Korfiatis, Panagiotis

Abstract Introduction: Allogeneic stem cell transplantation (allo-SCT) can be curative for patients with hematologic malignancies. Despite optimization of supportive measures, the risk of non-relapse mortality is a particularly important consideration in older individuals. Sarcopenia, defined as low skeletal muscle mass, has been shown to be associated with decreased overall survival in cancer patients and may identify individuals at higher risk of treatment-related complications. Methods: Using Mayo Clinic Arizona's hematopoietic stem cell transplantation database, patients aged 60 years or older who underwent allo-SCT between 2008 and 2018 were identified. Patients who underwent abdominal computed tomography (CT) at our institution within 90 days prior to their transplant date were included in the study. The skeletal muscle area (SMA) was obtained using a fully automated algorithm for segmenting abdominal CT images to quantify body composition based on a deep learning approach (Weston et al., Radiology, 2019). The skeletal muscle index (SMI) was then calculated using the SMA and the subject's height at the time the CT was obtained. Sarcopenia was defined based on previously described consensus (Cruz-Jentoft et al., Age Ageing, 2010; Magudia et al., Radiology, 2021). T-tests and Fisher's exact tests compared patient characteristics between groups, where appropriate. Kaplan-Meier methods were used to construct survival times. Log-rank tests compared survival distributions and Cox proportional hazards models estimated hazard ratios. Results: A total of 79 patients met inclusion criteria (Table 1). Of these, 50 (63%, n=50/79) had sarcopenia, and 29 (37%, n=29/79) had normal skeletal muscle mass. Among the sarcopenia group, 43 (86%, n=43/50) had SMI measurements. No differences were seen between groups in age at transplant, sex, Karnofsky performance scale, disease risk index, or conditioning regimen intent. There was no statistically significant difference in overall survival between those with sarcopenia and those with normal skeletal muscle mass (log-rank p=0.18). Median survival was 3.3 years (95% CI: 0.8, not estimable (NE)) for those with sarcopenia and 3.6 years (95% CI: 1.3, NE) for those with normal skeletal muscle mass. Given the infrequency of events, the upper bounds of the median survival confidence intervals were not estimable. The unadjusted hazard ratio (HR) for sarcopenia versus normal skeletal muscle mass was 1.65 (95% CI: 0.79, 3.47; p=0.18). Males had a higher mean SMI than females, as expected (50.4 versus 37.5, p<0.001; mean difference 13.0, 95% CI: 8.9, 17.1). Without considering sex, higher or lower SMI was not associated with survival time in a Cox model in those with sarcopenia (HR: 0.97, 95% CI: 0.92, 1.03; p=0.32). Sex alone was also not associated with any survival benefit among these 43 patients. After adjusting for the difference seen in SMI between sexes, on average, those with higher SMI were associated with a survival benefit (HR: 0.92, 95% CI: 0.84, 0.99; p=0.039). This amounts to roughly an 8% reduction in hazard rate as SMI increased. Conclusions: The presence of sarcopenia alone was not associated with overall survival in patients 60 years and older undergoing allo-SCT for hematologic malignancy. Rating sarcopenia severity using SMI may assist in identifying high risk individuals with sarcopenia as lower sarcopenia severity was associated with improved overall survival. Further studies are warranted to explore the possible impacts of interventions directed at improving skeletal muscle mass on transplant-related outcomes. Figure 1 Figure 1. Disclosures Gaulin: DeciBio: Honoraria; Olson Research Group: Honoraria. Jain: CareDx: Other: for advisory board participation; CTI Biopharma: Research Funding; Syneos Health: Research Funding; Bristol Myers Squibb: Other: for advisory board participation; Targeted Healthcare Communications: Consultancy. Palmer: PharmaEssentia: Research Funding; Incyte: Research Funding; Sierra Oncology: Consultancy, Research Funding; CTI BioPharma: Consultancy, Research Funding; Protagonist: Consultancy, Research Funding.

Abstract Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133 μ m to 35 μ m, which is smaller than the ultrasound wavelength (73 μ m). The proposed method was further tested in an in vivo human liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170 μ m (FWHM). Adjacent microvessels with a distance of 670 μ m, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.

PurposeTo develop a two‐stage three‐dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra‐reader and inter‐reader reliability at full dose and reduced radiation dose CTs on a public dataset.MethodsA dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75‐mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two‐stage 3D CNN model based on a modified U‐net architecture for automated volumetric pancreas segmentation. Model’s performance for pancreas segmentation and the differences in model‐predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra‐reader and inter‐reader reliability, model’s segmentation performance, and reliability between model‐predicted pancreatic volumes at full vs reduced dose were measured. Finally, model’s performance was tested on the benchmarking National Institute of Health (NIH)‐Pancreas CT (PCT) dataset.ResultsThree‐dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model’s performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model‐predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was −2.7 cc (percent difference: −2.4% of GT volume) with excellent correlation between model‐predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter‐reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model‐predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum–maximum DSC: 0.79 −0.96) on the NIH‐PCT dataset.ConclusionA 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection.

PurposeOrgan segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs‐at‐risk, which is laborious and time‐consuming. We present a fully automated segmentation method based on the three‐dimensional (3D) U‐Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels. Whole abdomen segmentation is capable of quantifying exposure beyond a handful of organs‐at‐risk to all tissues within the abdomen.MethodsSixty‐six (66) CT examinations of 64 individuals were included in the training and validation sets and 18 CT examinations from 16 individuals were included in the test set. All pixels in each examination were segmented by image analysts (with physician correction) and assigned one of 33 labels. Segmentation was performed with a 3D U‐Net variant architecture which included residual blocks, and model performance was quantified on 18 test cases. Human interobserver variability (using semiautomated segmentation) was also reported on two scans, and manual interobserver variability of three individuals was reported on one scan. Model performance was also compared to several of the best models reported in the literature for multiple organ segmentation.ResultsThe accuracy of the 3D U‐Net model ranges from a Dice coefficient of 0.95 in the liver, 0.93 in the kidneys, 0.79 in the pancreas, 0.69 in the adrenals, and 0.51 in the renal arteries. Model accuracy is within 5% of human segmentation in eight of 19 organs and within 10% accuracy in 13 of 19 organs.ConclusionsThe CNN approaches the accuracy of human tracers and on certain complex organs displays more consistent prediction than human tracers. Fully automated deep learning‐based segmentation of CT abdomen has the potential to improve both the speed and accuracy of radiotherapy dose prediction for organs‐at‐risk.

PurposeEarly identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In noncontrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyperacute phase (< 8 h from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry‐based analysis of texture features of ischemic lesions in noncontrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase.MethodsOne hundred thirty‐nine (139) ncCT scans of hyperacute ischemic stroke with follow‐up magnetic resonance diffusion‐weighted (MR‐DW) images were collected. The regions of stroke were identified in the MR‐DW images, which were spatially aligned to corresponding ncCT images. A state‐of‐the‐art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR‐DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co‐occurrence and run‐length texture‐based image features in the regions of ischemic stroke and their contralateral regions.ResultsThe classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions.ConclusionsThe results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in noncontrast CT. This work is an initial step toward development of an automated decision support system for detection of hyperacute ischemic stroke lesions on noncontrast CT of the brain.