Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Paranavithana, Iromi R.; Stirling, David; Ros, Montserrat; Field, Matthew

doi:10.3390/cancers15061750

Cited by 3 publications

(1 citation statement)

References 124 publications

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“…The statistical significance in DSC improvement for papers which excluded cross validation compared to those which included it indicates the potential of an overfitting problem in these cases, highlighting the importance of test sets and external validation for generalizability. While other reviews have investigated similar segmentation performance tasks applied to various lesions or whole organs, to the best of our knowledge, ours is the first to focus on deep learning techniques applied specifically to lesions of the bone ( 70 – 77 ). Additionally, ours is the first which specifically evaluates differences in segmentation performance specifically as they relate to imaging modality, imaging dimensionality, and predominant lesion characteristic.…”

Section: Discussionmentioning

confidence: 99%

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Rich,

Bhardwaj,

Shah

et al. 2023

Front. Radiol.

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IntroductionImage segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).MethodThe literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.ResultsThe majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85–0.9.DiscussionDeep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

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Section: Discussionmentioning

confidence: 99%

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Rich,

Bhardwaj,

Shah

et al. 2023

Front. Radiol.

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The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

Ataei,

Eggermont,

Verdonschot

et al. 2024

Bone

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Radiomics in Musculoskeletal Tumors

Brandenberger,

White

2024

Semin Musculoskelet Radiol

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Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

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Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Cited by 3 publications

References 124 publications

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

Radiomics in Musculoskeletal Tumors

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