOA5Δ40"). Mouse WT-APOA5, but not APOA5Δ40, suppressed ANGPTL3/8's capacity to inhibit LPL catalytic activity and sharply reduced plasma TG levels in mice. WT-APOA5, but not APOA5Δ40, increased intracapillary LPL levels and reduced plasma TG levels in Apoa5-/- mice (where TG levels are high and intravascular LPL levels are low). Also, WT-APOA5, but not APOA5Δ40, blocked the ability of ANGPTL3/8 to detach LPL from cultured cells. Finally, an antibody against a synthetic peptide corresponding to the last 26 amino acids of mouse APOA5 reduced intracapillary LPL levels and increased plasma TG levels in WT mice. We conclude that C-terminal sequences in APOA5 are crucial for suppressing ANGPTL3/8 activity in vitro and for regulating intracapillary LPL levels and plasma TG levels in vivo.

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Direct Patlak Reconstruction of [68Ga]Ga-PSMA PET for the Evaluation of Primary Prostate Cancer Prior Total Prostatectomy: Results of a Pilot Study.

To investigate the use of kinetic parameters derived from direct Patlak reconstructions of [68Ga]Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) to predict the histological grade of malignancy of the primary tumor of patients with prostate cancer (PCa). Thirteen patients (mean age 66 ± 10 years) with a primary, therapy-naïve PCa (median PSA 9.3 [range: 6.3-130 µg/L]) prior radical prostatectomy, were recruited in this exploratory prospective study. A dynamic whole-body [68Ga]Ga-PSMA-11 PET/CT scan was performed for all patients. Measured quantification parameters included Patlak slope (Ki: absolute rate of tracer consumption) and Patlak intercept (Vb: degree of tracer perfusion in the tumor). Additionally, the mean and maximum standardized uptake values (SUVmean and SUVmax) of the tumor were determined from a static PET 60 min post tracer injection. In every patient, initial PSA (iPSA) values that were also the PSA level at the time of the examination and final histology results with Gleason score (GS) grading were correlated with the quantitative readouts. Collectively, 20 individual malignant prostate lesions were ascertained and histologically graded for GS with ISUP classification. Six lesions were classified as ISUP 5, two as ISUP 4, eight as ISUP 3, and four as ISUP 2. In both static and dynamic PET/CT imaging, the prostate lesions could be visually distinguished from the background. The average values of the SUVmean, slope, and intercept of the background were 2.4 (±0.4), 0.015 1/min (±0.006), and 52% (±12), respectively. These were significantly lower than the corresponding parameters extracted from the prostate lesions (all p < 0.01). No significant differences were found between these values and the various GS and ISUP (all p > 0.05). Spearman correlation coefficient analysis demonstrated a strong correlation between static and dynamic PET/CT parameters (all r ≥ 0.70, p < 0.01). Both GS and ISUP grading revealed only weak correlations with the mean and maximum SUV and tumor-to-background ratio derived from static images and dynamic Patlak slope. The iPSA demonstrated no significant correlation with GS and ISUP grading or with dynamic and static PET parameter values. In this cohort of mainly high-risk PCa, no significant correlation between [68Ga]Ga-PSMA-11 perfusion and consumption and the aggressiveness of the primary tumor was observed. This suggests that the association between SUV values and GS may be more distinctive when distinguishing clinically relevant from clinically non-relevant PCa.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.

Comparison of cardiac image-derived input functions for quantitative whole body [18F]FDG imaging with arterial blood sampling.

