Machine learning and glioma imaging biomarkers

Neuroplasticity in Diffuse Low-grade Gliomas: Backward Modelling of Brain-tumor Interactions Prior to Diagnosis is Needed to Better Predict Recovery after Treatment

PURPOSE OF REVIEW

In low-grade glioma (LGG), besides the patient's neurological status and tumor characteristics on neuroimaging, current treatment guidelines mainly rely on the glioma's genetics at diagnosis to define therapeutic strategy, usually starting with surgical resection. However, this snapshot in time does not take into account the antecedent period of tumor progression and its interactions with the brain before presentation. This article reviews new concepts that pertain to reconstruct the history of previous interplay between the LGG's course and adaptive changes in the connectome within which the glioma is embedded over the years preceding the diagnosis.

RECENT FINDINGS

Microscale and macroscale parameters helpful for extrapolating backward in time are considered, both for the glioma (kinetics, migration vs. proliferation profile, metabolism with possible intratumoral heterogeneity, relationships with surrounding cerebral pathways) and for patterns of reconfiguration within and across neural networks in reaction to the LGG leading to considerable interindividual cerebral variability. Modelling these continuous variations at the time of LGG diagnosis is a prerequisite to predict recovery from treatment(s). It is important to go beyond the biology of the LGG at a given moment of its history, and instead construct a more comprehensive picture of the past and present dynamics of glioma-brain interactions, and their ongoing evolution, as a necessary stage to optimize a personalized management plan by thinking several steps ahead.

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM. Colocalization analysis was performed to validate genetic associations, while scPagwas analysis was used to evaluate the relevance of key UDIPs to GBM at the cellular level. Among 512 UDIPs tested, 23 were found to have significant causal associations with GBM. Notably, UDIPs such as T1-33 (OR = 1.007, 95% CI = 1.001 to 1.012, P = .022), T1-34 (OR = 1.012, 95% CI = 1.001-1.023, P = .028), and T1-96 (OR = 1.009, 95% CI = 1.001-1.019, P = .046) were found to have a genetic association with GBM. Furthermore, T1-34 and T1-96 were significantly associated with GBM recurrence, with P-values < .0001 and P < .001, respectively. In addition, scPagwas analysis revealed that T1-33, T1-34, and T1-96 are distinctively linked to different GBM subtypes, with T1-33 showing strong associations with the neural progenitor-like subtype (NPC2), T1-34 with mesenchymal (MES2) and neural progenitor (NPC1) cells, and T1-96 with the NPC2 subtype. T1-33, T1-34, and T1-96 hold significant potential for predicting tumor recurrence and aiding in the development of personalized GBM treatment strategies.

Correlation of Edema/Tumor Index With Histopathological Outcomes According to the WHO Classification of Cranial Tumors

BACKGROUND

Metastatic brain tumors are a prevalent challenge in neurosurgery, with vasogenic edema being a significant consequence of these lesions. Despite the critical role of peritumoral edema in prognosis and patient outcomes, few studies have quantified its diagnostic and prognostic implications. This study aims to evaluate the correlation between the edema/tumor index (ETI) and histopathological outcomes according to the 2021 WHO classification of cranial tumors.

METHODOLOGY

We conducted a retrospective analysis of Digital Imaging and Communications in Medicine (DICOM)-format magnetic resonance imaging (MRI) data from May 2023 to May 2024, applying manual 3D volumetric segmentation using Image Tool Kit-SNAP (ITK-SNAP, version 3.8.0, University of Pennsylvania) software. The ETI was calculated by dividing the volume of peritumoral edema by the tumor volume. The study included 60 patients, and statistical analyses were performed to assess the correlation between ETI and tumor histopathology, including Receiver Operating Characteristic (ROC) curve analysis for cutoff points.

