Strategy Use in Automation-Aided Decision Making

When human operators make signal detection judgments with assistance from an automated decision aid, they perform better than they could unaided but fail to reach optimal sensitivity. We investigated the decision strategies that produce this suboptimal performance. Participants ( N = 130) performed a two-response classification task that required them to mentally estimate the mean of a set of randomly sampled values each trial. The task was performed with and without assistance from a 93% reliable decision aid. Psychometric functions were fit to the classification data, and data were fit with two cognitive models of automation use. The first model assumed that participants made automation-aided judgments using a contingent criterion strategy, adjusting their response cutoff for yes vs. no responses following a cue from the aid. The second strategy, a discrete state model, assumed that participants made aided judgments by simply deferring to the aid on some proportion of trials. A measure of model fit favored the discrete-state process model, with parameter estimates indicating large individual differences in deferral rate between participants (range = 2% and 95%).

Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer

Rationale Recent studies have discovered several unique tumor response subgroups outside of response classification by Response Evaluation Criteria for Solid Tumors (RECIST), such as mixed response and oligometastasis. These subtypes have a distinctive property, lesion heterogeneity defined as diversity of tumor growth profiles in RECIST target lesions. Furthermore, many cancer clinical trials have been activated to evaluate various treatment options for heterogeneity-related subgroups (e.g., 29 trials so far listed in clinicaltrials.gov for cancer patients with oligometastasis). Some of the trials have shown survival benefit by tailored treatment strategies. This evidence presents the unmet need to incorporate lesion heterogeneity to improve RECIST response classification. Method An approach for Lesion Heterogeneity Classification (LeHeC) was developed using a contemporary statistical approach to assess target lesion variation, characterize patient treatment response, and translate informative evidence to improving treatment strategy. A mixed effect linear model was used to determine lesion heterogeneity. Further analysis was conducted to classify various types of lesion variation and incorporate with RECIST to enhance response classification. A study cohort of 110 target lesions from 36 lung cancer patients was used for evaluation. Results Due to small sample size issue, the result was exploratory in nature. By analyzing RECIST target lesion data, the LeHeC approach detected a high prevalence (n = 21; 58%) of lesion heterogeneity. Subgroup classification revealed several informative distinct subsets in a descending order of lesion heterogeneity: mix of progression and regression (n = 7), mix of progression and stability (n = 9), mix of regression and stability (n = 5), and non-heterogeneity (n = 15). Evaluation for association of lesion heterogeneity and RECIST best response classification showed lesion heterogeneity commonly occurred in each response group (stable disease: 16/27; 59%; partial response: 3/5; 60%; progression disease: 2/4; 50%). Survival analysis showed a differential trend of overall survival between heterogeneity and non-heterogeneity in RECIST response groups. Conclusion This is the first study to evaluate lesion heterogeneity, an underappreciated metric, for RECIST application in oncology clinical trials. Results indicated lesion heterogeneity is not an uncommon event. The LeHeC approach could enhance RECIST response classification by utilizing granular lesion level discovery of heterogeneity.

Interim PSMA PET/CT for response evaluation during LuPSMA treatment in mCRPC (INTERIM PET): An explorative, multicenter study.

5066 Background: The aim of this analysis was to evaluate the prognostic value of interim PSMA PET/CT in men with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA and to develop a novel framework for Response Evaluation Criteria In PSMA-imaging (RECIP). Methods: This was an explorative, multicenter, retrospective study; 124 men with mCRPC who underwent 177Lu-PSMA treatment and received PSMA-PET/CT at baseline (bPET) and at interim after two cycles of treatment (iPET) met the eligibility criteria and were included in this analysis. The primary endpoint was overall survival (OS). Pairs of bPET and iPET were interpreted by three independent readers for appearance of new lesions. Whole-body tumor lesions were segmented using qPSMA software and total PSMA-positive tumor volume (PSMA-VOL) was obtained. Changes in PSMA-VOL on iPET relative to bPET were calculated. After being tested separately for associations with OS, appearance of new lesions and changes in PSMA-VOL were combined to develop RECIP. Results: The median OS was 13.5 months (95%CI, 11.6-15.4). Appearance of at least one new lesion on iPET was observed in 73 (59%) patients and was associated with poor OS (hazard ratio [HR] 2.23; 95%CI, 1.51-3.28; P <.001). Based on the current data, RECIP were defined as: partial response (PSMA-PR) as a decline ≥20% in PSMA-VOL and no appearance of new lesions; progressive disease (PSMA-PD) as an increase ≥20% in PSMA-VOL and appearance of new lesions; stable disease (PSMA-SD) was defined as any condition but not PSMA-PR or PSMA-PD. The OS of men with PSMA-PD (n = 41) was significantly worse compared to men with PSMA-SD (n = 47; HR 2.52; 95%CI, 1.61–3.93; P <.001) and PSMA-PR (n = 36; HR 4.16; 95%CI, 2.54–6.78; P <.001). PSMA-SD was associated with significantly worse OS compared to PSMA-PR (HR 1.65; 95%CI, 1.02–2.65; P =.039). The time dependent C-index of associations with OS for response according to RECIP was 0.68 (95%CI, 0.63-0.72). Conclusions: Interim staging using PSMA-PET/CT and response classification by RECIP is prognostic for survival of men with mCRPC treated with 177Lu-PSMA. Validation of these findings in clinical trials is warranted.

