CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy.

Objectives: To develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC).

Methods: This retrospective study included immunotherapy-treated advanced HCC patients from 52 multi-national in-house centers between 2018 and 2022. A multi-modal prognostic model using baseline and the first follow-up CT images and 7 clinical variables was proposed.

Zanubrutinib is effective in non-germinal-center B-cell-like diffuse large B-cell lymphoma with mutated , high 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];  = 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.

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.

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.

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.

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.

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).