RESULTS

A total of 60 patients were included in the study, with 27 males (45%) and 33 females (55%). The average tumor volume measured by 3D segmentation was 46.9 cubic centimeters (cc) (standard deviation [SD] ± 25.6), and the average peritumoral edema volume was 79 cc (SD ± 37.5) for malignant tumors. The ETI was calculated for each case. Malignant tumors (WHO grades 3 and 4) had a mean ETI of 1.6 (SD ± 1.2), while non-malignant tumors (WHO grades 1 and 2) had a mean ETI of 1.2 (SD ± 1.1), but this difference was not statistically significant (P = 0.51). ROC curve analysis for the ETI did not provide a reliable cutoff point for predicting tumor malignancy (area under the curve [AUC] = 0.59, P = 0.20). Despite the larger edema volume observed in malignant tumors, the ETI did not correlate significantly with the histopathological grade.

CONCLUSIONS

This study found no significant correlation between the ETI and the histopathological grade of brain tumors according to the 2021 WHO classification. While malignant tumors were associated with larger volumes of both tumor and peritumoral edema, the ETI did not prove to be a reliable predictor of tumor malignancy. Therefore, the ETI should not be used as a standalone metric for determining tumor aggressiveness or guiding clinical decision-making. Further studies with larger cohorts are required to better understand the potential prognostic value of the ETI in brain tumors.

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.

Construction and validation of a machine learning-based immune-related prognostic model for glioma

BACKGROUND

Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine.

METHODS

Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments.

RESULTS

In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden. The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration.

CONCLUSION

This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.

Machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma and supratentorial glioblastoma

Objective

To develop a machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma (STEE) and supratentorial glioblastoma (GBM).

Methods

We conducted a retrospective analysis on MRI datasets obtained from 140 patients who were diagnosed with STEE (n=48) and GBM (n=92) from two institutions. Initially, we compared seven different machine learning algorithms to determine the most suitable signature (rad-score). Subsequently, univariate and multivariate logistic regression analyses were performed to identify significant clinical predictors that can differentiate between STEE and GBM. Finally, we developed a nomogram by visualizing the rad-score and clinical features for clinical evaluation.

Results

The TreeBagger (TB) outperformed the other six algorithms, yielding the best diagnostic efficacy in differentiating STEE from GBM, with area under the curve (AUC) values of 0.735 (95% CI: 0.625-0.845) and 0.796 (95% CI: 0.644-0.949) in the training set and test set. Furthermore, the nomogram incorporating both the rad-score and clinical variables demonstrated a robust predictive performance with an accuracy of 0.787 in the training set and 0.832 in the test set.

Conclusion

The nomogram could serve as a valuable tool for non-invasively discriminating between STEE and GBM.

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.

Morphological MRI features as prognostic indicators in brain metastases

BACKGROUND

Stereotactic radiotherapy is the preferred treatment for managing patients with fewer than five brain metastases (BMs). However, some lesions recur after irradiation. The purpose of this study was to identify patients who are at a higher risk of failure, which can help in adjusting treatments and preventing recurrence.

METHODS

In this retrospective multicenter study, we analyzed the predictive significance of a set of interpretable morphological features derived from contrast-enhanced (CE) T1-weighted MR images as imaging biomarkers using Kaplan-Meier analysis. The feature sets studied included the total and necrotic volumes, the surface regularity and the CE rim width. Additionally, we evaluated other nonmorphological variables and performed multivariate Cox analysis.

RESULTS

A total of 183 lesions in 128 patients were included (median age 61 [31-95], 64 men and 64 women) treated with stereotactic radiotherapy (57% single fraction, 43% fractionated radiotherapy). None of the studied variables measured at diagnosis were found to have prognostic value. However, the total and necrotic volumes and the CE rim width measured at the first follow-up after treatment and the change in volume due to irradiation can be used as imaging biomarkers for recurrence. The optimal classification was achieved by combining the changes in tumor volume before and after treatment with the presence or absence of necrosis (p <  < 0.001).

CONCLUSION

This study demonstrated the prognostic significance of interpretable morphological features extracted from routine clinical MR images following irradiation in brain metastases, offering valuable insights for personalized treatment strategies.

Glioma Grade and Molecular Markers: Comparing Machine-Learning Approaches Using VASARI (Visually AcceSAble Rembrandt Images) Radiological Assessment

OBJECTIVES

This study aimed to leverage Visually AcceSAble Rembrandt Images (VASARI) radiological features, extracted from magnetic resonance imaging (MRI) scans, and machine-learning techniques to predict glioma grade, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA methyltransferase (MGMT) methylation.

