Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet

Hsu, Sanford P.C.; Hsiao, Tien-Yu; Pai, Li-Chieh; Sun, Chia‐Wei

doi:10.1117/1.nph.9.1.015005

Cited by 4 publications

(9 citation statements)

References 40 publications

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“…According to the results in Fig. 6 , normal tissue exhibited homogeneous appearances, whereas glioma, in general, displayed irregular holes and abnormal attenuation, as reported before [ 36 ]. In contrast, instead of showing microstructural features, PCNSL surprisingly tended to be homogeneous with a few abnormalities of attenuation.…”

Section: Resultssupporting

confidence: 81%

“…The utilized OCT system remained consistent with the one previously published [ 36 ]. It was designed using a single-mode fiber-based balanced Mach-Zehnder interferometer configuration.…”

Section: Methodsmentioning

confidence: 59%

“…The training time of these models is long and large (especially the VGG series). Previous studies have confirmed that self-built attention ResNet model can achieve three classifications of brain tumor images from OCT [ 36 ]. However, the architecture of this model is very complex and takes time.…”

Section: Discussionmentioning

confidence: 84%

“…7 demonstrates the misclassification. According to our previous findings, NOR OCT images display homogeneous and no microstructural appearance, whereas glioma exhibits abnormal microstructure, such as microcystins, calcification, and hemorrhaging within tumoral regions [ 36 ]. On the other hand, these are relatively uncommon on PCNSL OCT images [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

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Brain tumor grading diagnosis using transfer learning based on optical coherence tomography

Hsu,

Lin,

Lin

et al. 2024

Biomed. Opt. Express

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In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

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Section: Resultssupporting

confidence: 81%

“…The utilized OCT system remained consistent with the one previously published [ 36 ]. It was designed using a single-mode fiber-based balanced Mach-Zehnder interferometer configuration.…”

Section: Methodsmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

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Brain tumor grading diagnosis using transfer learning based on optical coherence tomography

Hsu,

Lin,

Lin

et al. 2024

Biomed. Opt. Express

Self Cite

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“…[2][3][4][5][6][7][8][9][10][11] However, while many papers have dealt with other types of tumors such as gliomas, 12 only a limited number of studies have focused on brain lymphomas. Some works focused on classification 13,14 or survival analysis. 15 Automatic segmentation of PCNSL from MRI data has been performed in a few publications.…”

Section: Introductionmentioning

confidence: 99%

Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI

El Jurdi,

Nichelli,

Alentorn

et al. 2024

Medical Imaging 2024: Image Processing

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Unlike for other brain tumors, there has been little work on the automatic segmentation of primary central nervous system (CNS) lymphomas. This is a challenging task due the highly variable pattern of the tumor and its boundaries. In this work, we propose a new loss function that controls border irregularity for deep learning-based automatic segmentation of primary CNS lymphomas. We introduce a border irregularity loss which is based on the comparison of the segmentation and it smoothed version. The border irregularity loss is combined with a previously proposed topological loss to better control the different connected components. The approach is general and can be used with any segmentation network. We studied a population of 99 patients with primary CNS lymphoma. 40 patients were isolated from the very beginning and formed the independent test set. The segmentations were performed on post-contrast T1-weighted MRI. The MRI were acquired in clinical routine and were highly heterogeneous. The proposed approach substantially outperformed the baseline across the various evaluation metrics (by 6 percent points of Dice, 40mm of Hausdorff distance and 6mm of mean average surface distance). However, the overall performance was moderate, highlighting that automatic segmentation of primary CNS lymphomas is a difficult task, especially when dealing with clinical routine MRI.

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Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models

Yang,

Zhao,

Shi

et al. 2024

Neuroradiology

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Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet

Cited by 4 publications

References 40 publications

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography

Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI

Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models

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