Citations

AbstractSince the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on‐board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk‐based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management. A subset of TPS commissioning and QA checks in MPPG 5.a. applicable to tomotherapy are recommended. In addition, failure mode and effects analysis has been conducted among TG members to obtain multi‐institutional analysis on tomotherapy‐related failure modes and their effect ranking.

Hexanucleotide G 4 C 2 repeat expansions in the C9orf72 gene are the most common genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Dipeptide repeat proteins (DPRs) generated by translation of repeat-containing RNAs show toxic effects in vivo as well as in vitro and are key targets for therapeutic intervention. We generated human antibodies that bind DPRs with high affinity and specificity. Anti-GA antibodies engaged extra- and intra-cellular poly-GA and reduced aggregate formation in a poly-GA overexpressing human cell line. However, antibody treatment in human neuronal cultures synthesizing exogenous poly-GA resulted in the formation of large extracellular immune complexes and did not affect accumulation of intracellular poly-GA aggregates. Treatment with antibodies was also shown to directly alter the morphological and biochemical properties of poly-GA and to shift poly-GA/antibody complexes to more rapidly sedimenting ones. These alterations were not observed with poly-GP and have important implications for accurate measurement of poly-GA levels including the need to evaluate all centrifugation fractions and disrupt the interaction between treatment antibodies and poly-GA by denaturation. Targeting poly-GA and poly-GP in two mouse models expressing G 4 C 2 repeats by systemic antibody delivery for up to 16 mo was well-tolerated and led to measurable brain penetration of antibodies. Long-term treatment with anti-GA antibodies produced improvement in an open-field movement test in aged C9orf72 450 mice. However, chronic administration of anti-GA antibodies in AAV-(G 4 C 2 ) 149 mice was associated with increased levels of poly-GA detected by immunoassay and did not significantly reduce poly-GA aggregates or alleviate disease progression in this model.

PURPOSE We sought to evaluate the independent and interactive associations of planned treatment duration, celecoxib use, physical activity, body mass index (BMI), diabetes mellitus, and vitamin B6 with oxaliplatin-induced peripheral neuropathy (OIPN) among patients with stage III colon cancer enrolled in a clinical trial. METHODS We conducted a prospective, observational study of 2,450 patients with stage III colon cancer enrolled in the CALGB/SWOG 80702 trial, randomly assigned to 6 versus 12 cycles of adjuvant fluorouracil, leucovorin, and oxaliplatin chemotherapy with or without 3 years of celecoxib. OIPN was reported using the Common Terminology Criteria for Adverse Events (CTCAE) during and following completion of chemotherapy and the FACT/GOG-NTX-13 15-17 months after random assignment. Multivariate analyses were adjusted for baseline sociodemographic and clinical factors. RESULTS Patients assigned to 12 treatment cycles, relative to 6, were significantly more likely to experience higher-grade CTCAE- and FACT/GOG-NTX-13-reported neuropathy and longer times to resolution, while neither celecoxib nor vitamin B6 intake attenuated OIPN. Exercising ≥ 9 MET-hours per week after treatment relative to < 9 was associated with improvements in FACT/GOG-NTX-13-reported OIPN (adjusted difference in means, 1.47; 95% CI, 0.49 to 2.45; P = .003). Compared with patients with baseline BMIs < 25, those with BMIs ≥ 25 were at significantly greater risk of developing higher-grade CTCAE-reported OIPN during (adjusted odds ratio, 1.18; 95% CI, 1.00 to 1.40; P = .05) and following completion (adjusted odds ratio, 1.23; 95% CI, 1.01 to 1.50; P = .04) of oxaliplatin treatment. Patients with diabetes were significantly more likely to experience worse FACT/GOG-NTX-13-reported neuropathy relative to those without (adjusted difference in means, –2.0; 95% CI, –3.3 to –0.73; P = .002). There were no significant interactions between oxaliplatin treatment duration and any of these potentially modifiable exposures. CONCLUSION Lower physical activity, higher BMI, diabetes, and longer planned treatment duration, but not celecoxib use or vitamin B6 intake, may be associated with significantly increased OIPN severity.

Abstract Background. Clinical medicine relies heavily on the synthesis of information and data from multiple sources. However, often simple feature concatenation is used as a strategy for developing a multimodal machine learning model in the cardiovascular domain, and thus the models are often limited by pre-selected features and moderate accuracy. Method. We proposed a two-branched joint fusion model for fusing the 12-lead electrocardiogram (ECG) signal data with clinical variables from the electronic medical record (EMR) in an end-to-end deep learning architecture. The model follows the joint fusion scheme and learns complementary information from ECG and EMR. Retrospective data from the Mayo Clinic Health Systems across four sites for patients that underwent percutaneous coronary intervention (PCI) were obtained. Model performance was assessed by area under the receiver-operating characteristics (AUROC) and Delong’s test. Results. The final cohort included 17,356 unique patients with a mean age of 67.2 ± 12.6 year (mean ± std) and 9,163 (52.7%) were male. The joint fusion model outperformed the ECG time-domain model with statistical margin. The model with clinical data obtained the highest AUROC for all-cause mortality (0.91 at 6 months) but the joint fusion model outperformed for cardiovascular outcomes - heart failure hospitalization and ischemic stroke with a significant margin (Delong’s p < 0.05). Conclusion. To the best of our knowledge, this is the first study that developed a deep learning model with joint fusion architecture for the prediction of post-PCI prognosis and outperformed machine learning models developed using traditional single-source features (clinical variables or ECG features). Adding ECG data with clinical variables did not improve prediction of all-cause mortality as may be expected, but the improved performance of related cardiac outcomes shows that the fusion of ECG generates additional value.

