Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression

OBJECTIVE

The early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner.

METHODS

A total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy.

RESULTS

Radiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93-1).

CONCLUSION

The use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer

OBJECTIVE

We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.

METHODS

We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after 18F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets.

RESULTS

For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98-0.99, 95-98%, and 87-95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).

CONCLUSION

The 2D slice-based CNN model using 18F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer.

Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas

Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas' high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging's role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.

Magnetic Resonance-Guided Laser Interstitial Thermal Therapy for Recurrent Glioblastoma and Radiation Necrosis: A Single-Surgeon Case Series

OBJECTIVE

To evaluate long-term clinical outcomes among patients treated with laser interstitial thermal therapy (LITT) for predicted recurrent glioblastoma (rGBM).

METHODS

Patients with rGBM treated by LITT by a single surgeon (2013-2020) were evaluated for progression-free survival (PFS), overall survival (OS), and OS after LITT.

RESULTS

Forty-nine patients (33 men, 16 women; mean [SD] age at diagnosis, 58.7 [12.5] years) were evaluated. Among patients with genetic data, 6 of 34 (18%) had IDH-1 R132 mutations, and 7 of 21 (33%) had MGMT methylation. Patients underwent LITT at a mean (SD) of 23.8 (23.8) months after original diagnosis. Twenty of 49 (40%) had previously undergone stereotactic radiosurgery, 37 (75%) had undergone intensity-modulated radiation therapy, and 49 (100%) had undergone chemotherapy. Patients had undergone a mean of 1.2 (0.7) previous resections before LITT. Mean preoperative enhancing and T2 FLAIR volumes were 13.1 (12.8) cm3 and 35.0 (32.8) cm3, respectively. Intraoperative biopsies confirmed rGBM in 31 patients (63%) and radiation necrosis in 18 patients (37%). Six perioperative complications occurred: 3 (6%) cases of worsening aphasia, 1 (2%) seizure, 1 (2%) epidural hematoma, and 1 (2%) intraparenchymal hemorrhage. For the rGBM group, median PFS was 2.0 (IQR, 4.0) months, median OS was 20.0 (IQR, 29.5) months, and median OS after LITT was 6.0 (IQR, 10.5) months. For the radiation necrosis group, median PFS was 4.0 (IQR, 4.5) months, median OS was 37.0 (IQR, 58.0) months, and median OS after LITT was 8.0 (IQR, 23.5) months.

CONCLUSIONS

In a diverse rGBM cohort, LITT was associated with a short duration of posttreatment PFS.

High Radiation Dose to the Fornix Causes Symptomatic Radiation Necrosis in Patients with Anaplastic Oligodendroglioma

PURPOSE

Surgery, radiotherapy (RT), and chemotherapy have prolonged the survival of patients with anaplastic oligodendroglioma. However, whether RT induces long-term toxicity remains unknown. We analyzed the relationship between the RT dose to the fornix and symptomatic radiation necrosis (SRN).

MATERIALS AND METHODS

A total of 67 patients treated between 2009 and 2019 were analyzed. SRN was defined according to the following three criteria: 1) radiographic findings, 2) symptoms attributable to the lesion, and 3) treatment resulting in symptom improvement. Various contours, including the fornix, were delineated. Univariate and multivariate analyses of the relationship between RT dose and SRN, as well as receiver operating characteristic curve analysis for cut-off values, were performed.

RESULTS

The most common location was the frontal lobe (n=40, 60%). Gross total resection was performed in 38 patients (57%), and 42 patients (63%) received procarbazine, lomustine, and vincristine chemotherapy. With a median follow-up of 42 months, the median overall and progression-free survival was 74 months. Sixteen patients (24%) developed SRN. In multivariate analysis, age and maximum dose to the fornix were associated with the development of SRN. The cut-off values for the maximum dose to the fornix and age were 59 Gy (equivalent dose delivered in 2 Gy fractions) and 46 years, respectively. The rate of SRN was higher in patients whose maximum dose to the fornix was >59 Gy (13% vs. 43%, p=0.005).

CONCLUSION

The maximum dose to the fornix was a significant factor for SRN development. While fornix sparing may help maintain neurocognitive function, additional studies are needed.

Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning

OBJECTIVES

Distinguishing between radiation necrosis (RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning.

METHODS

Eighty-six patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification, logistic regression, and support vector classification were trained and subsequently validated using test set. The classification performance was measured by area under the curve (AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity.

