Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery

Petersen, Gabriel Cassinelli; Bousabarah, Khaled; Verma, Tej; Reppert, Marc von; Jekel, Leon; Gordem, Ayyüce; Jang, Benjamin; Merkaj, Sara; Fadel, Sandra Abi; Owens, Randy; Omuro, Antonio; Chiang, Veronica; Ikuta, Ichiro; Lin, MingDe; Aboian, Mariam

doi:10.1093/noajnl/vdac116

Cited by 8 publications

(6 citation statements)

References 19 publications

(25 reference statements)

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“…Other methods incorporate multimodal scans [ 6 ] and radiomics information [ 22 ], but none performs simultaneous lesion comparison and lesion matching. Cassinelli-Petersen [ 23 ] describe a method that incorporates validated prior lesion annotations for the segmentation of existing lesions; however, it does not detect new lesions and does not provide a comprehensive analysis of brain lesion changes as shown in Fig. 1 .…”

Section: Discussionmentioning

confidence: 99%

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline

Hammer,

Najjar,

Kahanov

et al. 2024

J Neurooncol

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Purpose Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans. Methods SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5 mm, 834 > 10 mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations. Results SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10 mm, 0.90±0.22 and 0.97±0.12 for metastases > 5 mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10 mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13. Conclusion Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.

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Section: Discussionmentioning

confidence: 99%

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline

Hammer,

Najjar,

Kahanov

et al. 2024

J Neurooncol

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“…Therefore, there is a need for automated segmentation tools that can detect and segment brain metastases on multiple imaging time points [5]. These tools would allow for more accurate and consistent measurements, which would improve patient care.…”

Section: Introductionmentioning

confidence: 99%

MLP-UNEXT for brain metastasis detection and segmentation in multiparametric MRI

Li,

Chang,

Yang

2024

Medical Imaging 2024: Clinical and Biomedical Imaging

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Brain metastases are a serious form of brain cancer that can significantly shorten a patient's life expectancy. Accurately detecting and tracking the volume of metastatic lesions is critical for patient prognosis. While transformer methods have been shown to be effective in natural images, they require large and annotated datasets to achieve state-of-the-art performance. Convolutional neural networks (CNNs), on the other hand, are easier to train and can achieve high performance even with smaller datasets, making them suitable for medical imaging data. Recently, the ConvNeXt architecture was proposed as a way to modernize the standard CNN by mirroring transformer blocks. In this work, we propose MLP-UNEXT, a hybrid architecture that combines CNNs and multi-layer perceptrons (MLPs) for segmenting brain tumor metastases on MRI scans. We show that MLP-UNEXT achieves state-of-the-art performance on the BRATS METS dataset, outperforming both CNN and transformer methods. MLP-UNEXT also demonstrates faster training and inference speed, lower computational complexity, and higher data-efficiency than other methods. We believe that MLP-UNEXT is a promising new approach for brain metastasis segmentation since it is fast, efficient, and accurate.

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“…The longest axial diameters (LD) in up to five lesions are added up as a summary measure, and the longitudinal change in the sum of LD, along with the patient's clinical status and use of corticosteroids, are finally used to define neuroradiological treatment response [7]. However, in similarity to other diameter-based neuroimaging response criteria, this method shows several limitations: while summation of LD offers a summary measure, it neglects the fact that individual lesions in a single patient may behave heterogeneously after SRT and, therefore, may need to be treated differentially [8]. Additionally, 2D measurements may have significant inter-reader variability since different radiologists may use different slices to measure the same lesion [9].…”

Section: Introductionmentioning

confidence: 99%

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

Kaur,

Cassinelli Petersen,

Jekel

et al. 2023

Cancers

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Stereotactic radiotherapy (SRT) is the standard of care treatment for brain metastases (METS) today. Nevertheless, there is limited understanding of how posttreatment lesional volumetric changes may assist prediction of lesional outcome. This is partly due to the paucity of volumetric segmentation tools. Edema alone can cause significant clinical symptoms and, therefore, needs independent study along with standard measurements of contrast-enhancing tumors. In this study, we aimed to compare volumetric changes of edema to RANO-BM-based measurements of contrast-enhancing lesion size. Patients with NSCLC METS ≥10 mm on post-contrast T1-weighted image and treated with SRT had measurements for up to seven follow-up scans using a PACS-integrated tool segmenting the peritumoral FLAIR hyperintense volume. Two-dimensional contrast-enhancing and volumetric edema changes were compared by creating treatment response curves. Fifty NSCLC METS were included in the study. The initial median peritumoral edema volume post-SRT relative to pre-SRT baseline was 37% (IQR 8–114%). Most of the lesions with edema volume reduction post-SRT experienced no increase in edema during the study. In over 50% of METS, the pattern of edema volume change was different than the pattern of contrast-enhancing lesion change at different timepoints, which was defined as incongruent. Lesions demonstrating incongruence at the first follow-up were more likely to progress subsequently. Therefore, edema assessment of METS post-SRT provides critical additional information to RANO-BM.

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Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery

Cited by 8 publications

References 19 publications

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline

MLP-UNEXT for brain metastasis detection and segmentation in multiparametric MRI

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

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