PACS-Integrated Tools for Peritumoral Edema Volumetrics Provide Additional Information to RANO-BM-Based Assessment of Lung Cancer Brain Metastases after Stereotactic Radiotherapy: A Pilot Study

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas

PURPOSE

This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes.

METHODS

A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses.

RESULTS

T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051).

CONCLUSION

T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

Magnetic Resonance Imaging Assessment of Morphological Changes and Molecular Behavior to Evaluate Treatment Response of Brain Metastatic Lesions After Stereotactic Radiosurgery

BACKGROUND

Brain metastases (BMs) are the most common type of intracranial tumors, frequently arising from primary cancers such as lung, breast, melanoma, and renal cell carcinoma. Magnetic resonance imaging (MRI) plays a crucial role in assessing both the morphological and molecular characteristics of BMs, particularly in evaluating treatment response following radiosurgery. However, the interpretation of these imaging changes remains complex, often influencing clinical decision-making.

OBJECTIVE

This study aims to evaluate the morphological changes and molecular behavior of BMs postradiosurgery using MRI to assess treatment response.

MATERIALS AND METHODS

A retrospective review was conducted at a high specialty medical center, including 41 patients with BMs treated with stereotactic radiosurgery (SRS) from 2018 to 2022. Patients had a baseline MRI (pre-SRS) prior to treatment and follow-ups at 2-3 months (MRI-2) and 5-6 months (MRI-3). The response assessment in neuro-oncology brain metastases (RANO-BM) criteria were used, and T1/T2 matching was analyzed for each follow-up. Logistic regression was performed relating the T1/T2 matching and susceptibility areas (susceptibility-weighted imaging (SWI)) for MRI-2 and MRI-3. Cross tables were created regarding treatment response and demographic characteristics according to Pearson's Chi-squared test.

RESULTS

The mean age was 56.7 years; 53.7% (n = 22) were female. Primary tumors included lung (29.3%, n = 12), breast (19.5%, n = 8), colon (12.2%, n = 5), and melanoma and kidney tumors (7.3%, n = 3). Post-SRS changes included transitions from solid to cystic lesions, reduced perilesional edema, size reduction, and increased areas of magnetic susceptibility. A mixed pattern (areas of T1/T2 match + mismatch) was noted at lesion margins during follow-ups (MRI-2: 70.7% (n = 29), MRI-3: 68.3% (n = 28)). Most patients exhibited a partial response at MRI-2 (43.9%, n = 18), while at MRI-3, disease progression occurred (43.9%, n = 18) due to an increase in lesion number. Logistic regression linking T1/T2 matching and SWI demonstrated a significantly central-peripheral SWI distribution for T1/T2 match during both follow-ups (MRI-2: p = 0.005, R2: 0.52; MRI-3: p = 0.002, R2: 0.56). SWI distribution was higher when a mixed T1/T2 matching was present. Significant associations were found with systemic treatment and response type at MRI-2 (p =0.001), predominantly showing a partial response for those receiving chemotherapy + targeted therapy.

CONCLUSIONS

SWI and T1/T2 mismatch are valuable tools reflecting changes in the tumor microenvironment postradiosurgery, aiding in treatment response monitoring. The appearance of susceptibility areas may precede changes in the enhancement of the lesion margin. Short-term follow-ups (2-3 months) are crucial due to prevalent progression, marked primarily by the appearance of new lesions in approximately 50% of patients.

Introduction to the Special Issue on "Role of Novel Imaging Technique in Brain Tumors"

In recent years, significant strides have been made in the field of neuro-oncology imaging, contributing to our understanding and management of brain tumors [...].