Feasibility of assessing male osteoporosis using MRI IDEAL-IQ sequence of proximal femur in prostate cancer patients

Yanagida, Kazuki; Kimura, Takahiro; Yoshida, Takahiro; Kawae, Norikazu; Nakamura, Takahiro; Kajihara, Hajime; Mizushima, Akio

doi:10.1080/13685538.2022.2112663

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…PCA-LDA diagnostic algorithm has been widely used in Raman spectrum analysis of various human samples. Firstly, the PCA is used to use mathematical model to find several main components variables (PCs) in high-dimensional spectral data by reducing the dimension of data [7] . Then, independent sample t-test is performed on all PCs calculated.…”

Section: Discussionmentioning

confidence: 99%

Analysis of prostate cancer using serum surface-enhanced Raman spectroscopy and multivariate statistical algorithm

Xin

Gao²,

Lin³

et al. 2022

First Conference on Biomedical Photonics and Cross-Fusion (BPC 2022)

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Prostate cancer (PCa) is an epithelial malignant tumor occurring in the prostate gland and is the most common malignancy of the male genitourinary system. Prostate cancer is often asymptomatic in its early stage, and the best treatment time is usually missed when it is found. Therefore, early diagnosis and treatment is the key to reduce the mortality of prostate cancer patients. In this work, we developed a method to identify patients with prostate cancer and healthy volunteers. We collected serum samples from patients with prostate cancer and healthy volunteers, detected the SERS spectra of these serum samples, and analyzed the SERS spectrum of the obtained serum samples with a preliminary spectral peak assignment. Then principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose the SERS spectra of serum from patients with prostate cancer and healthy volunteers. Difference spectrum analysis showed that there are obvious differences in several characteristic peaks between the serum of patients with prostate cancer and the serum of healthy volunteers, which may be related to the special changes of nucleic acids, proteins, lipids and other biological molecules in the process of carcinogenesis. Using principal component analysis (PCA) combined with linear discriminant analysis (LDA) multivariate statistical method, the diagnostic sensitivity and specificity were 85% and 95%, respectively. The receiver operating characteristic (ROC) curve further proves the effectiveness of diagnosis algorithm based on PCA-LDA technology, and the area under the curve (AUC) is 0.998. These results show that the combination of SERS technology and PCA-LDA algorithm has great potential in screening prostate cancer patients.

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Section: Discussionmentioning

confidence: 99%

Analysis of prostate cancer using serum surface-enhanced Raman spectroscopy and multivariate statistical algorithm

Xin

Gao²,

Lin³

et al. 2022

First Conference on Biomedical Photonics and Cross-Fusion (BPC 2022)

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“…However, no data on the relationship between BMI and indicators of fracture risk assessment, such as R2* and PDFF, exist. Thus, this study aimed to accumulate additional cases from preliminary research by Watanabe et al [17], investigate the R2* and PDFF of the proximal femur for the first time, and explore their correlations with BMI.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Femoral Proximal Bone Quality Assessment by MRI IDEAL-IQ Sequence and Body Mass Index in Elderly Men

Goto,

Watanabe,

Kawae

et al. 2024

Tomography

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Background: Bone assessment using the MRI DEAL-IQ sequence may have the potential to serve as a substitute for evaluating bone strength by quantifying the bone marrow hematopoietic region (R2*) and marrow adiposity (proton density fat fraction: PDFF). Higher body mass index (BMI) is associated with increased bone mineral density (BMD) in the proximal femur; however, the relationship between BMI and R2* or PDFF remains unclear. Herein, we investigated the correlation between BMI and MRI IDEAL-IQ based R2* or PDFF of the proximal femur. Methods: A retrospective single-cohort study was conducted on 217 patients diagnosed with non-metastatic prostate cancer between September 2019 and December 2022 who underwent MRI. The correlation between BMI and R2* or PDFF of the proximal femur was analyzed using Spearman’s rank correlation test. Results: Among 217 patients (median age, 74 years; median BMI, 23.8 kg/m2), there was a significant positive correlation between BMI and R2* at the right and left proximal femur (r = 0.2686, p < 0.0001; r = 0.2755, p < 0.0001, respectively). Furthermore, BMI and PDFF showed a significant negative correlation (r = −0.239, p = 0.0004; r = −0.2212, p = 0.001, respectively). Conclusion: In elderly men, the increased loading on the proximal femur due to elevated BMI was observed to promote a decrease in bone marrow adiposity in the proximal femur, causing a tendency for a transition from fatty marrow to red marrow with hematopoietic activity. These results indicate that the MRI IDEAL-IQ sequence may be valuable for assessing bone quality deterioration in the proximal femur.

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Correlation of R2* with fat fraction and bone mineral density and its role in quantitative assessment of osteoporosis

et al. 2023

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Objectives To investigate the correlation of R2* with vertebral fat fraction (FF) and bone mineral density (BMD), and to explore its role in the quantitative assessment of osteoporosis (OP). Methods A total of 83 patients with low back pain (59.77 ± 7.46 years, 30 males) were enrolled, which underwent lumbar MRI in IDEAL-IQ sequences and quantitative computed tomography (QCT) scanning within 48h. The FF, R2*, and BMD of all 415 lumbar vertebrae were respectively measured. According to BMD, all vertebrae were divided into BMD normal, osteopenia, and OP groups, and the difference of FF and R2* among groups was analyzed by one-way ANOVA. The correlation between R2*, FF, and BMD was analyzed by Pearson’s test. Taking BMD as the gold standard, the efficacies for FF and R2* in diagnosis of OP and osteopenia were assessed by receiver operating characteristic curve, and their area under the curve (AUC) was compared with DeLong’s test. Results The FF and R2* were statistically different among groups (F values of 102.521 and 11.323, both p < 0.05), and R2* were significantly correlated with FF and BMD, respectively (r values of −0.219 and 0.290, both p < 0.05). In diagnosis of OP and osteopenia, the AUCs were 0.776 and 0.778 for FF and 0.638 and 0.560 for R2*, and the AUCs of R2* were lower than those of FF, with Z values of 4.030 and 4.087, both p < 0.001. Conclusion R2* is significantly correlated with FF and BMD and can be used as a complement to FF and BMD for quantitative assessment of OP. Key Points • R2* based on IDEAL-IQ sequences has a definite but weak linear relationship with FF and BMD. • FF is significantly correlated with BMD and can effectively evaluate BMAT. • R2* can be used as a complement to FF and BMD for fine quantification of bone mineral loss and bone marrow fat conversion.

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Feasibility of assessing male osteoporosis using MRI IDEAL-IQ sequence of proximal femur in prostate cancer patients

Cited by 4 publications

References 22 publications

Analysis of prostate cancer using serum surface-enhanced Raman spectroscopy and multivariate statistical algorithm

Analysis of prostate cancer using serum surface-enhanced Raman spectroscopy and multivariate statistical algorithm

Relationship between Femoral Proximal Bone Quality Assessment by MRI IDEAL-IQ Sequence and Body Mass Index in Elderly Men

Correlation of R2* with fat fraction and bone mineral density and its role in quantitative assessment of osteoporosis

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