Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics

OBJECTIVES

Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation.

MATERIALS AND METHODS

The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges.

RESULTS

Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows.

CONCLUSION

To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized.

CRITICAL RELEVANCE STATEMENT

Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics.

KEY POINTS

Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.

Development of Personalized Strategies for Precisely Battling Malignant Melanoma

Melanoma is the most severe and fatal form of skin cancer, resulting from multiple gene mutations with high intra-tumor and inter-tumor molecular heterogeneity. Treatment options for patients whose disease has progressed beyond the ability for surgical resection rely on currently accepted standard therapies, notably immune checkpoint inhibitors and targeted therapies. Acquired resistance to these therapies and treatment-associated toxicity necessitate exploring novel strategies, especially those that can be personalized for specific patients and/or populations. Here, we review the current landscape and progress of standard therapies and explore what personalized oncology techniques may entail in the scope of melanoma. Our purpose is to provide an up-to-date summary of the tools at our disposal that work to circumvent the common barriers faced when battling melanoma.

The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective

Clinical dermatoscopy and pathological slide assessment are essential in the diagnosis and management of patients with cutaneous melanoma. For those presenting with stage IIC disease and beyond, radiological investigations are often considered. The dermatoscopic, whole slide and radiological images used during clinical care are often stored digitally, enabling artificial intelligence (AI) and convolutional neural networks (CNN) to learn, analyse and contribute to the clinical decision-making. A keyword search of the Medline database was performed to assess the progression, capabilities and limitations of AI and CNN and its use in diagnosis and management of cutaneous melanoma. Full-text articles were reviewed if they related to dermatoscopy, pathological slide assessment or radiology. Through analysis of 95 studies, we demonstrate that diagnostic accuracy of AI/CNN can be superior (or at least equal) to clinicians. However, variability in image acquisition, pre-processing, segmentation, and feature extraction remains challenging. With current technological abilities, AI/CNN and clinicians synergistically working together are better than one another in all subspecialty domains relating to cutaneous melanoma. AI has the potential to enhance the diagnostic capabilities of junior dermatology trainees, primary care skin cancer clinicians and general practitioners. For experienced clinicians, AI provides a cost-efficient second opinion. From a pathological and radiological perspective, CNN has the potential to improve workflow efficiency, allowing clinicians to achieve more in a finite amount of time. Until the challenges of AI/CNN are reliably met, however, they can only remain an adjunct to clinical decision-making.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Predicting the BRAF mutation with pretreatment MRI radiomics features for melanoma brain metastases receiving Gamma Knife radiosurgery

AIM

To develop a model using radiomics features extracted from magnetic resonance imaging (MRI) images of Gamma Knife radiosurgery (GKRS) to predict the BRAF mutation in patients with melanoma brain metastases (MBM).

MATERIALS AND METHODS

Data of 220 tumours were classified into two groups. One was a group whose BRAF mutation was identified, and the other group whose BRAF mutation was not identified. We extracted 1,962 radiomics features from gadolinium contrast-enhanced T1-weighted MRI treatment-planning images. Synthetic Minority Over-sampling TEchnique (SMOTE) was performed to address the unbalanced data-related issues. A single-layer neural network (NN) was used to build predictive models with radiomics features. The sensitivity, specificity, accuracy, and the area under the curve (AUC) were evaluated to assess the model performance.

RESULTS

The prediction performance for the final evaluation without the SMOTE had an accuracy of 77.14%, a specificity of 82.44%, a sensitivity of 81.85%, and an AUC of 0.79. The application of SMOTE improved the prediction model to an accuracy of 83.1%, a specificity of 87.07%, a sensitivity of 78.82%, and an AUC of 0.82.

CONCLUSION

The current study showed the feasibility of generating a highly accurate NN model for the BRAF mutation prediction. The prediction performance improved with SMOTE. The model assists physicians to obtain more accurate expectations of the treatment outcome without a genetic test.

Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas

OBJECTIVES

We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas.

METHODS

A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set.

RESULTS

The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%.

CONCLUSION

The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models.