METHODOLOGY

A retrospective evaluation was undertaken, analyzing MRI and molecular data from 107 glioma patients treated at a tertiary hospital. Patients underwent MRI scans using established protocols and were evaluated based on VASARI criteria. Tissue samples were assessed for glioma grade and underwent molecular testing for IDH mutations and MGMT methylation. Four machine learning models, namely, Random Forest, Elastic-Net, multivariate adaptive regression spline (MARS), and eXtreme Gradient Boosting (XGBoost), were trained on 27 VASARI features using fivefold internal cross-validation. The models' predictive performances were assessed using the area under the curve (AUC), sensitivity, and specificity.

RESULTS

For glioma grade prediction, XGBoost exhibited the highest AUC (0.978), sensitivity (0.879), and specificity (0.964), with f6 (proportion of non-enhancing) and f12 (definition of enhancing margin) as the most important predictors. In predicting IDH mutation status, XGBoost achieved an AUC of 0.806, sensitivity of 0.364, and specificity of 0.880, with f1 (tumor location), f12, and f30 (perpendicular diameter to f29) as primary predictors. For MGMT methylation, XGBoost displayed an AUC of 0.580, sensitivity of 0.372, and specificity of 0.759, highlighting f29 (longest diameter) as the key predictor.

CONCLUSIONS

This study underscores the robust potential of combining VASARI radiological features with machine learning models in predicting glioma grade, IDH mutation status, and MGMT methylation. The best and most balanced performance was achieved using the XGBoost model. While the prediction of glioma grade showed promising results, the sensitivity in discerning IDH mutations and MGMT methylation still leaves room for improvement. Follow-up studies with larger datasets and more advanced artificial intelligence techniques can further refine our understanding and management of gliomas.

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study)

BACKGROUND

The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion.

METHODS

Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection.

RESULTS

The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003).

CONCLUSIONS

A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Imaging predictors of 4q12 amplified and RB1 mutated glioblastoma IDH-wildtype

INTRODUCTION

Recent studies have identified that glioblastoma IDH-wildtype consists of different molecular subgroups with distinct prognoses. In order to accurately describe and classify gliomas, the Visually AcceSAble Rembrandt Images (VASARI) system was developed. The goal of this study was to evaluate the VASARI characteristics in molecular subgroups of IDH-wildtype glioblastoma.

METHODS

A retrospective analysis of glioblastoma IDH- wildtype with comprehensive next-generation sequencing and pre-operative and post-operative MRI was performed. VASARI characteristics and 205 genes were evaluated. Multiple comparison adjustment by the Bejamin-Hochberg false discovery rate (BH-FDR) was performed. A 1:3 propensity score match (PSM) with a Caliper of 0.2 was done.

RESULTS

178 patients with GBM IDH-WT met the inclusion criteria. 4q12 amplified patients (n = 20) were associated with cyst presence (30% vs. 12%, p = 0.042), decreased hemorrhage (35% vs. 62%, p = 0.028), and non-restricting/mixed (35%/60%) rather than restricting diffusion pattern (5%), meanwhile, 4q12 non-amplified patients had mostly restricting (47.4%) rather than a non-restricting/mixed diffusion pattern (28.4%/23.4%). This remained statistically significant after BH-FDR adjustment (p = 0.002). PSM by 4q12 amplification showed that diffusion characteristics continued to be significantly different. Among RB1-mutant patients, 96% had well-defined enhancing margins vs. 70.6% of RB1-WT (p = 0.018), however, this was not significant after BH-FDR or PSM.

CONCLUSIONS

Patients with glioblastoma IDH-wildtype harboring 4q12 amplification rarely have restricting DWI patterns compared to their wildtype counterparts, in which this DWI pattern is present in ~ 50% of patients. This suggests that some phenotypic imaging characteristics can be identified among molecular subtypes of IDH-wildtype glioblastoma.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Development and validation of a clinical prediction model for glioma grade using machine learning

BACKGROUND

Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

OBJECTIVE

This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading.

METHODS

Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model.

RESULTS

The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit.

