Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric <scp>MRI</scp> Using Deep Learning

Pennig, Lenhard; Hoyer, Ulrike Cornelia Isabel; Goertz, Lukas; Shahzad, Rahil; Persigehl, Thorsten; Thiele, Frank; Perkuhn, Michael; Ruge, Maximilian I.; Kabbasch, Christoph; Borggrefe, Jan; Caldeira, Liliana; Laukamp, Kai Roman

doi:10.1002/jmri.27288

Cited by 25 publications

(21 citation statements)

References 37 publications

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“…8 A large number of recently proposed deep learning algorithms enabling automated detection of BMs cannot generate segmentations of tumor tissue. [28][29][30] In contrast, the DLM of the present study provides 3D voxel-wise segmentations of BMs with a performance (DSC of 0.72), which is in line with the present literature (0.67-0.79 25,26 ) and comparable to DL-based segmentation of larger malignant tumors, eg, glioblastoma (0.62-0.86 21 ) or primary central nervous system lymphoma (0.73-0.76 20 ). In this context, automated segmentation of BMs might be useful for lesion contouring in stereotactic radiosurgery, leading to a reduction of time effort as well as of intra-and inter-rater variabilities.…”

Section: Discussionsupporting

confidence: 90%

“…Further, in this study, only MRI data at primary diagnosis of the BMs were included with unknown performance of the DLM after therapy. However, the DeepMedic architecture has already shown its potential for application in longitudinal tumor imaging in primary central nervous system lymphoma 20 . Finally, compared with other deep learning based approaches to automatically detect BMs, 25,30 the DeepMedic network requires multiparametric MRI datasets (FLAIR, T 1 ‐/T 2 ‐weighted, and T 1 CE).…”

Section: Discussionmentioning

confidence: 99%

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Fully Automated MR Detection and Segmentation of Brain Metastases in Non‐small Cell Lung Cancer Using Deep Learning

Jünger

Hoyer

Schaufler

et al. 2021

Magnetic Resonance Imaging

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Background: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. Purpose: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. Study Type: Retrospective. Population: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Fully Automated MR Detection and Segmentation of Brain Metastases in Non‐small Cell Lung Cancer Using Deep Learning

Jünger

Hoyer

Schaufler

et al. 2021

Magnetic Resonance Imaging

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“…Given that aneurysm detection on CTA proves to be challenging with misdiagnosis of aSAH eventually resulting in a poor clinical outcome, automated detection of intracranial aneurysms may be of valuable assistance to physicians [11][12][13][14]. Over the last decade, deep learning models (DLMs), in particular convolutional neural networks (CNNs), have shown great potential in performing diagnostic and analyzing tasks on medical imaging for different subspecialties [15][16][17][18]19].…”

Section: Introductionmentioning

confidence: 99%

Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage

et al. 2021

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Purpose To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). Methods Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. Results In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM). Conclusion Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.

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“…20 Using deep learning based segmentation algorithm had the potential to realize automatic tumor delineation, however, the previous brain tumor segmentation studies mainly focused on the GBM segmentation, and the performance of PCNSL segmentation had Dice similarity coefficient lower than 0.8, which indicated a not ideal results that needed further manual correction. 21,22 By utilizing convolutional neural networks (CNN), image features can be extracted without the delineation of ROIs. 23 For brain tumor diagnosis, previous studies have shown the CNN could be used for glioma grading and genetic mutation classification.…”

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confidence: 99%

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi‐Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model

Xia

et al. 2021

Magnetic Resonance Imaging

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Background Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies. Purpose To investigate the use of a convolutional neural network (CNN) model for differentiation of PCNSL and GBM without tumor delineation. Study Type Retrospective. Population A total of 289 patients with PCNSL (136) or GBM (153) were included, the average age of the cohort was 54 years, and there were 173 men and 116 women. Field Strength/Sequence 3.0 T Axial contrast‐enhanced T1‐weighted spin‐echo inversion recovery sequence (CE‐T1WI), T2‐weighted fluid‐attenuation inversion recovery sequence (FLAIR), and diffusion weighted imaging (DWI, b = 0 second/mm2, 1000 seconds/mm2). Assessment A single‐parametric CNN model was built using CE‐T1WI, FLAIR, and the apparent diffusion coefficient (ADC) map derived from DWI, respectively. A decision‐level fusion based multi‐parametric CNN model (DF‐CNN) was built by combining the predictions of single‐parametric CNN models through logistic regression. An image‐level fusion based multi‐parametric CNN model (IF‐CNN) was built using the integrated multi‐parametric MR images. The radiomics models were developed. The diagnoses by three radiologists with 6 years (junior radiologist Y.Y.), 11 years (intermediate‐level radiologist Y.T.), and 21 years (senior radiologist Y.L.) of experience were obtained. Statistical Analysis The 5‐fold cross validation was used for model evaluation. The Pearson's chi‐squared test was used to compare the accuracies. U‐test and Fisher's exact test were used to compare clinical characteristics. Results The CE‐T1WI, FLAIR, and ADC based single‐parametric CNN model had accuracy of 0.884, 0.782, and 0.700, respectively. The DF‐CNN model had an accuracy of 0.899 which was higher than the IF‐CNN model (0.830, P = 0.021), but had no significant difference in accuracy compared to the radiomics model (0.865, P = 0.255), and the senior radiologist (0.906, P = 0.886). Data Conclusion A CNN model can differentiate PCNSL from GBM without tumor delineation, and comparable to the radiomics models and radiologists. Level of Evidence 4 Technical Efficacy Stage 2

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Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning

Cited by 25 publications

References 37 publications

Fully Automated MR Detection and Segmentation of Brain Metastases in Non‐small Cell Lung Cancer Using Deep Learning

Fully Automated MR Detection and Segmentation of Brain Metastases in Non‐small Cell Lung Cancer Using Deep Learning

Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi‐Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model

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