Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [18F]DCFPyL (PSMA) imaging for standardized reporting.

PURPOSE: The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [18F]DCFPyL (PSMA) PET/CT scans. In the current study, we analytically validated aPROMISE, a software as a medical device that segments organs in low-dose CT images with deep learning, and subsequently detects and quantifies potential pathological lesions in PSMA PET/CT. METHODS: To evaluate the deep learning algorithm, the automated segmentations of the low-dose CT component of PSMA PET/CT scans from 20 patients were compared to manual segmentations. Dice scores were used to quantify the similarities between the automated and manual segmentations. Next, the automated quantification of tracer uptake in the reference organs and detection and pre-segmentation of potential lesions were evaluated in 339 patients with prostate cancer, who were all enrolled in the phase II/III OSPREY study. Three nuclear medicine physicians performed the retrospective independent reads of OSPREY images with aPROMISE. Quantitative consistency was assessed by the pairwise Pearson correlations and standard deviation between the readers and aPROMISE. The sensitivity of detection and pre-segmentation of potential lesions was evaluated by determining the percent of manually selected abnormal lesions that were automatically detected by aPROMISE. RESULTS: The Dice scores for bone segmentations ranged from 0.88 to 0.95. The Dice scores of the PSMA PET/CT reference organs, thoracic aorta and liver, were 0.89 and 0.97, respectively. Dice scores of other visceral organs, including prostate, were observed to be above 0.79. The Pearson correlation for blood pool reference was higher between any manual reader and aPROMISE, than between any pair of manual readers. The standard deviations of reference organ uptake across all patients as determined by aPROMISE (SD = 0.21 blood pool and SD = 1.16 liver) were lower compared to those of the manual readers. Finally, the sensitivity of aPROMISE detection and pre-segmentation was 91.5% for regional lymph nodes, 90.6% for all lymph nodes, and 86.7% for bone in metastatic patients. CONCLUSION: In this analytical study, we demonstrated the segmentation accuracy of the deep learning algorithm, the consistency in quantitative assessment across multiple readers, and the high sensitivity in detecting potential lesions. The study provides a foundational framework for clinical evaluation of aPROMISE in standardized reporting of PSMA PET/CT.

aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL Images of Veterans with Prostate Cancer.

Standardized staging and quantitative reporting are necessary to demonstrate the association of 18F-DCFPyL PET/CT imaging with clinical outcome. This work introduces an automated platform, aPROMISE, to implement and extend the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria. The objective is to validate the performance of aPROMISE in staging and quantifying disease burden in patients with prostate cancer who undergo prostate-specific antigen (PSMA) imaging. Methods: This was a retrospective analysis of 109 veterans with intermediate- or high-risk prostate cancer who underwent PSMA imaging. To validate the performance of aPROMISE, 2 independent nuclear medicine physicians conducted aPROMISE-assisted reads, resulting in standardized reports that quantify individual lesions and stage the patients. Patients were staged as having local disease only (miN0M0), regional lymph node disease only (miN1M0), metastatic disease only (miN0M1), or both regional and distant metastatic disease (miN1M1). The staging obtained from aPROMISE-assisted reads was compared with the staging by conventional imaging. Cohen pairwise κ-agreement was used to evaluate interreader variability. Correlation coefficients and intraclass correlation coefficients were used to evaluate the interreader variability of the quantitative assessment (molecular imaging PSMA [miPSMA] index) at each stage. Kendall tau and t testing were used to evaluate the association of miPSMA index with prostate-specific antigen and Gleason score. Results: All PSMA images of 109 veterans met the DICOM conformity and the requirements for the aPROMISE analysis. Both independent aPROMISE-assisted analyses demonstrated significant upstaging in patients with localized (23%, n = 20/87) and regional (25%, n = 2/8) tumor burden. However, a significant number of patients with bone metastases identified on conventional imaging (18F-NaF PET/CT) were downstaged (29%, n = 4/14). The comparison of the 2 independent aPROMISE-assisted reads demonstrated a high κ-agreement: 0.82 for miN0M0, 0.90 for miN1M0, and 0.77 for miN0M1. The Spearman correlation of quantitative miPSMA index was 0.93, 0.96, and 0.97, respectively. As a continuous variable, miPSMA index in the prostate was associated with risk groups defined by prostate-specific antigen and Gleason score. Conclusion: We demonstrated the consistency of the aPROMISE platform between readers and observed substantial upstaging in PSMA imaging compared with conventional imaging. aPROMISE may contribute to broader standardization of PSMA imaging assessment and to its clinical utility in the management of prostate cancer patients.

