Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics

Le, Nguyen Quoc Khanh

doi:10.1007/s00330-024-10586-x

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Although manual segmentation offers high precision, it is labor-intensive, subjective, and lacks standardization, leading to limited reproducibility and elevated time and labor expenses. Semi-automatic segmentation necessitates manual refinement, whereas automatic segmentation employs sophisticated computer algorithms for efficient and reproducible lesion boundary identification ( 41 , 42 ). However, it’s crucial to mention that the studies incorporated in this research exclusively used manual delineation for image segmentation.…”

Section: Discussionmentioning

confidence: 99%

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

Liu,

Liao,

Zhao

et al. 2024

Front. Oncol.

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ObjectiveWe aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC).MethodsThe present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman’s correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS).ResultsThe meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 17 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability.ConclusionThe present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings.Systematic Review RegistrationOpen Science Framework platform at https://osf.io/5zcnd.

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Section: Discussionmentioning

confidence: 99%

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

Liu,

Liao,

Zhao

et al. 2024

Front. Oncol.

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“…Machine learning and deep learning techniques have emerged as powerful tools in biomedical research, revolutionizing disease diagnosis, treatment planning, and personalized medicine (Le, 2024;Tran and Le, 2024). Medical image segmentation is the process of delineating regions of interest within medical images for diagnosis and treatment planning.…”

Section: Introductionmentioning

confidence: 99%

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

Sun,

Pan,

Kong

et al. 2024

Front. Bioeng. Biotechnol.

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Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image’s intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model’s capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

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Spatial structural abnormality maps associated with cognitive and physical performance in relapsing-remitting multiple sclerosis

Zhuo,

Zhang,

et al. 2024

Eur Radiol

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Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics

Cited by 7 publications

References 9 publications

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

Spatial structural abnormality maps associated with cognitive and physical performance in relapsing-remitting multiple sclerosis

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