Effect of MRI-based semiautomatic size-assessment in cerebral metastases on the RANO-BM classification

The value of AI for assessing longitudinal brain metastases treatment response

Background

Effective follow-up of brain metastasis (BM) patients post-treatment is crucial for adapting therapies and detecting new lesions. Current guidelines (Response Assessment in Neuro-Oncology-BM) have limitations, such as patient-level assessments and arbitrary lesion selection, which may not reflect outcomes in high tumor burden cases. Accurate, reproducible, and automated response assessments can improve follow-up decisions, including (1) optimizing re-treatment timing to avoid treating responding lesions or delaying treatment of progressive ones, and (2) enhancing precision in evaluating responses during clinical trials.

Methods

We compared manual and automatic (deep learning-based) lesion contouring using unidimensional and volumetric criteria. Analysis focused on (1) agreement in size and RANO-BM categories, (2) stability of measurements under scanner rotations and over time, and (3) predictability of 1-year outcomes. The study included 49 BM patients, with 184 MRI studies and 448 lesions, retrospectively assessed by radiologists.

Results

Automatic contouring and volumetric criteria demonstrated superior stability (P < .001 for rotation; P < .05 over time) and better outcome predictability compared to manual methods. These approaches reduced observer variability, offering reliable and efficient response assessments. The best outcome predictability, defined as 1-year response, was achieved using automatic contours and volumetric measurements. These findings highlight the potential of automated tools to streamline clinical workflows and provide consistency across evaluators, regardless of expertise.

Conclusion

Automatic BM contouring and volumetric measurements provide promising tools to improve follow-up and treatment decisions in BM management. By enhancing precision and reproducibility, these methods can streamline clinical workflows and improve the evaluation of response in trials and practice.

Feature pyramid network-based computer-aided detection and monitoring treatment response of brain metastases on contrast-enhanced MRI

AIM

To investigate the value of feature pyramid network (FPN)-based computer-aided detection (CAD) of brain metastases (BMs) before and after non-surgical treatment, and to evaluate its performance in monitoring treatment response of BM on contrast-enhanced (CE) magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Eighty-five cancer patients newly diagnosed with BM who had undergone initial and follow-up three-dimensional (3D) CE MRI at Liaocheng People's Hospital were included retrospectively in this study. Manual detection (MD) was performed by reviewer 1. Computer-aided detection (CAD) was performed by reviewer 2 using uAI Discover-BMs software. The treatment response was assessed by the two reviewers for each patient separately. A paired chi-square test was used to compare the differences in the detection of BM between MD and CAD. Agreement between MD and CAD in monitoring treatment response was assessed by kappa test.

RESULTS

The sensitivities of MD and CAD on initial 3D CE MRI were 78.65% and 99.13%, respectively. The sensitivities of MD and CAD on follow-up 3D CE MRI were 76.32% and 98.24%, respectively. There was a very good agreement between Reviewer 1 and Reviewer 2 in evaluating the treatment response of BM.

CONCLUSION

FPN-based CAD has a higher sensitivity of close to 100% and lower false negatives (FNs) for BM detection, compared to MD. Although CAD had a few shortcomings in reflecting changes of BMs after treatment, it had high performance in monitoring treatment response of BM on CE MRI.

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Background

Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM.

Methods

A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity).

Results

The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset.

Conclusion

The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.

Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI

BACKGROUND

Although accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning.

METHODS

We included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed.

RESULTS

In the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (κ, 0.52) and substantial (κ, 0.68) for one-dimensional and volumetric, respectively. In the geographic test set, sensitivity was 87.7% (95% CI: 77.2%, 94.5%), mean DSC was 0.68 ± 0.20, and FP rate per scan was 1.9 for BM ≥ 5 mm. In the temporal test set #2, sensitivity was 94.7% (95% CI: 74.0%, 99.9%), mean DSC was 0.82 ± 0.20, and FP per scan was 0.5 (6/12) for BM ≥ 5 mm.

CONCLUSIONS

Our CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.