[Prognostic significance of textbook outcome in advanced gastric patients who underwent neoadjuvant chemotherapy followed by surgical resection]

Objective: To investigate the risk factors and prognostic value of the textbook outcome (TO) in patients with advanced gastric cancer (AGC) who underwent neoadjuvant chemotherapy followed by surgical resection. Methods: This is a retrospective cohort study. A total of 253 patients with AGC who underwent neoadjuvant chemotherapy combined with gastrectomy and D2 lymphadenectomy in the Department of Gastric Surgery, Fujian Medical University Union Hospital from January 2010 to December 2019 were retrospectively included. There were 195 males and 58 females, aged (60.3±10.0) years (range: 27 to 75 years). The patients were then divided into the TO group (n=168) and the non-TO group (n=85). Multivariate Logistic regression was used to analyze the independent predictors of TO. Univariate and multivariate Cox analysis were used to analyze independent prognosis factors for overall survival (OS) and disease-free survival (DFS). Propensity score matching was performed to balance the TO and non-TO groups, and the Kaplan-Meier method was used to calculate survival rates and draw survival curves. Results: Among the 253 patients, 168 patients (66.4%) achieved TO. The Eastern Cooperative Oncology Group score (OR=0.488, 95%CI: 0.278 to 0.856, P=0.012) and ypN stage (OR=0.626, 95%CI:0.488 to 0.805, P<0.01) were independently predictive of TO. Multivariate analysis revealed that TO was an independent risk factor for both OS (HR=0.662, 95%CI: 0.457 to 0.959,P=0.029) and DFS (HR=0.687, 95%CI: 0.483 to 0.976, P=0.036). After matching, the 5-year OS rate (42.2% vs. 27.8%) and the 5-year DFS rate (37.5% vs. 27.8%) were significantly higher in the TO group than in the non-TO group (both P<0.05). Furthermore, patients in the non-TO group benefited significantly from postoperative chemotherapy (both P<0.05), but those in the TO group did not (both P>0.05). Conclusion: TO is an independent prognosis factor in patients undergoing neoadjuvant chemotherapy and surgery for AGC and is associated with postoperative chemotherapy benefits.

Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial

BACKGROUND AND PURPOSE

Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas.

MATERIALS AND METHODS

Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response.

RESULTS

Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories.

CONCLUSIONS

Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.

Intraprocedural C-arm dual-phase cone-beam enhancement patterns correlate with tumor absorbed dose after radioembolization

BACKGROUND

Recent studies have shown a clear relationship between absorbed dose and tumor response to treatment after hepatic radioembolization. These findings help to create more personalized treatment planning and dosimetry. However, crucial to this goal is the ability to predict the dose distribution prior to treatment. The microsphere distribution is ultimately determined by (i) the hepatic vasculature and the resulting blood flow dynamics and (ii) the catheter position.

PURPOSE

To show that pretreatment, intra-procedural imaging of blood flow patterns, as quantified by catheter-directed intra-arterial contrast enhancement, correlate with posttreatment microsphere accumulation and, consequently, absorbed dose.

MATERIALS AND METHODS

Patients who participated in a clinical trial (NCT01177007) and for whom both a pretreatment dual-phase contrast-enhanced cone-beam CT (CBCT) and a posttreatment 90Y PET/CT scan were available were included in this retrospective study. Tumors and perfused volumes were manually delineated on the CBCT by an experienced radiologist. The mean, sum, and standard deviation of the voxels in each volume were recorded. The delineations were transferred to the PET-based absorbed dose maps by coregistration of the corresponding CTs. Linear multiple regression was used to correlate pretreatment CBCT enhancement to posttreatment 90Y PET/CT-based absorbed dose in each region. Leave-one-out cross-validation and Bland-Altman analyses were performed on the predicted versus measured absorbed doses.

RESULTS

Nine patients, with a total of 23 tumors were included. All presented with hepatocellular carcinoma (HCC). Visually, all patients had a clear correspondence between CBCT enhancement and absorbed dose. The correlation between CBCT enhancement and posttherapy absorbed tumor dose based was strong (R2 = 0.91), and moderate for the non-tumor liver tissue (R2 = 0.61). Limits of agreement were approximately ±55 Gray for tumor tissue.

CONCLUSION

There is a linear relationship between pretreatment blood dynamics in HCC tumors and posttreatment absorbed dose, which, if shown to be generalizable, allows for pretreatment tumor absorbed dose prediction.

Risk of metastasis and survival in patients undergoing different treatment strategies with T1 colonic neuroendocrine tumors

PURPOSE

The efficacy and safety of local excision (LE) for small (< 1‒2 cm) colonic neuroendocrine tumors (NETs) is controversial due to the higher metastasis risk when compared with rectal NETs. The study aimed to evaluate the metastasis risk of T1 colonic NETs and compare patients' long-term prognosis after LE or radical surgery (RS).

METHODS

The Surveillance Epidemiology and End Results database was used to identify patients with T1 colonic NETs (2004‒2015). Multivariable logistic regression was performed to assess factors associated with metastasis risk. Propensity score matching was used to balance the variables. Cancer-specific survival (CSS) and overall survival (OS) were calculated to estimate the prognosis of patients with T1N0M0 colonic NETs who underwent LE or RS.

RESULTS

Of the 610 patients with colonic NETs, 46 (7.54%) had metastasis at diagnosis. Tumor size (11-20 mm) (OR = 9.51; 95% confidence interval (CI): 4.32‒21.45; P < 0.001), right colon (OR = 15.79; 95% CI 7.20‒38.56; P < 0.001), submucosal infiltration (OR = 2.08; 95% CI 0.84‒5.57; P = 0.125) were independent risk factors associated with metastasis. Of the 515 patients with T1N0M0 colonic NETs, the overall long-term prognosis of LE was as good as that of RS groups (after matching, 5-year CSS: 97.9% vs. 94.6%, P = 0.450; 5-year OS: 92.7% vs. 85.6%, P = 0.009).

CONCLUSION

Tumor size (11‒20 mm) and site (right colon) are associated with metastasis in T1 colonic NETs. In the absence of metastasis, LE could be a viable option for 0‒10 mm T1 colonic NETs with well/moderate differentiation in the left colon in terms of long-term survival.

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.