CONCLUSION

A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

A Pilot Remote Curriculum to Enhance Resident and Medical Student Understanding of Machine Learning in Healthcare

BACKGROUND

Despite the expanding role of machine learning (ML) in health care and patient expectations for clinicians to understand ML-based tools, few for-credit curricula exist specifically for neurosurgical trainees to learn basic principles and implications of ML for medical research and clinical practice. We implemented a novel, remotely delivered curriculum designed to develop literacy in ML for neurosurgical trainees.

METHODS

A 4-week pilot medical elective was designed specifically for trainees to build literacy in basic ML concepts. Qualitative feedback from interested and enrolled students was collected to assess students' and trainees' reactions, learning, and future application of course content.

RESULTS

Despite 15 interested learners, only 3 medical students and 1 neurosurgical resident completed the course. Enrollment included students and trainees from 3 different institutions. All learners who completed the course found the lectures relevant to their future practice as clinicians and researchers and reported improved confidence in applying and understanding published literature applying ML techniques in health care. Barriers to ample enrollment and retention (e.g., balancing clinical responsibilities) were identified.

CONCLUSIONS

This pilot elective demonstrated the interest, value, and feasibility of a remote elective to establish ML literacy; however, feedback to increase accessibility and flexibility of the course encouraged our team to implement changes. Future elective iterations will have a semiannual, 2-week format, splitting lectures more clearly between theory (the method and its value) and application (coding instructions) and will make lectures open-source prerequisites to allow tailoring of student learning to their planned application of these methods in their practice and research.

Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.

A systematic framework for identifying prognostic necroptosis-related lncRNAs and verification of lncRNA CRNDE/miR-23b-3p/IDH1 regulatory axis in glioma

Glioma remains the most frequent malignancy of the central nervous system. Recently, necroptosis has been identified as a cell death process that mediates the proliferation and development of tumor cells. LncRNAs play a key role in the diagnosis and treatment of various diseases. However, the impact that necrosis-related lncRNAs (NRLs) have on glioma remains unclear. In our studies, we selected 9 NRLs to construct a prognostic model. Meanwhile, we assessed the survival curves of these 9 NRLs. Our findings found ADGRA1-AS1 and WAC-AS1 were protective lncRNAs, while MIR210HG, LINC01503, CRNDE, HOXC-AS1, ZIM2-AS1, MIR22HG and PLBD1-AS1 were risk lncRNAs. Specifically, 12 immune cells, 25 immune-correlated pathways, and TME score were differentially expressed in the both risk groups. Additionally, the study predicted and validated the necroptosis-related lncRNA CRNDE/miR-23b-3p/IDH1 axis. CRNDE was strongly expressed in glioma specimens and several cell lines. Inhibiting CRNDE resulted in a substantial reduction in the proliferation and migration of U-118MG and U251 cells. Furthermore, the study predicted that CRNDE may exhibit oncogenic features by adsorbing miR-23b-3p and positively regulating IDH1 expression. Overall, the study constructed a prognostic model in glioma, and predicted a lncRNA CRNDE/miR-23b-3p/IDH1 axis, which could potentially be useful for gene therapy of glioma.

Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics

Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 μm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.

Screening the predictors for live birth failure in women after the first frozen embryo transfer based on the Lasso algorithm: a retrospective study

This study aimed to screen factors related to live birth outcomes of women with first frozen embryo transfer (FET). The enrolled women were divided into training and validation cohorts. The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning and the multiple regression model were then used to screen factors relevant to live birth failure (LBF) for the training dataset. A nomogram risk prediction model was established on the basis of the screened factors, and the consistency index (C-index) and calibration curve were derived for evaluating the model. The validation cohort was utilized to validate the nomogram model further. In total, 2083 women who accepted the first FET in our hospital were included and 44 factors were initially screened in this study. On the basis of the training cohort, the screened risk factors via multiple regression analysis with odds ratio (OR) values were female age (OR: 3.092, 95%CI: 1.065-4.852), body mass index (BMI; OR: 1.106, 95%CI: 1.015-1.546), caesarean section (OR: 1.909, 95%CI: 1.318-2.814), number of high-quality embryos (OR: 0.698, 95%CI: 0.599-0.812), and endometrial thickness (OR: 0.957, CI: 0.904-0.980). The nomogram model was generated based on five predictors. Furthermore, favourable results with C-indexes and calibration curves close to ideal curves indicated the accurate predictive ability of the nomogram. Female age, BMI, caesarean section, number of high-quality embryos, and endometrial thickness were independent predictors for LBF. The five factors of the risk assessment model may help to identify LBF with high accuracy in women who accept FET.

Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity. Systematic Review Registration: PROSPERO, CRD42022306922.

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

BACKGROUND

Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed.

METHODS

MEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random-effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC).

PROSPERO

CRD42021278454.

RESULTS

43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% - 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC.

CONCLUSION

AI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice.

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium

OBJECTIVE

To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models.

METHODS

MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules.

RESULTS

All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site.

CONCLUSION

The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres.

ADVANCES IN KNOWLEDGE

Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Reproducibility of 3 T APT-CEST in Healthy Volunteers and Patients With Brain Glioma

BACKGROUND

Amide proton transfer (APT) imaging is a chemical exchange saturation transfer (CEST) technique offering potential clinical applications such as diagnosis, characterization, and treatment planning and monitoring in glioma patients. While APT-CEST has demonstrated high potential, reproducibility remains underexplored.

PURPOSE

To investigate whether cerebral APT-CEST with clinically feasible scan time is reproducible in healthy tissue and glioma for clinical use at 3 T.

STUDY TYPE

Prospective, longitudinal.

SUBJECTS

Twenty-one healthy volunteers (11 females; mean age ± SD: 39 ± 11 years) and 6 glioma patients (3 females; 50 ± 17 years: 4 glioblastomas, 1 oligodendroglioma, 1 radiologically suspected low-grade glioma).

FIELD STRENGTH/SEQUENCE

3 T, Turbo Spin Echo - ampling perfection with application optimized contrasts using different flip angle evolution - chemical exchange saturation transfer (TSE SPACE-CEST).

ASSESSMENT

APT-CEST measurement reproducibility was assessed within-session (glioma patients, scan session 1; healthy volunteers scan sessions 1, 2, and 3), between-sessions (healthy volunteers scan sessions 1 and 2), and between-days (healthy volunteers, scan sessions 1 and 3). The mean APTCEST values and standard deviation of the within-subject difference (SDdiff ) were calculated in whole tumor enclosed by regions of interest (ROIs) in patients, and eight ROIs in healthy volunteers-whole-brain, cortical gray matter, putamen, thalami, orbitofrontal gyri, occipital lobes, central brain-and compared.

STATISTICAL TESTS

Brown-Forsythe tests and variance component analysis (VCA) were used to assess the reproducibility of ROIs for the three time intervals. Significance was set at P < 0.003 after Bonferroni correction.

RESULTS

Intratumoral mean APTCEST was significantly higher than APTCEST in healthy-appearing tissue in patients (0.5 ± 0.46%). The average within-session, between-sessions, and between-days SDdiff of healthy control brains was 0.2% and did not differ significantly with each other (0.76 > P > 0.22). The within-session SDdiff of whole-brain was 0.2% in both healthy volunteers and patients, and 0.21% in the segmented tumor. VCA showed that within-session factors were the most important (60%) for scanning variance.

DATA CONCLUSION

Cerebral APT-CEST imaging may show good scan-rescan reproducibility in healthy tissue and tumors with clinically feasible scan times at 3 T. Short-term measurement effects may be the dominant components for reproducibility.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.

Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical-functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient's brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.

A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans

AIM OF STUDY

Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans.

METHODOLOGY/APPROACH

The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for future treatment plans for GBM patients using clinical data, biomedical data, and different image data. A case study is presented based on the Cancer Genome Atlas Glioblastoma Multiforme dataset to prove the effectiveness of the proposed model. This dataset is analyzed using data preprocessing, experts' knowledge, and a feature reduction method based on the Principal Component Analysis. Then, the FCM clustering method is utilized to reinforce classifier learning.

