Volumetric Assessment Research Articles

Three-dimensional freehand ultrasonography to measure muscle volume of the lumbar multifidus: Reliability of processing technique and validity through comparison to magnetic resonance imaging.

There is a growing interest in muscle characteristics of the lumbar multifidus related to low back pain, but findings between studies are inconsistent. One of the issues explaining these conflicting findings might be the use of two-dimensional measures of cross-sectional area and thickness of the lumbar multifidus in most studies, which might be a suboptimal representation of the entire muscle volume. A three-dimensional volumetric assessment, combined with standardized imaging and processing measurement protocols, is highly recommended to quantify spinal muscle morphology. Three-dimensional freehand ultrasonography is a technique with large potential for daily clinical practice. It is achieved by combining conventional two-dimensional ultrasound with a motion-tracking system, recording the position and orientation of the ultrasound transducer during acquisition, resulting in a three-dimensional reconstruction. This study investigates intra- and interprocessor reliability for the quantification of muscle volume of the lumbar multifidus based on three-dimensional freehand ultrasound and its validity, in 31 patients with low back pain and 20 healthy subjects. Two processors manually segmented the lumbar multifidus on three-dimensional freehand ultrasound images using Stradwin software following a well-defined method. We assessed the concurrent validity of the measurement of multifidus muscle volume using three-dimensional freehand ultrasound compared with magnetic resonance imaging in 10 patients with low back pain. Processing reliability and agreement were determined using intraclass correlation coefficients, Bland-Altman plots, and calculation of the standard error of measurement and minimal detectable change, while validity was defined based on correlation analysis. The processing of three-dimensional freehand ultrasound images to measure lumbar multifidus volume was reliable. Good to excellent intraclass correlation coefficients were found for intraprocessor reliability. For interprocessor reliability, the intraclass correlation coefficients were moderate to good, emphasizing the importance of processing guidelines and training. A single processor analysis is preferred in clinical studies or when small differences in muscle volume are expected. The correlation between magnetic resonance imaging and three-dimensional freehand ultrasound measurements of lumbar multifidus volume was moderate to good but with a systematically smaller multifidus volume measured on three-dimensional freehand ultrasound. These results provide opportunities for both researchers and clinicians to reliably assess muscle structure using three-dimensional freehand ultrasound in patients with low back pain and to monitor changes related to pathology or interventions. To allow implementation in both research and clinical settings, guidelines on three-dimensional freehand ultrasound processing and training were provided.

10018-COT-4 COMPARATIVE VOLUMETRIC ANALYSES FOLLOWING BEVACIZUMAB THERAPY FOR A PATIENT WITH CO- INCIDENCE OF GLIOBLASTOMA, MENINGIOM

Abstract BACKGROUND Given that glioblastoma, meningioma and dural arteriovenous fistura (dAVF) represent angiogenic disease mainly caused by high expression of vascular endothelial growth factor (VEGF), bevacizumab is expected to be effective for these diseases. We experienced a patient with co-incidence of these 3 diseases was given neoadjuvant bevacizumab which provided a volume reduction of glioblastoma with improvement of clinical symptoms. CASE REPORT An eighty five year-old man has suffered from aphasia and dementia. Brain MRI showed a ring enhanced intraaxial tumor with perifocal edema in left temporal lobe, a dural-attached extraaxial tumor in the left sphenoid ridge and dAVF at left transverse-sigmoid sinus. Due to elderly with poor general condition, he was treated by administrate bevacizumab rather than the resection to confirm histological diagnosis. Three days after the administration, he began to walk and speech. RESULTS For comparison of the reactivity to bevacizumab among these three diseases, we calculated the volumetric quantitative assessment of glioblastoma and meningioma by the contrast-enhanced MRI and made a qualitative assesment for the dAVF by digital subtraction angiography (DSA) and MRA with comparison between before and afterbevacizumab administration. The volume reduction rates 1 and 5 months later after bevacizumab administration in glioblastoma and in meningioma were 0.34% and 94.5%, 13.7%, 6.8%, respectively. The volume reduction rate of glioblastoma was significantly larger than of Mg. The DSA showed the dAVF did not change even though bevacizumab therapy. DISCUSSION As previously reported, the VEGF concentration in the glioblastoma was significantly higher than that in other types of tumors including meningioma. In dAVF, other angiogenic factors would be produced in addition to VEGF. CONCLUSIONS Based on these evidences and the current rare case with co-incidence of glioblastoma, meningioma, and dAVF, it suggested that the effectiveness of bevacizumab therapy would be depend on the expression level of the VEGF concentration.

