Prediction Of Response Research Articles

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

BackgroundEarly prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients.MethodsThe DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways.ResultsA 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635–0.741 and AUC = 0.650, 95%CI: 0.595–0.705) and tested (AUC = 0.686, 95%CI: 0.594–0.778 and AUC = 0.626, 95%CI: 0.529–0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722–0.816 and test: 0.762, 95%CI: 0.679–0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665–0.767 and test AUC = 0.695, 95%CI: 0.656–0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system.ConclusionDynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Short-term extreme value prediction for the structural responses of the IEA 15 MW offshore wind turbine under extreme environmental conditions

As offshore wind turbines enter deep seas, large-scale models ranging from 15 MW to 20 MW are becoming the mainstream choice for the future market. The dynamic responses of 15 MW wind turbines under extreme environmental conditions should be considered in the design process. With increasing structure sizes, the mooring systems and tower base structures play essential roles in maintaining position and supporting wind turbines under harsh environmental conditions. Accurate estimation of the extreme mooring loads and dynamic responses at the tower base is crucial, particularly for the ultimate limit state (ULS) analysis. The dynamic responses of the offshore wind turbine are calculated by Open Fast and validated with other numerical tools. For the safety of deep-sea structures, it is necessary to consider the extreme environmental conditions with a 100-year return period, which is beyond the current standards. An environmental contour-based approach is proposed to select the short-term extreme conditions, enabling approximate prediction of the extreme values of the dynamic responses during the preliminary design stage. The Gumbel method and the ACER method are used to predict the short-term extreme dynamic responses. Comparative evaluations against the relevant standards for floating wind turbines are conducted to assess structural safety.

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.

Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g., scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. Pathformer is implemented and freely available at https://github.com/lulab/Pathformer. Supplementary data are available at Bioinformatics online.

Hepatocellular Carcinoma and the Multifaceted Relationship with Its Microenvironment: Attacking the Hepatocellular Carcinoma Defensive Fortress

Hepatocellular carcinoma is a malignant tumor that originates from hepatocytes in an inflammatory substrate due to different degrees of liver fibrosis up to cirrhosis. In recent years, there has been growing interest in the role played by the complex interrelationship between hepatocellular carcinoma and its microenvironment, capable of influencing tumourigenesis, neoplastic growth, and its progression or even inhibition. The microenvironment is made up of an intricate network of mesenchymal cells, immune system cells, extracellular matrix, and growth factors, as well as proinflammatory cytokines and translocated bacterial products coming from the intestinal microenvironment via the enterohepatic circulation. The aim of this paper is to review the role of the HCC microenvironment and describe the possible implications in the choice of the most appropriate therapeutic scheme in the prediction of tumor response or resistance to currently applied treatments and in the possible development of future therapeutic perspectives, in order to circumvent resistance and break down the tumor’s defensive fort.

Neoadjuvant chemoradiotherapy for thoracic esophageal squamous cell carcinoma: does everyone need it?

Objective. In accordance with clinical guidelines, neoadjuvant chemoradiotherapy (nCRT) is recommended as a beneficial option for treatment of thoracic esophageal squamous cell carcinoma (TESCC). However, some studies did not demonstrate potential benefits of nCRT. In this paper we compared treatment outcomes of surgery alone with preoperative CRT followed by surgery. Material and methods. Group 1 (n=147) consisted of patients who underwent surgery alone. Group 2 (n=3,337) was formed in the framework of the present systematic review and meta-analysis (15 publications captured 20 separate subgroups for analysis). Patients of group 2 received nCRT followed by esophagectomy. Overall survival (OS) and median OS were estimated. Results. The median OS was reached in group 1 and in 12 separate subgroups of group 2 (2310 of 3337 patients). In groups 1 and 2 the OS median estimates were 31.0 and 28.1, respectively (p=0.873). Respective 3-year and 5-year OS were 0.49 vs 0.48 (p=0.425) and 0.37 vs 0.41 (p=0.228). The median OS was not reached in 8 separate subgroups of group 2 (1027 of 3337 patients). In this case, group 2 patients had significantly higher 3- and 5-year OS compared to group 1 patients: 0.69 vs 0.49 (p=0.000) and 0.60 vs 0.37 (p=0.000), respectively. Pathological complete response to nCRT was associated with better survival. conclusion. nCRT is not appropriate for all patients with TESCC. Improved overall survival may be achieved in patients with a high grade of pathological tumor response to neoadjuvant treatment. Selective administration of nCRT based on highly probable prediction of complete tumor response seems promising. This concept needs further research.