Modeling and Bioinformatics Identify Responders to G-CSF in Patients With Amyotrophic Lateral Sclerosis

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS.Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response.Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI).Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

Antimicrobial Activity of Necklace Orchids is Phylogenetically Clustered and can be Predicted With a Biological Response Method

Necklace orchids (Coelogyninae, Epidendroideae) have been used in traditional medicine practices for centuries. Previous studies on a subset of unrelated orchid species utilized in these traditional practices revealed they possessed antimicrobial, anti-inflammatory, and anti-oxidant activity, providing experimental proof for their medicinal properties. To date however none of these species have been investigated ethno-botanically in a phylogenetic context. This study carried out comparative bioprospecting for a group of wild orchids using EBDCS (the Economic Botany Data Collection Standards) organ targeted and biological response methods. The traditional medicinal use of necklace orchids was recorded from books and journals published between 1984 and 2016. Two orchids, Coelogyne cristata and Coelogyne fimbriata, were selected, cultivated both indoors and outdoors, and the antimicrobial properties on extracts from their leaves and pseudobulbs tested against a selection of human pathogens. A molecular phylogeny of Coelogyninae based on nuclear ribosomal ITS and plastid matK DNA sequences obtained from 148 species was reconstructed with Maximum Likelihood (ML) using RAxML, Maximum Parsimony (MP) using PAUP and Bayesian Inference using MrBayes. Bioprospecting comparison of EBDCS and biological response was carried out using customized R scripts. Ethanolic extracts obtained from leaves of C. fimbriata inhibited growth of Bacillus cereus, Staphylococcus aureus, and Yersinia enterocolitica, confirming the antimicrobial properties of these extracts. Leaf extracts were found to have slightly stronger antimicrobial properties for plants cultivated outdoors than indoors. These differences were not found to be statistically significant though. Three hot nodes with high potency for antimicrobial activities were detected with the EBDCS organ targeted classification method, and eight hot nodes were detected with the biological response classification method. The biological response classification method is thus a more effective tool in finding hot nodes amongst clades of species with high medicinal potential.

Using physiological signals to predict temporal defense responses: a multi-modality analysis

Defense responses are a highly conserved behavioral response set across species. Defense responses motivate organisms to detect and react to threats and potential danger as a precursor to anxiety. Accurate measurement of temporal defense responses is important for understanding clinical anxiety and mood disorders, such as post-traumatic stress disorder, obsessive compulsive disorder, and generalized anxiety disorder. Within these conditions, anxiety is defined as a state of prolonged defense response elicitation to a threat that is ambiguous or unspecific. In this study, we aimed to develop a data-driven approach to capture temporal defense response elicitation through a multi-modality data analysis of physiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and eye-tracking information. A fear conditioning paradigm was adopted to develop a defense response classification model. From a classification model based on 42 feature sets, a higher order crossing feature set-based model was chosen for further analysis with cross-validation loss of 0.0462 (SEM: 0.0077). To validate our model, we compared predicted defense response occurrence ratios from a comprehensive situation that generates defense responses by watching movie clips with fear awareness and threat existence predictability, which have been reported to correlate with defense response elicitation in previous studies. We observed that defense response occurrence ratios are correlated with threat existence predictability, but not with fear awareness. These results are similar to those of previous studies using comprehensive situations. Our study provides insight into measurement of temporal defense responses via a novel approach, which can improve understanding of anxiety and related clinical disorders for neurobiological and clinical researchers.