Radiomics in neuro-oncological clinical trials

Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics

PURPOSE

To establish and validate novel predictive models for predicting bone metastasis (BM) in newly diagnosed prostate adenocarcinoma (PCa) via single-photon emission computed tomography radiomics.

METHOD

In a retrospective review of the clinical single-photon emission computed tomography (SPECT) database, 176 patients (training set: n = 140; validation set: n = 36) who underwent SPECT/CT imaging and were histologically confirmed to have newly diagnosed PCa from June 2016 to June 2022 were enrolled. Radiomic features were extracted from the region of interest (ROI) in a targeted lesion in each patient. Clinical features, including age, total prostate-specific antigen (t-PSA), and Gleason grades, were included. Statistical tests were then employed to eliminate irrelevant and redundant features. Finally, four types of optimized models were constructed for the prediction. Furthermore, fivefold cross-validation was applied to obtain sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. The clinical usefulness of the multivariate models was estimated through decision curve analysis (DCA).

RESULTS

A radiomics signature consisting of 27 selected features which were obtained by radiomics' LASSO treatment was significantly correlated with bone status (P < 0.01 for both training and validation sets). Collectively, the models showed good predictive efficiency. The AUC values ranged from 0.87 to 0.98 in four models. The AUC values of the human experts were 0.655 and 0.872 in the training and validation groups, respectively. Most radiomic models showed better diagnostic accuracy than human experts in the training and validation groups. DCA also demonstrated the superiority of the radiomics models compared to human experts.

CONCLUSION

Radiomics models are superior to humans in differentiating between benign bone and prostate cancer bone metastases; it can be used to facilitate personalized prediction of BM in newly diagnosed PCa patients.

Radiomics incorporating deep features for predicting Parkinson's disease in 123I-Ioflupane SPECT

PURPOSE

123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0.

METHODS

In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.

RESULTS

For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.

CONCLUSION

The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

PET imaging of gliomas: Status quo and quo vadis?

PET imaging, particularly using amino acid tracers, has become a valuable adjunct to anatomical MRI in the clinical management of patients with glioma. Collaborative international efforts have led to the development of clinical and technical guidelines for PET imaging in gliomas. The increasing readiness of statutory health insurance agencies, especially in European countries, to reimburse amino acid PET underscores its growing importance in clinical practice. Integrating artificial intelligence and radiomics in PET imaging of patients with glioma may significantly improve tumor detection, segmentation, and response assessment. Efforts are ongoing to facilitate the clinical translation of these techniques. Considerable progress in computer technology developments (eg quantum computers) may be helpful to accelerate these efforts. Next-generation PET scanners, such as long-axial field-of-view PET/CT scanners, have improved image quality and body coverage and therefore expanded the spectrum of indications for PET imaging in Neuro-Oncology (eg PET imaging of the whole spine). Encouraging results of clinical trials in patients with glioma have prompted the development of PET tracers directing therapeutically relevant targets (eg the mutant isocitrate dehydrogenase) for novel anticancer agents in gliomas to improve response assessment. In addition, the success of theranostics for the treatment of extracranial neoplasms such as neuroendocrine tumors and prostate cancer has currently prompted efforts to translate this approach to patients with glioma. These advancements highlight the evolving role of PET imaging in Neuro-Oncology, offering insights into tumor biology and treatment response, thereby informing personalized patient care. Nevertheless, these innovations warrant further validation in the near future.

Challenges, limitations, and pitfalls of PET and advanced MRI in patients with brain tumors: A report of the PET/RANO group