Exploring Recent Decreases in First Molar Sealants among US Children

Analyses of National Health and Nutrition Examination Survey (NHANES) data suggested a significant decrease in sealant prevalence among children between 2011 to 2014 and 2015 to 2018. We explore whether this decrease could be associated with possible changes in 1) clinical sealant delivery, 2) dental materials (i.e., increased use of glass ionomer [GI] sealants resulting in an inability to detect sealant fragments that still provide preventive benefits or increased use of composite restorations leading to misclassifying sealants as restorations), and 3) examination sensitivity and specificity. We used NHANES data to estimate the prevalences of sealants, untreated caries, and restorations in ≥1 first permanent molar among children aged 7 to 10 y and used Medical Expenditure Panel Survey data to estimate the annual clinical delivery of sealants and fluoride treatments. We examined changes in outcomes between 2 periods (P < 0.05) controlling for selected sociodemographic characteristics. NHANES sealant examination quality was based on the reference examiner's replicate examinations. The adjusted prevalence of sealants decreased relatively by 27.5% (46.6% vs. 33.8%). Overall, untreated caries decreased. Untreated caries and restoration decreased among children without sealants. Annual clinical sealant delivery did not change, whereas fluoride treatment delivery increased. The decrease in sealant prevalence held when assessed for various age ranges and NHANES cycle combinations. While sealant examination specificity remained similar between the periods, sensitivity (weighted by the proportion of exams by each examiner) decreased relatively by 17.4% (0.92 vs. 0.76). These findings suggest that decreased sealant prevalence was not supported by decreased clinical sealant delivery nor increased use of composite restorations. Decreased examination sensitivity, which could be due to an increased use of GI sealants, could contribute to the decrease in sealant prevalence. The decrease in caries among children without sealants could suggest the increased use of GI sealants. However, we could not rule out that the decrease in caries could be attributable to increased fluoride treatment delivery.

[A multiscale carotid plaque detection method based on two-stage analysis]

OBJECTIVE

To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.

METHODS

We proposed a two-stage carotid plaque detection method based on deep convolutional neural network (SM-YOLO).A series of algorithms such as median filtering, histogram equalization, and Gamma transformation were used to preprocess the dataset to improve image quality. In the first stage of the model construction, a candidate plaque set was built based on the YOLOX_l target detection network, using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes. In the second stage, the Histogram of Oriented Gradient (HOG) features and Local Binary Pattern (LBP) features were extracted and fused, and a Support Vector Machine (SVM) classifier was used to screen the candidate plaque set to obtain the final detection results. This model was compared quantitatively and visually with several target detection models (YOLOX_l, SSD, EfficientDet, YOLOV5_l, Faster R-CNN).

RESULTS

SM-YOLO achieved a recall of 89.44%, an accuracy of 90.96%, a F1-Score of 90.19%, and an AP of 92.70% on the test set, outperforming other models in all performance indicators and visual effects. The constructed model had a much shorter detection time than the Faster R-CNN model (only one third of that of the latter), thus meeting the requirements of real-time detection.

CONCLUSION

The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

[Single - nucleotide polymorphisms of artemisinin resistance - related Pfubp1 and Pfap2mu genes in Bioko Island, Equatorial Guinea from 2018 to 2020]

OBJECTIVE

To investigate the prevalence of single nucleotide polymorphisms (SNPs) of artemisinin resistance-related Pfubp1 and Pfap2mu genes in Plasmodium falciparum isolates from Bioko Island, Equatorial Guinea, so as to to provide baseline data for the formulation of malaria control strategies in Bioko Island.

METHODS

A total of 184 clinical blood samples were collected from patients with P. falciparum malaria in Bioko Island, Equatorial Guinea from 2018 to 2020, and genomic DNA was extracted. The Pfubp1 and Pfap2mu gene SNPs of P. falciparum were determined using a nested PCR assay and Sanger sequencing, and the gene sequences were aligned.

RESULTS

There were 159 wild-type P. falciparum isolates (88.83%) from Bioko Island, Equatorial Guinea, and 6 SNPs were identified in 20 Pfubp1-mutant P. falciparum isolates (11.17%), in which 4 non-synonymous mutations were detected, including E1516G, K1520E, D1525E, E1528D. There was only one Pfubp1gene mutation site in 19 Pfubp1-mutant P. falciparum isolates (95.00%), in which non-synonymous mutations accounted for 68.42% (13/19). D1525E and E1528D were identified as major known epidemic mutation sites in the Pfubp1 gene associated with resistance to artemisinin-based combination therapies (ACTs). At amino acid position 1525, there were 178 wild-type P. falciparum isolates (99.44%) and 1 mutant isolate (0.56%), with such a mutation site identified in blood samples in 2018, and at amino acid position 1528, there were 167 wild-type P. falciparum isolates (93.30%) and 12 mutant isolates (6.70%). The proportions of wild-type P. falciparum isolates were 95.72% (134/140), 79.25% (126/159) and 95.83% (161/168) in the target amplification fragments of the three regions in the Pfap2mu gene (Pfap2mu-inner1, Pfap2mu-inner2, Pfap2mu-inner3), respectively. There were 16 different SNPs identified in all successfully sequenced P. falciparum isolates, in which 7 non-synonymous mutations were detected, including S160N, K199T, A475V, S508G, I511M, L595F, and Y603H. There were 7 out of 43 Pfap2mu-mutant P. falciparum isolates (16.28%) that harbored only one gene mutation site, in which non-synonymous mutations accounted for 28.57% (2/7). For the known delayed clearance locus S160N associated with ACTs, there were 143 wild-type (89.94%) and 16 Pfap2mu-mutant P. falciparum isolates (10.06%).

CONCLUSIONS

Both Pfubp1 and Pfap2mu gene mutations were detected in P. falciparum isolates from Bioko Island, Equatorial Guinea from 2018 to 2020, with a low prevalence rate of Pfubp1 gene mutation and a high prevalence rate of Pfap2mu gene mutation. In addition, new mutation sites were identified in the Pfubp1 (E1504E and K1520E) and Pfap2mu genes (A475V and S508G).

Bronchiolar adenoma/ciliated muconodular papillary tumour: advancing clinical, pathological, and imaging insights for future perspectives

Bronchiolar adenoma/ciliated muconodular papillary tumour (BA/CMPT) is a benign peripheral lung tumour composed of bilayered bronchiolar-type epithelium containing a continuous basal cell layer; however, the similarities in imaging and tissue biopsy findings at histopathology between BA/CMPT and malignant tumours, including lung adenocarcinoma, pose significant challenges in accurately diagnosing BA/CMPT preoperatively. This difficulty in differentiation often results in misdiagnosis and unnecessary overtreatment. The objective of this article is to provide a comprehensive and systematic review of BA/CMPT, encompassing its clinical manifestations, pathological basis, imaging features, and differential diagnosis. By enhancing healthcare professionals' understanding of this disease, we aim to improve the accuracy of preoperative BA/CMPT diagnosis. This improvement is crucial for the development of appropriate therapeutic strategies and the overall improvement of patient prognosis.

Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial

Background

Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods.

Methods

An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images.

Results

There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD.

Conclusions

Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.

Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.

Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction

BACKGROUND

The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor.

PURPOSE

We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging.

DATA SOURCES

Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science.

STUDY SELECTION

Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria.

DATA ANALYSIS

We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.

DATA SYNTHESIS

Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles.

LIMITATIONS

The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias.

CONCLUSIONS

While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.

A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.

[The mechanism of berberine on Methicillin resistant Staphylococcus aureus in vitro]

Objective: To explore the impact of traditional Chinese medicine berberine (BBR) on membrane integrity and permeability of Methicillin-resistant Staphylococcus aureus (MRSA) and the change of bacterial cell wall structure, laying a foundation for the clinical application of berberine in antibacterial. Methods: This study used a non-randomized concurrent controlled trial. The 3 MRSA strains were isolated and cultured from lower respiratory tract samples of geriatric patients from Shanghai Eighth People's Hospital between 2019 and 2020.The Meirier VETEK MS fully automated rapid microbial mass spectrometry detection system and VETEK 2 Compact fully automated microbial identification instrument were used to identify bacterial drug sensitivity experiments to detect bacterial species and drug sensitivity. The minimal inhibitory concentration (MIC) of BBR on MRSA strains was determined by broth microdilution. This study used conductivity tests to assess the changes in membrane permeability in response to different concentration of BBR on MRSA, while also investigating the changes in MRSA morphology by transmission electron microscopy. GraphPad Prism5 was used to analyze the differences in the electrical conductivity experimental results. Results: The MIC of BBR on MRSA was 64 μg/ml. After co-culturing MRSA with BBR for 4 h at 8 μg/ml, 16 μg/ml, 32 μg/ml, 64 μg/ml and 128 μg/ml, respectively, the electrical conductivity increased, compared with the control group, by 24.49%,34.59%,208.92%,196.40% and 208.68%, respectively. By transmission electron microscopy, This study found that low concentration of BBR (8 μg/ml,1/8 MIC) caused no significant damage to MRSA, and the bacterial structure of MRSA remained intact. The cell wall of MRSA became thinner after treatment with berberine at medium concentration (64 μg/ml,1 MIC), while high concentration of BBR (512 μg/ml,8 MIC) induced the destruction and dissolution of MRSA cell wall structure and the leakage of bacterial contents, leading to bacterial lysis. Conclusion: Berberine can kill bacteria by altering the permeability of MRSA cell membrane and destroying and dissolving the structure of the cell wall.

AAPM Task Group Report 238: 3D C-arms with volumetric imaging capability

This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

[STIP1 correlates with tumor immune infiltration and prognosis as a potential immunotherapy target: a pan-cancer bioinformatics analysis]

OBJECTIVE

To investigate the correlation of stress-inducible phosphoprotein 1 (STIP1) expression level with prognosis of different cancers and its potential role in immunotherapy.

METHODS

TCGA, TARGET and GTEx databases were used for bioinformatic analysis of STIP1 expression level and its prognostic value in different cancers. We also detected STIP1 expression immunohistochemically in 10 pairs of colorectal cancer and adjacent tissues. We further analyzed the correlation of STIP1 expression level with tumor mutational burden, microsatellite instability, immune cell infiltration, immune regulators and outcomes of different cancers. STIP1- related proteins were identified using protein- protein interaction (PPI) network analysis, and functional enrichment analysis was performed to analyze the regulatory pathways involving STIP1.

RESULTS

Bioinformatics analysis showed that STIP1 was highly expressed in most tumors compared with the normal tissues (P < 0.05), which was confirmed by immunohistochemistry of the 10 pairs of colorectal cancer tissues. STIP1 expression level was correlated with clinical stages of multiple cancers (P < 0.05), and in some cancer types, an upregulated STIP1 expression was correlated with a poor prognosis of the patients in terms of overall survival, disease-specific survival, disease-free survival and progression-free survival (P < 0.05). STIP1 expression was significantly correlated with tumor mutational burden, microsatellite instability, immune cell infiltration and immunomodulatory factors in most tumors (P < 0.05). PPI network analysis indicated that STIP1-related proteins included HSPA4, HSPA8, and HSP90AA1. KEGG enrichment analysis suggested that the high expression of STIP1 in liver cancer was related mainly with valerate metabolism, tryptophan metabolism, and butyrate metabolism pathways; HALLMARK enrichment analysis suggested high STIP1 expression in liver cancer was involved in bile acid and fatty acid metabolism.

CONCLUSION

STIP1 is up-regulated in multiple cancer types and its expression level is correlated with clinical tumor stage, tumor mutational burden, microsatellite instability, immune cell infiltration and immunomodulatory factors.

PACS-integrated machine learning breast density classifier: clinical validation

OBJECTIVE

To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training.

MATERIALS AND METHODS

This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading.

RESULTS

For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category.

CONCLUSIONS

The automated breast density tool showed high agreement with radiologists' assessments of breast density.

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.

Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning

Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1-6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC-ROC). After prediction, the model's clinical relevance was evaluated using Kaplan-Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan-Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.

Asymmetric control of food intake by left and right vagal sensory neurons

We investigated the lateralization of gut-innervating vagal sensory neurons and their roles in feeding behavior. Using genetic, anatomical, and behavioral analyses, we discovered a subset of highly lateralized vagal sensory neurons with distinct sensory responses to intestinal stimuli. Our results demonstrated that left vagal sensory neurons (LNG) are crucial for distension-induced satiety, while right vagal sensory neurons (RNG) mediate preference for nutritive foods. Furthermore, these lateralized neurons engage different central circuits, with LNG neurons recruiting brain regions associated with energy balance and RNG neurons activating areas related to salience, memory, and reward. Altogether, our findings unveil the diverse roles of asymmetrical gut-vagal-brain circuits in feeding behavior, offering new insights for potential therapeutic interventions targeting vagal nerve stimulation in metabolic and neuropsychiatric diseases.

[Mechanisms mediating the inhibitory effects of quercetin against phthalates-induced testicular oxidative damage in rats]

OBJECTIVE

To explore the mechanism underlying the inhibitory effect of quercetin against testicular oxidative damage induced by a mixture of 3 commonly used phthalates (MPEs) in rats.

