The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

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

BACKGROUND: Early 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. METHODS: The 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. RESULTS: A 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. CONCLUSION: Dynamic 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.

Baseline [18F]FDG PET/CT and MRI first-order breast tumor features do not improve pathological complete response prediction to neoadjuvant chemotherapy.

PURPOSE: To verify the ability of pretreatment [18F]FDG PET/CT and T1-weighed dynamic contrast-enhanced MRI to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. METHODS: This retrospective study includes patients with BC of no special type submitted to baseline [18F]FDG PET/CT, NAC and surgery. [18F]FDG PET-based features reflecting intensity and heterogeneity of tracer uptake were extracted from the primary BC and suspicious axillary lymph nodes (ALN), for comparative analysis related to NAC response (pCR vs. non-pCR). Multivariate logistic regression was performed for response prediction combining the breast tumor-extracted PET-based features and clinicopathological features. A subanalysis was performed in a patients' subsample by adding breast tumor-extracted first-order MRI-based features to the multivariate logistic regression. RESULTS: A total of 170 tumors from 168 patients were included. pCR was observed in 60/170 tumors (20/107 luminal B-like, 25/45 triple-negative and 15/18 HER2-enriched surrogate molecular subtypes). Higher intensity and higher heterogeneity of [18F]FDG uptake in the primary BC were associated with NAC response in HER2-negative tumors (immunohistochemistry score 0, 1 + or 2 + non-amplified by in situ hybridization). Also, higher intensity of tracer uptake was observed in ALN in the pCR group among HER2-negative tumors. No [18F]FDG PET-based features were associated with pCR in the other subgroup analyses. A subsample of 103 tumors was also submitted to extraction of MRI-based features. When combined with clinicopathological features, neither [18F]FDG PET nor MRI-based features had additional value for pCR prediction. The only significant predictors were estrogen receptor status, HER2 expression and grade. CONCLUSION: Pretreatment [18F]FDG PET-based features from primary BC and ALN are not associated with response to NAC, except in HER2-negative tumors. As compared with pathological features, no breast tumor-extracted PET or MRI-based feature improved response prediction.

Preoperative treatment response prediction for pancreatic cancer by multiple microRNAs in plasma exosomes: Optimization using machine learning and network analysis.

BACKGROUND/OBJECTIVES: MicroRNAs (miRNAs) are involved in chemosensitivity through their biological activities in various malignancies, including pancreatic cancer (PC). However, single-miRNA models offer limited predictability of treatment response. We investigated whether a multiple-miRNA prediction model optimized via machine learning could improve treatment response prediction. METHODS: A total of 20 and 66 patients who underwent curative resection for PC after gemcitabine-based preoperative treatment were included in the discovery and validation cohorts, respectively. Patients were classified according to their response to preoperative treatment. In the discovery cohort, miRNA microarray and machine learning were used to identify candidate miRNAs (in peripheral plasma exosomes obtained before treatment) associated with treatment response. In the validation cohort, miRNA expression was analyzed using quantitative reverse transcription polymerase chain reaction to validate its ability to predict treatment response. RESULTS: In the discovery cohort, six and three miRNAs were associated with good and poor responders, respectively. The combination of these miRNAs significantly improved predictive accuracy compared with using each single miRNA, with area under the curve (AUC) values increasing from 0.485 to 0.672 to 0.909 for good responders and from 0.475 to 0.606 to 0.788 for poor responders. In the validation cohort, improved predictive performance of the miRNA combination over single-miRNA prediction models was confirmed, with AUC values increasing from 0.461 to 0.669 to 0.777 for good responders and from 0.501 to 0.556 to 0.685 for poor responders. CONCLUSIONS: Peripheral blood miRNA profiles using an optimized combination of miRNAs may provide a more advanced prediction model for preoperative treatment response in PC.

Neural predictors of cognitive-behavior therapy outcome in anxiety-related disorders: a meta-analysis of task-based fMRI studies.

BACKGROUND: Cognitive-behavior therapy (CBT) is a well-established first-line intervention for anxiety-related disorders, including specific phobia, social anxiety disorder, panic disorder/agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, and posttraumatic stress disorder. Several neural predictors of CBT outcome for anxiety-related disorders have been proposed, but previous results are inconsistent. METHODS: We conducted a systematic review and meta-analysis of task-based functional magnetic resonance imaging (fMRI) studies investigating whole-brain predictors of CBT outcome in anxiety-related disorders (17 studies, n = 442). RESULTS: Across different tasks, we observed that brain response in a network of regions involved in salience and interoception processing, encompassing fronto-insular (the right inferior frontal gyrus-anterior insular cortex) and fronto-limbic (the dorsomedial prefrontal cortex-dorsal anterior cingulate cortex) cortices was strongly associated with a positive CBT outcome. CONCLUSIONS: Our results suggest that there are robust neural predictors of CBT outcome in anxiety-related disorders that may eventually lead (probably in combination with other data) to develop personalized approaches for the treatment of these mental disorders.