Brain tumor diagnostics have significantly evolved with the use of positron emission tomography (PET) and advanced magnetic resonance imaging (MRI) techniques. In addition to anatomical MRI, these modalities may provide valuable information for several clinical applications such as differential diagnosis, delineation of tumor extent, prognostication, differentiation between tumor relapse and treatment-related changes, and the evaluation of response to anticancer therapy. In particular, joint recommendations of the Response Assessment in Neuro-Oncology (RANO) Group, the European Association of Neuro-oncology, and major European and American Nuclear Medicine societies highlighted that the additional clinical value of radiolabeled amino acids compared to anatomical MRI alone is outstanding and that its widespread clinical use should be supported. For advanced MRI and its steadily increasing use in clinical practice, the Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition provided more recently an updated acquisition protocol for the widely used dynamic susceptibility contrast perfusion MRI. Besides amino acid PET and perfusion MRI, other PET tracers and advanced MRI techniques (e.g. MR spectroscopy) are of considerable clinical interest and are increasingly integrated into everyday clinical practice. Nevertheless, these modalities have shortcomings which should be considered in clinical routine. This comprehensive review provides an overview of potential challenges, limitations, and pitfalls associated with PET imaging and advanced MRI techniques in patients with gliomas or brain metastases. Despite these issues, PET imaging and advanced MRI techniques continue to play an indispensable role in brain tumor management. Acknowledging and mitigating these challenges through interdisciplinary collaboration, standardized protocols, and continuous innovation will further enhance the utility of these modalities in guiding optimal patient care.

Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases

OBJECTIVE

Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS.

METHODS

Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed.

RESULTS

Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence.

CONCLUSIONS

Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it's utility in patient care.

Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach

Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.

Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Medical image foundation models in assisting diagnosis of brain tumors: a pilot study

OBJECTIVES

To build self-supervised foundation models for multicontrast MRI of the whole brain and evaluate their efficacy in assisting diagnosis of brain tumors.

METHODS

In this retrospective study, foundation models were developed using 57,621 enhanced head MRI scans through self-supervised learning with a pretext task of cross-contrast context restoration with two different content dropout schemes. Downstream classifiers were constructed based on the pretrained foundation models and fine-tuned for brain tumor detection, discrimination, and molecular status prediction. Metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were used to evaluate the performance. Convolutional neural networks trained exclusively on downstream task data were employed for comparative analysis.

RESULTS

The pretrained foundation models demonstrated their ability to extract effective representations from multicontrast whole-brain volumes. The best classifiers, endowed with pretrained weights, showed remarkable performance with accuracies of 94.9, 92.3, and 80.4%, and corresponding AUC values of 0.981, 0.972, and 0.852 on independent test datasets in brain tumor detection, discrimination, and molecular status prediction, respectively. The classifiers with pretrained weights outperformed the convolutional classifiers trained from scratch by approximately 10% in terms of accuracy and AUC across all tasks. The saliency regions in the correctly predicted cases are mainly clustered around the tumors. Classifiers derived from the two dropout schemes differed significantly only in the detection of brain tumors.

CONCLUSIONS

Foundation models obtained from self-supervised learning have demonstrated encouraging potential for scalability and interpretability in downstream brain tumor-related tasks and hold promise for extension to neurological diseases with diffusely distributed lesions.

CLINICAL RELEVANCE STATEMENT

The application of our proposed method to the prediction of key molecular status in gliomas is expected to improve treatment planning and patient outcomes. Additionally, the foundation model we developed could serve as a cornerstone for advancing AI applications in the diagnosis of brain-related diseases.

Application of radiomics in ischemic stroke

In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses

OBJECTIVES

To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses.

MATERIALS AND METHODS

A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model's performance.

RESULTS

Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity.

CONCLUSION

The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

Advances in the field of developing biomarkers for re-irradiation: a how-to guide to small, powerful data sets and artificial intelligence

Introduction

Patient selection remains challenging as the clinical use of re-irradiation (re-RT) increases. Re-RT data is limited to retrospective studies and small prospective single-institution reports, resulting in small, heterogenous data sets. Validated prognostic and predictive biomarkers are derived from large-volume studies with long-term follow-up. This review aims to examine existing re-RT publications and available data sets and discuss strategies using artificial intelligence (AI) to approach small data sets to optimize the use of re-RT data.

Methods

Re-RT publications were identified where associated public data was present. The existing literature on small data sets to identify biomarkers was also explored.

Results

Publications with associated public data were identified, with glioma and nasopharyngeal cancers emerging as the most common tumor sites where the use of re-RT was the primary management approach. Existing and emerging AI strategies have been used to approach small data sets including data generation, augmentation, discovery, and transfer learning.

Conclusions

Further data is needed to generate adaptive frameworks, improve the collection of specimens for molecular analysis, and improve the interpretability of results in re-RT data.

Robustness of [18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity

BACKGROUND AND OBJECTIVES

Standardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity.

METHODS

Image post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated.

