Citations

Limb ischaemia is a clinically relevant complication of venoarterial extracorporeal membrane oxygenation (VA ECMO) with femoral artery cannulation. No selective distal perfusion or other advanced techniques were used in the past to maintain adequate distal limb perfusion. A more recent trend is the shift from the reactive or emergency management to the pro-active or prophylactic placement of a distal perfusion cannula to avoid or reduce limb ischaemia-related complications. Multiple alternative cannulation techniques to the distal perfusion cannula have been developed to maintain distal limb perfusion, including end-to-side grafting, external or endovascular femoro-femoral bypass, retrograde limb perfusion (e.g., via the posterior tibial, dorsalis pedis or anterior tibial artery), and, more recently, use of a bidirectional cannula. Venous congestion has also been recognized as a potential contributing factor to limb ischaemia development and specific techniques have been described with facilitated venous drainage or bilateral cannulation being the most recent, to reduce or avoid venous stasis as a contributor to impaired limb perfusion. Advances in monitoring techniques, such as near-infrared spectroscopy and duplex ultrasound analysis, have been applied to improve decision-making regarding both the monitoring and management of limb ischaemia. This narrative review describes the evolution of techniques used for distal limb perfusion during peripheral VA ECMO.

OBJECTIVE Congenital anomalies of the atlanto-occipital articulation may be present in patients with Chiari malformation type I (CM-I). However, it is unclear how these anomalies affect the biomechanical stability of the craniovertebral junction (CVJ) and whether they are associated with an increased incidence of occipitocervical fusion (OCF) following posterior fossa decompression (PFD). The objective of this study was to determine the prevalence of condylar hypoplasia and atlas anomalies in children with CM-I and syringomyelia. The authors also investigated the predictive contribution of these anomalies to the occurrence of OCF following PFD (PFD+OCF). METHODS The authors analyzed the prevalence of condylar hypoplasia and atlas arch anomalies for patients in the Park-Reeves Syringomyelia Research Consortium database who underwent PFD+OCF. Condylar hypoplasia was defined by an atlanto-occipital joint axis angle (AOJAA) ≥ 130°. Atlas assimilation and arch anomalies were identified on presurgical radiographic imaging. This PFD+OCF cohort was compared with a control cohort of patients who underwent PFD alone. The control group was matched to the PFD+OCF cohort according to age, sex, and duration of symptoms at a 2:1 ratio. RESULTS Clinical features and radiographic atlanto-occipital joint parameters were compared between 19 patients in the PFD+OCF cohort and 38 patients in the PFD-only cohort. Demographic data were not significantly different between cohorts (p > 0.05). The mean AOJAA was significantly higher in the PFD+OCF group than in the PFD group (144° ± 12° vs 127° ± 6°, p < 0.0001). In the PFD+OCF group, atlas assimilation and atlas arch anomalies were identified in 10 (53%) and 5 (26%) patients, respectively. These anomalies were absent (n = 0) in the PFD group (p < 0.001). Multivariate regression analysis identified the following 3 CVJ radiographic variables that were predictive of OCF occurrence after PFD: AOJAA ≥ 130° (p = 0.01), clivoaxial angle < 125° (p = 0.02), and occipital condyle–C2 sagittal vertical alignment (C–C2SVA) ≥ 5 mm (p = 0.01). A predictive model based on these 3 factors accurately predicted OCF following PFD (C-statistic 0.95). CONCLUSIONS The authors’ results indicate that the occipital condyle–atlas joint complex might affect the biomechanical integrity of the CVJ in children with CM-I and syringomyelia. They describe the role of the AOJAA metric as an independent predictive factor for occurrence of OCF following PFD. Preoperative identification of these skeletal abnormalities may be used to guide surgical planning and treatment of patients with complex CM-I and coexistent osseous pathology.

The immune system substantially influences age-related cognitive decline and Alzheimer’s disease (AD) progression, affected by genetic and environmental factors. In a Mayo Clinic Study of Aging cohort, we examined how risk factors like APOE genotype, age, and sex affect inflammatory molecules and AD biomarkers in cerebrospinal fluid (CSF). Among cognitively unimpaired individuals over 65 ( N = 298), we measured 365 CSF inflammatory molecules, finding age, sex, and diabetes status predominantly influencing their levels. We observed age-related correlations with AD biomarkers such as total tau, phosphorylated tau-181, neurofilament light chain (NfL), and YKL40. APOE4 was associated with lower Aβ42 and higher SNAP25 in CSF. We explored baseline variables predicting cognitive decline risk, finding age, CSF Aβ42, NfL, and REG4 to be independently correlated. Subjects with older age, lower Aβ42, higher NfL, and higher REG4 at baseline had increased cognitive impairment risk during follow-up. This suggests that assessing CSF inflammatory molecules and AD biomarkers could predict cognitive impairment risk in the elderly.

