XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma

Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI

Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including "MGMT", "methylation", "glioma", "glioblastoma", "machine learning", "deep learning", and "radiomics", were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice.

Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic

Objective

This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence.

Materials and Methods

We analyzed electronic health records (EHRs) capturing a wide array of demographic, socio-economic, and clinical variables from 2168 patients with scheduled appointments at the PDC from September 2022 to August 2023. Logistic regression, decision trees, and eXtreme Gradient Boosting (XGBoost) algorithms were employed to construct predictive models for appointment adherence.

Results

The XGBoost machine learning model outperformed logistic regression and decision trees with an area under the curve (AUC) of 72% vs 65% and 67%, respectively, in predicting missed appointments, despite limited availability of historical data. Key predictors included patient age, number of days between appointment scheduling and occurrence, insurance status, marital status, and mental health and cardiac disease conditions.

Discussion

Findings underscore the potential of machine learning predictive analytics to significantly enhance patient engagement and operational efficiency in emerging healthcare settings. Optimizing predictive models can help balance the early identification of patients at risk of non-adherence with the efficient allocation of resources.

Conclusion

The study highlights the potential value of employing machine learning techniques to inform interventions aimed at improving appointment adherence in a post-discharge transition clinic environment.

APOD: A biomarker associated with oxidative stress in acute rejection of kidney transplants based on multiple machine learning algorithms and animal experimental validation

BACKGROUND

Oxidative stress is an unavoidable process in kidney transplantation and is closely related to the development of acute rejection after kidney transplantation. This study aimed to investigate the biomarkers associated with oxidative stress and their potential biological functions during acute rejection of kidney transplants.

METHODS

We identified Hub genes using five machine learning algorithms based on differentially expressed genes (DEGs) in the kidney transplant acute rejection dataset GSE50058 and oxidative stress-related genes (OS) obtained from the MSigDB database, and validated them with the datasets GSE1563 and GSE9493, as well as with animal experiments; Subsequently, we explored the potential biological functions of Hub genes using single-gene GSEA enrichment analysis; The Cibersort algorithm was used to explore the altered levels of infiltration of 22 immune cells during acute rejection of renal transplantation, and a correlation analysis between Hub genes and immune cells was performed; Finally, we also explored transcription factors (TFs), miRNAs, and potential drugs that regulate Hub genes.

RESULTS

We obtained a total of 57 genes, which we defined as oxidative stress-associated differential genes (DEOSGs), after intersecting DEGs during acute rejection of kidney transplants with OSs obtained from the MSigDB database; The results of enrichment analysis revealed that DEOSGs were mainly enriched in response to oxidative stress, response to reactive oxygen species, and regulation of oxidative stress and reactive oxygen species; Subsequently, we identified one Hub gene as APOD using five machine learning algorithms, which were validated by validation sets and animal experiments; The results of single-gene GSEA enrichment analysis revealed that APOD was closely associated with the regulation of immune signaling pathways during acute rejection of kidney transplants; The Cibersort algorithm found that the infiltration levels of a total of 10 immune cells were altered in acute rejection, while APOD was found to correlate with the expression of multiple immune cells; Finally, we also identified 154 TFs, 12 miRNAs, and 12 drugs or compounds associated with APOD regulation.

CONCLUSION

In this study, APOD was identified as a biomarker associated with oxidative stress during acute rejection of kidney transplants using multiple machine learning algorithms, which provides a potential therapeutic target for mitigating oxidative stress injury and reducing the incidence of acute rejection in kidney transplantation.

Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis

OBJECTIVES

To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas.

METHODS

PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity.

RESULTS

We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01).

CONCLUSIONS

Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice.

CLINICAL RELEVANCE STATEMENT

MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery.

KEY POINTS

• Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.

Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status

Background.Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis.O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients.Purpose.To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.Methods.Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.Results.The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.Conclusions.SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.

Multi-modal deep learning from imaging genomic data for schizophrenia classification

Background

Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises.

Methods

Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC).

Results

Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%.

Conclusion

We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.