RESULTS

The effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified.

CONCLUSIONS

Gray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.

Radiomic tractometry reveals tract-specific imaging biomarkers in white matter

Tract-specific microstructural analysis of the brain's white matter (WM) using diffusion MRI has been a driver for neuroscientific discovery with a wide range of applications. Tractometry enables localized tissue analysis along tracts but relies on bare summary statistics and reduces complex image information along a tract to few scalar values, and so may miss valuable information. This hampers the applicability of tractometry for predictive modelling. Radiomics is a promising method based on the analysis of numerous quantitative image features beyond what can be visually perceived, but has not yet been used for tract-specific analysis of white matter. Here we introduce radiomic tractometry (RadTract) and show that introducing rich radiomics-based feature sets into the world of tractometry enables improved predictive modelling while retaining the localization capability of tractometry. We demonstrate its value in a series of clinical populations, showcasing its performance in diagnosing disease subgroups in different datasets, as well as estimation of demographic and clinical parameters. We propose that RadTract could spark the establishment of a new generation of tract-specific imaging biomarkers with benefits for a range of applications from basic neuroscience to medical research.

The surgical management of diffuse gliomas: Current state of neurosurgical management and future directions

After recent updates to the World Health Organization pathological criteria for diagnosing and grading diffuse gliomas, all major North American and European neuro-oncology societies recommend a maximal safe resection as the initial management of a diffuse glioma. For neurosurgeons to achieve this goal, the surgical plan for both low- and high-grade gliomas should be to perform a supramaximal resection when feasible based on preoperative imaging and the patient's performance status, utilizing every intraoperative adjunct to minimize postoperative neurological deficits. While the surgical approach and technique can vary, every effort must be taken to identify and preserve functional cortical and subcortical regions. In this summary statement on the current state of the field, we describe the tools and technologies that facilitate the safe removal of diffuse gliomas and highlight intraoperative and postoperative management strategies to minimize complications for these patients. Moreover, we discuss how surgical resections can go beyond cytoreduction by facilitating biological discoveries and improving the local delivery of adjuvant chemo- and radiotherapies.

Advances in diffuse glial tumors diagnosis

In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

Clinical Applications of Radiomics in Nuclear Medicine

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.

Radiomics in Carotid Plaque: A Systematic Review and Radiomics Quality Score Assessment

Imaging modalities provide information on plaque morphology and vulnerability; however, they are operator dependent and miss a great deal of microscopic information. Recently, many radiomics models for carotid plaque that identify unstable plaques and predict cardiovascular outcomes have been proposed. This systematic review was aimed at assessing whether radiomics is a reliable and reproducible method for the clinical prediction of carotid plaque. A systematic search was conducted to identify studies published in PubMed and Cochrane library from January 1, 2001, to September 30, 2022. Both retrospective and prospective studies that developed and/or validated machine learning models based on radiomics data to classify or predict carotid plaques were included. The general characteristics of each included study were selected, and the methodological quality of radiomics reports and risk of bias were evaluated using the radiomics quality score (RQS) tool and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. Two investigators independently reviewed each study, and the consensus data were used for analysis. A total of 2429 patients from 16 studies were included. The mean area under the curve of radiomics models for diagnostic or predictive performance of the included studies was 0.88 ± 0.02, with a range of 0.741-0.989. The mean RQS was 9.25 (standard deviation: 6.04), representing 25.7% of the possible maximum value of 36, whereas the lowest point was -2, and the highest score was 22. Radiomics models have revealed additional information on patients with carotid plaque, but with respect to methodological quality, radiomics reports are still in their infancy, and many hurdles need to be overcome.

Radiomics for characterization of the glioma immune microenvironment

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

A nomogram based on radiomics intermuscular adipose analysis to indicate arteriosclerosis in patients with newly diagnosed type 2 diabetes

Objective

Early identifying arteriosclerosis in newly diagnosed type 2 diabetes (T2D) patients could contribute to choosing proper subjects for early prevention. Here, we aimed to investigate whether radiomic intermuscular adipose tissue (IMAT) analysis could be used as a novel marker to indicate arteriosclerosis in newly diagnosed T2D patients.

