Preoperative Computed Tomography Imaging Research Articles

Association of mid-thigh quadriceps muscle quality with postoperative outcomes in patients with hip osteoarthritis undergoing total hip arthroplasty: a retrospective cohort study.

Muscle quality is more important than muscle mass for assessing physical function. Computed tomography (CT) is used to evaluate intramuscular fatty infiltration. The mid-thigh quadriceps CT attenuation values (CTV) expressed in Hounsfield units (HU) negatively correlate with physical function. Patients with hip osteoarthritis (HOA) have lower extremity muscle atrophy, including decreased cross-sectional area (CSA), CTV, and muscle strength. Using preoperative CT images, we investigated the association between mid-thigh quadriceps CSA, CTV, and postoperative outcomes in patients with HOA. This study included 62 patients who had unilateral HOA (62 hips) and underwent total hip arthroplasty (THA). We investigated the association between preoperative and postoperative Japanese Orthopaedic Association (JOA) hip scores, 12-item Short Form survey (SF-12), mid-thigh quadriceps CSA, and CTV. The mean age was 64.7 ± 10.1years, with 15 men (24.2%), and the mean body mass index was 24.3 ± 4.3kg/m2. Secondary HOA was present in 79.0% of patients. The mean CSA and CTV of the mid-thigh quadriceps on the operative side were 38.8 ± 9.8 cm2 and 40.3 ± 7.8 HU, respectively. Multiple regression analyses adjusted for age and sex showed that preoperative mid-thigh quadriceps CSA was not associated with preoperative and postoperative JOA hip scores or SF-12. The preoperative mid-thigh quadriceps CTV was associated with the postoperative JOA hip score in the gait ability domain and SF-12 in the physical component summary domain. Preoperative muscle quality is associated with postoperative outcomes in patients who have HOA regardless of age and sex.

Sarcopenia impacts perioperative and survival outcomes after esophagectomy for cancer: a multicenter study

BackgroundThe impact of sarcopenia on outcomes after esophagectomy is controversial. Most data are currently derived from Asian populations. This study aimed to correlate sarcopenia to short-term perioperative complication rates and long-term survival and recurrence outcomes. MethodsA retrospective analysis was performed of patients undergoing esophagectomy for cancer from 3 tertiary referral centers in Australia. Sarcopenia was defined using cutoffs for skeletal muscle index (SMI), assessed on preoperative computed tomography images. Outcomes measured included complications, overall survival (OS), and disease-free survival (DFS). ResultsOf 462 patients (78.4% male; median age, 67 years), sarcopenia was evident in 276 (59.7%). Patients with sarcopenia had a higher rate of major (Clavien-Dindo ≥ 3b) complications (27.9% vs 14.5%; P < .001), including higher rates of postoperative cardiac arrythmia (16.3% vs 9.7%; P = .042), pneumonia requiring antibiotics (14.5% vs 9.1%; P = .008), and 30-day mortality (5.1% vs 0%; P = .002). In the sarcopenic group, the median OS was lower (37 months [95% CI, 27.1–46.9] vs 114 months [95% CI, 75.8–152.2]; P < .001), as was the median DFS (27 months [95% CI, 18.9–35.1] vs 77 months [95% CI, 36.4–117.6]; P < .001). Sarcopenia was an independent risk factor for lower survival on multivariate analysis (hazard ratio, 1.688; 95% CI, 1.223–2.329; P = .001). ConclusionPatients with preoperative sarcopenia based on analysis of SMI are at a higher risk of major complications and have inferior survival and oncologic outcomes after esophagectomy for esophageal cancer.

Longitudinal Study on Pre- and Post-Operation CT Imaging for Predicting Pedicle Screw Loosening in Patients with Lumbar Degenerative Disease.