Background: Clinical and anatomical characteristics are often considered key factors in deciding between percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) in patients with complex coronary artery disease (CAD) such as left-main CAD or 3-vessel disease. However, little is known about the interaction between self-reported preprocedural physical/mental health and clinical outcomes after revascularization. Methods: This subgroup analysis of the SYNTAXES trial (SYNTAX Extended Survival), which is the extended follow-up of the randomized SYNTAX trial (Synergy Between PCI With Taxus and Cardiac Surgery) comparing PCI with CABG in patients with left-main CAD or 3-vessel disease, stratified patients by terciles of Physical (PCS) or Mental Component Summary (MCS) scores derived from the preprocedural 36-Item Short Form Health Survey, with higher PCS and MCS scores representing better physical and mental health, respectively. The primary end point was all-cause death at 10 years. Results: A total of 1656 patients with preprocedural 36-Item Short Form Health Survey data were included in the present study. Both higher PCS and MCS were independently associated with lower 10-year mortality (10-point increase in PCS adjusted hazard ratio, 0.84 [95% CI, 0.73–0.97]; P =0.021; in MCS adjusted hazard ratio, 0.85 [95% CI, 0.76–0.95]; P =0.005). A significant survival benefit with CABG over PCI was observed in the highest PCS (>45.5) and MCS (>52.3) terciles with significant treatment-by-subgroup interactions (PCS P interaction =0.033, MCS P interaction =0.015). In patients with both high PCS (>45.5) and MCS (>52.3), 10-year mortality was significantly higher with PCI compared with CABG (30.5% versus 12.2%; hazard ratio, 2.87 [95% CI, 1.55–5.30]; P =0.001), whereas among those with low PCS (≤45.5) or low MCS (≤52.3), there were no significant differences in 10-year mortality between PCI and CABG, resulting in a significant treatment-by-subgroup interaction ( P interaction =0.002). Conclusions: Among patients with left-main CAD or 3-vessel disease, patient-reported preprocedural physical and mental health status was strongly associated with long-term mortality and modified the relative treatment effects of PCI versus CABG. Patients with the best physical and mental health had better 10-year survival with CABG compared with PCI. Assessment of self-reported physical and mental health is important when selecting the optimal revascularization strategy. Registration: URL: https://www.clinicaltrials.gov ; SYNTAXES Unique identifier: NCT03417050. URL: https://www.clinicaltrials.gov ; SYNTAX Unique identifier: NCT00114972.

Abstract Background Topological data analysis (TDA) is the state-of-the-art unsupervised machine learning framework that can provide insight into the dataset and visualize condensed information via the topological network graph. This robust approach was never used to assess the heterogeneous MitraClip population. Purpose We aim to develop a TDA model that will identify prognostically-distinct phenogroups in MitraClip patients without a priori knowledge of the population and their outcomes. Method Patients who underwent MitraClip (June 2014-September 2020) at Mayo Clinic sites were identified from the institutional database for baseline and follow-up data. Thirteen variables were used for TDA. The topological network graph was created using the Python Scikit-TDA Kepler-Mapper package (v. 2.0.1), and clustering was performed at the graph level with Louvain's modularity method. Kaplan-Meier survival analysis was used to assess the all-cause mortality endpoint of each cluster identified in an unsupervised manner. The dataset with cluster labels was also used to train a Light Gradient Boosted Machine model, and SHapley Additive exPlanations (SHAP) analysis was applied to determine the feature importance. Results A total of 389 consecutive patients were included in the final analysis and two major clusters consisting of 384 patients were identified. The mean age was 80.3±8.7 years; 256 (65.8%) were male. The mean Society of Thoracic Surgeons Mitral Valve Replacement risk score was 9.6±6.9%. Fifty-five (14.5%) patients died during the mean follow duration (185 days). Kaplan-Meier analysis showed significant survival differences among the two clusters (HR: 2.38; 95% CI: 1.39–4.06, p=0.001; Figure 1). Clusters 1 (n=195) was associated with &gt; mild residual mitral regurgitation and worse survival performance and was characterized with worse tricuspid regurgitation severity, a higher proportion of patients with atrial fibrillation/flutter, anterior leaflet prolapse, and mitral annular/leaflet calcification, as summarized in Table 1. Conclusion TDA can identify distinct phenotype clusters with prognostic significance in MitraClip patients based on mitral valve morphology and clinical risk factors. This simple model can facilitate clinical risk stratification for MitraClip patients regarding procedural success and survival performance. Funding Acknowledgement Type of funding sources: None.

Aims Increased LV wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4 chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). Conclusion The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.