Methods

A total of 549 patients with newly diagnosed T2D were included in this study. The clinical information of the patients was recorded and the carotid plaque burden was used to indicate arteriosclerosis. Three models were constructed to evaluate the risk of arteriosclerosis: a clinical model, a radiomics model (a model based on IMAT analysis proceeded on chest CT images), and a clinical-radiomics combined model (a model that integrated clinical-radiological features). The performance of the three models were compared using the area under the curve (AUC) and DeLong test. Nomograms were constructed to indicate arteriosclerosis presence and severity. Calibration curves and decision curves were plotted to evaluate the clinical benefit of using the optimal model.

Results

The AUC for indicating arteriosclerosis of the clinical-radiomics combined model was higher than that of the clinical model [0.934 (0.909, 0.959) vs. 0.687 (0.634, 0.730), P < 0.001 in the training set, 0.933 (0.898, 0.969) vs. 0.721 (0.642, 0.799), P < 0.001 in the validation set]. Similar indicative efficacies were found between the clinical-radiomics combined model and radiomics model (P = 0.5694). The AUC for indicating the severity of arteriosclerosis of the combined clinical-radiomics model was higher than that of both the clinical model and radiomics model [0.824 (0.765, 0.882) vs. 0.755 (0.683, 0.826) and 0.734 (0.663, 0.805), P < 0.001 in the training set, 0.717 (0.604, 0.830) vs. 0.620 (0.490, 0.750) and 0.698 (0.582, 0.814), P < 0.001 in the validation set, respectively]. The decision curve showed that the clinical-radiomics combined model and radiomics model indicated a better performance than the clinical model in indicating arteriosclerosis. However, in indicating severe arteriosclerosis, the clinical-radiomics combined model had higher efficacy than the other two models.

Conclusion

Radiomics IMAT analysis could be a novel marker for indicating arteriosclerosis in patients with newly diagnosed T2D. The constructed nomograms provide a quantitative and intuitive way to assess the risk of arteriosclerosis, which may help clinicians comprehensively analyse radiomics characteristics and clinical risk factors more confidently.

Understanding the activity of antibody-drug conjugates in primary and secondary brain tumours

Antibody-drug conjugates (ADCs), a class of targeted cancer therapeutics combining monoclonal antibodies with a cytotoxic payload via a chemical linker, have already been approved for the treatment of several cancer types, with extensive clinical development of novel constructs ongoing. Primary and secondary brain tumours are associated with high mortality and morbidity, necessitating novel treatment approaches. Pharmacotherapy of brain tumours can be limited by restricted drug delivery across the blood-brain or blood-tumour barrier, although data from phase II studies of the HER2-targeted ADC trastuzumab deruxtecan indicate clinically relevant intracranial activity in patients with brain metastases from HER2⁺ breast cancer. However, depatuxizumab mafodotin, an ADC targeting wild-type EGFR and EGFR variant III, did not provide a definitive overall survival benefit in patients with newly diagnosed or recurrent EGFR-amplified glioblastoma in phase II and III trials, despite objective radiological responses in some patients. In this Review, we summarize the available data on the central nervous system activity of ADCs from trials involving patients with primary and secondary brain tumours and discuss their clinical implications. Furthermore, we explore pharmacological determinants of intracranial activity and discuss the optimal design of clinical trials to facilitate development of ADCs for the treatment of gliomas and brain metastases.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review

Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.

Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation

BACKGROUND

Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4-5 months after radiological and clinical progression.

METHODS

A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were extracted from MRI T1 and T2 sequences using LIFEx software. Statistical analysis included normal distribution tests and the Mann-Whitney U test, ROC analysis, and calculation of cut-off values.

RESULTS

A total of 5760 radiomic values were included in the analyses. AUROC demonstrated 13 radiomics with statistical significance for progression-free survival (PFS) and overall survival (OS). Diagnostic performance tests showed nine radiomics with specificity for PFS above 90% and one with a sensitivity of 97.2%. For OS, 3 out of 4 radiomics demonstrated between 80 and 90% sensitivity.

CONCLUSIONS

Several radiomic features demonstrated statistical significance and have the potential to further aid DMG diagnostic assessment non-invasively. The most significant radiomics were first- and second-order features with GLCM texture profile, GLZLM_GLNU, and NGLDM_Contrast.

MRI radiomics and potential applications to glioblastoma

MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis via radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.