Deep learning-based segmentation of multisite disease in ovarian cancer

PurposeTo determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.MethodsA deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.ResultsOur model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10–7, 3 × 10–4, 4 × 10–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.ConclusionAutomated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.Relevance statementAutomated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.Key points• The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented.• Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists.• Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.Graphical

ADPrep ‐ A fully automated Alzheimer’s Disease Neuroimaging Preprocessing Pipeline for MRI and multi‐tracer PET data

AbstractBackgroundState‐of‐the‐art preprocessing of MRI and PET data is crucial for Alzheimer’s disease (AD) neuroimaging research. Therefore, standardization and harmonization of neuroimaging preprocessing across sites is key to generate comparable and sharable datasets and to reduce potential bias introduced by different preprocessing strategies. Further, neuroimaging preprocessing requires strong expertise in different programming languages (R/MATLAB/Python/Bash) and software packages (SPM/FSL/AFNI/FreeSurfer), i.e., a major barrier for non‐expert users. Therefore, we developed a docker‐based, state‐of‐the‐art, fully automated, neuroimaging toolbox that integrates robust preprocessing of structural/functional MRI, and multi‐tracer PET (amyloid/tau/FDG/TSPO), generating easy to use spreadsheet and nifti outputs. The toolbox requires no programming expertise and can facilitate neuroimaging analyses, data harmonization and sharing across the AD neuroimaging community.MethodThe pipeline works on standardized bids‐formatted data and was fully developed in nipype (see Figure 1). Preprocessing for structural MRI includes volumetric and cortical thickness assessments for widely used brain atlases (Desikan‐Killiany/Schaefer/LPBA/Hammers/Neuromorphometrics/Cobra/Destrieux), plus spatially normalized and smoothed grey/white‐matter segments for voxel‐based morphometry and generation of subject‐specific PET‐reference regions. Functional MRI processing includes slice‐timing and motion correction, nuisance regression, spatial normalization, and functional connectivity assessments for the above‐mentioned atlases. PET processing includes generation of spatially normalized SUVR images for different tracer‐specific reference regions, as well as extraction of atlas‐based SUVRs (PVE and non‐PVE‐corrected). Standardized imaging outputs in FreeSurfer space were benchmarked against available ADNI data to illustrate comparability with existing pipelines (see Figure 2). Cluster implementation is provided to ensure large‐scale data processing.ResultThe toolbox was tested successfully on large‐scale multimodal datasets, including several thousand scans from ADNI, ADNI‐DOD, and A4 with an overall processing failure rate of <4%. Using ADNI data as a benchmark, our data closely reproduce global amyloid‐PET (r = 0.99, p<0.001, Fig.2A) and temporal meta tau‐PET SUVRs (r = 0.98, p<0.001, Fig.2B), suggesting that PET‐thresholds can be applied. Runtime is ∼1h for a structural MRI and ∼5hrs for a PET including PVE‐correction.ConclusionWe propose a user‐friendly multimodal robust neuroimaging preprocessing toolbox, that can be applied to different neuroimaging datasets by non‐expert users. The toolbox provides outputs that can be directly used for statistical analyses. Future work involves implementing other imaging modalities, e.g., diffusion tensor imaging.

Olfactory groove meningiomas: supraorbital keyhole versus orbitofrontal, frontotemporal, or bifrontal approaches.

Olfactory groove meningiomas (OGMs) often require surgical removal. The introduction of recent keyhole approaches raises the question of whether these tumors may be better treated through a smaller cranial opening. One such approach, the supraorbital keyhole craniotomy, has never been compared with more traditional open transcranial approaches with regard to outcome. In this study, the authors compared clinical, radiographic, and functional quality of life (QOL) outcomes between the keyhole supraorbital approach (SOA) and traditional transcranial approach (TTA) for OGMs. They sought to examine the potential advantages and disadvantages of open/TTA versus keyhole SOA for the resection of OGMs in a relatively case-matched series of patients. A retrospective, single-institution review of 57 patients undergoing a keyhole SOA or larger traditional transcranial (frontotemporal, pterional, or bifrontal) craniotomy for newly diagnosed OGMs between 2005 and 2023 was performed. Extent of resection, olfaction, length of stay (LOS), radiographic volumetric assessment of postoperative vasogenic and cytotoxic edema, and QOL (using the Anterior Skull Base Questionnaire) were assessed. Thirty-two SOA and 25 TTA patients were included. The mean EOR was not significantly different by approach (TTA: 99.1% vs SOA: 98.4%, p = 0.91). Olfaction was preserved or improved at similar rates (TTA: 47% vs SOA: 43%, p = 0.99). The mean LOS was significantly shorter for SOA patients (4.1 ± 2.8 days) than for TTA patients (9.4 ± 11.2 days) (p = 0.002). The authors found an association between an increase in postoperative FLAIR cerebral edema and TTA (p = 0.031). QOL as assessed by the ASQB at last follow-up did not differ significantly between groups (p = 0.74). The keyhole SOA was associated with a statistically significant decrease in LOS and less postoperative edema relative to traditional open approaches.