We conducted a longitudinal study to examine the predictive role of risk factors in the occurrence of pedicle screw loosening, assessed through pre- and post-operative computed tomography (CT) scans. A total of 103 patients with degenerative lumbar disease who had undergone L4/5 pedicle screw fixation (involving 412 screws) were included in this study. They were subsequently categorized into two groups-the "loosening group" and the "non-loosening group". The axial and sagittal angles of the screw trajectory in pre- and post-operative CT images were measured, and the deviation angles were computed. Additionally, measurements were taken of the Hounsfield unit (HU) within the screw entry point area, the pedicle, and the vertebral body in preoperative CT images. Logistic regression analysis was employed to ascertain the risk factors influencing the occurrence of screw loosening. Elderly patients who experienced screw loosening tended to have bilateral screw issues at the L5 level (p < 0.005). The HU of the pedicle (p < 0.001), age (p < 0.001), and the axial deviation angle (p = 0.014) were identified as independent factors predicting screw loosening. Additionally, when HU of the pedicle < 126.5 or age ≥ 53.5 years, the axial deviation angle was found to be smaller in the group experiencing screw loosening (p = 0.018 and p = 0.019). Loosening of screws positioned at L5 was found to be more prevalent in elderly patients, particularly exhibiting a bilateral occurrence. Independent predictors of this phenomenon included a low HU value in the pedicle, advanced age in patients, and a substantial axial deviation angle. In the case of elderly patients with a low HU value in the pedicle, a reduced axial surgical deflection was necessitated to prevent the occurrence of screw loosening.

Reproducibility and efficiency of liver volumetry using manual method and liver analysis software

BackgroundFor liver volumetry, manual tracing on computed tomography (CT) images is time-consuming and operator dependent. To overcome these disadvantages, several three-dimensional simulation software programs have been developed; however, their efficacy has not fully been evaluated. MethodsThree physicians performed liver volumetry on preoperative CT images on 30 patients who underwent formal right hepatectomy, using manual tracing volumetry and two simulation software programs, SYNAPSE and syngo.via. The future liver remnant (FLR) was calculated using each method of volumetry. The primary endpoint was reproducibility and secondary outcomes were calculation time and learning curve. ResultsThe mean FLR was significantly lower for manual volumetry than for SYNAPSE or syngo.via; there was no significant difference in mean FLR between the two software-based methods. Reproducibility was lower for the manual method than for the software-based methods. Mean calculation time was shortest for SYNAPSE. For the two physicians unfamiliar with the software, no obvious learning curve was observed for using SYNAPSE, whereas learning curves were observed for using syngo.via. ConclusionsLiver volumetry was more reproducible and faster with three-dimensional simulation software, especially SYNAPSE software, than with the conventional manual tracing method. Software can help even inexperienced physicians learn quickly how to perform liver volumetry.

Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study.

Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis. To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics. This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors. Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05). A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.

Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty.

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2-3 years, and 3-5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.

Preprocedural Computed Tomography Planning for Surgical Aortic Valve Replacement

BackgroundSelection of transcatheter valve size using preprocedural computed tomography (CT) is standardized and well established. However, valve sizing for surgical aortic valve replacement (SAVR) is currently performed intraoperatively by using sizers, which may result in variation among operators and risk for prosthesis-patient mismatch. This study evaluated the usefulness of CT annulus measurement for SAVR valve sizing. MethodsThis study included patients who underwent SAVR using Inspiris or Magna Ease and received preoperative electrocardiogram-gated CT imaging. Starting from June 2022, study investigators applied a CT sizing algorithm using CT-derived annulus size to guide minimum SAVR label size. The final decision of valve selection was left to the operating surgeon during SAVR. The study compared the appropriateness of valve selection (comparing implanted size with CT-predicted size) and prosthesis-patient mismatch rates without aortic root enlargement between 2 cohorts: 102 cases since June 2022 (CT sizing cohort) and 180 cases from 2020 to 2021 (conventional sizing cohort). ResultsImplanted size smaller than CT predicted size and severe prosthesis-patient mismatch were significantly lower by CT sizing than by conventional sizing (12% vs 31% [P = .001] and 0% vs 6% [P = .039], respectively). Interoperator variability was a factor associated with implanted size smaller than CT predicted with conventional sizing, whereas it became nonsignificant with CT sizing. ConclusionsApplying CT sizing to SAVR led to improved valve size selection, less prosthesis-patient mismatch, and less interoperator variability. CT sizing for SAVR could also be used to predict prosthesis-patient mismatch before SAVR and identify patients who need aortic root enlargement.