Importance The association of tumor-infiltrating lymphocyte (TIL) abundance in breast cancer tissue with cancer recurrence and death in patients with early-stage triple-negative breast cancer (TNBC) who are not treated with adjuvant or neoadjuvant chemotherapy is unclear. Objective To study the association of TIL abundance in breast cancer tissue with survival among patients with early-stage TNBC who were treated with locoregional therapy but no chemotherapy. Design, Setting, and Participants Retrospective pooled analysis of individual patient-level data from 13 participating centers in North America (Rochester, Minnesota; Vancouver, British Columbia, Canada), Europe (Paris, Lyon, and Villejuif, France; Amsterdam and Rotterdam, the Netherlands; Milan, Padova, and Genova, Italy; Gothenburg, Sweden), and Asia (Tokyo, Japan; Seoul, Korea), including 1966 participants diagnosed with TNBC between 1979 and 2017 (with follow-up until September 27, 2021) who received treatment with surgery with or without radiotherapy but no adjuvant or neoadjuvant chemotherapy. Exposure TIL abundance in breast tissue from resected primary tumors. Main Outcomes and Measures The primary outcome was invasive disease-free survival [iDFS]. Secondary outcomes were recurrence-free survival [RFS], survival free of distant recurrence [distant RFS, DRFS], and overall survival. Associations were assessed using a multivariable Cox model stratified by participating center. Results This study included 1966 patients with TNBC (median age, 56 years [IQR, 39-71]; 55% had stage I TNBC). The median TIL level was 15% (IQR, 5%-40%). Four-hundred seventeen (21%) had a TIL level of 50% or more (median age, 41 years [IQR, 36-63]), and 1300 (66%) had a TIL level of less than 30% (median age, 59 years [IQR, 41-72]). Five-year DRFS for stage I TNBC was 94% (95% CI, 91%-96%) for patients with a TIL level of 50% or more, compared with 78% (95% CI, 75%-80%) for those with a TIL level of less than 30%; 5-year overall survival was 95% (95% CI, 92%-97%) for patients with a TIL level of 50% or more, compared with 82% (95% CI, 79%-84%) for those with a TIL level of less than 30%. At a median follow-up of 18 years, and after adjusting for age, tumor size, nodal status, histological grade, and receipt of radiotherapy, each 10% higher TIL increment was associated independently with improved iDFS (hazard ratio [HR], 0.92 [0.89-0.94]), RFS (HR, 0.90 [0.87-0.92]), DRFS (HR, 0.87 [0.84-0.90]), and overall survival (0.88 [0.85-0.91]) (likelihood ratio test, P &amp;lt; 10e-6). Conclusions and Relevance In patients with early-stage TNBC who did not undergo adjuvant or neoadjuvant chemotherapy, breast cancer tissue with a higher abundance of TIL levels was associated with significantly better survival. These results suggest that breast tissue TIL abundance is a prognostic factor for patients with early-stage TNBC.

Abstract Background: Recent studies indicate a correlation between increased stromal composition in tumors and resistance to immune checkpoint inhibitors (ICIs) across various cancer types. Specifically, the extracellular matrix (ECM) within the stromal compartment significantly influence the tumor microenvironment, affecting cancer cell proliferation and immune cell recruitment. However, identifying and quantifying the ECM at the collagen fiber level in whole slide images (WSIs) is challenging due to its inherent heterogeneity and complexity. In this work, we utilized statistical analysis and a deep learning (DL)-based approach to identify and assess collagen in the ECM, examining its characteristics in relation to the ICIs treatment response in gastric cancer. Methods: We analyzed 116 WSIs of hematoxylin and eosin (H&E) stained slides before ICIs treatment from three institutions. Responders were defined as those with a complete response or partial response (N=18); those with stable disease (SD) (N=34) or progressive disease (PD) (N=64) were considered non-responders (N=98). For non-responders, post-treatment H&E slides were available for 21 patients; SD (N=5) and PD (N=16). We applied a DL-based second harmonic generation (SHG) synthesis algorithm to tumor regions identified by a tumor detection model. We then calculated various collagen properties, including volume, fiber orientation and alignment, waviness, straightness, and fiber thickness, based on collagen fiber centerlines extracted from the generated SHG images. An attention-based multiple instance learning (ABMIL) model was trained to automatically predict the treatment response solely based on collagen features. The dataset of 116 pre-treatment slides was divided into training and validation subsets using stratified 3-fold cross-validation. The area under the curve (AUC) was used to assess the performance of the trained models. Results: Collagen fiber features were significantly associated with ICIs response. Notably, ICIs responders have a decrease in collagen waviness and fiber orientation variation (p-value&lt;0.05, by Mann-Whitney U test). Among non-responders, the PD group showed a statistical decrease in waviness after treatment (p-value&lt;0.05, by Mann-Whitney U test). The ABMIL model achieved a mean AUC score of 0.823, with a standard deviation of 0.023. Conclusion: This study provides a comprehensive analysis of collagen in the ECM, and its properties in relation to gastric cancer ICIs response within Tumor Immune Microenvironment. We also developed a model using collagen features and demonstrated high accuracy in predicting treatment response. Citation Format: Minji Kim, Ekaterina Redekop, Yoonho Choi, Sung Hak Lee, Jae-Ho Cheong, Yu Aoki, Kohei Shitara, Tae Hyun Hwang. AI-driven analysis of collagen characteristics in the tumor immune microenvironment predicts immune checkpoint inhibitors treatment responsiveness in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4262.