Developing Topics.

Amyloid-negative amnestic mild cognitive impairment (MCI) patients have a conversion rate to dementia of approximately 10% within 2 years. This study aimed to investigate whether brain age, calculated using quantitative data obtained from brain magnetic resonance imaging (MRI) artificial intelligence (AI) software can be an important factor in predicting the conversion of dementia in amyloid-negative amnestic MCI patients. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain MRI, and [18F]-florbetaben (FBB) positron emission tomography (PET) scan. Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Brain age was determined through volumetric assessment of 12 distinct brain regions using commercially available segmentation software, Neurophet AQUA. During the follow-up period, 38% (35/91) of patients converted to dementia from amnestic MCI. 73% (66/91) of patients had a higher brain age than their actual chronological age. In the conversion group, all participants except three (91%, 32/35) had a brain age older than their chronological age. Conversely, the conversion rate to dementia was significantly lower in the group with a younger brain age than their actual age. The subjects were divided into eight groups based on sex, education level, and brain age. When defining the 'survival' as the non-conversion of MCI to dementia, these groups significantly differed in survival probability with a p-value of 0.036. The low-educated female group with a higher brain age than their actual age had the lowest survival rate of all groups. Our findings suggest that MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.

Usefulness of nasal cavity evaluation before high-resolution esophageal manometry in high-risk patients.

A catheter is inserted through the nasal cavity during high-resolution esophageal manometry (HRM), which may cause adverse events such as pain or epistaxis. Despite these possible safety considerations, studies on this subject are very limited. We aimed to investigate the usefulness of nasal cavity evaluation before HRM to reduce the risk of adverse events and test failure. Patients who underwent HRM after consultation with the ear-nose-throat department for nasal evaluation were retrospectively enrolled between December 2021 and May 2022. The included patients had a previous history of sinonasal disease or surgery or had subjective nasal discomfort. All patients answered the Sino-Nasal Outcome Test (SNOT-22) questionnaire, and subjective nasal discomfort was scored using a visual analog scale. Nasal endoscopy and acoustic rhinometry were performed for disease evaluation and volumetric assessment. The analysis included 22 patients with a mean age of 58.9 years. The mean SNOT-22 score was 24.2, and 16 patients (72.7%) complained of subjective nasal obstruction. The HRM catheter was successfully inserted in 20 patients (90.9%), without any significant adverse events. The objective measurement outcomes of acoustic rhinometry and sinus endoscopy did not always correspond to subjective symptoms. Narrowed nasal airways unresponsive to decongestants were observed in two patients with failed catheter insertion. To reduce the risk of adverse events and test failure during HRM, a site-specific questionnaire to evaluate nasal obstruction might be helpful. When nasal obstruction is suspected, objective nasal cavity evaluation could be recommended for the safe and successful performance of HRM.

A longitudinal analysis of brain volume changes in myelin oligodendrocyte glycoprotein antibody-associated disease.

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a relapsing demyelinating condition. There are several cross-sectional studies showing evidence of brain atrophy in people with MOGAD (pwMOGAD), but longitudinal brain volumetric assessment is still an unmet need. Current recommendations do not include monitoring with MRI and assume distinct attacks. Evidence of ongoing axon loss will have diagnostic and therapeutic implications. In this study, we assessed brain volume changes in pwMOGAD over a mean follow-up period of 2years and compared this to changes in people with multiple sclerosis (pwMS). This is a retrospective single-center study over a 7-year period from 2014 to 2021. MRI brain scans at the time of diagnosis and follow-up in remission were collected from 14 Caucasian pwMOGAD, confirmed by serum myelin oligodendrocyte glycoprotein immunoglobulin G antibody presence, detected by live cell-based assays. Total brain volume (TBV), white matter (WM), gray matter (GM), and demyelinating lesion volumes were assessed automatically using the Statistical Parametric Mapping and FMRIB automated segmentation tools. MRI brain scans at diagnosis and follow-up on remission were collected from 32-matched pwMS for comparison. Statistical analysis was done using analysis of variance. There is evidence of TBV loss, affecting particularly GM, over an approximately 2-year follow-up period in pwMOGAD (p<.05), comparable to pwMS. WM and lesion volume change over the same period were not statistically significant (p>.1). We found evidence of loss of GM and TBV over time in pwMOGAD, similar to pwMS, although the WM and lesion volumes were unchanged.

Efficacy of 2% Paradichlorobenzene, 10% Citric Acid, 17% Ethylenediaminetetraacetic Acid, and 0.2% Chitosan at Removing Calcium Hydroxide From the Root Canals.