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study.

Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy. The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not. The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images

IntroductionThe automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. MethodsTo overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. Results and discussionBased on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.

Estimation of Cochlear Implant Insertion Depth Using 2D-3D Registration of Postoperative X-Ray and Preoperative CT Images.

To improve estimation of cochlear implant (CI) insertion depth in postoperative skull x-rays using synthesized information from preoperative CT scans. Retrospective cohort. Tertiary referral center. Ten adult cochlear implant recipients with preoperative and postoperative temporal bone computed tomography (CT)scans and postoperative skull x-ray imaging. Postoperative x-rays and digitally reconstructed radiographs (DRR) from preoperative CTs were registered using 3D Slicer and MATLAB to enhance localization of the round window and modiolus. Angular insertion depth (AID) was estimated in unmodified and registration-enhanced x-rays and DRRs in the cochlear view. Linear insertion depth (LID) was estimated in registered images by two methods that localized the proximal CI electrode or segmented the cochlea. Ground truth assessments were made in postoperative CTs. Errors of insertion depth estimates were calculated relative to ground truth measurements and compared with paired t t ests. Pearson correlation coefficient was used to assess inter-rater reliability of two reviewer's measurements of AID in unmodified x-rays. In postoperative x-rays, AID estimation errors were similar with and without registration enhancement (-1.3 ± 20.7° and -4.8 ± 24.9°, respectively; mean ± SD; p = 0.6). AID estimation in unmodified x-rays demonstrated strong interrater agreement (ρ = 0.79, p < 0.05) and interrater differences (-15.0 ± 35.3°) comparable to estimate errors. Registering images allowed measurement of AID in the cochlear view with estimation errors of 14.6 ± 30.6° and measurement of LID, with estimate errors that were similar between proximal electrode localization and cochlear segmentation methods (-0.9 ± 2.2 mm and -2.1 ± 2.7 mm, respectively; p = 0.3). 2D-3D image registration allows measurement of AID in the cochlear view and LID using postoperative x-rays and preoperative CT imaging. The use of this technique may reduce the need for postimplantation CT studies to assess these metrics of CI electrode position. Further work is needed to improve the accuracy of AID assessment in the postoperative x-ray view with registered images compared with established methods.

Surgical Treatment for Troublesome Mastoid Cavities: Canal Wall Reconstruction With Bony Obliteration Versus Subtotal Petrosectomy.

A chronically discharging modified radical mastoid cavity may require surgical intervention. We aim to explore two techniques. To compare outcomes of subtotal petrosectomy (STP) and canal wall reconstruction with bony obliteration technique (CWR-BOT). Retrospective cohort study. A tertiary referral center. All patients with a chronically discharging mastoid cavity surgically treated at the Radboud University Medical Center by STP or CWR-BOT in 2015 to 2020, excluding patients with preoperative cholesteatoma. Dry ear rate, audiometry, and rehabilitation. Healing time, number of postoperative visits, complications, cholesteatoma, and need for revision surgeries. Thirty-four (58%) patients underwent STP, and 25 (42%) CWR-BOT. A dry ear was established in 100% of patients (STP) and 87% (CWR-BOT). The air-bone gap (ABG) increased by 12 dB in STP, and decreased by 11 dB in CWR-BOT. Postoperative ABG of CWR-BOT patients was better when preoperative computed tomography imaging showed aerated middle ear aeration. ABG improvement was higher when ossicular chain reconstruction took place. Mean follow-up time was 32.5 months (STP) versus 40.5 months (CWR-BOT). Healing time was 1.2 months (STP) versus 4.1 months (CWR-BOT). The number of postoperative visits was 2.5 (STP) versus 5 (CWR-BOT). Cholesteatoma was found in 15% (STP) versus 4% (CWR-BOT) of patients. Complication rate was 18% (STP) and 24% (CWR-BOT) with a need for revision in 21% (STP) and 8% (CWR-BOT), including revisions for cholesteatoma. STP and CWR-BOT are excellent treatment options for obtaining a dry ear in patients with a chronically discharging mastoid cavity. This article outlines essential contributing factors in counseling patients when opting for one or the other. Magnetic resonance imaging with diffusion-weighted imaging follow-up should be conducted at 3 and 5 years postoperatively.