RESULTS

The best performance was achieved using random forest classification with a Gradient filter (AUC=0.910±0.047, accuracy 0.8±0.071, sensitivity=0.796±0.055, specificity=0.922±0.059). For, support vector classification the best result obtains using wavelet_HHH with a high AUC of 0.890±0.89, accuracy of 0.777±0.062, sensitivity=0.701±0.084, and specificity=0.85±0.112. Logistic regression using wavelet_HHH provides a poor result with AUC=0.882±0.051, accuracy of 0.753±0.08, sensitivity=0.717±0.208, and specificity=0.816±0.123.

CONCLUSION

This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.

A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.

Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment's lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.

One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.

Assessment of brain cancer atlas maps with multimodal imaging features

BACKGROUND

Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity.

MAIN TEXT

Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers.

CONCLUSIONS

The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.

What effective technique to differentiate radiation brain necrosis from a tumor progression in patients treated with radiation: A monocentric retrospective study combining the MRI TRAMs technique and the (18F)-dopa PET/CT

PURPOSE

Brain necrosis after radiotherapy is a challenging diagnosis, since it has similar radiological appearance on standard MRI to tumor progression. Consequences on treatment decisions can be important. We compare recent imaging techniques in order to adopt a reliable diagnostic protocol in doubtful situations.

PATIENTS AND METHOD

This is a retrospective study comparing the performance of three imaging techniques after radiotherapy of brain metastasis: Perfusion-MRI, TRAMs technique and F-dopa PET-CT. The evolution of the treated metastasis volume was also analyzed by contouring all patients MRIs. All included patients were suspected of relapse and had the three exams once the volume of treated metastasis increased.

RESULTS

The majority of our patients were treated by stereotactic radiotherapy. Suspicion of relapse was on average around 17months after treatment. Four cases of radionecrosis were diagnosed and six cases of real tumor progression. Neurological symptoms were less present in radionecrosis cases. All of our radionecrosis cases had relative cerebral blood volume below 1. F-dopa PET-CT succeeded to set the good diagnosis in eight cases, although we found one false positive and one false negative exam. The TRAMs technique failed in one case of false negative exam.

CONCLUSIONS

Perfusion-MRI showed high performance in the diagnosis of radionecrosis, especially when calculating relative cerebral blood volume rate. The TRAMs technique showed interesting results and deserves application in daily routine combined with the perfusion-MRI. F-dopa CT might induce false results because of different metabolic uptake according to tumor type, medication and brain blood barrier leak.

The Role of Apparent Diffusion Coefficient Values in Glioblastoma: Differentiating Tumor Progression Versus Treatment-Related Changes

OBJECTIVE

Glioblastoma represents the most common primary brain malignancy with a median survival of 15 months. Follow-up examinations are crucial to establish the presence of tumor recurrence, as well as treatment-associated changes such as ischemic infarction and radiation effects. Even though magnetic resonance imaging is a valuable tool, a histopathological diagnosis is often required because of imaging overlap between tumor recurrence and treatment associated changes. We set out to measure the apparent diffusion coefficient (ADC) values of the lesions in magnetic resonance imaging scans of treated glioblastoma patients to investigate if ADC values could accurately differentiate between tumor progression, radiation-related changes, and ischemic infarctions.

METHODS

We evaluated ADC values among 3 groups, patients with tumor progression, radiation necrosis, and ischemic infarctions. The regions of interest were placed in the areas of greatest hypointensity among solid lesions using the ADC maps, excluding areas with necrotic, cystic, or hemorrhagic changes. The ADC values of the contralateral normal appearing white matter were also measured as the reference value for each patient. The relative ADC (rADC) values were measured for all 3 groups. Comparison between lesions and normal white matter was evaluated by Wilcoxon signed test.

RESULTS

A total of 157 patients were included in the study; 49 patients classified as tumor progression, 58 patients as radiation necrosis, and 50 patients as ischemic infarctions. The mean ± SD ADC value was 752.8 ± 132.5 for tumor progression, 479.0 ± 105.2 for radiation-related changes, and 250.1 ± 57.2 for ischemic infarctions. The mean ± SD rADC value was 1.07 ± 0.22 for tumor progression, 0.66 ± 0.14 for radiation necrosis, and 0.34 ± 0.08 for ischemic infarctions. The mean rADC values were significantly higher in tumor progression, compared with both radiation necrosis and ischemic changes (P < 0.001).

CONCLUSIONS

The present study demonstrates that ADC values are a helpful tool to differentiate between tumor progression, radiation necrosis, and posttreatment ischemic changes.

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Introduction

There is a cumulative risk of 20–40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT.

Methods

Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated.

Results

The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient.

Conclusion

Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.

Systemic Therapy Type and Timing Effects on Radiation Necrosis Risk in HER2+ Breast Cancer Brain Metastases Patients Treated With Stereotactic Radiosurgery

Background

There is a concern that HER2-directed systemic therapies, when administered concurrently with stereotactic radiosurgery (SRS), may increase the risk of radiation necrosis (RN). This study explores the impact of timing and type of systemic therapies on the development of RN in patients treated with SRS for HER2+ breast cancer brain metastasis (BCBrM).