Chronic infections often require the use of an intracanal medicament. Calcium hydroxide is the most commonly used intracanal medicament. However, retrieval of calcium hydroxide (CH) medicaments is a challenge. This study evaluated the efficacy of 2% paradichlorobenzene, 10% citric acid, 17% ethylenediaminetetraacetic acid (EDTA), and 0.2% chitosan in removing calcium hydroxide from the root canals. Forty single-rooted mandibular premolars extracted for orthodontic reasons were selected for the study. After access opening, cleaning and shaping were performed following a standardized irrigation protocol, and the samples were pre-weighed for baseline weight. CH was placed in canals until the apex, and the specimen was incubated at 37 °C and 100% relative humidity for seven days. These samples were weighed again for quantitative analysis and subjected to cone beam computed tomography (CBCT) for volumetric analysis using ITK SNAP software (Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA). The samples were randomly divided into four groups based on the irrigant used for the removal of medicament. Group 1: 2% paradichlorobenzene; group 2: 10% citric acid; group 3: 17% EDTA; and group 4: 0.2% chitosan, all in combination with sonic agitation. After the removal of CH, weight and volumetric analyses were repeated, and the percentage difference was calculated. Statistical analysis was done using the one-way ANOVA test for both weight and volumetric assessment, and the inter-group comparison was made using the post hoc Tukey test. The maximum retrieval was observed with 2% paradichlorobenzene by both weight (96.75%) and volumetric (91.42%) assessment, with p=0.00 and p=0.01, respectively. This was followed by 0.2% chitosan, 10% citric acid, and the least, 17% EDTA. Two percent paradichlorobenzene combined with sonic agitation was most efficient in removing CH, followed by 0.2% chitosan, 10% citric acid, and 17% EDTA chitosan.

Analysis of PET-CT Derived Radiomic Biomarkers with Efficacy, Safety, and Expansion of Axicabtagene Ciloleucel (Axi-Cel) in Patients with Relapsed or Refractory Large B-Cell Lymphoma (LBCL)

Introduction In LBCL, the presence of bulky disease is known to be an adverse prognostic factor, indicating poorer outcomes. The ZUMA-1 clinical trial (Neelapu, NEJM 2017) showed that high tumor burden, as measured by the sum of the product of diameters (SPD), was associated with lower durable response after Chimeric Antigen Receptor T-Cell Therapy (CAR T ). Radiomic biomarkers such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been recently studied to assess three-dimensional measurement of tumor activity and metabolic volume in LBCL. There is a paucity of understanding on the impact of radiomic biomarkers on outcomes and CAR T expansion kinetics in LBCL CAR T recipients. We studied the association of these radiomic biomarkers with efficacy, toxicity, and CAR T expansion in this single institution retrospective study of patients with LBCL who received axi-cel. Methods 189 patients with relapsed or refractory LBCL who were treated with axi-cel from 12/2017 to 5/2022 were studied. The closest PET-CT scan to CAR T leukapheresis date was identified for each patient. After excluding cases with unavailable pre-apheresis PET-CT scans, 172 patients were included in the statistical analysis. Two reviewers independently used a semi-automated contouring software (MIM, v. 7.1.2) to measure maximum standardized uptake value (SUV Max), mean SUV, total MTV and TLG values of malignant lesions. CD19 CAR T expansion was measured by real-time flow cytometry with anti-idiotype-FMC63 conjugated to Dylight 65013. Areas under the curve (AUCs) were calculated for CAR T expansion in the 28 days following infusion using the linear trapezoidal method on raw values, and the results were transformed using a base-10 logarithm. Patients were categorized into high and low biomarker groups based on whether their values exceeded or were below the respective median. For univariate analyses, continuous variables were compared using a Mann-Whitney U-test, categorical using a Chi-square test, and time-to-event using a log-rank test. Within a multivariable framework, regressions (linear, logistic, Cox) were fit to control for potential confounders. Results Median duration of follow up was 33 months (IQR: 17.9, 44.7). MTV was highly correlated with TLG (r=0.87). High MTV was associated with bulky disease defined as &amp;gt;10cm (p&amp;lt;0.01), high IPI at apheresis (p&amp;lt;0.04), bridging treatment (p&amp;lt;0.01), pre-lymphodepletion lactate dehydrogenase (p&amp;lt;0.01), and autologous stem cell transplant (p=0.02). Complete response (CR) rate was greater for those in the low MTV group (79.3%) compared to high MTV group (52.3%), (p&amp;lt;0.01), as well as those with high TLG (77.9%) compared to low TLG group (46%), (p&amp;lt;0.01). Low MTV was significantly associated with better progression free survival (PFS) in a univariate analysis (p=0.03); median PFS was 33.5 months (IQR 3.06, 39.55) for low MTV versus 6.1 months (IQR 6.38, NA) for high MTV. Low MTV was also significantly associated with better overall survival (OS) in a univariate analysis (p&amp;lt;0.01); median OS was not reached for the low MTV group but was 31.8 months for the high MTV group (IQR 16.5, NA) (Figure 1). After multivariable regression analysis, PFS was no longer different by MTV biomarker level ( p= 0.07), but OS was still significantly different ( p= 0.01). Neither PFS nor OS were significantly associated with the binary TLG variable. The proportion of cytokine release syndrome (CRS) or immune cell associated neurologic toxicity syndrome (ICANS) of any grade or severe grade ICANS were significantly higher for the high MTV and TLG groups as compared to their respective low groups. Length of stay was significantly longer for those in the high MTV and TLG groups (Table 1). A higher day 14 CAR T cell expansion was seen in the high TLG as compared to the low group (0.89 vs 0.63 cells/μL, p=0.002). However, there was no statistically significant difference in CAR T expansion as measured by AUC between the high and low MTV or TLG cohort. Conclusion Our study demonstrates poor outcomes for efficacy, safety, and healthcare utilization endpoints in patients with high tumor bulk as measured by volumetric tumor assessment on PET-CT. Furthermore, we show a differential peripheral day 14 CAR T expansion with higher expansion seen in patients with higher tumor bulk. Strategies like tumor debulking prior to CAR T therapy and earlier referral in patients with high tumor bulk may improve clinical outcomes.