Remission of Nonalcoholic Fatty Liver Disease After Radical Surgery in Patients with Colorectal Cancer: A Single-Center Retrospective Study.

Purpose: The purpose of this study was to investigate the relationship between remission of nonalcoholic fatty liver disease (NAFLD) and radical surgery for colorectal cancer (CRC) patients. Methods: From January 2014 to December 2021, data of patients with concurrent CRC and NAFLD who underwent radical surgery in a single-center hospital were retrospectively collected. NAFLD was defined as a mean computed tomography (CT) liver attenuation value of <40 Hounsfield units (HUs). Comparison of preoperative and 1-year postoperative CT images was performed to evaluate the change of NAFLD. Multivariate logistic regression analysis was performed to identify independent predictive factors for NAFLD remission. The Kaplan-Meier method was used to estimate overall survival (OS) and disease-free survival (DFS) between the remission group and no remission group. Results: In this study, a total of 55 eligible patients were included. The remission group had 33 (60.0%) patients and the no remission group had 22 (40.0%) patients. The mean preoperative weight was 66.1 ± 9.9 kg. The mean preoperative body mass index (BMI) was 25.4 ± 2.5 kg/m2. We found that the average weight was significantly decreased (P < 0.01), average BMI was significantly decreased (P < 0.01), and HU score was significantly increased (P < 0.01). By comparing baseline characteristics between the remission group and no remission group, we found that the remission group exhibited larger tumor sizes (P = 0.036) than the no remission group. In the multivariate logistic regression analysis, we found that weight change was a predictor for NAFLD (odds ratio = 0.764, 95% confidence interval = 0.618-0.944, P = 0.013). We did not find any statistically significant differences in OS (P = 0.182) or DFS (P = 0.248) between the remission group and no remission group. Conclusions: The NAFLD remission rate reached 60.0% for CRC patients 1 year after radical surgery. In addition, we found that weight change was a predictor of NAFLD remission.

Hemodynamic-based virtual surgery design of double-patch repair for pulmonary arterioplasty in tetralogy of Fallot

Background and objectiveSurgical correction of pulmonary artery stenosis (PAS) is essential to the prognosis of patients with tetralogy of Fallot (TOF). The double-patch method of pulmonary arterioplasty is usually applied in case of multiple stenosis in TOF patients’ pulmonary artery (PA) and when PAS cannot be relieved by the single-patch method. The surgical planning for the double-patch design remains challenging. The purpose of this study is to investigate the double-patch design with different angulations between the left pulmonary artery (LPA) and the right pulmonary artery (RPA), and to understand postoperative hemodynamic alterations by the application of computer-aided design (CAD) and computational fluid dynamics (CFD) techniques. MethodsThe three-dimensional model of the PA was reconstructed based on preoperative computed tomography imaging data obtained from the patient with TOF. Three postoperative models with different designs of double-patch were created by “virtual surgery” using the CAD technique. Double-Patch 120 Model was created with double patches implanted in the main pulmonary artery (MPA) and the PA bifurcation and without changing the spatial position of PA. The angulation between the LPA and the RPA was defined as θ, which equaled to 120° in Pre-Operative Model and Double-Patch 120 Model. Based on Double-Patch 120 Model, Double-Patch 110 Model and Double-Patch 130 Model were generated with θ equaled to 110° and 130°, respectively. Combined with CFD, the differences of velocity streamlines, wall shear stress (WSS), flow distribution ratio (FDR), and energy loss (EL) were compared to analyze postoperative pulmonary flow characteristics. ResultsThe values of velocity and WSS decreased significantly after virtual surgery. Obvious vortices and swirling flows were observed downstream of the stenosis of RPA and LPA in Pre-Operative Model, while fewer vortices developed along the anterior wall of the expanded lumens of RPA, especially in Double-Patch 110 Model. With the relief of PAS, two relatively higher WSS regions were observed at the posterior walls of RPA and LPA. The maximum WSS values in these regions of Double-Patch 110 Model were lower than those in Double-Patch 120 Model and Double-Patch 130 Model. Furthermore, the FDRs were elevated and the ELs were greatly reduced. It was found that Double-Patch 110 Model with the angulation between the LPA and the RPA equaled to 110° showed relatively better properties of hemodynamics than other models. ConclusionsThe angulation between the LPA and the RPA is an important factor that should be integrated in the double-patch design for TOF repair. Virtual surgery based on patient-specific vascular model and computational hemodynamics can be used to provide assistance for individualized surgical planning of double-patch arterioplasty.