Genomic fingerprints of the world's soil ecosystems.

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes-more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.IMPORTANCEWe address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1,512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. Our work reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step toward comprehending the intricate world of soil microorganisms and its role in the global climate.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit.

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

BackgroundThe prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets.ResultsThis study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model’s predicted results.ConclusionsOur method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub (https://github.com/pangweixiong/DrugGene).

Interpretable machine learning for selection of site-specific soil constitutive models and consolidation settlement analysis

Machine learning (ML) has achieved great breakthroughs in many fields, such as computer vision and speech recognition, and it also gains an increasing attention in geotechnical engineering. However, it is often criticized for a lack of interpretability. This study proposes an interpretable sparse dictionary learning (ISDL) approach for selection of suitable soil constitutive models (SCMs) and geotechnical consolidation analysis in a specific site. ISDL is inherently interpretable since it expresses prediction of geotechnical responses (e.g., consolidation settlement) as a weighted summation of many elementary datasets (i.e., dictionary atoms in ISDL). The dictionary atoms are constructed using numerical analyses (e.g., finite element model) with different candidate SCMs and parameters. Interpretability of ISDL is improved by a small number of non-trivial atoms (or SCMs) selected for model prediction using site-specific monitoring data and further enhanced by contribution ranking of the selected SCMs using a game theory-based approach. The proposed method is illustrated using a real reclamation project in Hong Kong, and it is shown to effectively select the most suitable constitutive models for the given site, quantify contributions of the selected models, and significantly improve settlement predictions with rigorously quantified prediction uncertainty, particularly at locations without monitoring or future time steps.

Prediction and mediation analysis for treatment responses to combined cognitive and physical training for older adults

Diminished cognitive and physical functions negatively affect the daily functions of individuals. Although combined cognitive and physical training prevents instrumental activities of daily living (IADL) disability in older adults, no predictive model or mediation analysis of IADL after combined training exists. This study aims to employ prediction and mediation analysis to identify the predictors of IADL performance and to elucidate the mediators of the association between baseline global cognition and subsequent IADL performance following combined cognitive and physical training. This study involved 177 participants aged 60 years and older who underwent combined training. Cognitive function was measured with the Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Color Trails Test, Word List, and a dual task; physical function with the Timed Up and Go (TUG) test; daily function with the Lawton IADL Scale. We conducted regression analyses to identify the predictors of IADL performance, and mediation analysis to examine whether DSST and TUG mediate the relationship between MoCA and IADL. The pre-intervention DSST and TUG were significant independent predictors of post-intervention IADL. The association between the pre-intervention MoCA and post-intervention IADL was mediated by pre-intervention DSST and TUG. This study highlighted the importance of measuring and improving processing speed and functional mobility before and during interventions to enhance IADL outcomes.Trial registration: NCT03619577, 23/07/2018; NCT04689776, 29/12/2020.

Broadening the horizon of immune thrombocytopenia through Omics approaches.

Immune thrombocytopenia (ITP) is a highly heterogeneous autoimmune bleeding disorder characterized by low platelet counts due to an immune-mediated platelet destruction and impaired platelet production. The pathophysiology is multifactorial and remains to be fully unravelled. Consequently, disease trajectories and responses to therapeutics, despite the availability of multiple agents, can be unpredictable and differing between patients. There is an urgent need for the identification of diagnostic and therapeutic biomarkers, but this has proven to be challenging to achieve. To shed light on this, two studies in this issue of the British Journal of Haematology have recognized the opportunity of using high-throughput Omics technologies in ITP. Sun etal. performed proteomics, and Li etal. metabolomics, on bone marrow biopsy samples of patients with ITP. This was conducted using mass spectrometry and, due to the generation of large datasets, in combination with machine learning. These studies set the stage for further investigations exploring the high potential of multi-omics technologies in order to shed light on the heterogeneity in ITP, accelerating the path towards a much needed personalized medicine approach. Commentary on: Li etal. Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19391 Commentary on: Sun etal. Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19420.

Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review

IntroductionOnline mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare.MethodWe searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence.ResultsTwenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low.DiscussionThe review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

An Adaptive Contextual Relation Model for Improving Response Generation

An Adaptive Contextual Relation Model for Improving Response Generation

A road surface reconstruction dataset for autonomous driving

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

Volumetric parameters of 68[Ga]Ga-DOTA-TATE PET/CT in the prediction of response to treatment with long-acting somatostatin analogues in patients with well-differentiated NETs

Volumetric parameters of 68[Ga]Ga-DOTA-TATE PET/CT in the prediction of response to treatment with long-acting somatostatin analogues in patients with well-differentiated NETs

Clinical Response Predictive Model for Omalizumab in Moderate-to-Severe Asthma Patients

Objective. Our study aimed to develop a predictive model for evaluating the clinical response of omalizumab treatment in moderate-to-severe asthma patients. Methods. This single-center, prospective study collected patients who meet the diagnostic criteria for moderate-to-severe bronchial asthma set by the National Asthma Prevention and Treatment Group in 2016 in the first hospital affiliated with Soochow University. Patients recruited were treated with omalizumab once per four weeks; at the beginning of each injection, blood eosinophils and the level of total serum IgE (IU/mL) were tested. After four injections of omalizumab, asthma control test (ACT), the 15-item Mini Asthma Quality of Life Questionnaire (MiniAQLQ), global treatment effectiveness (GETE), and lung function of all patients were evaluated in the 16th week. We used the selection operator method to build a logistic model and evaluated the clinical response of omalizumab in these patients. Results. This study included 108 moderate-to-severe patients (aged 39.86 ± 14.59 years). Eighty-nine patients finished treatment for 16 weeks, and 74 patients (83.1%) had an excellent or good response. The serum level of total IgE increased significantly after injection of omalizumab, while blood eosinophils count decreased significantly from baseline. Using the GETE as a clinical outcome, several clinical variables were significant predictors of clinical response. The corrected AUC and Brier scores were 0.872 and 0.111, which showed good discrimination. Significant variables included age, weight, family allergic history, acute exacerbations, the ratio of total serum IgE level at the 4th week to the baseline level, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), and commodities of rhinitis. Using the improvement in maximal expiratory flow 25% of the measured value to the predicted value (MEF25%pre) as clinical outcome, the significant variables included weight, duration of asthma, use of oral corticosteroids (OCS), total serum IgE level at the 4th week, and history of rhinitis. Its corrected AUC and Brier scores were 0.674 and 0.225 after internal validation. Conclusion. Omalizumab treatment remarkably improved asthma control and pulmonary function in Chinese patients with moderate-to-severe asthma. The response prediction model we developed provides convenient approaches to help identify better clinical response patients to omalizumab treatment.

A clinicopathological-imaging nomogram for the prediction of pathological complete response in breast cancer cases administered neoadjuvant therapy

ObjectiveTo construct a user-friendly nomogram with MRI and clinicopathological parameters for the prediction of pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC). MethodsWe retrospectively enrolled consecutive female patients pathologically confirmed with breast cancer who received NAT followed by surgery between January 2018 and December 2022 as the development cohort. Additionally, we prospectively collected eligible candidates between January 2023 and December 2023 as an external validation group at our institution. Pretreatment MRI features and clinicopathological variables were collected, and the pre- and post-treatment background parenchymal enhancement (BPE) and the changes in BPE on two MRIs were compared between patients who achieved pCR and those who did not. Multivariable logistic regression analysis was used to identify independent variables associated with pCR in the development cohort. These independent variables were combined into a predictive nomogram for which performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis, and external validation. ResultsIn the development cohort, there were a total of 276 female patients with a mean age of 48.3 ± 8.7 years, while in the validation cohort, there were 87 female patients with a mean age of 49.0 ± 9.5 years. Independent prognostic factors of pCR included small tumor size, HER2(+), high Ki-67 index,high signal enhancement ratio (SER), low minimum value of apparent diffusion coefficient (ADCmin), and significantly decreased BPE after NAT(change of BPE). The nomogram, which incorporates the above parameters, demonstrated excellent predictive performance in both the development and external validation cohorts, with AUC values of 0.900 and 0.850, respectively. Additionally, the nomogram showed excellent calibration capacities, as indicated by Hosmer-Lemeshow test p values of 0.508 and 0.423 in the two cohorts. Furthermore, the nomogram provided greater net benefits compared to the default simple schemes in both cohorts. ConclusionA nomogram constructed using tumor size, HER2 status, Ki-67 index, SER, ADCmin, and changes in pre- and post-NAT BPE demonstrated strong predictive performance, calibration ability, and greater net benefits for predicting pCR in patients with BC after NAT. This suggests that the user-friendly nomogram could be a valuable imaging biomarker for identifying suitable candidates for NAT.

Systematic literature review to inform the EULAR recommendations for the use of imaging in crystal-induced arthropathies in clinical practice

ObjectiveTo summarise current data regarding the use of imaging in crystal-induced arthropathies (CiAs) informing a European Alliance of Associations for Rheumatology task force.MethodsWe performed four systematic searches in Embase, Medline...