Identifying key factors for predicting O6-Methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer

PURPOSE

This study aimed to perform multimodal analysis by vision transformer (vViT) in predicting O6-methylguanine-DNA methyl transferase (MGMT) promoter status among adult patients with diffuse glioma using demographics (sex and age), radiomic features, and MRI.

METHODS

The training and test datasets contained 122 patients with 1,570 images and 30 patients with 484 images, respectively. The radiomic features were extracted from enhancing tumors (ET), necrotic tumor cores (NCR), and the peritumoral edematous/infiltrated tissues (ED) using contrast-enhanced T1-weighted images (CE-T1WI) and T2-weighted images (T2WI). The vViT had 9 sectors; 1 demographic sector, 6 radiomic sectors (CE-T1WI ET, CE-T1WI NCR, CE-T1WI ED, T2WI ET, T2WI NCR, and T2WI ED), 2 image sectors (CE-T1WI, and T2WI). Accuracy and area under the curve of receiver-operating characteristics (AUC-ROC) were calculated for the test dataset. The performance of vViT was compared with AlexNet, GoogleNet, VGG16, and ResNet by McNemar and Delong test. Permutation importance (PI) analysis with the Mann-Whitney U test was performed.

RESULTS

The accuracy was 0.833 (95% confidence interval [95%CI]: 0.714-0.877) and the area under the curve of receiver-operating characteristics was 0.840 (0.650-0.995) in the patient-based analysis. The vViT had higher accuracy than VGG16 and ResNet, and had higher AUC-ROC than GoogleNet (p<0.05). The ED radiomic features extracted from the T2-weighted image demonstrated the highest importance (PI=0.239, 95%CI: 0.237-0.240) among all other sectors (p<0.0001).

CONCLUSION

The vViT is a competent deep learning model in predicting MGMT status. The ED radiomic features of the T2-weighted image demonstrated the most dominant contribution.

Revisiting Temozolomide's role in solid tumors: Old is gold?

Temozolomide is an imidazotetrazine with a long history in oncology especially for the high grade malignant glioma and metastatic melanoma. However, last year's new indications for its use are added. Its optimum pharmacodynamic profile, its ability to penetrate the blood-brain barrier, the existence of methylation of MGMT in solid tumors which enhances its efficacy, the identification of new agents that can overcome temozolomide's resistance, the promising role of temozolomide in turning immune cold tumors to hot ones, are leading to expand its use in other solid tumors, giving oncologists an additional tool for the treatment of advanced and aggressive neoplasms.

AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis

BACKGROUND

Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions.

METHODS

Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups.

RESULTS

By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category.

CONCLUSION

Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma

OBJECTIVES

This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM).

METHODS

We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression. The machine learning models were established with the maximum relevance and minimum redundancy algorithm and the extreme gradient boosting classifier. The area under the receiver operating curve (AUC) and accuracy were calculated for the training and validation sets.

RESULTS

Positive VEGF in GBM was 63.1% (137/217), with a high expression ratio of 53.3% (73/137). To predict the positive and negative VEGF expression, 7 radiomic features were selected, with 3 features from T1CE and 4 from T2WI. The accuracy and AUC were 0.83 and 0.81, respectively, in the training set and were 0.73 and 0.74, respectively, in the validation set. To predict high and low levels, 7 radiomic features were selected, with 2 from T1CE, 1 from T2WI, and 4 from the data combinations of T1CE and T2WI. The accuracy and AUC were 0.88 and 0.88, respectively, in the training set and were 0.72 and 0.72, respectively, in the validation set.

CONCLUSION

The VEGF expression status in GBM can be predicted using a machine learning model. Radiomic features resulting from data combinations of different MRI sequences could be helpful.

Prognostic Value of Immunohistochemical Expression of MTAP and AKIP1 in IDH1 Mutant Astrocytoma

BACKGROUND

Definite treatment for glioma is not exist, and with increased drug resistance, more effort should be paid to identify new prognostic biomarkers and molecular targets for therapy for glioma patients.

AIM

The current study aimed to evaluate the immunohistochemical (IHC) expression of MTAP and A-Kinase Interacting Protein 1 (AKIP1) in astrocytoma and to investigate their association with the clinicopathological characters of these cases.