Preoperative 3-dimensional computed tomography bone density measures provide objective bone quality classifications for stemless anatomic total shoulder arthroplasty

Reproducible methods for determining adequate bone densities for stemless anatomic total shoulder arthroplasty (aTSA) are currently lacking. The purpose of this study was to evaluate the utility of preoperative computed tomography (CT) imaging for assessing the bone density of the proximal humerus for supportive differentiation in the decision making for stemless humeral component implantation. It was hypothesized that preoperative 3-dimensional (3-D) CT bone density measures provide objective classifications of the bone quality for stemless aTSA. A 3-part study was performed that included the analysis of cadaveric humerus CT scans followed by retrospective application to a clinical cohort and classification with a machine learning model. Thirty cadaveric humeri were evaluated with clinical CT and micro-CT (μCT) imaging. Phantom-calibrated CT data were used to extract 3-D regions of interest and defined radiographic scores. The final image processing script was applied retrospectively to a clinical cohort (n=150) that had a preoperative CT and intraoperative bone density assessment using the "thumb test," followed by placement of an anatomic stemmed or stemless humeral component. Postscan patient-specific calibration was used to improve the functionality and accuracy of the density analysis. A machine learning model (Support vector machine [SVM]) was utilized to improve the classification of bone densities for a stemless humeral component. The image processing of clinical CT images demonstrated good to excellent accuracy for cylindrical cancellous bone densities (metaphysis [ICC=0.986] and epiphysis [ICC=0.883]). Patient-specific internal calibration significantly reduced biases and unwanted variance compared with standard HU CT scans (P<.0001). The SVM showed optimized prediction accuracy compared with conventional statistics with an accuracy of 73.9% and an AUCof0.83 based on the intraoperative decision of the surgeon. The SVM model based on density clusters increased the accuracy of the bone quality classification to 87.3% with an AUC of 0.93. Preoperative CT imaging allows accurate evaluation of the bone densities in the proximal humerus. Three-dimensional regions of interest, rescaling using patient-specific calibration, and a machine learning model resulted in good to excellent prediction for objective bone quality classification. This approach may provide an objective tool extending preoperative selection criteria for stemless humeral component implantation.

The effects of sarcopenic obesity on immediate postoperative outcomes after pancreatoduodenectomy: a retrospective cohort study.

Several studies have evaluated the impact of sarcopenic obesity (SO) on postoperative complications, including postoperative pancreatic fistula (POPF), in patients undergoing pancreatoduodenectomy (PD). Previous studies have shown that SO increases POPF, but it remains unclear whether SO increases postoperative complications. In this study, we aimed to determine the relationship between SO and immediate postoperative complications. From January 2005 to December 2019, the medical records of patients who underwent PD for periampullary cancer were retrospectively reviewed. Skeletal muscle index (SMI) and visceral fat area (VFA) were calculated from preoperative computed tomography images. Patients with high VFA were classified as obese, while those with low SMI were classified as sarcopenic. Patients were divided into 4 groups: normal group, sarcopenia only group, obesity only group, and SO group. Postoperative outcomes were compared between groups, and factors affecting postoperative complications were analyzed by multivariate analysis. Normal group (n = 176), sarcopenia only group (n = 130), obesity only group (n = 207), and SO group (n = 117) were analyzed retrospectively. SO group had significantly more frequent major complications compared to the normal group (P = 0.006), as well as a significantly more frequent clinically relevant POPF compared to the other groups (P = 0.002). In multivariate analysis, SO was an independent risk factor for major complications (P = 0.008) and clinically relevant POPF (P = 0.003). SO is a factor associated with poor immediate postoperative outcomes after PD for periampullary cancer.