METHODS

Forty male Sprague-Dawley rats were randomly divided into control group, MPEs exposure group, and MPEs with low-, median- and high-dose quercetin treatment groups. For MPEs exposure, the rats were subjected to intragastric administration of MPEs at the daily dose of 900 mg/kg for 30 consecutive days; Quercetin treatments were administered in the same manner at the daily dose of 10, 30, and 90 mg/kg. After the treatments, serum levels of testosterone, luteinizing hormone (LH), follicle stimulating hormone (FSH), and testicular malondialdeyhde (MDA), catalase (CAT) and superoxide dismutase (SOD) were detected, and testicular pathologies of the rats were observed with HE staining. The expressions of nuclear factor-E2-related factor 2 (Nrf2), Kelch-like ECH2 associated protein 1 (Keap1) and heme oxygenase 1 (HO-1) in the testis were detected using immunofluorescence assay and Western blotting.

RESULTS

Compared with the control group, the rats with MPEs exposure showed significant reductions of the anogenital distance, weight of the testis and epididymis, and the coefficients of the testis and epididymis with lowered serum testosterone, LH and FSH levels (P < 0.05). Testicular histological examination revealed atrophy of the seminiferous tubules, spermatogenic arrest, and hyperplasia of the Leydig cells in MPEs-exposed rats. MPEs exposure also caused significant increments of testicular Nrf2, MDA, SOD, CAT and HO-1 expressions and lowered testicular Keap1 expression (P < 0.05). Treatment with quercetin at the median and high doses significantly ameliorated the pathological changes induced by MPEs exposure (P < 0.05).

CONCLUSION

Quercetin treatment inhibits MPEs-induced oxidative testicular damage in rats possibly by direct scavenging of free radicals to lower testicular oxidative stress and restore the regulation of the Nrf2 signaling pathway.

[Latent period and incubation period with associated factors of COVID-19 caused by Omicron variant]

Objective: To estimate the latent period and incubation period of Omicron variant infections and analyze associated factors. Methods: From January 1 to June 30, 2022, 467 infected persons and 335 confirmed cases in five local Omicron variant outbreaks in China were selected as the study subjects. The latent period and incubation period were estimated by using log-normal distribution and gamma distribution models, and the associated factors were analyzed by using the accelerated failure time model (AFT). Results: The median (Q₁, Q₃) age of 467 Omicron infections including 253 males (54.18%) was 26 (20, 39) years old. There were 132 asymptomatic infections (28.27%) and 335 (71.73%) symptomatic infections. The mean latent period of 467 Omicron infections was 2.65 (95%CI: 2.53-2.78) days, and 98% of infections were positive for nucleic acid detection within 6.37 (95%CI: 5.86-6.82) days after infection. The mean incubation period of 335 symptomatic infections was 3.40 (95%CI: 3.25-3.57) days, and 97% of them developed clinical symptoms within 6.80 (95%CI: 6.34-7.22) days after infection. The results of the AFT model analysis showed that compared with the group aged 18~49 years old, the latent period [exp(β)=1.36 (95%CI: 1.16-1.60), P<0.001] and incubation period [exp(β)=1.24 (95%CI: 1.07-1.45), P=0.006] of infections aged 0~17 year old were also prolonged. The latent period [exp(β)=1.38 (95%CI: 1.17-1.63), P<0.001] and the incubation period [exp(β)=1.26 (95%CI: 1.06-1.48), P=0.007] of infections aged 50 years old and above were also prolonged. Conclusion: The latent period and incubation period of most Omicron infections are within 7 days, and age may be the influencing factor of the latent period and incubation period.

[Establishment and preliminary evaluation of a fluorescent recombinase-aided amplification/CRISPR-Cas12a system for rapid detection of Plasmodium falciparum]

OBJECTIVE

To establish a fluorescent assay for rapid detection of Plasmodium falciparum based on recombinaseaided amplification (RAA) and CRISPR-Cas12a system,and to preliminarily evaluate the diagnostic efficiency of this system.

METHODS

The 18S ribosomal RNA (rRNA) gene of P. falciparum was selected as the target sequence, and three pairs of RAA primers and CRISPR-derived RNA (crRNA) were designed and synthesized. The optimal combination of RAA primers and crRNA was screened and the reaction conditions of the system were optimized to create a fluorescent RAA/CRISPR-Cas12a system. The plasmid containing 18S rRNA gene of the P. falciparum strain 3D7 was generated, and diluted into concentrations of 1 000, 100, 10, 1 copy/μL for the fluorescent RAA/CRISPR-Cas12a assay, and its sensitivity was evaluated. The genomic DNA from P. vivax, P. malariae, P. ovum, hepatitis B virus, human immunodeficiency virus and Treponema pallidum was employed as templates for the fluorescent RAA/CRISPR-Cas12a assay, and its specificity was evaluated. Fifty malaria clinical samples were subjected to the fluorescent RAA/CRISPR-Cas12a assay and nested PCR assay, and the consistency between two assays was compared. In addition, P. falciparum strain 3D7 was cultured in vitro. Then, the culture was diluted into blood samples with parasite densities of 1 000, 500, 200, 50, 10 parasites/μL with healthy volunteers' O-positive red blood cells for the RAA/CRISPR-Cas12a assay, and the detection efficiency was tested.

RESULTS

The Pf-F3/Pf-R3/crRNA2 combination, 2.5 μL as the addition amount of B buffer, 40 min as the RAA reaction time, 37 °C as the reaction temperature of the CRISPR-Cas12a system were employed to establish the fluorescent RAA/CRISPR-Cas12a system. Such a system was effective to detect the plasmid containing 18S rRNA gene of the P. falciparum strain 3D7 at a concentration of 1 copy/μL, and presented fluorescent signals for detection of P. falciparum, but failed to detect P. ovum, P. malariae, P. vivax, T. pallidum, hepatitis B virus or human immunodeficiency virus. The fluorescent RAA/CRISPR-Cas12a system and nested PCR assay showed completely consistent results for detection of 50 malaria clinical samples (kappa = 1.0, P < 0.001). Following 6-day in vitro culture of the P. falciparum strain 3D7, 10 mL cultures were generated and the fluorescent RAA/CRISPR-Cas12a system showed the minimal detection limit of 50 parasites/μL.

CONCLUSIONS

The fluorescent RAA/CRISPR-Cas12a system is rapid, sensitive and specific for detection of P. falciparum, which shows promising value for rapid detection and risk monitoring of P. falciparum.

Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.

Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study

BACKGROUND. Posttreatment recurrence is an unpredictable complication after liver transplant for hepatocellular carcinoma (HCC) that is associated with poor survival. Biomarkers are needed to estimate recurrence risk before organ allocation. OBJECTIVE. This proof-of-concept study evaluated the use of machine learning (ML) to predict recurrence from pretreatment laboratory, clinical, and MRI data in patients with early-stage HCC initially eligible for liver transplant. METHODS. This retrospective study included 120 patients (88 men, 32 women; median age, 60.0 years) with early-stage HCC diagnosed who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation between June 2005 and March 2018. Patients underwent pretreatment MRI and posttreatment imaging surveillance. Imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network. Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models (clinical model, imaging model, combined model) for predicting recurrence within six time frames ranging from 1 through 6 years after treatment. Kaplan-Meier analysis with time to recurrence as the endpoint was used to assess the clinical relevance of model predictions. RESULTS. Tumor recurred in 44 of 120 (36.7%) patients during follow-up. The three models predicted recurrence with AUCs across the six time frames of 0.60-0.78 (clinical model), 0.71-0.85 (imaging model), and 0.62-0.86 (combined model). The mean AUC was higher for the imaging model than the clinical model (0.76 vs 0.68, respectively; p = .03), but the mean AUC was not significantly different between the clinical and combined models or between the imaging and combined models (p > .05). Kaplan-Meier curves were significantly different between patients predicted to be at low risk and those predicted to be at high risk by all three models for the 2-, 3-, 4-, 5-, and 6-year time frames (p < .05). CONCLUSION. The findings suggest that ML-based models can predict recurrence before therapy allocation in patients with early-stage HCC initially eligible for liver transplant. Adding MRI data as model input improved predictive performance over clinical parameters alone. The combined model did not surpass the imaging model's performance. CLINICAL IMPACT. ML-based models applied to currently underutilized imaging features may help design more reliable criteria for organ allocation and liver transplant eligibility.

DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT

PURPOSE

Myocardial perfusion imaging (MPI) using single-photon emission-computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In clinical practice, the long scanning procedures and acquisition time might induce patient anxiety and discomfort, motion artifacts, and misalignments between SPECT and computed tomography (CT). Reducing the number of projection angles provides a solution that results in a shorter scanning time. However, fewer projection angles might cause lower reconstruction accuracy, higher noise level, and reconstruction artifacts due to reduced angular sampling. We developed a deep-learning-based approach for high-quality SPECT image reconstruction using sparsely sampled projections.

METHODS

We proposed a novel deep-learning-based dual-domain sinogram synthesis (DuDoSS) method to recover full-view projections from sparsely sampled projections of cardiac SPECT. DuDoSS utilized the SPECT images predicted in the image domain as guidance to generate synthetic full-view projections in the sinogram domain. The synthetic projections were then reconstructed into non-attenuation-corrected and attenuation-corrected (AC) SPECT images for voxel-wise and segment-wise quantitative evaluations in terms of normalized mean square error (NMSE) and absolute percent error (APE). Previous deep-learning-based approaches, including direct sinogram generation (Direct Sino2Sino) and direct image prediction (Direct Img2Img), were tested in this study for comparison. The dataset used in this study included a total of 500 anonymized clinical stress-state MPI studies acquired on a GE NM/CT 850 scanner with 60 projection angles following the injection of 99m Tc-tetrofosmin.

RESULTS

Our proposed DuDoSS generated more consistent synthetic projections and SPECT images with the ground truth than other approaches. The average voxel-wise NMSE between the synthetic projections by DuDoSS and the ground-truth full-view projections was 2.08% ± 0.81%, as compared to 2.21% ± 0.86% (p < 0.001) by Direct Sino2Sino. The averaged voxel-wise NMSE between the AC SPECT images by DuDoSS and the ground-truth AC SPECT images was 1.63% ± 0.72%, as compared to 1.84% ± 0.79% (p < 0.001) by Direct Sino2Sino and 1.90% ± 0.66% (p < 0.001) by Direct Img2Img. The averaged segment-wise APE between the AC SPECT images by DuDoSS and the ground-truth AC SPECT images was 3.87% ± 3.23%, as compared to 3.95% ± 3.21% (p = 0.023) by Direct Img2Img and 4.46% ± 3.58% (p < 0.001) by Direct Sino2Sino.

CONCLUSIONS

Our proposed DuDoSS is feasible to generate accurate synthetic full-view projections from sparsely sampled projections for cardiac SPECT. The synthetic projections and reconstructed SPECT images generated from DuDoSS are more consistent with the ground-truth full-view projections and SPECT images than other approaches. DuDoSS can potentially enable fast data acquisition of cardiac SPECT.

Association of Baseline Frailty with Patient-Reported Outcomes in Systemic Lupus Erythematosus at 1 Year

The relationship of baseline frailty with subsequent patient-reported outcomes in systemic lupus erythematosus (SLE) remains unclear. We assessed these associations in a pilot prospective cohort study. Frailty based on the FRAIL scale and the Fried phenotype and patient-reported outcomes, namely Patient Reported Outcomes Measurement Information System computerized adaptive tests and Valued Life Activities disability, were measured at baseline and 1 year among women aged 18-70 years with SLE enrolled at a single center. Differences in Patient Reported Outcomes Measurement Information System computerized adaptive tests between frail and non-frail participants were evaluated using Wilcoxon rank sum tests, and the association of baseline frailty with self-report disability at 1 year was estimated using linear regression. Of 51 participants, 24% (FRAIL scale) and 16% (Fried phenotype) met criteria for frailty at baseline despite median age of 55.0 and 56.0 years, respectively. Women with (versus without) baseline frailty using either measure had worse 1-year Patient Reported Outcomes Measurement Information System computerized adaptive test scores across multiple domains and greater self-report disability. Baseline frailty was significantly associated with self-report disability at 1 year (FRAIL scale: parameter estimate 0.55, 95% confidence interval (CI) 0.21-0.89, p<0.01; Fried phenotype: parameter estimate 0.61, 95% CI 0.22-1.00, p<0.01), including only slight attenuation after adjustment for SLE cumulative organ damage (FRAIL scale: parameter estimate 0.45, 95% CI 0.09-0.81, p=0.02; Fried phenotype: parameter estimate 0.49, 95% CI 0.09-0.90, p=0.02). These preliminary findings support frailty as an independent risk factor for clinically relevant patient-reported outcomes, including disability onset, among women with SLE.