KEY POINTS

• MYCN amplification in neuroblastomas is an important determinant of disease prognosis. • Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas. • Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.

Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal

Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.

Computed tomography-based radiomics nomogram for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer: a double-center study

OBJECTIVE

To develop and validate a radiomics nomogram based on CT for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer (CRC).

METHODS

A total of 451 CRC patients (training cohort = 190; internal validation cohort = 125; external validation cohort = 136) from 2 centers were retrospectively included. Least absolute shrinkage and selection operator regression was used to select radiomics features and the radiomics score (Radscore) was calculated. Nomogram was constructed by combining Radscore and significant clinical predictors. Receiver operating characteristic curve analysis, calibration curve and decision curve analysis were used to evaluate the predictive performance of the nomogram. Kaplan‒Meier survival curves based on the radiomics nomogram were used to assess overall survival (OS) of the entire cohort.

RESULTS

The Radscore consisted of nine radiomics features which were the most relevant to BRAF mutation. The radiomics nomogram integrating Radscore and clinical independent predictors (age, tumor location and cN stage) showed good calibration and discrimination with AUCs of 0.86 (95% CI: 0.80-0.91), 0.82 (95% CI: 0.74-0.90) and 0.82 (95% CI: 0.75-0.90) in the training cohort, internal validation and external validation cohorts, respectively. Furthermore,the performance of nomogram was significantly better than that of the clinical model (p < 0.05). The radiomics nomogram-predicted BRAF mutation high-risk group had a worse OS than the low-risk group (p < 0.0001).

CONCLUSION

The radiomics nomogram showed good performance in predicting BRAF mutation and OS of CRC patients, which could provide valuable information for individualized treatment.

ADVANCES IN KNOWLEDGE

The radiomics nomogram could effectively predict BRAF mutation and OS in patients with CRC. High-risk BRAF mutation group identified by the radiomics nomogram was independently associated with poor OS.

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review

Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.

Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer

BACKGROUND

The expression level of the programmed cell death ligand 1 (PD-L1) appears to be a predictor for response to immunotherapy using checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). As differences in terms of PD-L1 expression levels in the extracranial primary tumor and the brain metastases may occur, a reliable method for the non-invasive assessment of the intracranial PD-L1 expression is, therefore of clinical value. Here, we evaluated the potential of radiomics for a non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to NSCLC.

PATIENTS AND METHODS

Fifty-three NSCLC patients with brain metastases from two academic neuro-oncological centers (group 1, n = 36 patients; group 2, n = 17 patients) underwent tumor resection with a subsequent immunohistochemical evaluation of the PD-L1 expression. Brain metastases were manually segmented on preoperative T1-weighted contrast-enhanced MRI. Group 1 was used for model training and validation, group 2 for model testing. After image pre-processing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. The radiomics model was trained and validated using random stratified cross-validation. Finally, the best-performing radiomics model was applied to the test data. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analyses.

RESULTS

An intracranial PD-L1 expression (i.e., staining of at least 1% or more of tumor cells) was present in 18 of 36 patients (50%) in group 1, and 7 of 17 patients (41%) in group 2. Univariate analysis identified the contrast-enhancing tumor volume as a significant predictor for PD-L1 expression (area under the ROC curve (AUC), 0.77). A random forest classifier using a four-parameter radiomics signature, including tumor volume, yielded an AUC of 0.83 ± 0.18 in the training data (group 1), and an AUC of 0.84 in the external test data (group 2).

CONCLUSION

The developed radiomics classifiers allows for a non-invasive assessment of the intracranial PD-L1 expression in patients with brain metastases secondary to NSCLC with high accuracy.

Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases

Background

Lung cancer is the most common primary tumor metastasizing to the brain. A significant proportion of lung cancer patients show epidermal growth factor receptor (EGFR) mutation status discordance between the primary cancer and the corresponding brain metastases, which can affect prognosis and therapeutic decision-making. However, it is not always feasible to obtain brain metastases samples. The aim of this study was to establish a radiomic model to predict the EGFR mutation status of lung cancer brain metastases.