Effect of visceral obesity on outcomes of fluorescence-guided lymphadenectomy during laparoscopic gastrectomy for gastric cancer: Post hoc analysis of a randomized phase 3 trial.

To explore the impact of visceral fat area (VFA) on the short- and long-term efficacy of indocyanine green (ICG)-guided D2 lymphadenectomy for gastric cancer (GC). A post hoc analysis was performed in patients who participated in a phase 3 randomized clinical trial of ICG-guided laparoscopic radical gastrectomy vs. conventional laparoscopic radical gastrectomy from November 2018 to July 2019. The VFA was calculated based on preoperative computed tomography images. Short-term efficacy included the quality of lymph node (LN) dissection and surgical outcomes, while long-term efficacy included overall survival (OS) and recurrence-free survival (RFS). This study included 126 patients each in the ICG (high-VFA, n=43) and non-ICG groups (high-VFA, n=38). Compared with the non-ICG group, the ICG group had significantly more retrieved LNs (low-VFA: 50.1 vs. 43.9, P=0.001; high-VFA: 49.6 vs. 37.5, P<0.001) and a significantly lower LN noncompliance rate (low-VFA: 32.5% vs. 50.0%, P=0.020; high-VFA: 32.6% vs. 73.7%, P<0.001), regardless of the VFA. The ICG group had a shorter postoperative hospital stay and fewer intra-abdominal infections than the ICG group in the high-VFA patients (P=0.025 and P=0.020, respectively) but not in the low-VFA patients. Regardless of the VFA, the 3-year OS (RFS) was better in the ICG group than in the non-ICG group [low-VFA: 83.1% (76.9%) vs. 73.9% (67.0%); high-VFA: 90.7% (90.7%) vs. 73.7% (73.5%); P for interaction =0.474 (0.547)]. The short- and long-term efficacies of ICG tracing were not influenced by visceral obesity.

Navigated ultrasound bronchoscopy with integrated positron emission tomography-A human feasibility study.

Patients suspected to have lung cancer, undergo endobronchial ultrasound bronchoscopy (EBUS) for the purpose of diagnosis and staging. For presumptive curable patients, the EBUS bronchoscopy is planned based on images and data from computed tomography (CT) images and positron emission tomography (PET). Our study aimed to evaluate the feasibility of a multimodal electromagnetic navigation platform for EBUS bronchoscopy, integrating ultrasound and segmented CT, and PET scan imaging data. The proof-of-concept study included patients with suspected lung cancer and pathological mediastinal/hilar lymph nodes identified on both CT and PET scans. Images obtained from these two modalities were segmented to delineate target lymph nodes and then incorporated into the CustusX navigation platform. The EBUS bronchoscope was equipped with a sensor, calibrated, and affixed to a 3D printed click-on device positioned at the bronchoscope's tip. Navigation accuracy was measured postoperatively using ultrasound recordings. The study enrolled three patients, all presenting with suspected mediastinal lymph node metastasis (N1-3). All PET-positive lymph nodes were displayed in the navigation platform during the EBUS procedures. In total, five distinct lymph nodes were sampled, yielding malignant cells from three nodes and lymphocytes from the remaining two. The median accuracy of the navigation system was 7.7 mm. Our study introduces a feasible multimodal electromagnetic navigation platform that combines intraoperative ultrasound with preoperative segmented CT and PET imaging data for EBUS lymph node staging examinations. This innovative approach holds promise for enhancing the accuracy and effectiveness of EBUS procedures.

Preoperative computed tomography-imaging with patient-specific computer simulation in transcatheter aortic valve implantation: Design and rationale of the GUIDE-TAVI trial