Conventional vs. Drug-Eluting Beads Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma-A Propensity Score Weighted Comparison of Efficacy and Safety

This study compared the efficacy and safety of conventional transarterial chemoembolization (cTACE) with drug-eluting beads (DEB)-TACE in patients with unresectable hepatocellular carcinoma (HCC). This retrospective analysis included 370 patients with HCC treated with cTACE (n = 248) or DEB-TACE (n = 122) (January 2000-July 2014). Overall survival (OS) was assessed using uni- and multivariate Cox proportional hazards models and Kaplan-Meier analysis. Additionally, baseline imaging was assessed, and clinical and laboratory toxicities were recorded. Propensity score weighting via a generalized boosted model was applied to account for group heterogeneity. There was no significant difference in OS between cTACE (20 months) and DEB-TACE patients (24.3 months, ratio 1.271, 95% confidence interval 0.876-1.69; p = 0.392). However, in patients with infiltrative disease, cTACE achieved longer OS (25.1 months) compared to DEB-TACE (9.2 months, ratio 0.366, 0.191-0.702; p = 0.003), whereas DEB-TACE proved more effective in nodular disease (39.4 months) than cTACE (18 months, ratio 0.458, 0.308-0681; p = 0.007). Adverse events occurred with similar frequency, except for abdominal pain, which was observed more frequently after DEB-TACE (101/116; 87.1%) than cTACE (119/157; 75.8%; p = 0.02). In conclusion, these findings suggest that tumor morphology and distribution should be used as parameters to inform decisions on the selection of embolic materials for TACE for a more personalized treatment planning in patients with unresectable HCC.

NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET

PURPOSE

Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of volumetric segmentations in neuroradiology clinical workflow. Material: 1990 patients from Yale Radiation Oncology Registry (2012-2019) were identified. Segmentations were performed using a UNETR algorithm trained on BRaTS 2021 and an internal dataset of manually segmented tumors. Segmentations were validated by a board-certified neuro-radiologist and natively embedded PyRadiomics in PACS was used for feature extraction.

RESULTS

In 7 Months (05/2021 - 08/2021, 03/2022 - 05/2022) segmentations and annotations were performed in 835 patients (322 female, 467 male, 46 unknown, mean age 53 yrs). Dataset includes 275 Grade 4 Gliomas (54 Grade 3, 100 Grade 2, 31 Grade 1, 375 unknown). Molecular subtypes include IDH (113 mutated, 498 wildtype, 2 Equivocal, 222 unknown), 1p/19q (87 deleted or co-deleted, 122 intact, 626 unknown), MGMT promotor (182 methylated, 95 partially methylated, 275 unmethylated, 283 unknown), EGFR (76 amplified, 177 not amplified, 582 unknown), ATRX (40 mutated, 157 retained, 638 unknown), Ki-67 (616 known, 219 unknown) and p53 (549 known, 286 unknown). Classification of gliomas between grade 3/4 and grade 1/2, yielded AUC of 0.85.

CONCLUSION

We developed a method for incorporation of volumetric segmentation, feature extraction, and classification that is easily incorporated into neuroradiology workflow. These tools allowed us to annotate over 100 gliomas per month, thus establishing a proof of concept for rapid development of annotated imaging database for AI applications.

NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET

PURPOSE

Monitoring metastatic disease to the brain is laborious and time-consuming, especially in the setting of multiple metastases and when performed manually. Response assessment in brain metastases based on maximal unidimensional diameter as per the RANO-BM guideline is commonly performed1, however, accurate volumetric lesion estimates can be crucial for clinical decision-making2 and enhance outcome prediction3. We propose a deep learning (DL)-based auto-segmentation approach with the potential for improvement of time-efficiency, reproducibility and robustness against inter-rater variability. Materials and

METHODS

We retrospectively retrieved 259 patients with a total number of 916 lesions from our institutional database from 2014 - 2021. Patients with prior history of local radiation therapy or surgery were excluded. Manually generated trainee segmentations were revised and adjusted by a board-certified radiologist and served as ground truth for evaluation of segmentation accuracy. Model performance was tested via dice-similarity-coefficient (DSC). Volumetric measurements were then obtained within our PACS-integrated workflow on Visage 7 (Visage Imaging, Inc., San Diego, CA) at the click of one button.

RESULTS

For model training and evaluation, a train-test split of 90:10 on patient-level was performed (n= 234:25 (Patients), n= 861:55 (Lesions). A DL-algorithm (nnUNet) was incrementally trained on 10 batches of 23 patients. The DSC of the U-Net gradually increased throughout the training process and heuristically reached a plateau of 0.85. The sensitivity of the algorithm was 83% with detection of 46 out of 55 lesions in the testing dataset. The lesions that were not detected by the algorithm were below 5 mm in size. The false positive rate was 8% (n=4/50).

CONCLUSION

Our study demonstrates the feasibility of PACS-based integration of automatized segmentation workflows of brain metastases. An incremental-training approach is recommended to adapt DL algorithms to specific hospital settings.

NIMG-07. APPLYING A GLIOMA-TRAINED DEEP LEARNING AUTO-SEGMENTATION TOOL ON BM PRE- AND POST-RADIOSURGERY

PURPOSE

Stereotactic radiosurgery (SRS) has become the mainstay to treat BM. Follow-up MRI provides important information on lesion treatment response and guides future therapy planning. Volumetric measurements of BM have shown promise over traditional uni- and two-dimensional measurements in more accurate and repeatable assessment. However, routine clinical use has yet to be achieved because the workflow is laborious. In previous work, we developed a PACS-integrated deep learning algorithm for automatic high- and low-grade glioma 3D segmentation. In this work, we applied this U-Net to segment BM on pre- and post-Gamma Knife (GK) MRI and evaluated the performance.

METHODS

10 pre- and post-GK studies were autosegmented in five randomly selected patients (melanoma n= 3, breast n= 2). The glioma trained algorithm segmented the “Whole Tumor” (tumor core+peritumoral edema on T2w-FLAIR) and “Tumor Core” (CE tumor core+necrosis on SPGR). The AI generated segmentation was then revised as needed by a board-certified neuroradiologist and the dice-similarity-coefficient (DSC) between the revised and automatic volumetric segmentations were calculated.

RESULTS

Four patients had multicentric (2-4 BM) lesions. The mean± SD DSC for Whole Tumor and Tumor Core were 0.92±0.06 and 0.46±0.30 for pretreatment, 0.84±0.09 and 0.41±0.25 for posttreatment BM, respectively. The tool detected lesions with a sensitivity of 45% (5/11) for pretreatment and 50% (3/6) for posttreatment lesions. Three pretreatment and all posttreatment lesions that were not detected by the autosegmentation tool showed a very faint hyperintense peritumoral edema in T2w-FLAIR.

CONCLUSION

Volumetric segmentation of edema on FLAIR using the glioma-trained segmentation algorithm on pre- and post-GK BM did not require major adjustment of segmentation if it detects the lesion. On the other hand, with low sensitivity of lesion detection and low DSC for enhancing component, dedicated training of the algorithm on annotated BM data will be needed.

Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction

Purpose

Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient’s medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction.