METHODS

Totally 66 cases of astrocytoma patients involved in this study. Cases underwent tumor resection and tissue sections were stained with MTAP, AKIP1 and IDH1 by IHC and evaluated in different grades of astrocytoma and their association with survival and response to therapy was investigated.

RESULTS

High AKIP1 expression was positively correlated with treatment resistance and progressive disease. Positive IDH and retained MTAP expressions had shown better treatment response rather than negative IDH and lost MTAP. High AKIP, negative IDH and loss of MTAP expressions were significantly associated with poor survival outcome.

CONCLUSION

Irrespective to grade and IDH status, the loss of MTAP immunoreactivity and high AKIP1 expression are predictive factors in astrocytoma, and they may be used as a biomarker for guiding astrocytoma management and prognosis surveillance.

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics

Background

Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.

Methods

In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared.

Results

The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003).

Conclusions

The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Insight into the Progress in CAR-T Cell Therapy and Combination with Other Therapies for Glioblastoma

Glioblastoma (GBM) is the most common malignant primary brain cancer in adults. It is always resistant to existing treatments, including surgical resection, postoperative radiotherapy, and chemotherapy, which leads to a dismal prognosis and a high relapse rate. Therefore, novel curative therapies are urgently needed for GBM. Chimeric antigen receptor T (CAR-T) cell therapy has significantly improved life expectancy for hematological malignancies patients, and thus it increases the interest in applying CAR-T cell therapy for solid tumors. In the recently published research, it is indicated that there are numerous obstacles to achieve clinical benefits for solid tumors, especially for GBM, because of GBM anatomical characteristics (the blood-brain barrier and suppressive tumor microenvironment) and the tumor heterogeneity. CAR-T cells are difficult to penetrate blood-brain barrier, and immunosuppressive tumor microenvironment (TME), which induces CAR-T cell exhaustion, impairs CAR-T cell therapy response. Moreover, under the pressure of CAR-T cell therapy, the tumor heterogeneity and tumor plasticity drive tumor evolution and therapy resistance, such as antigen escape. Nonetheless, scientists strive for strategies to overcome these hurdles, including novel CAR-T cell designs and regional delivery. For instance, the structure of multi-antigen-targeted CAR-T cells can enrich CAR-T accumulation in tumor TME and eliminate abundant tumor cells to avoid tumor antigen heterogeneity. Additionally, paired with an immune modifier and one or more stimulating domains, different generation of innovations in the structure and manufacturing of CAR-T cells have improved efficacy and persistence. While single CAR-T cell therapy receives limited clinical survival benefit. Compared with single CAR-T cell therapy, the combination therapies have supplemented the treatment paradigm. Combinatorial treatment methods consolidate the CAR-T cells efficacy by regulating the tumor microenvironment, optimizing the CAR structure, targeting the CAR-T cells to the tumor cells, reversing the tumor-immune escape mechanisms, and represent a promising avenue against GBM, based on multiple impressive research. Moreover, exciting results are also reported to be realized through combining effective therapies with CAR-T cells in preclinical and clinical trials samples, have aroused inspiration to explore the antitumor function of combination therapies. In summary, this study aims to summarize the limitation of CAR-T cell therapies and introduces novel strategies to enhance CAR-T cell function as well as prospect the potential of the therapeutic combination.

A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Considering the complications of the tissue-based methods, an imaging-based approach is preferred. This study aimed to compare three different deep learning-based approaches for predicting MGMT promoter methylation status. We obtained 576 T2WI with their corresponding tumor masks, and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32 × 32 × 32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch's coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction, then for final prediction, we used majority voting. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, and voxel-wise, accuracy was 65.42% (SD 3.97%), 61.37% (SD 1.48%), and 56.84% (SD 4.38%), respectively.

Prediction Framework on Early Urine Infection in IoT-Fog Environment Using XGBoost Ensemble Model

Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.

MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation

Glioblastoma is the most aggressive primary brain tumor, which almost systematically relapses despite surgery (when possible) followed by radio-chemotherapy temozolomide-based treatment. Upon relapse, one option for treatment is another chemotherapy, lomustine. The efficacy of these chemotherapy regimens depends on the methylation of a specific gene promoter known as MGMT, which is the main prognosis factor for glioblastoma. Knowing this biomarker is a key issue for the clinician to personalize and adapt treatment to the patient at primary diagnosis for elderly patients, in particular, and also upon relapse. The association between MRI-derived information and the prediction of MGMT promoter status has been discussed in many studies, and some, more recently, have proposed the use of deep learning algorithms on multimodal scans to extract this information, but they have failed to reach a consensus. Therefore, in this work, beyond the classical performance figures usually displayed, we seek to compute confidence scores to see if a clinical application of such methods can be seriously considered. The systematic approach carried out, using different input configurations and algorithms as well as the exact methylation percentage, led to the following conclusion: current deep learning methods are unable to determine MGMT promoter methylation from MRI data.

LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation

The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed, has accumulated over 200,000 articles with millions of accesses. Approximately 10,000 new articles are added to LitCovid every month. A main curation task in LitCovid is topic annotation where an article is assigned with up to eight topics, e.g., Treatment and Diagnosis. The annotated topics have been widely used both in LitCovid (e.g., accounting for ∼18% of total uses) and downstream studies such as network generation. However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth. This study proposes LITMC-BERT, a transformer-based multi-label classification method in biomedical literature. It uses a shared transformer backbone for all the labels while also captures label-specific features and the correlations between label pairs. We compare LITMC-BERT with three baseline models on two datasets. Its micro-F1 and instance-based F1 are 5% and 4% higher than the current best results, respectively, and only requires ∼18% of the inference time than the Binary BERT baseline. The related datasets and models are available via https://github.com/ncbi/ml-transformer.

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.

Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach

O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.

Prediction Model of Bone Marrow Infiltration in Patients with Malignant Lymphoma Based on Logistic Regression and XGBoost Algorithm

Objective

The prediction model of bone marrow infiltration (BMI) in patients with malignant lymphoma (ML) was established based on the logistic regression and the XGBoost algorithm. The model's prediction efficiency was evaluated.

Methods

A total of 120 patients diagnosed with ML in the department of hematology from January 2018 to January 2021 were retrospectively selected. The training set (n = 84) and test set (n = 36) were randomly divided into 7 : 3, and logistic regression and XGBoost algorithm models were constructed using the training set data. Predictors of BMI were screened based on laboratory indicators, and the model's efficacy was evaluated using test set data.

Results

The prediction algorithm model's top three essential characteristics are the blood platelet count, soluble interleukin-2 receptor, and non-Hodgkin's lymphoma. The area under the curve of the logistic regression model for predicting the BMI of patients with ML was 0.843 (95% CI: 0.761~0.926). The area under the curve of the XGBoost model is 0.844 (95% CI: 0.765~0.937).

Conclusion

The prediction model constructed in this study based on logistic regression and XGBoost algorithm has a good prediction model. The results showed that blood platelet count and soluble interleukin-2 receptor were good predictors of BMI in ML patients.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Simple Summary

Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices.

Abstract

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Stratification From Heterogeneity of the Cell-Death Signal Enables Prognosis Prediction and Immune Microenvironment Characterization in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is the primary subtype of esophageal cancer (EC) characterized by a high incidence rate and extremely poor prognosis worldwide. Previous studies suggested that the specific cell death signal was linked to different immune subtypes in multiple cancers, while a comprehensive investigation on ESCC is to be performed yet. In the current study, we dissected different cell death signals in ESCC tumors and then integrated that functional information to stratify ESCC patients into different immunogenic cell death (ICD) subtypes. By systematically analyzing the transcriptomes of 857 patients and proteomic profile of 124 patients, we found that the signals of necroptosis, pyroptosis, and ferroptosis are positively associated with activated immunity in ESCC. We identified two ICD pattern terms, namely, ICD-high and ICD-low subtypes that positively correlated to both progression-free survival and overall survival. In addition, cell fraction deconvolution analysis revealed that more infiltrated leukocytes were enriched in ICD-high types, especially antigen-presenting cells, such as dendritic cells and macrophages. With the XGBoost algorithm, we further developed a 14-gene signature which can simplify the subtyping for allocating new samples, by which we validated the prognosis value of the signature and proved that the ICD score scheme could serve as a promising biomarker for stratifying patients with immunotherapy in several immune checkpoint blockade treatment cohorts. Collectively, we successfully constructed the ICD scheme, which enables predicting of the prognosis or immunotherapy efficacy in ESCC patients and uncovered the critical interplay between cell death signals and immune status in ESCC.