Evaluation of upper airway characteristics in patients with and without sleep apnea using cone-beam computed tomography and computational fluid dynamics.

To determine if upper airway characteristics and airway pressure change significantly between low risk, healthy non-OSA subjects, and OSA subjects during respiration using cone-beam computed tomography (CBCT) imaging and steady-state k-ω model computational fluid dynamics (CFD) fluid flow simulations, respectively. CBCT scans were collected at both end-inhalation and end-exhalation for 16 low-risk non-OSA subjects and compared to existing CBCT data from 7 OSA subjects. The CBCT images were imported into Dolphin Imaging and the upper airway was segmented into stereolithography (STL) files for area and volumetric measurements. Subject models that met pre-processing criteria underwent CFD simulations using ANSYS Fluent Meshing (Canonsburg, PA) in which unstructured mesh models were generated to solve the standard dual equation turbulence model (k-ω). Objective and supplemental descriptive measures were obtained and statistical analyses were performed with both parametric and non-parametric tests to evaluate statistical significance at P < .05. Regarding area and volumetric assessments, there were statistically significant mean differences in Total Volume and Minimum CSA between non-OSA and OSA groups at inhalation and exhalation (P = .002, .003, .004, and .007), respectively. There were also statistically significant mean differences in volume and min CSA between the inhalation and exhalation for the non-OSA group (P < .001 and .002), respectively. While analysis of the CFD simulation was limited by the collected data available, a finding consistent with published literature was that the OSA subject group simulation models depicted the point of lowest pressure coinciding with the area of maximum constriction.

Large in-stream wood yield during an extreme flood (Storm Alex, October 2020, Roya Valley, France): Estimating the supply, transport, and deposition using GIS

During major floods, rivers erode their banks and thus recruit large wood pieces from the riparian zones. There is still a lack of knowledge about the transport of large wood, the volumes involved and the flux distribution, i.e. the large wood connectivity at catchment scale. During storm Alex (October 2020), the French Roya catchment (394 km2) experienced a paroxysmal morphogenic flood involving massive bank erosion. The riparian vegetation was largely recruited, with large wood contributing to logjams and bridge destruction. This paper presents a methodology for volumetric assessment of the large wood fluxes involved. Simple approaches are used to (i) quantify the inputs from stand density data from the national forest inventory and from source areas based on diachronic analysis of active channels highlighting the erosion of 87 ha of wooded areas; and (ii) quantify the volumes deposited via an exhaustive manual digitisation of 16,846 pieces of large wood deposited on 59 km of channels on the Roya and its tributaries. This catchment-scale, large wood connectivity analysis shows that the flood recruited and transported downstream a volume of around 14,000 m3 of large wood (uncertainty range: 7000–29,500 m3). Drone observations of the Roya River mouth in Italy and satellite images showing a raft of driftwood, several km long, drifting off the Roya River mouth in the aftermath of the flood corroborate our findings.

Analysis of baseline circulating tumor cells integrated with PET/CT findings in transplant-ineligible multiple myeloma