Materials and methods

An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations.

Results

UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study.

Conclusion

Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.

Analysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis

PURPOSE

To assess the value of quantitative analysis of tumor burden on baseline MRI for prediction of survival in patients with neuroendocrine tumor liver metastases (NELM) undergoing intra-arterial therapies.

MATERIALS AND METHODS

This retrospective single-center analysis included 122 patients with NELM who received conventional (n = 74) or drug-eluting beads, (n = 20) chemoembolization and radioembolization (n = 28) from 2000 to 2014. Overall tumor diameter (1D) and area (2D) of up to 3 largest liver lesions were measured on baseline arterially contrast enhanced MR images. Three-dimensional quantitative analysis was performed using the qEASL tool (IntelliSpace Portal Version 8, Philips) to calculate enhancing tumor burden (the ratio between enhancing tumor volume and total liver volume). Based on Q-statistics, patients were stratified into low tumor burden (TB) or high TB.

RESULTS

The survival curves were significantly separated between low TB and high TB groups for 1D (p < 0.001), 2D (p < 0.001) and enhancing TB (p = 0.008) measurements, with, respectively, 2.7, 2.6 and 2.2 times longer median overall survival (MOS) in the low TB group (p < 0.001, p < 0.001 and p = 0.008). Multivariate analysis showed that 1D, 2D, and enhancing TB were independent prognostic factors for MOS, with respective hazard ratios of 0.4 (95%CI: 0.2-0.6, p < 0.001), 0.4 (95%CI: 0.3-0.7, p < 0.001) and 0.5 (95%CI: 0.3-0.8, p = 0.003).

CONCLUSION

The overall tumor diameter, overall tumor area, and enhancing tumor burden are strong prognostic factors of overall survival in patients with neuroendocrine tumor liver metastases undergoing intra-arterial therapies.

[Factors associated with death and attrition in HIV-infected children under initial antiretroviral therapy in Guangxi Zhuang Autonomous Region, 2004 - 2019]

Objective: To investigate death and attrition in HIV-infected children under initial antiretroviral therapy (ART) and associated factors in Guangxi Zhuang autonomous region. Methods: This retrospective cohort study was conducted in HIV-infected children under initial ART in Guangxi from 2004 to 2019, data from ART information system of National comprehensive AIDS prevention and treatment information system. Cox proportional hazards models were used to assess factors associated with the death and attrition. Results: In 943 HIV-infected children, the overall mortality and attrition rates were 1.00/100 person-years and 0.77/100 person-years, respectively. The mortality and attrition rates within the first year of ART were 3.90/100 person-years and 1.67/100 person-years, respectively. The cumulative survival rate during the first, second, fifth and tenth year after ART was 96.14%, 95.80%, 93.68% and 91.54%, respectively. Multivariate Cox proportional hazards models results showed that being female (aHR=2.00, 95%CI: 1.17-3.40), CD4⁺T lymphocytes (CD4) counts before ART <200 cells/μl (aHR=2.79, 95%CI: 1.54-5.06), weight-for-age Z score before ART <-2 (aHR=2.38, 95%CI: 1.32-4.26), hemoglobin before ART <80 g/L (aHR=2.47, 95%CI: 1.24-4.92), initial ART with LPV/r (aHR=5.05, 95%CI: 1.15-22.12) were significantly associated with death; being female (aHR=2.23, 95%CI: 1.22-4.07) and initial ART with LPV/r (aHR=2.02, 95%CI: 1.07-3.79) were significantly associated with attrition. Conclusions: The effect of ART in HIV-infected children in Guangxi was better, but the mortality and attrition rates were high within the first year of treatment. It is necessary to strengthen the training in medical staff and health education in HIV-infected children and their parents in order to improve the treatment effect.

COVID-19 Pandemic Impact on US Childhood Caries and Potential Mitigation

Non-Hispanic Black (NHB) and Hispanic and low-income US children have a higher prevalence of untreated caries than their higher-income and non-Hispanic White (NHW) counterparts. Due to the COVID-19 pandemic, many dental offices and school sealant programs closed beginning March 2020. We examine the effect of reduced access to restorative care and sealants on the oral health of children from low-income households overall and by race/ethnicity and how increased sealant delivery in September 2022 could mitigate these effects. We used Markov chain Monte Carlo simulation to model COVID-19's impact on first permanent molar (1M) caries incidence and loss in quality of life (disability-adjusted life years [DALYs]) due to time lived with 1M untreated caries. Our model followed a cohort of children aged 7 y in March 2020 until February 2024. Model inputs were primarily obtained from published studies and nationally representative data. Excess DALYs per 1,000 children attributable to reduced access to care during the pandemic were 1.48 overall and greater for Hispanic (2.07) and NHB (1.75) children than for NHW children (0.94). Excess incidence of 1M caries over 4 y was 2.28 percentage points overall and greater for Hispanic (2.63) and NHB (2.40) children than for NHW (1.96) children. Delivering sealants to 50% of eligible 1Ms in September 2022 would not completely mitigate COVID-19's health access impact: overall excess DALYs would decrease to 1.05, and absolute disparities in excess DALYs between NHW children and Hispanic and NHB children would remain but decrease by 0.38 and 0.33, respectively. Sealing 40% of eligible 1Ms, however, would bring overall 4-y caries incidence down to pre-COVID-19 levels and eliminate the differential effect of the pandemic on children from minority groups. The pandemic's negative impact on the oral health of children from low-income households and increased disparities could be partially mitigated with increased sealant delivery.

Holter electrocardiogram research trends and hotspots: bibliometrics and visual analysis

OBJECTIVE

With the help of metrology, we can identify research hotspots and development trends in dynamic electrocardiography, and thereby provide corresponding reference material to aid further theoretical research.

MATERIALS AND METHODS

All research data derived from the core collection of Web of Science, and all searches were completed on the same day (February 6, 2022). The obtained data were stored in plain text format and imported into CiteSpace for subsequent analysis. Citation analysis and visualization technology were used to draw a visual map of the research elements, using factors such as annual literature volume, country, journal, author, abstract, keywords, and citation.

RESULTS

After screening, 2,937 papers were obtained. Research on ambulatory electrocardiography is increasing worldwide every year. Using research hotspots, keyword-clustering time-zone maps, and high-frequency emerging words, the research in this field was roughly divided into two stages, with 2017 as the divider. The first stage primarily focuses on areas such as atrial fibrillation, stroke, autonomic nerve function, catheter ablation, and T-wave alternation. The second stage saw the focus shift to wearable devices, sudden cardiac death, obstructive sleep apnea, feature extraction, cryptogenic stroke, and similar topics.