Brain Tumor Imaging: Applications of Artificial Intelligence

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.

Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review

Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.

An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification

Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine the nature of brain tumors. The current genetic analysis approach is time-consuming and requires surgical extraction of brain tissue samples. Accurate classification of multi-modal brain tumor images can speed up the detection process and alleviate patient suffering. Medical image fusion refers to effectively merging the significant information of multiple source images of the same tissue into one image, which will carry abundant information for diagnosis. This paper proposes a novel attentive deep-learning-based classification model that integrates multi-modal feature aggregation, lite attention mechanism, separable embedding, and modal-wise shortcuts for performance improvement. We evaluate our model on the RSNA-MICCAI dataset, a scenario-specific medical image dataset, and demonstrate that the proposed method outperforms the state-of-the-art (SOTA) by around 3%.

Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model

BACKGROUND

New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE.

METHODS AND RESULTS

We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified.

CONCLUSIONS

Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.

Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis

PURPOSE

We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients.

METHODS

A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis.

RESULTS

A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance.

CONCLUSION

This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.

Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

Since consuming gutter oil does great harm to people's health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.

Precision Neurosurgery: A Path Forward

Since the inception of their profession, neurosurgeons have defined themselves as physicians with a surgical practice. Throughout time, neurosurgery has always taken advantage of technological advances to provide better and safer care for patients. In the ongoing precision medicine surge that drives patient-centric healthcare, neurosurgery strives to effectively embrace the era of data-driven medicine. Neuro-oncology best illustrates this convergence between surgery and precision medicine with the advent of molecular profiling, imaging and data analytics. This convenient convergence paves the way for new preventive, diagnostic, prognostic and targeted therapeutic perspectives. The prominent advances in healthcare and big data forcefully challenge the medical community to deeply rethink current and future medical practice. This work provides a historical perspective on neurosurgery. It also discusses the impact of the conceptual shift of precision medicine on neurosurgery through the lens of neuro-oncology.

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC's early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist's experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

Urdu text in natural scene images: a new dataset and preliminary text detection

Text detection in natural scene images for content analysis is an interesting task. The research community has seen some great developments for English/Mandarin text detection. However, Urdu text extraction in natural scene images is a task not well addressed. In this work, firstly, a new dataset is introduced for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. Secondly, the channel enhanced Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text regions as candidates in an image. Two-stage filtering mechanism is applied to eliminate non-candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, a support vector machine classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. Extensive experimentation is performed on the locally developed dataset to evaluate the performance. The experimental results show good performance on test set images. The dataset will be made available for research use. To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction.

The IL13α 2R paves the way for anti-glioma nanotherapy

Glioblastoma (GBM) is one of the most aggressive (grade IV) gliomas characterized by a high rate of recurrence, resistance to therapy and a grim survival prognosis. The long-awaited improvement in GBM patients' survival rates essentially depends on advances in the development of new therapeutic approaches. Recent preclinical studies show that nanoscale materials could greatly contribute to the improvement of diagnosis and management of brain cancers. In the current review, we will discuss how specific features of glioma pathobiology can be employed for designing efficient targeting approaches. Moreover, we will summarize the main evidence for the potential of the IL-13R alpha 2 receptor (IL13α2R) targeting in GBM early diagnosis and experimental therapy.

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases

Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms

Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F ₁ score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique.

Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study

Background

Tuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB.

Objective

We aim to predict the status of liver injury in patients with TB at the clinical treatment stage.

Methods

We designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019.

Results

In total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients’ most recent alanine transaminase levels, average rate of change of patients’ last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days.