Abstract Background: Microsatellite instability-high (MSI-H) tumors respond well to immune checkpoint blockade and oftentimes not to chemotherapeutics. Despite value of determining MSI status to guide therapy, many patients’ samples are not untested. Deep learning models may be able to predict MSI status by analyzing hematoxylin and eosin (H&E) stained whole-slide images (WSIs). Validation of this tool will provide another tool to increase MSI testing. However, to be deployed into clinical routine care, prediction models need be externally validated in large and diverse patient cohorts. In addition, these models should provide uncertainty of prediction to help clinicians make informed decisions. Results: In this study, we introduce a novel Bayesian prediction model using whole-slide images (WSIs) and deep Gaussian processes (DGPs) in a weakly supervised learning context. Extensively tested on large datasets (n=3,146) from multiple centers, our model not only demonstrated superior performance but also showed that incorporating uncertainty into predictions significantly improves MSI prediction accuracy, achieving AUROC of &gt;0.91 in colon cancer and &gt;0.94 in gastric cancer. Additionally, an in-depth analysis within the gastric cancer cohort indicated a significant correlation between predicted MSI-H regions and immunotherapy response. Patients who responded to immune checkpoint inhibitors had a higher proportion of MSI-H regions (P-value &lt; 0.05). This correlation was also observed in patients categorized as MSS but responsive to ICIs, highlighting the potential of our model to identify MSI-H patterns not detected through standard laboratory tests. This advancement could lead to more precise patient selection for ICI therapy, enhancing personalized treatment strategies in gastrointestinal cancer. Citation Format: Sunho Park, Minj Kim, Ji-Youn Sung, Jeong Hwan Park, Sung Hak Lee, Sam C. Wang, Jaeho Cheong, Tae Hyun Hwang. Explainable AI model incorporating uncertainty estimation for enhanced MSI-H prediction and immunotherapy response in gastrointestinal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7385.

Abstract Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer. Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7400.

Background: Salivary duct carcinomas (SDC) are a rare and aggressive subtype of salivary gland neoplasm. They can present with distinct immunoprofiles, such as androgen receptor (AR) and HER-2/Neu-positivity. To date, no consensus exists on how to best manage this entity. Methods: All patients diagnosed with nonmetastatic AR+ SDC of the parotid from 2013 to 2019 treated with curative intent were included. Immunologic tumor profiling was conducted using 24 distinct markers. Kaplan–Meier analyses were used to estimate locoregional recurrence (LRR), distant control, and overall survival (OS). Results: Fifteen patients were included. Nine (60%) patients presented with T4 disease and eight (53%) had positive ipsilateral cervical lymphadenopathy. Ten (67%) patients underwent trimodality therapy, including surgery followed by adjuvant radiation and concurrent systemic therapy. The median follow-up was 5.5 years (interquartile range, 4.8–6.1). The estimated 5-year rates of LRR, distant progression, and OS were 6%, 13%, and 87%, respectively. Conclusion: Despite only including AR+ SDC of the parotid, immunoprofiles, such as expression of HER-2, were highly variable, highlighting the potential to tailor systemic regimens based on individual histologic profiles in the future. Studies with larger patient numbers using tumor-specific molecular profiling and tumor heterogeneity analyses are justified to better understand the biology of these tumors. Molecularly informed treatment approaches, including the potential use of AR- and HER-2/Neu-directed therapies upfront in the definitive setting, may hold future promise to further improve outcomes for these patients.

Abstract Background Variations in climate have been associated with a greater risk of surgical site infections, urinary tract infections, and changes in the skin microbiome; however, limited data exist on the impact of climate on inflatable penile prosthesis (IPP) infections. Aim We sought to evaluate the impact of climate on the risk of IPP infections in a large international, multicenter cohort. Methods We performed a multi-institutional, retrospective study of patients undergoing IPP surgery. We then evaluated whether the month or season, during which surgery was performed, affected device infections. Implant infections were defined as infections requiring device explantation. A univariate logistic regression analysis was undertaken. Outcomes Our primary outcome was implant infection. Results A total of 5289 patients with a mean age of 62.2 ± 10.8 years received IPP placement. There was a fairly even distribution of implants performed in each season. A total of 103 (1.9%) infections were recorded. There were 32 (31.1%) IPP infections in patients who underwent surgery in the summer, followed by 28 (27.2%) in the winter, 26 (25.2%) in the spring, and 17 (16.5%) in the fall. No statistically significant differences were recorded in terms of season (P = .19) and month (P = .29). The mean daily temperature (P = .43), dew point (P = .43), and humidity (P = .92) at the time of IPP placement was not associated with infection. Clinical Implications These findings provide reassurance to prosthetic urologists that infection reduction strategies do not need to be tailored to local climate. Strengths and Limitations Climate data were not directly recorded for each hospital, but rather based on the monthly averages in the city where the surgery was performed. Conclusion The climate at time of IPP placement and time of year of surgery is not associated with IPP infection risk.