What is a "unimodal" cell population? Using statistical tests as criteria for unimodality in automated gating and quality control.

Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data preprocessing are given. © 2017 International Society for Advancement of Cytometry.

BayesFlow: latent modeling of flow cytometry cell populations.

BACKGROUND: Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another. RESULTS: We introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation. CONCLUSIONS: Modeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

Bystander hyperactivation of preimmune CD8+ T cells in chronic HCV patients.

Chronic infection perturbs immune homeostasis. While prior studies have reported dysregulation of effector and memory cells, little is known about the effects on naïve T cell populations. We performed a cross-sectional study of chronic hepatitis C (cHCV) patients using tetramer-associated magnetic enrichment to study antigen-specific inexperienced CD8(+) T cells (i.e., tumor or unrelated virus-specific populations in tumor-free and sero-negative individuals). cHCV showed normal precursor frequencies, but increased proportions of memory-phenotype inexperienced cells, as compared to healthy donors or cured HCV patients. These observations could be explained by low surface expression of CD5, a negative regulator of TCR signaling. Accordingly, we demonstrated TCR hyperactivation and generation of potent CD8(+) T cell responses from the altered T cell repertoire of cHCV patients. In sum, we provide the first evidence that naïve CD8(+) T cells are dysregulated during cHCV infection, and establish a new mechanism of immune perturbation secondary to chronic infection.

Low Bias Local Intrinsic Dimension Estimation from Expected Simplex Skewness.

In exploratory high-dimensional data analysis, local intrinsic dimension estimation can sometimes be used in order to discriminate between data sets sampled from different low-dimensional structures. Global intrinsic dimension estimators can in many cases be adapted to local estimation, but this leads to problems with high negative bias or high variance. We introduce a method that exploits the curse/blessing of dimensionality and produces local intrinsic dimension estimators that have very low bias, even in cases where the intrinsic dimension is higher than the number of data points, in combination with relatively low variance. We show that our estimators have a very good ability to classify local data sets by their dimension compared to other local intrinsic dimension estimators; furthermore we provide examples showing the usefulness of local intrinsic dimension estimation in general and our method in particular for stratification of real data sets.

A multicellular basis for the origination of blast crisis in chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is characterized by a specific chromosome translocation, and its pathobiology is considered comparatively well understood. Thus, quantitative analysis of CML and its progression to blast crisis may help elucidate general mechanisms of carcinogenesis and cancer progression. Hitherto, it has been widely postulated that CML blast crisis originates mainly via cell-autonomous mechanisms such as secondary mutations or genomic instability. However, recent results suggest that carcinogenic transformation may be an inherently multicellular event, in departure from the classic unicellular paradigm. We investigate this possibility in the case of blast crisis origination in CML. A quantitative, mechanistic cell population dynamics model was employed. This model used recent data on imatinib-treated CML; it also used earlier clinical data, not previously incorporated into current mathematical CML/imatinib models. With the pre-imatinib data, which include results on many more blast crises, we obtained evidence that the driving mechanism for blast crisis origination is a cooperation between specific cell types. Assuming leukemic-normal interactions resulted in a statistically significant improvement over assuming either cell-autonomous mechanisms or interactions between leukemic cells. This conclusion was robust with regard to changes in the model's adjustable parameters. Application of the results to patients treated with imatinib suggests that imatinib may act not only on malignant blast precursors, but also, to a limited degree, on the malignant blasts themselves.