OUTCOMES OF STUDY

The proposed model finds the best combination of Wrapper feature selection and classifier for each cluster based on different measures, including accuracy, sensitivity, specificity, precision, F-score, and G-mean according to a hierarchical structure. It has the best performance among other reinforced classifiers. Besides, this model is compatible with real-world medical processes for GBM patients based on clinical, biomedical, and image data.

Machine learning based survival prediction in Glioma using large-scale registry data

Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000-2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.

Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information

Fat deposition in pigs is not only closely related to pig production efficiency and pork quality but also an ideal model for human obesity. Transcriptome sequencing is widely used to study fat deposition. However, due to small sample sizes, high false positive rates, and poor consistency of results from different studies, new strategies are urgently needed. Machine learning, a new analysis method, can effectively fit complex data and accurately identify samples and genes. In this study, 36 samples of adipose tissue, muscle tissue, and liver tissue were collected from Songliao black pigs and Landrace pigs, and the mRNA of all the samples was sequenced. In addition, we collected transcriptome data for 64 samples in the GEO database from four different sources. After standardization and imputation of missing values in the data set comprising 100 samples, traditional differential expression analysis was carried out, and different numbers of expressed genes were selected as features for the training model of eight machine learning methods. In the 1000 replications of fourfold cross validation with 100 samples, AdaBoost performed best, with an average prediction accuracy greater than 93% and the highest mean area under the curve in predicting the high- and low-fat content groups among the eight ML methods. According to their performance-based ranks inferred by AdaBoost, 12 genes related to fat deposition were identified; among them, FASN and APOD were specifically expressed in adipose tissue, and APOA1 was specifically expressed in the liver, which could be important candidate biomarkers affecting fat deposition.

Detection of mutation profiles and tumor mutation burden of cerebrospinal fluid circulating DNA by a cancer genomic panel sequencing in glioma patients

BACKGROUND AND AIMS

Circulating tumor DNA (ctDNA) has been recognized as a reliable source to reflect the molecular and genetic landscape of corresponding tumors in recent years. In this study, we tested the application of a cancer genomic panel sequencing on the cerebrospinal fluid (CSF)-derived ctDNA for the less invasive detection and diagnosis of glioma.

MATERIALS AND METHODS

CtDNA was extracted from 26 CSF samples and subject to a cancer genomic panel sequencing of 520 genes to analyze the mutation profiles and tumor mutation burden (TMB), which were compared with their corresponding tumor DNA samples. Associations between mutations or TMB and clinical characteristics were also evaluated.

RESULTS

A high detection rate of ctDNA (24/26, 92.3%) was observed in CSF. CtDNA mutations had high concordance rates with tumor DNA, especially in non-copy number variations and in glioblastoma. CSF ctDNA TMB also exhibited a strong correlation with tumor DNA TMB (R² = 0.879, P < 0.001), particularly in glioblastoma (R² = 0.992, P < 0.001). Age was significantly associated with CSF ctDNA TMB in glioma patients.

CONCLUSION

We established a less invasive but effective molecular diagnostic approach using a cancer genomic panel sequencing system targeting CSF ctDNA for glioma, especially in glioblastoma.

Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging

Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.

Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis

The application of artificial intelligence (AI) systems is emerging in many fields in recent years, due to the increased computing power available at lower cost. Although its applications in various branches of medicine, such as pediatric oncology, are many and promising, its use is still in an embryonic stage. The aim of this paper is to provide an overview of the state of the art regarding the AI application in pediatric oncology, through a systematic review of systematic reviews, and to analyze current trends in Europe, through a bibliometric analysis of publications written by European authors. Among 330 records found, 25 were included in the systematic review. All papers have been published since 2017, demonstrating only recent attention to this field. The total number of studies included in the selected reviews was 674, with a third including an author with a European affiliation. In bibliometric analysis, 304 out of the 978 records found were included. Similarly, the number of publications began to dramatically increase from 2017. Most explored AI applications regard the use of diagnostic images, particularly radiomics, as well as the group of neoplasms most involved are the central nervous system tumors. No evidence was found regarding the use of AI for process mining, clinical pathway modeling, or computer interpreted guidelines to improve the healthcare process. No robust evidence is yet available in any of the domains investigated by systematic reviews. However, the scientific production in Europe is significant and consistent with the topics covered in systematic reviews at the global level. The use of AI in pediatric oncology is developing rapidly with promising results, but numerous gaps and challenges persist to validate its utilization in clinical practice. An important limitation is the need for large datasets for training algorithms, calling for international collaborative studies.