Transcatheter aortic valve implantation (TAVI) is an established treatment option for patients with severe aortic valve stenosis, but is still associated with relatively high rates of pacemaker implantation and paravalvular regurgitation. Routine preoperative computed tomography (CT) combined with patient-specific computer modelling can predict the interaction between the TAVI device and the patient's unique anatomy, allowing physicians to assess the risk for paravalvular regurgitation and conduction disorders in advance to the procedure. The aim of this trial is to assess potential improvement in the procedural outcome of TAVI by applying CT-based patient-specific computer simulations in patients with suitable anatomy for TAVI. The GUIDE-TAVI trial is an international multicenter randomized controlled trial including patients accepted for TAVI by the Heart Team. Patients enrolled in the study will be randomized into 2 arms of each 227 patients. In patients randomized to the use of FEops HEARTGuide (FHG), patient-specific computer simulation with FHG is performed in addition to routine preoperative CT imaging and results of the FHG are available to the operator(s) prior to the scheduled intervention. In patients randomized to no use of FHG, only routine preoperative CT imaging is performed. The primary objective is to evaluate whether the use of FHG will reduce the incidence of mild to severe PVR, according to the Valve Academic Research Consortium 3. Secondary endpoints include the incidence of new conduction disorders requiring permanent pacemaker implantation, the difference between preoperative and final selected valve size, the difference between target and final implantation depth, change of preoperative decision, failure to implant valve, early safety composite endpoint and quality of life. The GUIDE-TAVI trial is the first multicenter randomized controlled trial to evaluate the value of 3-dimensional computer simulations in addition to standard preprocedural planning in TAVI procedures.

Three-dimensional body composition parameters using automatic volumetric segmentation allow accurate prediction of colorectal cancer outcomes.

Parameters obtained from two-dimensional (2D) cross-sectional images have been used to determine body composition. However, data from three-dimensional (3D) volumetric body images reflect real body composition more accurately and may be better predictors of patient outcomes in cancer. This study aimed to assess the 3D parameters and determine the best predictive factors for patient prognosis. Patients who underwent surgery for colorectal cancer (CRC) between 2010 and 2016 were included in this study. Preoperative computed tomography images were analysed using an automatic segmentation program. Body composition parameters for muscle, muscle adiposity, subcutaneous fat (SF) and abdominal visceral fat (AVF) were assessed using 2D images at the third lumbar (L3) level and 3D images of the abdominal waist (L1-L5). The cut-off points for each parameter were determined using X-tile software. A Cox proportional hazards regression model was used to identify the association between the parameters and the treatment outcomes, and the relative influence of each parameter was compared using a gradient boosting model. Overall, 499 patients were included in the study. At a median follow-up of 59months, higher 3D parameters of the abdominal muscles and SF from the abdominal waist were found to be associated with longer overall survival (OS) and disease-free survival (all P<0.001). Although the 3D parameters of AVF were not related to survival outcomes, patients with a high AVF volume and mass experienced higher rate of postoperative complications than those with low AVF volume (27.4% vs. 18.7%, P=0.021, for mass; 27.1% vs. 19.0%, P=0.028, for volume). Low muscle mass and volume (hazard ratio [HR] 1.959, P=0.016; HR 2.093, P=0.036, respectively) and low SF mass and volume (HR 1.968, P=0.008; HR 2.561, P=0.003, respectively), both in the abdominal waist, were identified as independent prognostic factors for worse OS. Along with muscle mass and volume, SF mass and volume in the abdominal waist were negatively correlated with mortality (all P<0.001). Both AVF mass and volume in the abdominal waist were positively correlated with postoperative complications (P<0.05); 3D muscle volume and SF at the abdominal waist were the most influential factors for OS. 3D volumetric parameters generated using an automatic segmentation program showed higher correlations with the short- and long-term outcomes of patients with CRC than conventional 2D parameters.

A spatial registration method based on 2D-3D registration for an augmented reality spinal surgery navigation system.

In order to provide accurate and reliable image guidance for augmented reality (AR) spinal surgery navigation, a spatial registration method has been proposed. In the AR spinal surgery navigation system, grayscale-based 2D/3D registration technology has been used to register preoperative computed tomography images with intraoperative X-ray images to complete the spatial registration, and then the fusion of virtual image and real spine has been realised. In the image registration experiment, the success rate of spine model registration was 90%. In the spinal model verification experiment, the surface registration error of the spinal model ranged from 0.361 to 0.612mm, and the total average surface registration error was 0.501mm. The spatial registration method based on 2D/3D registration technology can be used in AR spinal surgery navigation systems and is highly accurate and minimally invasive.