We aimed to improve prognostic predictors in transplant-ineligible multiple myeloma (TIE-MM) patients by combining baseline circulating clonal tumor cells (CTCs) and positron emission tomography/computed tomography (PET/CT) findings. The factors associated with prognosis were retrospectively investigated in 126 TIE-MM patients who underwent CTC quantification by multiparameter flow cytometry and PET/CT at the initial presentation. The total lesion glycolysis (TLG) level was calculated using Metavol software. The median percentage of CTC was 0.06% (range, 0%–4.82%), and 54 patients (42.9%) demonstrated high CTC levels. High CTC levels were associated with significantly poorer progression-free survival (PFS, 2-year 43.4% vs. 68.1%, P <0.001) and overall survival (OS, 5-year 39.0% vs. 68.3%, P <0.001). Similarly, high TLG levels significantly worsened the PFS (2-year, 41.2% vs. 67.6%, P = 0.038) and OS (5-year, 37.7% vs. 63.1%, P = 0.019). The multivariate analyses showed that Revised International Staging System (R-ISS) III, high CTC and TLG levels, and complete response were significant prognostic factors for PFS and OS. A novel predictive model was constructed using CTCs, TLG, and R-ISS III. The patients were stratified into three groups according to the number of risk factors, revealing an extremely high-risk group with a 2-year PFS of 0% and a 5-year OS of 20%. Patients without any high-risk features had better prognosis, with a 2-year PFS of 78.6% and a 5-year OS of 79.5%. The combination of CTCs and volumetric assessment of PET/CT at diagnosis augments the existing stratification systems and may pave the way for a risk-adapted treatment approach.

Damage volumetric assessment and digital twin synchronization based on LiDAR point clouds

Point clouds are widely used for structure inspection and can provide damage spatial information. However, how to update a digital twin (DT) with local damage based on point clouds has not been sufficiently studied. This research presents an efficient framework for assessing and DT synchronizing local damage on a planar surface using point clouds. The pipeline starts from damage detection via DeepLabV3+ on the pseudo grayscale images from the point depth. It avoids the drawbacks of image and point cloud fusion. The target point cloud is separated according to the detected damage. Then, it can be converted into a 3D binary matrix through voxelization and binarization, which is highly lightweight and can be losslessly compressed for DT synchronization. The framework is validated via two case studies, demonstrating that the proposed voxel-based method can be easily applied to real-world damage with non-convex geometry instead of convex-hull fitting; finite-element (FE) models and BIM models can be updated automatically through the framework.

NIMG-52. CORRELATION OF BIDIMENSIONAL AND VOLUMETRIC TUMOR SIZING IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma (GBM) is the most common malignant primary nervous system tumor and remains incurable with a poor prognosis despite current therapy. The standard for monitoring response to treatment, as defined by the Response Assessment in Neuro-Oncology (RANO) criteria, relies on a bidimensional (2D) measurement product of T1-gadolinium enhancing disease. However, due to the variability of bidimensional tumor characterization, there is considerable interest in volumetric segmentation to assess tumor burden, which could be implemented using machine learning models offering workflow automatization. METHODS In this study, we evaluate a commercially available automated tumor segmentation tool and correlate its volumetric output to the manual assessment of bidimensional 2D enhancing tumor burden by experts. MRI examinations during systemic treatment for two retrospective cohorts of patients with either newly diagnosed (nGBM) or recurrent GBM (rGBM) were assessed. The 2D diameter product in cm^2 of enhancing tumor was determined by blinded readers and volumetric segmentations in cm^3 were automatically generated. Spearman's correlation between the manual 2D and automated volume measurements was calculated for each cohort. RESULTS 315 patients with nGBM, and 482 with rGBM were evaluated, comprising 3073 total brain MRIs in the nGBM cohort and 2439 MRIs in the rGBM cohort. Spearman's correlation between the manual 2D and automated volumetric measurements was 0.7463 (0.730, 0.762) in the nGBM cohort and 0.8598 (0.849, 0.870) in the rGBM cohort. CONCLUSION Automated segmentation tools have the potential to revolutionize radiology workflows and increase sensitivity to subtle changes on imaging. However, the differential performance of this volumetric assessment confirms the need for further refinement and customization of such automated tools to account for tumor- and patient-specific factors.

NIMG-76. A DEEP LEARNING ALGORITHM FOR FULLY AUTOMATED VOLUMETRIC MEASUREMENT OF MENINGIOMA BURDEN

Abstract PURPOSE Meningiomas are the most common primary central nervous system tumor. A subset of these tumors is multiply recurrent despite aggressive treatment with surgery or radiation and causes significant morbidity and mortality. Longitudinal measurement of meningioma burden on post-contrast MRI is the basis of treatment response assessment. As per Response Assessment in Neuro-Oncology (RANO), estimates of tumor size are based on the product of bidirectional diameters of enhancing tumor. However, these 2D segmentations as well as manual volumetric segmentations have high rates of intra- and inter-rater variability, which prohibits accurate longitudinal assessment of tumor size. A rapid and reproducible quantitative method for determining meningioma volume is needed to expedite response assessment in clinical trials. To address this unmet need, we developed a deep learning algorithm that enables fully automated 3D segmentation and volumetric assessment of meningioma burden. METHODS 351 post-contrast T1-weighted brain MRIs from 86 patients with meningioma were obtained from Massachusetts General Hospital and Dana-Farber Cancer Institute. The cohort was unique compared to prior DL segmentation models as it encompassed diverse clinical scenarios (e.g., meningioma of all grades, post-operative and post-radiated meningioma). The MRIs were resampled to 1 mm isotropic resolution and preprocessed using N4 bias field correction and zero-mean intensity normalization. The dataset was split 70%/15%/15% for training/validation/testing on a per patient level. The training data were used with manually generated tumor segmentations to train a convolutional neural network (CNN) with a U-Net architecture and the Dice loss function. RESULTS The median Dice score was 0.6758 on the test set. Additional work is ongoing to optimize performance, with primary focus directed toward improved data preprocessing and ground truth segmentation as the variability in segmentation quality contributed to the lower Dice score, highlighting the need to develop a less biased and more reliable tool to segment meningiomas.