CONCLUSIONS

With the development of various wearable technologies, the daily monitoring of healthy people engaged in sporting activities and the development of innovative analysis algorithms providing more accurate data may represent the hotspots and direction of future research.

PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients

The image quality in clinical PET scan can be severely degraded due to high noise levels in extremely obese patients. Our work aimed to reduce the noise in clinical PET images of extremely obese subjects to the noise level of lean subject images, to ensure consistent imaging quality. The noise level was measured by normalized standard deviation (NSTD) derived from a liver region of interest. A deep learning-based noise reduction method with a fully 3D patch-based U-Net was used. Two U-Nets, U-Nets A and B, were trained on datasets with 40% and 10% count levels derived from 100 lean subjects, respectively. The clinical PET images of 10 extremely obese subjects were denoised using the two U-Nets. The results showed the noise levels of the images with 40% counts of lean subjects were consistent with those of the extremely obese subjects. U-Net A effectively reduced the noise in the images of the extremely obese patients while preserving the fine structures. The liver NSTD improved from 0.13±0.04 to 0.08±0.03 after noise reduction (p = 0.01). After denoising, the image noise level of extremely obese subjects was similar to that of lean subjects, in terms of liver NSTD (0.08±0.03 vs. 0.08±0.02, p = 0.74). In contrast, U-Net B over-smoothed the images of extremely obese patients, resulting in blurred fine structures. In a pilot reader study comparing extremely obese patients without and with U-Net A, the difference was not significant. In conclusion, the U-Net trained by datasets from lean subjects with matched count level can provide promising denoising performance for extremely obese subjects while maintaining image resolution, though further clinical evaluation is needed.

Response assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization

PURPOSE

This study assessed the response to conventional transarterial chemoembolization (cTACE) in patients with liver metastases from rare tumor primaries using one-dimensional (1D) and three-dimensional (3D) quantitative response assessment methods, and investigate the relationship of lipiodol deposition in predicting response.

MATERIALS AND METHODS

This retrospective bicentric study included 16 patients with hepatic metastases from rare tumors treated with cTACE between 2002 and 2017. Multi-phasic MR imaging obtained before and after cTACE was used for assessment of response. Response evaluation criteria in solid tumors (RECIST) and modified-RECIST (mRECIST) were utilized for 1D response assessment, and volumetric RECIST (vRECIST) and enhancement-based quantitative European Association for Study of the Liver EASL (qEASL) were used for 3D response assessment. The same day post-cTACE CT scan was analyzed to quantify intratumoral lipiodol deposition (%).

RESULTS

The mean and standard deviation (SD) of diameter of treated lesions per targeted area was 7.5 ± 5.4 cm, and the mean and SD of number of metastases in each targeted area was 4.2 ± 4.6. cTACE was technically successful in all patients, without major complications. While RECIST and vRECIST methods did not allocate patients with partial response, mRECIST and qEASL identified patients with partial response. Intratumoral lipiodol deposition significantly predicted treatment response according qEASL (R² = 0.470, p < 0.01), while no association was shown between lipiodol deposition within treated tumor area and RECIST or mRECIST (p > 0.212).

CONCLUSION

3D quantitative volumetric response analysis can be used for stratification of response to cTACE in patients with hepatic metastases originating from rare primary tumors. Lipiodol deposition could potentially be used as an early surrogate to predict response to cTACE.

Dual-energy x-ray absorptiometry scanner mismatch in follow-up bone mineral density testing

Scanner mismatch occurs frequently with follow-up dual-energy x-ray absorptiometry (DXA) scans. Nearly one-in-five follow-up DXA scans were conducted on non-cross-calibrated scanners (scanner mismatch) and more than a quarter of patients who had a follow-up DXA scan had experienced scanner mismatch.

INTRODUCTION

Detecting significant changes in bone mineral density (BMD) with dual-energy x-ray absorptiometry (DXA) scanners relies on the least significant change (LSC). Results from two different DXA scanners can only be compared, albeit with decreased sensitivity for change, if the LSC between the two scanners has been directly determined through cross-calibration. Performing follow-up DXA scans on non-cross-calibrated scanners (scanner mismatch) has safety and economic implications. This study aims to determine the proportion of scanner mismatch occurring at a population level.

METHODS

All patients who completed at least two DXA scans between 1 April 2009 and 31 December 2018 in the province of Alberta, Canada, were identified using population-based health services databases. Scanner mismatch was defined as a follow-up DXA scan completed on a DXA scanner that differed from and was not cross-calibrated to the previous DXA scanner. Multivariate logistic regression models were used to assess predictive factors that may contribute to scanner mismatch.

RESULTS

A total of 264,866 patients with 470,641 follow-up DXA scans were identified. Scanner mismatch occurred in 18.9% of follow-up DXA scans; 28.7% of patients experienced at least one scanner mismatch. Longer duration between scans (OR 1.25, 95% CI 1.24-1.26) and major osteoporotic fracture history before index scan (OR 1.06, 95% CI 1.03-1.08) increased risk of scanner mismatch. Osteoporosis medication use before index scan (OR 0.89; 95% CI 0.88-0.91), recency of follow-up scans (OR 0.98, 95% CI 0.73-0.98), female sex (OR 0.97, 95% CI 0.94-1.00), and age at last scan (OR 0.99, 95% CI 0.99-1.00) were associated with lower risk of scanner mismatch.

CONCLUSION

Scanner mismatch is a common problem, occurring in one-in-five follow-up DXA scans and affecting more than a quarter of patients. Interventions to reduce this large proportion of scanner mismatch are necessary.

Adopting a systems view of disrupting crisis-driven food insecurity

Objectives

During the COVID crisis, the incidence of food insecurity worsened around the globe. We were reminded that: food insecurity existed before COVID, worsened during this crisis, and will unfortunately be a persistent phenomenon in the post-COVID world. It is evident that to counter this public health threat, systematic changes will need to happen. In this short communication, we introduce the notion of a systems-oriented framework that can guide appropriate actions for us to disrupt future food insecurity crises.

Study design

This short communication identifies preliminary observations based on relevant past studies that documented the impact of COVID-19 on food insecurity, and the researchers’ conceptualization of a framework on how we may address future crisis-driven food insecurity challenges.

Methods

Systems-oriented framework was conceptualized based on preliminary observations in studies that investigated food insecurity during the COVID-19 pandemic.

Results

This short communication explores the notion of a systems-oriented framework as a guide to future action to prevent crisis-driven food insecurity.

Conclusions

The systems-oriented framework emphasizes the importance of action across macro, meso, and micro levels, and synchronization to maximize synergies.