Methods

Data from 162 patients with resected brain metastases originating from lung cancer (70 with mutant EGFR, 92 with wild-type EGFR) were retrospectively analyzed. Radiomic features were extracted using preoperative brain magnetic resonance (MR) images (contrast-enhanced T1-weighted imaging, T1CE; T2-weighted imaging, T2WI; T2 fluid-attenuated inversion recovery, T2 FLAIR; and combinations of these sequences), to establish machine learning-based models for predicting the EGFR status of excised brain metastases (108 metastases for training and 54 metastases for testing). The least absolute shrinkage selection operator was used to select informative features; radiomics models were built with logistic regression of the training cohort, and model performance was evaluated using an independent test set.

Results

The best-performing model was a combination of 10 features selected from multiple sequences (two from T1CE, five from T2WI, and three from T2 FLAIR) in both the training and test sets, resulting in classification area under the curve, accuracy, sensitivity, and specificity values of 0.85 and 0.81, 77.8% and 75.9%, 83.7% and 73.1%, and 73.8% and 78.6%, respectively.

Conclusions

Radiomic signatures integrating multi-sequence MR images have the potential to noninvasively predict the EGFR mutation status of lung cancer brain metastases.

The Role of [68Ga]Ga-DOTA-SSTR PET Radiotracers in Brain Tumors: A Systematic Review of the Literature and Ongoing Clinical Trials

Simple Summary

[⁶⁸Ga]Ga-DOTA-SSTR PET imaging has recently been introduced in the management of patients with brain tumors, mostly meningiomas and pituitary adenomas or carcinomas. The current literature demonstrated the superior diagnostic accuracy of this imaging modality, especially for lesions difficult to be detected or characterized on conventional imaging protocols, such as skull base or transosseous meningiomas. [⁶⁸Ga]Ga-DOTA-SSTR PET tracers also seem to provide superior volume contouring for radiotherapy planning and may also be used to evaluate the tumor’s overexpression of somatostatin receptors for devising patient-tailored peptide receptor radionuclide therapy. In this review, we comprehensively analyzed the current literature discussing the implementation of [⁶⁸Ga]Ga-DOTA-SSTR PET imaging in brain tumors, further presenting ongoing clinical trials and suggesting potential future applications.

Abstract

Background: The development of [⁶⁸Ga]Ga-DOTA-SSTR PET tracers has garnered interest in neuro-oncology, to increase accuracy in diagnostic, radiation planning, and neurotheranostics protocols. We systematically reviewed the literature on the current uses of [⁶⁸Ga]Ga-DOTA-SSTR PET in brain tumors. Methods: PubMed, Scopus, Web of Science, and Cochrane were searched in accordance with the PRISMA guidelines to include published studies and ongoing trials utilizing [⁶⁸Ga]Ga-DOTA-SSTR PET in patients with brain tumors. Results: We included 63 published studies comprising 1030 patients with 1277 lesions, and 4 ongoing trials. [⁶⁸Ga]Ga-DOTA-SSTR PET was mostly used for diagnostic purposes (62.5%), followed by treatment planning (32.7%), and neurotheranostics (4.8%). Most lesions were meningiomas (93.6%), followed by pituitary adenomas (2.8%), and the DOTATOC tracer (53.2%) was used more frequently than DOTATATE (39.1%) and DOTANOC (5.7%), except for diagnostic purposes (DOTATATE 51.1%). [⁶⁸Ga]Ga-DOTA-SSTR PET studies were mostly required to confirm the diagnosis of meningiomas (owing to their high SSTR2 expression and tracer uptake) or evaluate their extent of bone invasion, and improve volume contouring for better radiotherapy planning. Some studies reported the uncommon occurrence of SSTR2-positive brain pathology challenging the diagnostic accuracy of [⁶⁸Ga]Ga-DOTA-SSTR PET for meningiomas. Pre-treatment assessment of tracer uptake rates has been used to confirm patient eligibility (high somatostatin receptor-2 expression) for peptide receptor radionuclide therapy (PRRT) (i.e., neurotheranostics) for recurrent meningiomas and pituitary carcinomas. Conclusion: [⁶⁸Ga]Ga-DOTA-SSTR PET studies may revolutionize the routine neuro-oncology practice, especially in meningiomas, by improving diagnostic accuracy, delineation of radiotherapy targets, and patient eligibility for radionuclide therapies.