Neuroplasticity of Glioma Patients: Brain Structure and Topological Network

Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, grades, and molecular characteristics of gliomas can lead to different behavioral deficits and prognosis, which are closely related to patients' quality of life and associated with neuroplasticity. Some advanced magnetic resonance imaging (MRI) technologies can explore the neuroplasticity of structural, topological, biochemical metabolism, and related mechanisms, which may contribute to the improvement of prognosis and function in glioma patients. In this review, we summarized the studies conducted on structural and topological plasticity of glioma patients through different MRI technologies and discussed future research directions. Previous studies have found that glioma itself and related functional impairments can lead to structural and topological plasticity using multimodal MRI. However, neuroplasticity caused by highly heterogeneous gliomas is not fully understood, and should be further explored through multimodal MRI. In addition, the individualized prediction of functional prognosis of glioma patients from the functional level based on machine learning (ML) is promising. These approaches and the introduction of ML can further shed light on the neuroplasticity and related mechanism of the brain, which will be helpful for management of glioma patients.

Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment

BACKGROUND

Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.

PURPOSE

Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.

DATA SOURCES

Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

STUDY SELECTION

From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.

DATA ANALYSIS

Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.

DATA SYNTHESIS

The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.

LIMITATIONS

Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.

CONCLUSIONS

Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Objective

To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments.

Methods

The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging).

Results

The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application.

Conclusion

Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies

OBJECTIVE

Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018-01/2021. Included study participants were adults with glioblastoma having undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide), and follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics, the target condition). Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy (sensitivity, specificity, precision, F1-score, balanced accuracy). We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965).

RESULTS

Eighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies (10/18, 56%) included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC.

CONCLUSION

ML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement.

Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach

BACKGROUND

Machine learning (ML) approaches can significantly improve the classical Rout-based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus.

OBJECTIVE

To develop a ML model that accurately identifies patients as candidates for permanent cerebral spinal fluid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT.

METHODS

This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal fluid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training-testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms.

RESULTS

The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and specificity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout-based manual classification commonly used in clinical practice with an accuracy of 62.5%.

CONCLUSION

This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to benefit from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility of LIT in management.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.

Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.

Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.

Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme

PURPOSE

Our current study aims to consider the image biomarkers extracted from the MRI images for exploring their effects on glioblastoma multiforme (GBM) patients' survival. Determining its biomarker helps better manage the disease and evaluate treatments. It has been proven that imaging features could be used as a biomarker. The purpose of this study is to investigate the features in MRI and clinical features as the biomarker association of survival of GBM.

METHODS

55 patients were considered with five clinical features, 10 qualities pre-operative MRI image features, and six quantitative features obtained using BraTumIA software. It was run ANN, C5, Bayesian, and Cox models in two phases for determining important variables. In the first phase, we selected the quality features that occur at least in three models and quantitative in two models. In the second phase, models were run with the extracted features, and then the probability value of variables in each model was calculated.

RESULTS

The mean of accuracy, sensitivity, specificity, and area under curve (AUC) after running four machine learning techniques were 80.47, 82.54, 79.78, and 0.85, respectively. In the second step, the mean of accuracy, sensitivity, specificity, and AUC were 79.55, 78.71, 79.83, and 0.87, respectively.

CONCLUSION

We found the largest size of the width, the largest size of length, radiotherapy, volume of enhancement, volume of nCET, satellites, enhancing margin, and age feature are important features.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Glioma-Targeted Therapeutics: Computer-Aided Drug Design Prospective

Amongst the several types of brain cancers known to humankind, glioma is one of the most severe and life-threatening types of cancer, comprising 40% of all primary brain tumors. Recent reports have shown the incident rate of gliomas to be 6 per 100,000 individuals per year globally. Despite the various therapeutics used in the treatment of glioma, patient survival rate remains at a median of 15 months after undergoing first-line treatment including surgery, radiation, and chemotherapy with Temozolomide. As such, the discovery of newer and more effective therapeutic agents is imperative for patient survival rate. The advent of computer-aided drug design in the development of drug discovery has emerged as a powerful means to ascertain potential hit compounds with distinctively high therapeutic effectiveness against glioma. This review encompasses the recent advances of bio-computational in-silico modeling that have elicited the discovery of small molecule inhibitors and/or drugs against various therapeutic targets in glioma. The relevant information provided in this report will assist researchers, especially in the drug design domains, to develop more effective therapeutics against this global disease.

Chromatin insulation dynamics in glioblastoma: challenges and future perspectives of precision oncology

Glioblastoma (GBM) is the most aggressive primary brain tumor, having a poor prognosis and a median overall survival of less than two years. Over the last decade, numerous findings regarding the distinct molecular and genetic profiles of GBM have led to the emergence of several therapeutic approaches. Unfortunately, none of them has proven to be effective against GBM progression and recurrence. Epigenetic mechanisms underlying GBM tumor biology, including histone modifications, DNA methylation, and chromatin architecture, have become an attractive target for novel drug discovery strategies. Alterations on chromatin insulator elements (IEs) might lead to aberrant chromatin remodeling via DNA loop formation, causing oncogene reactivation in several types of cancer, including GBM. Importantly, it is shown that mutations affecting the isocitrate dehydrogenase (IDH) 1 and 2 genes, one of the most frequent genetic alterations in gliomas, lead to genome-wide DNA hypermethylation and the consequent IE dysfunction. The relevance of IEs has also been observed in a small population of cancer stem cells known as glioma stem cells (GSCs), which are thought to participate in GBM tumor initiation and drug resistance. Recent studies revealed that epigenomic alterations, specifically chromatin insulation and DNA loop formation, play a crucial role in establishing and maintaining the GSC transcriptional program. This review focuses on the relevance of IEs in GBM biology and their implementation as a potential theranostic target to stratify GBM patients and develop novel therapeutic approaches. We will also discuss the state-of-the-art emerging technologies using big data analysis and how they will settle the bases on future diagnosis and treatment strategies in GBM patients.

A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma

RATIONALE AND OBJECTIVES

To comprehensively evaluate robustness and variations of DCE-MRI derived generalized-tracer-kinetic-model (GTKM) parameters in healthy and tumor tissues and impact of normalization in mitigating these variations on application to glioma.

MATERIALS (PATIENTS) AND METHODS

A retrospective study included pre-operative 31 high-grade-glioma(HGG), 22 low-grade-glioma(LGG) and 33 follow-up data from 10 patients a prospective study with 4 HGG subjects. Voxel-wise GTKM was fitted to DCE-MRI data to estimate K^trans, v_e, v_b. Simulations were used to evaluate noise sensitivity. Variation of parameters with-respect-to arterial-input-function (AIF) variation and data length were studied. Normalization of parameters with-respect-to mean values in gray-matter (GM) and white-matter (WM) regions (GM-Type-2, WM-Type-2) and mean curves (GM-Type-1, WM-Type-1) were also evaluated. Co-efficient-of-variation(CoV), relative-percentage-error (RPE), Box-Whisker plots, bar graphs and t-test were used for comparison.

RESULTS

GTKM was fitted well in all tissue regions. K^trans and v_e in contrast-enhancing (CE) has shown improved noise sensitivity in longer data. v_b was reliable in all tissues. Mean AIF and C(t) peaks showed ~38% and ~35% variations. During simulation, normalizations have mitigated variations due to changes in AIF amplitude in K^trans and v_b.. v_e was less sensitive to normalizations. CoV of K^trans and v_b has reduced ~70% after GM-Type-1 normalization and ~80% after GM-Type-2 normalization, respectively. GM-Type-1 (p = 0.003) and GM-Type-2 (p = 0.006) normalizations have significantly improved differentiation of HGG and LGG using K^trans.

CONCLUSION

K^trans and v_b can be reliably estimated in normal-appearing brain tissues and can be used for normalization of corresponding parameters in tumor tissues for mitigating inter-subject variability due to errors in AIF. Normalized K^trans and v_b provided improved differentiation of HGG and LGG.

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.