Cerebral ischemia and reperfusion initiate cellular events in brain that lead to neurological disability. Investigating these cellular events provides ample targets for developing new treatments. Despite considerable work, no such therapy has translated into successful stroke treatment. Among other issues—such as incomplete mechanistic knowledge and faulty clinical trial design—a key contributor to prior translational failures may be insufficient scientific rigor during preclinical assessment: nonblinded outcome assessment; missing randomization; inappropriate sample sizes; and preclinical assessments in young male animals that ignore relevant biological variables, such as age, sex, and relevant comorbid diseases. Promising results are rarely replicated in multiple laboratories. We sought to address some of these issues with rigorous assessment of candidate treatments across 6 independent research laboratories. The Stroke Preclinical Assessment Network (SPAN) implements state-of-the-art experimental design to test the hypothesis that rigorous preclinical assessment can successfully reduce or eliminate common sources of bias in choosing treatments for evaluation in clinical studies. SPAN is a randomized, placebo-controlled, blinded, multilaboratory trial using a multi-arm multi-stage protocol to select one or more putative stroke treatments with an implied high likelihood of success in human clinical stroke trials. The first stage of SPAN implemented procedural standardization and experimental rigor. All participating research laboratories performed middle cerebral artery occlusion surgery adhering to a common protocol and rapidly enrolled 913 mice in the first of 4 planned stages with excellent protocol adherence, remarkable data completion and low rates of subject loss. SPAN stage 1 successfully implemented treatment masking, randomization, prerandomization inclusion/exclusion criteria, and blinded assessment to exclude bias. Our data suggest that a large, multilaboratory, preclinical assessment effort to reduce known sources of bias is feasible and practical. Subsequent SPAN stages will evaluate candidate treatments for potential success in future stroke clinical trials using aged animals and animals with comorbid conditions.

Human diseases may be modeled in animals to allow preclinical assessment of putative new clinical interventions. Recent, highly publicized failures of large clinical trials called into question the rigor, design, and value of preclinical assessment. We established the Stroke Preclinical Assessment Network (SPAN) to design and implement a randomized, controlled, blinded, multi-laboratory trial for the rigorous assessment of candidate stroke treatments combined with intravascular thrombectomy. Efficacy and futility boundaries in a multi-arm multi-stage statistical design aimed to exclude from further study highly effective or futile interventions after each of four sequential stages. Six independent research laboratories performed a standard focal cerebral ischemic insult in five animal models that included equal numbers of males and females: young mice, young rats, aging mice, mice with diet-induced obesity, and spontaneously hypertensive rats. The laboratories adhered to a common protocol and efficiently enrolled 2615 animals with full data completion and comprehensive animal tracking. SPAN successfully implemented treatment masking, randomization, prerandomization inclusion and exclusion criteria, and blinded assessment of outcomes. The SPAN design and infrastructure provide an effective approach that could be used in similar preclinical, multi-laboratory studies in other disease areas and should help improve reproducibility in translational science.

Despite curative-intent radical cystectomy (RC), patients with muscle-invasive bladder cancer (MIBC) are at high risk of recurrence. Biomarkers are urgently needed to refine prognostication and selection of appropriate perioperative systemic therapies. Our aim was to evaluate the prognostic and predictive value of tumor-informed circulating tumor DNA (ctDNA) results in a multicenter cohort of patients with bladder cancer who underwent RC. We performed a retrospective analysis of real-world data for a commercial ctDNA test (Signatera; Natera, Austin, TX, USA) performed in 167 patients (852 plasma samples) before RC and during molecular residual disease (MRD; adjuvant decision) and surveillance windows. We assessed the correlation between recurrence and ctDNA status before and after RC using Cox regression analysis. During study-defined postoperative MRD and surveillance windows, detectable ctDNA was associated with shorter disease-free survival (DFS) when compared to undetectable ctDNA (MRD: hazard ratio 6.93; p < 0.001; surveillance: hazard ratio 23.02; p < 0.001). Of note, patients with undetectable ctDNA did not appear to benefit from adjuvant therapy (p = 0.34). Detectable ctDNA in the pre-RC (p = 0.045), MRD (p = 0.002), and surveillance (p < 0.001) windows was the only risk factor independently associated with shorter DFS. Limitations include the retrospective and nonrandomized nature of the study. ctDNA testing in patients with bladder cancer undergoing RC was prognostic and potentially predictive. Identification of patients at high risk of recurrence may aid in patient counseling and decision-making. We found that outcomes for patients with muscle-invasive bladder cancer are strongly linked to detection of tumor DNA in blood samples. The results show the value of tumor-informed testing for tumor DNA in blood for decisions on the best treatment for each individual patient.

Key words: Fibrolamellar hepatocellular carcinoma; Liver cancer; Liver neoplasms; Hepatocellular carcinoma (fibrolamellar variant)

Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI. This study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models. Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation.

One of the potentially important applications of three-dimensional (3D) intracranial vessel wall (IVW) cardiovascular magnetic resonance (CMR) is to monitor disease progression and regression via quantitative measurement of IVW morphology during medical management or drug development. However, a prerequisite for this application is to validate that IVW morphologic measurements based on the modality are reliable. In this study we performed comprehensive reliability analysis for the recently proposed whole-brain IVW CMR technique. Thirty-four healthy subjects and 10 patients with known intracranial atherosclerotic disease underwent repeat whole-brain IVW CMR scans. In 19 of the 34 subjects, two-dimensional (2D) turbo spin-echo (TSE) scan was performed to serve as a reference for the assessment of vessel dimensions. Lumen and wall volume, normalized wall index, mean and maximum wall thickness were measured in both 3D and 2D IVW CMR images. Scan-rescan, intra-observer, and inter-observer reproducibility of 3D IVW CMR in the quantification of IVW or plaque dimensions were respectively assessed in volunteers and patients as well as for different healthy subjectsub-groups (i.e. < 50 and ≥ 50 years). The agreement in vessel wall and lumen measurements between the 3D technique and the 2D TSE method was also investigated. In addition, the sample size required for future longitudinal clinical studies was calculated. The intra-class correlation coefficient (ICC) and Bland-Altman plots indicated excellent reproducibility and inter-method agreement for all morphologic measurements (All ICCs > 0.75). In addition, all ICCs of patients were equal to or higher than that of healthy subjects except maximum wall thickness. In volunteers, all ICCs of the age group of ≥50 years were equal to or higher than that of the age group of < 50 years. Normalized wall index and mean and maximum wall thickness were significantly larger in the age group of ≥50 years. To detect 5% - 20% difference between placebo and treatment groups, normalized wall index requires the smallest sample size while lumen volume requires the highest sample size. Whole-brain 3D IVW CMR is a reliable imaging method for the quantification of intracranial vessel dimensions and could potentially be useful for monitoring plaque progression and regression.