The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma.

Angiogenesis plays the critical roles in promoting tumor progression, aggressiveness, and metastasis. Although few studies have revealed some angiogenesis-related genes (ARGs) could serve as prognosis-related biomarkers for the prostate cancer (PCa), the integrated role of ARGs has not been systematically studied. The RNA-sequencing data and clinical information of prostate adenocarcinoma (PRAD) were downloaded from The Cancer Genome Atlas (TCGA) as discovery dataset. Twenty-three ARGs in total were identified to be correlated with prognosis of PRAD by the univariate Cox regression analysis, and a 19-ARG signature was further developed with significant correlation with the disease-free survival (DFS) of PRAD by the least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation. The signature stratified PRAD patients into high- and low-ARGs signature score groups, and those with high ARGs signature score were associated with significantly poorer outcomes (median DFS: 62.71 months vs unreached, p < 0.0001). The predicting ability of ARGs signature was subsequently validated in two independent cohorts of GSE40272 & PRAD_MSKCC. Notably, the 19-ARG signature outperformed the typical clinical features or each involved ARG in predicting the DFS of PRAD. Furthermore, a prognostic nomogram was constructed with three independent prognostic factors, including the ARGs signature, T stage and Gleason score. The predicted results from the nomogram (C-index = 0.799, 95%CI = 0.744-0.854) matched well with the observed outcomes, which was verified by the calibration curves. The values of area under receiver operating characteristic curve (AUC) for DFS at 1-, 3-, 5-year for the nomogram were 0.82, 0.83, and 0.83, respectively, indicating the performance of nomogram model is of reasonably high accuracy and robustness. Moreover, functional enrichment analysis demonstrated the potential targets of E2F targets, G2M checkpoint pathways, and cell cycle pathways to suppress the PRAD progression. Of note, the high-risk PRAD patients were more sensitive to immune therapies, but Treg might hinder benefits from immunotherapies. Additionally, this established tool also could predict response to neoadjuvant androgen deprivation therapy (ADT) and some chemotherapy drugs, such as cisplatin, paclitaxel, and docetaxel, etc. The novel ARGs signature, with prognostic significance, can further promote the application of targeted therapies in different stratifications of PCa patients.

Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting-state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction.

Urinary dipeptidyl peptidase-4 protein is increased by linagliptin and is a potential predictive marker of urine albumin-to-creatinine ratio reduction in patients with type 2 diabetes.

Results of a post hoc analysis of urinary dipeptidyl peptidase-4 (DPP-4) protein as a predictor of urine albumin-to-creatinine ratio (UACR) response to linagliptin treatment based on MARLINA-T2D trial data are described. MARLINA was a 24-week, phase 3b, multinational, placebo-controlled clinical trial, in which patients with type 2 diabetes (T2D), HbA1c 6.5%-10.0% and UACR 30-3000 mg/g (n = 360) were treated with linagliptin or placebo. After 24 weeks of treatment, linagliptin significantly inhibited urinary DPP-4 activity and increased urinary DPP-4 protein. Furthermore, medium urinary DPP-4 protein levels (between 5.5 and 7.5 natural logarithmic [ln] µg/g creatinine) at baseline allowed for prediction of improved UACR in linagliptin-treated individuals. In patients with lower or higher levels of urinary DPP-4 protein at baseline, no association between linagliptin treatment and improved UACR was present. This might suggest a varying degree of importance of DPP-4 as a pathophysiological factor in T2D-associated kidney disease. In summary, urinary DPP-4 might be a useful predictive biomarker for UACR improvement by linagliptin.

Patient-derived tumor organoids for prediction of cancer treatment response.

Cancer treatment, in particular radiotherapy and chemotherapy, is often hindered by an inherent resistance of cancer cells. Cancer stem cells in particular have previously been shown to be more resistant than other cells within a tumor and are thought repopulate the tumour after therapies. Therefore, it is of utmost importance to develop tools and techniques that can be used to study mechanisms of resistance of cancer stem cells as potential treatment targets. Organoids (and cancer-derived organoids), are three-dimensional tissue-resembling cellular clusters derived from tissue or tumor specific stem cells that mimic the in vivo (tumor) characteristics, as well as (tumor) cell heterogeneity. Cancer organoids may further enhance the in vitro and in vivo models that are currently available, improve our understanding of cancer stem cell resistance and can be used to develop novel cancer treatments by improved targeting of cancer stem cells. In this review, we compare organoids with the more traditional laboratory models, such as cell lines and xenografts, and review the literature of the current role of cancer organoids in determining treatment responses.

PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy.

PURPOSE: To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. METHODS: Seventy-nine patients who had undergone pretreatment staging 18F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models. RESULTS: Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint. CONCLUSIONS: Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients.

[Clinical significance of quantitative level of hepatitis B core antibody].