Conclusions

Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

BACKGROUND

Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs.

METHODS

The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model.

RESULTS

The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%.

CONCLUSIONS

The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it.

Regional Population Forecast and Analysis Based on Machine Learning Strategy

Regional population forecast and analysis is of essence to urban and regional planning, and a well-designed plan can effectively construct a sound national infrastructure and stabilize positive population growth. Traditionally, either urban or regional planning relies on the opinions of demographers in terms of how the population of a city or a region will grow. Multi-regional population forecast is currently possible, carried out mainly on the basis of the Interregional Cohort-Component model. While this model has its unique advantages, several demographic rates are determined based on the decisions made by primary planners. Hence, the only drawback for cohort-component type population forecasting is allowing the analyst to specify the demographic rates of the future, and it goes without saying that this tends to introduce a biased result in forecasting accuracy. To effectively avoid this problem, this work proposes a machine learning-based method to forecast multi-regional population growth objectively. Thus, this work, drawing upon the newly developed machine learning technology, attempts to analyze and forecast the population growth of major cities in Taiwan. By effectively using the advantage of the XGBoost algorithm, the evaluation of feature importance and the forecast of multi-regional population growth between the present and the near future can be observed objectively, and it can further provide an objective reference to the urban planning of regional population.

Identification of KIF4A as a pan-cancer diagnostic and prognostic biomarker via bioinformatics analysis and validation in osteosarcoma cell lines

Background

Cancer is a disease of abnormal cell proliferation caused by abnormal expression of cancer-related genes. However, it is still difficult to distinguish benign and malignant lesions in many cases. KIF4A has been reported to be associated with a variety of cancer lesions. We aimed to explore whether KIF4A could be used as a biomarker of pan-cancer diagnostic.

Methods

We identified twenty-eight cell cycle-related genes that were overexpressed in no less than ten types of cancer. We determined KIF4A mRNA and protein expression in osteosarcoma (OS) cells. Furthermore, to determine the effect of KIF4A in OS, we silenced KIF4A in OS cells and detected cell viability, colony formation, invasion, migration, apoptosis and cell cycle parameters.

Results

KIF4A exhibited upregulated expression in eleven types of cancer. Cell cycle-related genes are extensively overexpressed in various types of cancers. KIF4A overexpression can serve as a diagnostic and prognostic marker in various cancers. Silencing KIF4A inhibited the viability, colony formation, invasion and migration and induced apoptosis and cell cycle arrest of OS cells. Our findings revealed that high expression of KIF4A could serve as a diagnostic and prognostic marker in OS cancers.

Conclusion

KIF4A could serve as a pan-cancer diagnostic and prognostic marker. KIF4A could be used as a novel therapeutic target for OS.

Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning

Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length V, word-size W, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA’s performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with W=3 and V=3 yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals.

Construction of a Soundscape-Based Media Art Exhibition to Improve User Appreciation Experience by Using Deep Neural Networks

The objective of this study was to improve user experience when appreciating visual artworks with soundscape music chosen by a deep neural network based on weakly supervised learning. We also propose a multi-faceted approach to measuring ambiguous concepts, such as the subjective fitness, implicit senses, immersion, and availability. We showed improvements in appreciation experience, such as the metaphorical and psychological transferability, time distortion, and cognitive absorption, with in-depth experiments involving 70 participants. Our test results were similar to those of “Bunker de Lumières: van Gogh”, which is an immersive media artwork directed by Gianfranco lannuzzi; the fitness scores of our system and “Bunker de Lumières: van Gogh” were 3.68/5 and 3.81/5, respectively. Moreover, the concordance of implicit senses between artworks and classical music was measured to be 0.88%, and the time distortion and cognitive absorption improved during the immersion. Finally, the proposed method obtained a subjective satisfaction score of 3.53/5 in the evaluation of its usability. Our proposed method can also help spread soundscape-based media art by supporting traditional soundscape design. Furthermore, we hope that our proposed method will help people with visual impairments to appreciate artworks through its application to a multi-modal media art guide platform.

Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer

The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.

Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning

In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions.

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network

Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision–recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI.