Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography

Chen, Yung-Chieh; Li, Yi-Tien; Kuo, Po-Chih; Cheng, Shih‐Ping; Chung, Yi-Hsiang; Kuo, Duen-Pang; Chen, Cheng-Yu

doi:10.1007/s00330-023-09421-6

Cited by 16 publications

(9 citation statements)

References 47 publications

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“…All included studies were published between 2018 and 2023, with most being published after 2020. X-ray (17/40) 14 – 25 and CT (16/40) 26 – 41 images were the most frequently used modalities; digital projection radiography (DPR) 42 – 44 was used in three studies and MRI 45 , 46 in two studies. Two studies 47 , 48 using vertebral fracture assessment (VFA) images were considered in this review.…”

Section: Resultsmentioning

confidence: 99%

“…Twenty studies 18 , 19 , 24 , 26 – 29 , 34 , 42 , 44 , 50 – 52 focused on osteoporosis screening and classification. The convolutional neural network (CNN) was widely used in all of these studies.…”

Section: Resultsmentioning

confidence: 99%

“…In their research, Chen et al. 27 developed a two-level classifier that combines ML techniques with radiomics texture analysis. The proposed classifier used SVM binary classifiers, which were trained using nested cross-validation.…”

Section: Resultsmentioning

confidence: 99%

“…In their research, Chen et al 27 developed a two-level classifier that combines ML techniques with radiomics texture analysis.…”

Section: Studies On Osteoporosis Screeningmentioning

confidence: 99%

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Deep learning in the radiologic diagnosis of osteoporosis: a literature review

He,

Lin,

Zhu

et al. 2024

J Int Med Res

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Objective Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. Methods We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. Results A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). Conclusions Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…In their research, Chen et al 27 developed a two-level classifier that combines ML techniques with radiomics texture analysis.…”

Section: Studies On Osteoporosis Screeningmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

He,

Lin,

Zhu

et al. 2024

J Int Med Res

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“…In this study, the elliptical ROI tool was used to define the ROI, ensuring it encompassed the largest circle diameter possible without including the vertebral cortex and posterior venous plexus, aligning with common practices in previous studies using elliptical ROIs. [13,14,[21][22][23][24] However, alternative approaches, including circular ROI, [12,[29][30][31][32][33][34][35] ROI with a fixed surface area, [36] and ROI in the anterior ½ to ¾ of the vertebral body [37] have been used in previous studies.…”

Section: Discussionmentioning

confidence: 99%

Preoperative abnormal bone mineral density as a prognostic indicator in patients undergoing gastrectomy for gastric cancer: A cohort study

An,

2024

Medicine

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Predicting postgastrectomy relapse and mortality in patients with gastric cancer (GC) remains challenging, with limitations to traditional staging systems such as the tumor–node–metastasis (TNM) system. This study aimed to investigate the impact of preoperative Hounsfield unit (HU) values, which serve as a surrogate marker for bone mineral density (BMD), in predicting survival outcomes in patients with GC. A retrospective analysis was conducted on data from patients with GC who underwent curative-intent gastrectomy. Opportunistic abdominopelvic computed tomography images were used to assess HU values at the 3rd lumbar vertebra (L3). These values were then categorized using a cutoff value of 110 HU, which has been established in previous studies as a determinant for abnormal versus normal BMD. Cox regression analysis established predictor models for overall survival (OS). Among 501 initial patients, 478 met the inclusion criteria. Multivariate analyses revealed HU values (hazard ratio, 1.51), along with other factors (the 5-factor modified frailty index, type of gastrectomy, TNM stage, anemia, and serum albumin level), as significant predictors of OS. The full model (FM) incorporating these variables demonstrated superior discrimination ability compared to the baseline model (BM), which is based solely on the TNM stage (concordance index: 0.807 vs 0.709; P < .001). Furthermore, the FM outperformed the BM in predicting OS risks at 36- and 60-months post-surgery. In conclusion, among patients undergoing gastrectomy for GC, those with HU values ≤ 110 (indicating abnormal BMD) at the L3 level, as determined through opportunistic CT scans, exhibited a poorer prognosis than those with HU values > 110 (indicating normal BMD). Integrating HU with other clinicopathological parameters enhances predictive accuracy, facilitating individualized risk stratification and treatment decision-making, which could potentially lead to improved survival outcomes.

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