Introduction: Dynamic positron emission tomography (PET) and the application of kinetic models can provide important quantitative information based on its temporal information. This however requires arterial blood sampling, which can be challenging to acquire. Nowadays, state-of-the-art PET/CT systems offer fully automated, whole-body (WB) kinetic modelling protocols using image-derived input functions (IDIF) to replace arterial blood sampling. Here, we compared the validity of an automatic WB kinetic model protocol to the reference standard arterial input function (AIF) for both clinical and research settings. Methods: Sixteen healthy participants underwent dynamic WB [18F]FDG scans using a continuous bed motion PET/CT system with simultaneous arterial blood sampling. Multiple processing pipelines that included automatic and manually generated IDIFs derived from the aorta and left ventricle, with and without motion correction were compared to the AIF. Subsequently generated quantitative images of glucose metabolism were compared to evaluate performance of the different input functions. Results: We observed moderate to high correlations between IDIFs and the AIF regarding area under the curve (r = 0.49-0.89) as well as for the cerebral metabolic rate of glucose (CMRGlu) (r = 0.68-0.95). Manual placing of IDIFs and motion correction further improved their similarity to the AIF. Discussion: In general, the automatic vendor protocol is a feasible approach for the quantification of CMRGlu for both, clinical and research settings where expertise or time is not available. However, we advise on a rigorous inspection of the placement of the volume of interest, the resulting IDIF, and the quantitative values to ensure valid interpretations. In protocols requiring longer scan times or where cohorts are prone to involuntary movement, manual IDIF definition with additional motion correction is recommended, as this has greater accuracy and reliability.

Sex-specific radiomic features of L-[S-methyl-11C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability.

Introduction: Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl-11Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status. Methods: MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated. Results: There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well. Discussion: We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers.

Feasibility of dose reduction for [18F]FDG-PET/MR imaging of patients with non-lesional epilepsy.

The aim of the study was to evaluate the effect of reduced injected [18F]FDG activity levels on the quantitative and diagnostic accuracy of PET images of patients with non-lesional epilepsy (NLE).Nine healthy volunteers and nine patients with NLE underwent 60-min dynamic list-mode (LM) scans on a fully-integrated PET/MRI system. Injected FDG activity levels were reduced virtually by randomly removing counts from the last 10-min of the LM data, so as to simulate the following activity levels: 50 %, 35 %, 20 %, and 10 % of the original activity. Four image reconstructions were evaluated: standard OSEM, OSEM with resolution recovery (PSF), the A-MAP, and the Asymmetrical Bowsher (AsymBowsher) algorithms. For the A-MAP algorithms, two weights were selected (low and high). Image contrast and noise levels were evaluated for all subjects while the lesion-to-background ratio (L/B) was only evaluated for patients. Patient images were scored by a Nuclear Medicine physician on a 5-point scale to assess clinical impression associated with the various reconstruction algorithms.The image contrast and L/B ratio characterizing all four reconstruction algorithms were similar, except for reconstructions based on only 10 % of total counts. Based on clinical impression, images with diagnostic quality can be achieved with as low as 35 % of the standard injected activity. The selection of algorithms utilizing an anatomical prior did not provide a significant advantage for clinical readings, despite a small improvement in L/B (< 5 %) using the A-MAP and AsymBowsher reconstruction algorithms.In patients with NLE who are undergoing [18F]FDG-PET/MR imaging, the injected [18F]FDG activity can be reduced to 35 % of the original dose levels without compromising.

A 2022 International Survey on the Status of Prostate Cancer Theranostics.

Growing interest in PSMA imaging using [68Ga]- or [18F]-labeled ligands and PSMA-based radioligand therapy (RLT) of prostate cancer (PCa) prompted us to survey the global community on their experiences and expectations. Methods: A web-based survey was composed to interrogate areas specific to PET imaging, the clinical value chain, and RLT applications. International responses were collected in early 2022. In total, over 300 valid responses were received and evaluated. Results: Most responses (83%) were given by nuclear medicine specialists with extensive experience in PET. At 22% of sites, PCa ranked "top" in cancer-type-specific PET indications, with an average and median of 15% and 10% of all cases, respectively. The most frequently used PSMA PET tracers were [68Ga]PSMA (32%) and [18F]PSMA-1007 (31%). Users reported a steady growth in PSMA PET and RLT over the past 5 y, averaging 50% and 82%, respectively, with a further 100% median growth projected over the next 5 y. Of note, more respondents indicated cognizance of personalized dosimetry than actually used it routinely. The most commonly identified barriers to future growth in PCa theranostics were radiopharmaceutical supply, reimbursement, staff availability, and buy-in of medical oncologists. Conclusion: Despite enthusiasm, this survey indicates variable adoption of PSMA imaging and RLT globally. Several challenges need to be addressed by the medical community, authorities, and patient advocacy groups in integrating PSMA-targeted theranostics into personalized medicine.

Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer.

PURPOSE: Head and neck squamous cell carcinomas (HNSCCs) are a molecularly, histologically, and clinically heterogeneous set of tumors originating from the mucosal epithelium of the oral cavity, pharynx, and larynx. This heterogeneous nature of HNSCC is one of the main contributing factors to the lack of prognostic markers for personalized treatment. The aim of this study was to develop and identify multi-omics markers capable of improved risk stratification in this highly heterogeneous patient population. METHODS: In this retrospective study, we approached this issue by establishing radiogenomics markers to identify high-risk individuals in a cohort of 127 HNSCC patients. Hybrid in vivo imaging and whole-exome sequencing were employed to identify quantitative imaging markers as well as genetic markers on pathway-level prognostic in HNSCC. We investigated the deductibility of the prognostic genetic markers using anatomical and metabolic imaging using positron emission tomography combined with computed tomography. Moreover, we used statistical and machine learning modeling to investigate whether a multi-omics approach can be used to derive prognostic markers for HNSCC. RESULTS: Radiogenomic analysis revealed a significant influence of genetic pathway alterations on imaging markers. A highly prognostic radiogenomic marker based on cellular senescence was identified. Furthermore, the radiogenomic biomarkers designed in this study vastly outperformed the prognostic value of markers derived from genetics and imaging alone. CONCLUSION: Using the identified markers, a clinically meaningful stratification of patients is possible, guiding the identification of high-risk patients and potentially aiding in the development of effective targeted therapies.

Application of a Deep Learning Approach to Analyze Large-Scale MRI Data of the Spine.

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future.

Automated data preparation for in vivo tumor characterization with machine learning.

Background: This study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts. Methods: A collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts. Results: Sixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps. Conclusions: This study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings.

Elective transfemoral amputation and simultaneous implantation of a transcutaneous osseointegrated prosthesis stem as salvage treatment after knee joint arthrodesis with poor function: A case report.

Background: Surgical reconstruction of anterior cruciate ligament ruptures is a well-established procedure, and although it is for the vast majority of patients without severe complications, total knee joint arthroplasty, arthrodesis of the knee, and finally transfemoral amputation have to be considered in the worst-case scenario. The case: We report a case of a patient with a 13-year history of recurrent failure after anterior cruciate ligament reconstruction. She claimed she had severely impaired mobility secondary to a knee joint arthrodesis via an Ilizarov circular frame 2 years ago and chronic immobilizing pain, making a permanent medication with opioids necessary. She was aware of the therapeutic options and asked for transfemoral amputation and concomitant supply with a transcutaneous osseointegrated prosthesis system (TOPS). Procedures: After careful evaluation and clinical work-up, the indication for transfemoral amputation and concomitant implantation of the prosthetic stem into the femoral cavity was secured. Six weeks after the creation of the stoma for coupling of the artificial limb and onset of physiotherapy, balance and gait training were scheduled. Full weight-bearing and walking without crutches were allowed 12 weeks after the index procedure. This sequence of events was paralleled by a series of pre-defined examinations, that is, questionnaires and mobility scores addressing the situation of transfemoral amputees, as well as standardized clinical gait analysis. The latter was performed before surgery and 6, 9, and 18 months after the index procedure. Outcome: At the time of the index procedure, opioids could be tapered to zero, and the patient quickly regained her walking abilities during the rehabilitation period. Clinical gait analysis confirmed the restoration of bilateral symmetry by mutual approximation of kinematics and kinetics to a standard gait pattern. Conclusion: The outcome of our patient strengthens the therapeutic potential of a unilateral transfemoral amputation in combination with TOPS. Nevertheless, long-term follow-up is necessary to detect future complications of this approach.