Evaluating diagnostic accuracy and determining optimal diagnostic thresholds of different approaches to [68Ga]-DOTATATE PET/MRI analysis in patients with meningioma

Multiple approaches with [⁶⁸Ga]-DOTATATE, a somatostatin analog PET radiotracer, have demonstrated clinical utility in evaluation of meningioma but have not been compared directly. Our purpose was to compare diagnostic performance of different approaches to quantitative brain [⁶⁸Ga]-DOTATATE PET/MRI analysis in patients with suspected meningioma recurrence and to establish the optimal diagnostic threshold for each method. Patients with suspected meningioma were imaged prospectively with [⁶⁸Ga]-DOTATATE brain PET/MRI. Lesions were classified as meningiomas and post-treatment change (PTC), using follow-up pathology and MRI as reference standard. Lesions were reclassified using the following methods: absolute maximum SUV threshold (SUV), SUV ratio (SUVR) to superior sagittal sinus (SSS) (SUVRsss), SUVR to the pituitary gland (SUVRpit), and SUVR to the normal brain parenchyma (SUVRnorm). Diagnostic performance of the four methods was compared using contingency tables and McNemar’s test. Previously published pre-determined thresholds were assessed where applicable. The optimal thresholds for each method were identified using Youden’s J statistics. 166 meningiomas and 41 PTC lesions were identified across 62 patients. SUV, SUVRsss, SUVRpit, and SUVRnorm of meningioma were significantly higher than those of PTC (P < 0.0001). The optimal thresholds for SUV, SUVRsss, SUVRpit, and SUVRnorm were 4.7, 3.2, 0.3, and 62.6, respectively. At the optimal thresholds, SUV had the highest specificity (97.6%) and SUVRsss had the highest sensitivity (86.1%). An ROC analysis of SUV, SUVRsss, SUVRpit, and SUVRnorm revealed AUC of 0.932, 0.910, 0.915, and 0.800, respectively (P < 0.0001). Developing a diagnostic threshold is key to wider clinical translation of [⁶⁸Ga]-DOTATATE PET/MRI in meningioma evaluation. We found that the SUVRsss method may have the most robust combination of sensitivity and specificity in the diagnosis of meningioma in the post-treatment setting, with the optimal threshold of 3.2. Future studies validating our findings in different patient populations are needed to continue optimizing the diagnostic performance of [⁶⁸Ga]-DOTATATE PET/MRI in meningioma patients.

Trial registration: ClinicalTrials.gov Identifier: NCT04081701. Registered 9 September 2019. https://clinicaltrials.gov/ct2/show/NCT04081701.

PET Imaging in Neuro-Oncology: An Update and Overview of a Rapidly Growing Area

Simple Summary

Positron emission tomography (PET) is a functional imaging technique which plays an increasingly important role in the management of brain tumors. Owing different radiotracers, PET allows to image different metabolic aspects of the brain tumors. This review outlines currently available PET radiotracers and their respective indications in neuro-oncology. It specifically focuses on the investigation of gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in the production of PET radiotracers, image analyses and translational applications to peptide radionuclide receptor therapy, which allow to treat brain tumors with radiotracers, are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain magnetic resonance imaging (MRI).

Abstract

PET plays an increasingly important role in the management of brain tumors. This review outlines currently available PET radiotracers and their respective indications. It specifically focuses on ¹⁸F-FDG, amino acid and somatostatin receptor radiotracers, for imaging gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in radiopharmaceuticals, image analyses and translational applications to therapy are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain MRI for all medical professionals implicated in brain tumor diagnosis and care.

Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression

PURPOSE

To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas.

PATIENTS AND METHODS

One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBR_mean, TBR_max) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBR_mean, TBR_max, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis.

RESULTS

Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBR_mean and TBR_max resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%).

CONCLUSION

The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

Radiomics as an emerging tool in the management of brain metastases

Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.