ABSTRACTAccurately predicting individual antidepressant treatment response could expedite the lengthy trial‐and‐error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning‐based methods that predict individual‐level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA‐MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4–12 weeks post‐initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel‐wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross‐validation methods (i.e., k‐fold and leave‐one‐site‐out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10‐fold cross‐validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non‐)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non‐)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non‐)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response‐related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

• Book : 46(1)
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

The disposal of radioactive waste within the UK is managed through a comprehensive regulatory framework.This framework requires radioactive waste to be sufficiently well characterized to ensure its disposal is compliant with the regulations and the acceptance criteria for any receiving facility. This is the responsibility of both the waste consignor and the receiving facility.Characterization of solid radioactive waste is extremely challenging. This is due to the wastes exhibiting a high degree of heterogeneity which leads to significant uncertainty. Understanding the total uncertainty requires combining the uncertainties associated with numerous stages of the characterization process.Experience suggests that whilst uncertainties are included in waste characterization, approaches are variable in quality. This makes it challenging to present an appropriate level of confidence in the waste characterization and the subsequent decisions made to stakeholders.This paper introduces the concept and principles of uncertainty and uncertainty management in waste characterization, summarizing challenges, and gaps in the current approach. It recommends a solution is sought to address shortfalls in the understanding of uncertainty; identify sources of uncertainty across the whole characterization lifecycle; and agree how specialists might combine these uncertainties and communicate them to stakeholders.

• Book : 57(1)
• Pub. Date : 2025
• Page : pp.1-7
• Keyword :

In this work, a new Time Domain Random Walk (TDRW) algorithm is proposed to estimate the tracer distribution profile within the rock matrix. The development of the new algorithm stems from the statistical properties of the analytical solution to a single fracture-matrix system, in which the particle position at a certain time is calculated and recorded. With the position of each particle determined, the resulting distribution will then provide an estimate of the tracer distribution profile directly. In addition, the newly developed algorithm can readily be extended to a case of more complicated fracture-matrix system, in which an arbitrary injection boundary condition may also be used. To verify the accuracy and applicability of the new algorithm, three benchmark simulations are made, in which the results of different approaches are found to be identical. Nevertheless, the new algorithm has a higher computational efficiency, due to its lower calculation demand.

• Book : 57(1)
• Pub. Date : 2025
• Page : pp.1-10
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

• Book : ()
• Pub. Date : 2025
• Page :
• Keyword :

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area. Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version. Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

• Book : 25(1)
• Pub. Date : 2025
• Page : pp.5-114
• Keyword :