Incremental prognostic value of fully automatic LVEF measured at stress using machine learning

Abstract Background Cardiovascular magnetic resonance (CMR) is the gold standard to measure left ventricular ejection fraction (LVEF), and novel artificial intelligence-based automatic analyses have been proposed for less user interaction and time saving. However, whether automatic LVEF delivers similar information for risk stratification remains unknown. Purpose To assess the prognostic value for all-cause mortality of LVEF measured by stress CMR using a fully automatic machine learning algorithm without human correction. Methods Between 2016 and 2018, all consecutive patients referred for vasodilator stress CMR were included and followed for the occurrence of all-cause death. A fully automatic machine learning algorithm was trained on 3,700 scans and validated on 1,719 unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to identify end-diastolic and end-systolic phases and segment LV volumes from short-axis cine images at stress. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Manual and automatic LVEF measured at stress were compared with the paired Wilcoxon test, Pearson correlation and Bland-Altman analysis. Cox regressions were performed to determine the prognostic value of automatic LVEF measured at stress. Results Among 9,883 included patients included to this study, the automatic LVEF was successfully computed in 9,712 (98.3%) patients (66.6% male, mean age 66±12 years). The agreement between manual and automatic LVEF was good (bias = -0.01%, 95% limits of agreement, -6.7% to 6.7%; Pearson’s correlation coefficient r=0.94). A total of 504 (5.2%) deaths were observed during a median (IQR) follow-up period of 4.5 (3.7-5.2) years. Both manual and automatic volumetric assessments showed similar impact on outcome in univariate analyses (manual LVEF per 5%: hazard ratio [HR], 0.80 [95% CI 0.77–0.83]; p&amp;lt;0.001; automatic LVEF per 5%: HR, 0.84 [95% CI, 0.81–0.86]; p&amp;lt;0.001) and multivariable analyses (manual LVEF per 5%: HR, 0.81 [95% CI, 0.78–0.84]; p&amp;lt;0.001; automatic LVEF per 5%: HR, 0.84 [95% CI, 0.82–0.87]; p&amp;lt;0.001). Fully automatic stress LVEF showed an incremental prognostic value to predict all-cause mortality above all traditional risk factors, LVEF measured at rest, the presence of inducible ischemia and LGE (C-statistic improvement: 0.04; NRI=0.221; IDI=0.049; all p&amp;lt;0.001). Conclusions Automatic LVEF is equally predictive of all-cause mortality compared to manual LVEF, and has an incremental prognostic value compared to traditional risk factors, and other stress CMR parameters. Figures: Figure 1: Linear regression plots (A) and Bland-Altman plots (B) comparing stress LVEF measured by experts (LVEFexpert) and computed by fully-automated artificial-intelligence algorithms (LVEFAI). Figure 2: Kaplan-Meier curves for MACE stratified by fully-automated LVEF value. Test comparing the three groups is based on the log-rank test.

Abstract 15625: Left Atrial Appendage Volumetric and Functional Assessment Using Cardiac CT-Derived Three-Dimensional Models

Introduction: The left atrial appendage (LAA) is the most common site of thrombus formation in atrial fibrillation (AF) patients who develop strokes, yet its dynamic volumetric parameters remain uncharacterized. We present a quantitative analysis of cardiac phase-specific LAA volumes and function using cardiac CT-derived three-dimensional (3D) models of 920 LAA segmentations. Methods: We measured LAA volumes at 10% increments of the cardiac cycle (R-R interval) in patients with AF and controls with sinus rhythm (SR). After identifying the LAA orifice using anatomical landmarks on multiplanar CT images, we performed computational LAA segmentation using specialized software. For precise mapping, we used iterative active region-growing and cutting tools to separate the LAA from surrounding structures. We used the resulting 3D model for volumetric assessment of the LAA during the cardiac cycle. Results: We analysed 580 LAA segmentations in patients with AF and 340 segmentations in patients with SR. Both groups displayed LAA volume increase from 0 to 40% of the cardiac cycle followed by a gradual decrease during diastole. LAA volumes were significantly higher in AF at 0%, 10%, 70%, 80%, and 90% of the cardiac cycle (p =0.01, 0.021, 0.047, 0.025 and 0.011, respectively). LAA volumetric variance within the AF group was significantly greater (p&lt;0.001). LAA ejection fraction was significantly lower in AF compared to SR (40.8±16.1% vs 49.7±12.6%, p=0.003). Polynomial plots of incremental volume changes showed an accelerated rate of LAA filling in early systole in the SR, which was significantly dampened in AF. Conclusions: We present the first comparative analysis of LAA volumetric and functional assessment in patients with AF and SR. We conclude that impaired LAA filling and emptying in AF can contribute to blood stasis and thrombogenicity. Further investigations into the utility of LAA metrics as prognostic and risk-stratification tools in patients with AF are warranted.