Hepatitis B core antibody (anti-HBc) targets viral core protein and is produced in patients with hepatitis B virus (HBV) infection, and seroconversion occurs in the early stage of infection and often lasts for a lifetime. Qualitative detection of anti-HBc has been used in clinical practice for many years, while the clinical significance of its quantitative level remains unclear. A novel anti-HBc immunoassay based on double-antigen sandwich ELISA has been developed in recent years and lays a foundation for illustrating the change in the quantitative level of anti-HBc (qAnti-HBc) in HBV infection and its clinical significance. Several recent studies have revealed that qAnti-HBc is associated with the degree of hepatitis activity and response to pharmacotherapy and may become an important basis for selecting antiviral drugs, optimizing therapeutic regimen, and predicting treatment outcome.

Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.

PURPOSE: To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. RESULTS: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). CONCLUSION: The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.

Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset".

The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI scans. The proposed algorithm attains impressive performance on the test dataset with a mean Area Under the Receiver-Operating Characteristic curve of 0.98 and a mean accuracy of 88%. In this letter, I raise concerns that the reported results can be explained by inadvertent data leakage between training and test datasets. More precisely, I conjecture that the random split of the full dataset in training and test sets did not occur on a patient level, but rather on the level of 2D MRI slices. This allows the neural network to "memorize" a patient's anatomy and their treatment outcome, as opposed to discovering useful features for treatment response prediction. To provide evidence for these claims, I present results of similar experiments I conducted on a public breast MRI dataset, where I demonstrate that the suspected data leakage mechanism closely reproduces the results reported on in the cited work.

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease.

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

A susceptibility gene signature for ERBB2-driven mammary tumour development and metastasis in collaborative cross mice.

BACKGROUND: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. METHODS: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice. FINDINGS: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. INTERPRETATION: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC. FUNDING: The U.S. Department of Defense (DoD) Breast Cancer Research Program (BCRP) (BC190820), United States; MCIN/AEI/10.13039/501100011039 (PID2020-118527RB-I00, PDC2021-121735-I00), the "European Union Next Generation EU/PRTR," the Regional Government of Castile and León (CSI144P20), European Union.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.79 for the most precise model (FDR = 0.01). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

MRI-based radiomics for pretreatment prediction of response to concurrent chemoradiotherapy in locally advanced cervical squamous cell cancer.

PURPOSE: To investigate the value of magnetic resonance imaging (MRI)-based radiomics in predicting the treatment response to concurrent chemoradiotherapy (CCRT) in patients with locally advanced cervical squamous cell cancer (LACSC). METHODS: In total, 198 patients (training: n = 138; testing: n = 60) with LACSC treated with CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Responses were evaluated by MRI and clinical data performed at one month after completion of CCRT according to RECIST standards, and patients were divided into the residual group and nonresidual group. Overall, 200 radiomics features were extracted from T2-weighted imaging and apparent diffusion coefficient maps. The radiomics score (Rad-score) was constructed with a feature selection strategy. Logistic regression analysis was used for multivariate analysis of radiomics features and clinical variables. The performance of all models was assessed using receiver operating characteristic curves. RESULTS: Among the clinical variables, tumor grade and FIGO stage were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.741 and 0.749 in the training and testing groups. The Rad-score, consisting of 4 radiomics features selected from 200 radiomics features, showed good predictive performance with an AUC of 0.819 in the training group and 0.776 in the testing group, which were higher than the clinical model, but the difference was not statistically significant. The combined model constructed with tumor grade, FIGO stage, and Rad-score achieved the best performance, with an AUC of 0.857 in the training group and 0.842 in the testing group, which were significantly higher than the clinical model. CONCLUSION: MRI-based radiomics features could be used as a noninvasive biomarker to improve the ability to predict the treatment response to CCRT in patients with LACSC.

Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.

Cohort Profile: A European Multidisciplinary Network for the Fight against HIV Drug Resistance (EuResist Network).

The EuResist cohort was established in 2006 with the purpose of developing a clinical decision-support tool predicting the most effective antiretroviral therapy (ART) for persons living with HIV (PLWH), based on their clinical and virological data. Further to continuous extensive data collection from several European countries, the EuResist cohort later widened its activity to the more general area of antiretroviral treatment resistance with a focus on virus evolution. The EuResist cohort has retrospectively enrolled PLWH, both treatment-naïve and treatment-experienced, under clinical follow-up from 1998, in nine national cohorts across Europe and beyond, and this article is an overview of its achievement. A clinically oriented treatment-response prediction system was released and made available online in 2008. Clinical and virological data have been collected from more than one hundred thousand PLWH, allowing for a number of studies on the response to treatment, selection and spread of resistance-associated mutations and the circulation of viral subtypes. Drawing from its interdisciplinary vocation, EuResist will continue to investigate clinical response to antiretroviral treatment against HIV and monitor the development and circulation of HIV drug resistance in clinical settings, along with the development of novel drugs and the introduction of new treatment strategies. The support of artificial intelligence in these activities is essential.