Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.

Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10-4). The pipeline can make precise lesion tracking with accuracies of 85.7% and 90.8% then finally yield the RECIST accuracies of 82.1% and 81.4% on the validation and test sets. Our proposed pipeline can provide precise and convenient RECIST outcome assessments and has the potential to aid clinicians with more efficient therapeutic decisions.

Zanubrutinib is effective in non-germinal-center B-cell-like diffuse large B-cell lymphoma with mutated CD79B, high TCL1A expression, or over- expressed MYC/BCL-2.

To evaluate the effects of gene mutations on Bruton tyrosine kinase inhibitor, zanubrutinib's effectiveness in patients with diffuse large B-cell lymphoma (DLBCL), we examined pooled data from four single-arm studies (BGB-3111-AU-003 [NCT02343120], BGB-3111-207 [NCT03145064], BGB-3111_GA101_Study_001 [NCT02569476], BGB-3111-213 [NCT03520920]; n = 121). Objective response rate (ORR) was higher, though not statistically significant, in patients with activated B-cell-like (ABC)- and unclassified DLBCL (42.9% [21/49]) versus those with germinal-center B-cell-like DLBCL (14.3% [1/7]; p = 0.15). Patients with CD79B mutations had better ORR (60%) versus patients with wild-type alleles (25.9%, p < 0.01). Higher TCL1A expression correlated with better zanubrutinib response (p = 0.03), longer progression-free survival (p = 0.01), and longer overall survival (p = 0.12). TCL1A expression was higher in ABC-DLBCL (p < 0.001) and MYD88/CD79B-mutated subtypes (p < 0.0001). Eighteen patients with high MYC/BCL-2 expression responded better to zanubrutinib (ORR = 61 vs. 29%, p = 0.02). Our results support assessing CD79B mutations, co-expressor DLBCL, and TCL1A expression status to identify patients with DLBCL who will benefit from zanubrutinib.

Optimal test procedures for multiple hypotheses controlling the familywise expected loss.

We consider the problem of testing multiple null hypotheses, where a decision to reject or retain must be made for each one and embedding incorrect decisions into a real-life context may inflict different losses. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise error rate may not be appropriate. Using a decision-theoretic approach, we define suitable loss functions for a given decision rule, where incorrect decisions can be treated unequally by assigning different loss values. Taking expectation with respect to the sampling distribution of the data allows us to control the familywise expected loss instead of the conventional familywise error rate. Different loss functions can be adopted, and we search for decision rules that satisfy certain optimality criteria within a broad class of decision rules for which the expected loss is bounded by a fixed threshold under any parameter configuration. We illustrate the methods with the problem of establishing efficacy of a new medicinal treatment in non-overlapping subgroups of patients.

Profile clustering in clinical trials with longitudinal and functional data methods.

Repeated measurements are widely encountered in medical or pharmaceutical studies, which can be analyzed by both longitudinal data and functional data analysis methods, particularly when the underlying process is continuous and the number of measurement points is not too small. Motivated by real problems of clustering patient profiles in clinical trials, this paper gives an overview of the clustering methods for repeated measurement data and compares three longitudinal data methods and two functional data methods with extensive simulation studies. Methods with appropriate properties are applied to the real data to produce interpretable results.

Antimalarial Activity of KAF156 in Falciparum and Vivax Malaria.

BACKGROUND: KAF156 belongs to a new class of antimalarial agents (imidazolopiperazines), with activity against asexual and sexual blood stages and the preerythrocytic liver stages of malarial parasites. METHODS: We conducted a phase 2, open-label, two-part study at five centers in Thailand and Vietnam to assess the antimalarial efficacy, safety, and pharmacokinetic profile of KAF156 in adults with acute Plasmodium vivax or P. falciparum malaria. Assessment of parasite clearance rates in cohorts of patients with vivax or falciparum malaria who were treated with multiple doses (400 mg once daily for 3 days) was followed by assessment of the cure rate at 28 days in a separate cohort of patients with falciparum malaria who received a single dose (800 mg). RESULTS: Median parasite clearance times were 45 hours (interquartile range, 42 to 48) in 10 patients with falciparum malaria and 24 hours (interquartile range, 20 to 30) in 10 patients with vivax malaria after treatment with the multiple-dose regimen and 49 hours (interquartile range, 42 to 54) in 21 patients with falciparum malaria after treatment with the single dose. Among the 21 patients who received the single dose and were followed for 28 days, 1 had reinfection and 7 had recrudescent infections (cure rate, 67%; 95% credible interval, 46 to 84). The mean (±SD) KAF156 terminal elimination half-life was 44.1±8.9 hours. There were no serious adverse events in this small study. The most common adverse events included sinus bradycardia, thrombocytopenia, hypokalemia, anemia, and hyperbilirubinemia. Vomiting of grade 2 or higher occurred in 2 patients, 1 of whom discontinued treatment because of repeated vomiting after receiving the single 800-mg dose. More adverse events were reported in the single-dose cohort, which had longer follow-up, than in the multiple-dose cohorts. CONCLUSIONS: KAF156 showed antimalarial activity without evident safety concerns in a small number of adults with uncomplicated P. vivax or P. falciparum malaria. (Funded by Novartis and others; ClinicalTrials.gov number, NCT01753323 .).

Parameter Estimation of Partial Differential Equation Models.

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

A Bayesian approach to the detection of small low emission sources.

This paper addresses the problem of detecting the presence and location of a small low emission source inside an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The goal is to reach the signal-to-noise ratio levels in the order of 10(-3). A Bayesian approach to this problem is implemented in 2D. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov chain Monte Carlo simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source.