Brain MRI Volumetric Assessment of Patients With Multiple Sclerosis: The Volume of Basal Ganglia, Thalamus, and Posterior Fossa.

Multiple sclerosis (MS) is an inflammatory demyelinating and neurodegenerative disorder of the central nervous system, which is associated with brain atrophy and volume changes in some brain structures. This study aimed to compare the volume of the basal ganglia, thalamus, cerebellum, and brainstem in patients with relapsing-remitting MS with that of the control group using magnetic resonance imaging (MRI). In this cross-sectional study, MRI brain scans were obtained from 25 patients with relapsing-remitting MS and 25 healthy control subjects. Volumetric analyses were performed using Brain Suite software. The mean age of the MS and the control groups was 33.96±8.75 and 40.40±8.72, respectively. No statistically significant difference was found in gender (P=0.747). The bilateral putamen and caudate nuclei volumes were significantly higher in the case group than in the control group (P<0.001). Moreover, lower the volume of the brainstem, cerebellum, bilateral thalamus, and globus pallidus were identified in the MS patients compared to the control group (P<0.001). There was an inverse correlation between the disease and treatment duration with the thalamus and cerebellum volume in MS patients (P=0.001). Treatment duration also had an inverse correlation with brainstem volume (P=0.047). The volume of some structures of the brain, including globus pallidus, thalamus, cerebellum, and brainstem is lower in MS and can be one of the markers of disease progression and disability among MS patients. Due to the degenerative process in multiple sclerosis, some cerebral structures may face volume change.The present study demonstrated that the volume of globus pallidus, thalamus, cerebellum, and brainstem is lower in MS patients compared to the controls. Multiple sclerosis (MS) is defined as an inflammatory disease involving the white matter of the brain, but experience has shown that many non-white matter structures also change in MS. In this study, we aimed to examine some parts of the brain, such as the thalamus, basal ganglia, brainstem, and cerebellum, for volume changes. The results showed that all these structures can have a smaller volume in MS patients than in healthy people. Especially in the case of the thalamus and cerebellum, this difference increases with increasing the disease duration. Changes in the size of these structures can be the result of degeneration of the neurons in these areas. These changes can cause significant disability in patients; however, there may not be significant changes in the number of plaques in patients. Attention to these changes can be essential in interpreting patients' clinical changes, including motor and cognitive disabilities.

Longitudinal follow-up of incidental renal calculi on computed tomography.

Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT. This retrospective study included 4266 scans from 2030 asymptomatic patients who underwent two or more nonenhanced CT scans for colorectal screening between 2004 and 2016. The DL software identified and measured the volume, location, and attenuation of 883 stones. The corresponding scans were manually evaluated, and patients without follow-up were excluded. At each follow-up, the stones were categorized as new, growing, persistent, or resolved. Stone size (volume and diameter), attenuation, and location were correlated with the outcome and growth rates of the stones. The stone cohort comprised 407 scans from 189 (M: 124, F: 65, median age: 55.4years) patients. The median number of stones per scan was 1 (IQR: [1, 2]). The median stone volume was 17.1mm3 (IQR: [7.4, 43.6]) and the median peak attenuation was 308 HU (IQR: [204, 532]. The 189 initial scans contained 291stones; 91 (31.3%) resolved, 142 (48.8%) grew, and 58 (19.9) remained persistent at the first follow-up. At the second follow-up (for 27 patients with 2 follow-ups), 14/44 (31.8%) stones had resolved, 19/44 (43.2%) grew and 11/44 (25%) were persistent. The median growth rate of growing stones was 3.3mm3/year, IQR: [1.4,7.4]. Size and attenuation had a moderate correlation (Spearman rho 0.53, P < .001 for volume, and 0.50 P < .001 for peak attenuation) with the growth rate. Growing and persistent stones had significantly greater maximum axial diameter (2.7 vs 2.3mm, P =.047) and peak attenuation (300 vs 258 HU, P =.031) CONCLUSION: We report a 12.7% prevalence of incidental kidney stones in asymptomatic adults, of which about half grew during follow-up with a median growth rate of about 3.3mm3/year.