Methods

This was a single-institution, retrospective study including patients >18 years of age with HER2+ BCBrM who received SRS between 2013 and 2018 and with at least 12-month post-SRS follow-up. Presence of RN was determined via imaging at one-year post-SRS, with confirmation by biopsy in some patients. Demographics, radiotherapy parameters, and timing (“during” defined as four weeks pre- to four weeks post-SRS) and type of systemic therapy (e.g., chemotherapy, HER2-directed) were evaluated.

Results

Among 46 patients with HER2+ BCBrM who received SRS, 28 (60.9%) developed RN and 18 (39.1%) did not based on imaging criteria. Of the 11 patients who underwent biopsy, 10/10 (100%) who were diagnosed with RN on imaging were confirmed to be RN positive on biopsy and 1/1 (100%) who was not diagnosed with RN was confirmed to be RN negative on biopsy. Age (mean 53.3 vs 50.4 years, respectively), radiotherapy parameters (including total dose, fractionation, CTV and size target volume, all p>0.05), and receipt of any type of systemic therapy during SRS (60.7% vs 55.6%, p=0.97) did not differ between patients who did or did not develop RN. However, there was a trend for patients who developed RN to have received more than one agent of HER2-directed therapy independent of SRS timing compared to those who did not develop RN (75.0% vs 44.4%, p=0.08). Moreover, a significantly higher proportion of those who developed RN received more than one agent of HER2-directed therapy during SRS treatment compared to those who did not develop RN (35.7% vs 5.6%, p=0.047).

Conclusions

Patients with HER2 BCBrM who receive multiple HER2-directed therapies during SRS for BCBrM may be at higher risk of RN. Collectively, these data suggest that, in the eight-week window around SRS administration, if HER2-directed therapy is medically necessary, it is preferable that patients receive a single agent.

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.

Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Objective

Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines.

Materials and Methods

PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines.

Results

External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively.

Conclusion

The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.

Efficacy of Whole-Ventricular Radiotherapy in Patients Undergoing Maximal Tumor Resection for Glioblastomas Involving the Ventricle

Background and Purpose

Patients with glioblastoma (GBM) involving the ventricles are at high risk of ventricle opening during surgery and potential ventricular tumor spread. We evaluated the effectiveness of whole-ventricular radiotherapy (WVRT) in reducing intraventricular seeding in patients with GBM and identified patients who could benefit from this approach.

Methods and Materials

We retrospectively reviewed the data of 382 patients with GBM who underwent surgical resection and temozolomide-based chemoradiotherapy. Propensity score matching was performed to compensate for imbalances in characteristics between patients who did [WVRT (+); n=59] and did not [WVRT (–); n=323] receive WVRT. Local, outfield, intraventricular, and leptomeningeal failure rates were compared.

Results

All patients in the WVRT (+) group had tumor ventricular involvement and ventricle opening during surgery. In the matched cohort, the WVRT (+) group exhibited a significantly lower 2-year intraventricular failure rate than the WVRT (–) group (2.1% vs. 11.8%; P=0.045), with no difference in other outcomes. Recursive partitioning analysis stratified the patients in the WVRT (–) group at higher intraventricular failure risk (2-year survival, 14.2%) due to tumor ventricular involvement, MGMT unmethylation, and ventricle opening. WVRT reduced the intraventricular failure rate only in high-risk patients (0% vs. 14.2%; P=0.054) or those with MGMT-unmethylated GBM in the matched cohort (0% vs. 17.3%; P=0.036).

Conclusions

WVRT reduced the intraventricular failure rate in patients with tumor ventricular involvement and ventricle opening during surgery. The MGMT-methylation status may further stratify patients who could benefit from WVRT. Further prospective evaluation of WVRT in GBM is warranted.

Reverse Engineering Glioma Radiomics to Conventional Neuroimaging

A novel radiological research field pursuing comprehensive quantitative image, namely “Radiomics,” gained traction along with the advancement of computational technology and artificial intelligence. This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic alteration of gliomas noninvasively. Although quite a few promising results were published regarding MRI-based diagnosis of isocitrate dehydrogenase (IDH) mutation in gliomas, it has become clear that an ample amount of effort is still needed to render this technology clinically applicable. At the same time, many significant insights were discovered through this research project, some of which could be “reverse engineered” to improve conventional non-radiomic MR image acquisition. In this review article, the authors aim to discuss the recent advancements and encountering issues of radiomics, how we can apply the knowledge provided by radiomics to standard clinical images, and further expected technological advances in the realm of radiomics and glioma.