Volumetric Regression in Brain Metastases After Stereotactic Radiotherapy: Time Course, Predictors, and Significance

Oft, Dominik; Schmidt, Manuel; Weissmann, Thomas; Roesch, Johannes; Mengling, Veit; Masitho, Siti; Bert, Christoph; Lettmaier, Sebastian; Frey, Benjamin; Distel, Luitpold; Fietkau, Rainer; Putz, Florian

doi:10.3389/fonc.2020.590980

Cited by 19 publications

(36 citation statements)

References 32 publications

(56 reference statements)

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“…The presence of any new lesion, regardless of its size, was assessed as progressive disease. In addition to the conventional criteria using one-dimensional measurement of the longest diameter of BM, we assessed volumetric response using the modified criteria suggested by Oft et al ( 31 ). The regions-of-interest in all BM nodules were carefully drawn along the enhancing tumor margin by an experienced radiologist (T.Q.N., with 11 years of clinical experience) using an in-house developed software.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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BackgroundAlthough 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.MethodsWe 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.ResultsIn 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.ConclusionsOur CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…These findings agree with a previous study [ 31 ], where an anti-vascular agent (BEV) was used for treatment in this model. Response to treatment is associated with change in tumour volume [ 67 , 68 , 69 , 70 ]. Both the tumour models demonstrated a trend in reduction of tumour volumes with treatment with a significant reduction in the F98 cases.…”

Section: Discussionmentioning

confidence: 99%

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

Bhaduri

Lesbats

Sharkey

et al. 2022

Cancers

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To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters , Ktrans , ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans , Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

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“…In total, 920 contrast enhanced T1 volumes using the MRI magnetization-prepared rapid gradient echo (MPRAGE) [48] sequence from a longitudinal study [49], [50] are used for evaluation. MPRAGE is a standard 3D T1 inversion recovery gradient echo (IR-GRE) MRI sequence and 3D T1 IR-GRE sequences are standard sequences for BM imaging [51].…”

Section: Datasetsmentioning

confidence: 99%

Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

Huang¹,

Bert²,

Fischer³

et al. 2022

Preprint

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Due to data privacy constraints, data sharing among multiple centers is restricted, which impedes the development of high performance deep learning models from multicenter collaboration. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development by sharing intermediate models instead of training data to bypass data privacy restrictions. This work aims to investigate the feasibility of continual learning for multicenter collaboration on an exemplary application of brain metastasis identification. The DeepMedic neural network is trained for brain metastasis segmentation. 920 T1 MRI contrast enhanced volumes are split to simulate multicenter collaboration scenarios. A continual learning algorithm, synaptic intelligence (SI), is applied to preserve important model weights for training one center after another. In a bilateral collaboration scenario, continual learning with SI achieves a sensitivity of 0.917, and naive continual learning without SI achieves a sensitivity of 0.906, while two models trained on internal data solely without continual learning achieve sensitivity of 0.853 and 0.831 only. In a seven-center multilateral collaboration scenario, the models trained on internal datasets (100 volumes each center) without continual learning obtain a mean sensitivity value of 0.699. With single-visit continual learning (i.e., the shared model visits each center only once during training), the sensitivity is improved to 0.788 and 0.849 without SI and with SI, respectively. With iterative continual learning (i.e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0.914, which is identical to the sensitivity using mixed data for training. Our experiments demonstrate the feasibility of applying continual learning for peer-to-peer federated learning in multicenter collaboration.

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Volumetric Regression in Brain Metastases After Stereotactic Radiotherapy: Time Course, Predictors, and Significance

Cited by 19 publications

References 32 publications

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

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

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

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