Imaging of brain metastasis in non-small-cell lung cancer: indications, protocols, diagnosis, post-therapy imaging, and implications regarding management

Extraordinary linear non-enhancing brainstem leptomeningeal metastasis from lung adenocarcinoma: A case report

In patients with lung adenocarcinoma, angiogenesis-altering drugs can alter the appearance of leptomeningeal metastasis on magnetic resonance imaging (MRI) scans. In the ventral brainstem, this can manifest as a unique, linear, non-enhancing T2-hyperintense signal.

Converging survival trends in non-small cell lung cancer patients with and without brain metastasis receiving state-of-the-art treatment

INTRODUCTION

Historically, patients with brain metastasis (BM) have been excluded from clinical trials investigating treatments for non-small cell lung cancer (NSCLC) due to their unfavorable prognosis. Advanced treatments have increased survival prospects for NSCLC patients with BM. This study evaluated the life expectancy of NSCLC patients with and without BM in the context of contemporary treatments.

METHODS

Outcome data were collected for patients with advanced NSCLC attending a tertiary medical center between 2015 and 2020. Patients were stratified according to BM status and compared for overall survival (OS) using log-rank and Cox regression analyses.

RESULTS

The cohort included 360 patients with NSCLC of whom 134 (37.2%) had BM. Most (95%) of cases of BM developed within the first two years: 63% at diagnosis, 18% during the first year, 14% during the second year. There was no significant difference in OS between patients without BM and those with BM (median 23.7 vs. 22.3 months, HR = 0.97, p = 0.82); patients with BM and a targetable or non-targetable mutation (40.2 vs. 31.4 months, HR = 0.93, p = 0.84, and 20.7 vs. 19.87 months, HR = 0.95, p = 0.75, respectively); and patients with symptomatic BM (23.7 vs. 19.8 months, HR = 0.95, p = 0.78). Treatment for BM (95% of patients) consisted of stereotactic radiosurgery or tyrosine kinase inhibitors, with corresponding intracranial control rates of 90% and 86%.

CONCLUSION

The results imply that the presence of BM has no impact on the prognosis of NSCLC. The practice of excluding NSCLC patients with BM from clinical trials warrants reconsideration.

Developments in Image Processing Using Deep Learning and Reinforcement Learning

The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

Aim

The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging.

Methods

Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI).

Results

In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model.

Conclusions

Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.