Brain metastases from breast cancer using magnetic resonance imaging: A systematic review

Post-COVID-19 conditions: a systematic review on advanced magnetic resonance neuroimaging findings

Post-COVID conditions (PCCs) cover a wide spectrum of lingering symptoms experienced by survivors of coronavirus disease 2019 (COVID-19). Neurological and neuropsychiatric sequelae are common in PCCs. Advanced magnetic resonance imaging (MRI) techniques can reveal subtle alterations in brain structure, function, and perfusion that underlie these sequelae. This systematic review aimed to synthesize findings from studies that used advanced MRI to characterize brain changes in individuals with PCCs. A detailed literature search was conducted in the PubMed and Scopus databases to identify relevant studies that used advanced MRI modalities, such as structural MRI (sMRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and perfusion-weighted imaging (PWI), to evaluate brain changes in PCCs. Twenty-five studies met the inclusion criteria, comprising 1219 participants with PCCs. The most consistent findings from sMRI were reduced gray matter volume (GMV) and cortical thickness (CTh) in cortical and subcortical regions. DTI frequently reveals increased mean diffusivity (MD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in white matter tracts (WMTs) such as the corpus callosum, corona radiata, and superior longitudinal fasciculus. fMRI demonstrated altered functional connectivity (FC) within the default mode, salience, frontoparietal, somatomotor, subcortical, and cerebellar networks. PWI showed decreased cerebral blood flow (CBF) in the frontotemporal area, thalamus, and basal ganglia. Advanced MRI shows changes in the brain networks and regions of the PCCs, which may cause neurological and neuropsychiatric problems. Multimodal neuroimaging may help understand brain-behavior relationships. Longitudinal studies are necessary to better understand the progression of these brain anomalies.

Breast percent density changes in digital mammography pre- and post-radiotherapy

INTRODUCTION

Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy.

METHODS

This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density.

CONCLUSIONS

This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects

INTRODUCTION

Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC.

METHODS

This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies.

RESULTS

Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival.

CONCLUSION

Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.

Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research

AIM

Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients.

METHOD

A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the "black box" problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed.

RESULTS

The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p ≤ 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) genes were also determined to increase the risk of metastasis in BC.

CONCLUSION

The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

INTRODUCTION AND IMPORTANCE

Accurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms.

CASE PRESENTATION AND METHODS

CE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations.

CLINICAL DISCUSSION

The approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods.

CONCLUSION

The MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Iron quantification in basal ganglia using quantitative susceptibility mapping in a patient with ALS: a case report and literature review

Background

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can measure the magnetic susceptibility of tissues, which can reflect their iron content. QSM has been used to detect iron accumulation in cortical and subcortical brain regions. However, its application in subcortical regions such as the basal ganglia, particularly the putamen, is rare in patients with amyotrophic lateral sclerosis (ALS).

Case presentation and literature review

We present the case of a 40-year-old male patient with ALS who underwent an MRI for QSM. We compared his QSM images with those of a control subject and performed a quantitative analysis of the magnetic susceptibility values in the putamen regions. We also reviewed the literature on previous QSM studies in ALS and summarized their methods and findings. Our QSM analysis revealed increased magnetic susceptibility values in the bilateral putamen of the ALS patient compared to controls, indicating iron overload. This finding is consistent with previous studies reporting iron dysregulation in subcortical nuclei in ALS. We also discussed the QSM processing techniques used in our study and in the literature, highlighting their advantages and limitations.

Conclusion

This case report demonstrates the potential of QSM as a sensitive MRI biomarker for evaluating iron levels in subcortical regions of ALS patients. QSM can provide quantitative information on iron deposition patterns in both motor and extra-motor areas of ALS patients, which may help understand the pathophysiology of ALS and monitor disease progression. Further studies with larger samples are needed to validate these results and explore the clinical implications of QSM in ALS.