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OBJECTIVE: Accurate preoperative assessment of extramural vascular invasion (EMVI) is critical for the treatment and prognosis of rectal cancer. The aim of our research was to develop an assessment model by texture analysis for preoperative prediction of EMVI. MATERIALS AND METHODS: This study enrolled 44 rectal patients as train cohort, 7 patients as validation cohort and 18 patients as test cohort. A total of 236 texture features from DCE MR imaging quantitative parameters were extracted for each patient (59 features of Ktrans, Kep, Ve and Vp), and key features were selected by least absolute shrinkage and selection operator regression (LASSO). Finally, clinical independent risk factors, conventional MRI assessment, and T-score were incorporated to construct an assessment model using multivariable logistic regression. RESULTS: The T-score calculated using the 4 selected key features were significantly correlated with EMVI (p < 0.010). The area under the receiver operating characteristic curve (AUC) was 0.797 for discriminating between EMVI-positive and EMVI-negative patients with a sensitivity of 88.2% and specificity of 70.4%. The conventional MRI assessment of EMVI had a sensitivity of 23.53% and a specificity of 96.30%. The assessment model showed a greatly improved performance with an AUC of 0.954 (sensitivity, 88.2%; specificity, 92.6%) in train cohort, 0.833 (sensitivity, 66.7%; specificity, 100%) in validation cohort and 0.877 in test cohort, respectively. CONCLUSIONS: The assessment model showed an excellent performance in preoperative assessment of EMVI. It demonstrates strong potential for improving the accuracy of EMVI assessment and provide a reliable basis for individualized treatment decisions.
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Background: To investigate the feasibility of quantitative ultrashort echo time magnetic resonance imaging (UTE-MRI) techniques for assessing early cartilage degeneration in vivo. Methods: A total of 46 patients with knee pain due to osteoarthritis (OA) as the main complaint were recruited into the study. We performed MRI examinations with different quantitative UTE-MRI techniques, including UTE-based magnetization transfer (MT), UTE-adiabaticT1ρ, and UTE-T2* mapping on a 3.0T clinical magnetic resonance (MR) scanner (MR750; GE Healthcare, Milwaukee, WI, USA). Three regions of interest (ROIs) were manually drawn on the medial and lateral femoral condyles and the corresponding medial and lateral tibial plateaus, respectively. A total of 561 ROIs (12 ROIs for each knee) were finally included and divided into 3 groups according to the MRI Osteoarthritis Knee Score (MOAKS): normal (MOAKS 0, n=175), mild degeneration (MOAKS 1, n=283), and moderate degeneration (MOAKS 2, n=103). One-way analysis of variance (ANOVA) and Tamhane's T2 test were used to compare the differences of quantitative UTE-biomarkers among different groups. The analysis of Spearman's correlation was used to assess the correlation between the UTE-biomarkers and MOAKS grading. The diagnostic efficacy of different quantitative UTE-MRI techniques for detecting mild cartilage degeneration was evaluated using the receiver operating characteristic (ROC) curve. Results: The UTE-MT ratio (UTE-MTR) and the UTE-adiabatic T1ρ values had a moderate correlation with the MOAKS grading (r=-0.523, P<0.001; r=0.531, P<0.001, respectively), while the UTE-T2* was weakly correlated with the MOAKS grading (r=-0.396, P<0.001). For the normal group (MOAKS 0) and the mild group (MOAKS 1), the UTE-MTR values were 21.09%±3.03% and 17.30%±3.22%, respectively. The UTE-adiabatic T1ρ values were 30.43±6.26 ms and 35.05±8.78 ms for the normal group (MOAKS 0) and the mild group (MOAKS 1), respectively. With respect to the UTE-T2* values, the normal group (MOAKS 0) values were 21.49±3.96 ms and the mild group (MOAKS 1) values were 19.86±3.08 ms. All the differences between the 2 groups of the 3 UTE-MRI values were significant. The AUCs of the UTE-MTR, UTE-adiabatic T1ρ, and UTE-T2* mapping were 0.794, 0.732, and 0.651, respectively. Conclusions: The quantitative UTE-MRI techniques (UTE-MT, UTE-adiabatic T1ρ, and UTE-T2* mapping) show great promise for assessing the early degeneration of articular cartilage in vivo, and the UTE-MT and UTE-adiabatic T1ρ values show better diagnostic efficacy than UTE-T2* mapping.
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BACKGROUND: Recent studies suggest that bone marrow adipose tissue might play a role in the pathogenesis of osteoporosis. There are inconsistent findings on the relationship among marrow fat content, bone mineral density and apparent diffusion coefficient (ADC). This study aimed to prospectively explore the efficacy of MR spectroscopy (MRS) and diffusion-weighted MR imaging (DWI) in detecting vertebral marrow changes in postmenopausal women with varying bone densities. METHODS: Both MRS and DWI of the lumber spine were performed in 102 postmenopausal women (mean age, (67.3 +/- 6.5) years; range, 55 - 83 years), who underwent dual X-ray absorptiometry. Marrow fat content and ADC were compared and correlated among three groups: 24 with normal bone density, 31 with osteopenia and 47 with osteoporosis. RESULTS: Vertebral marrow fat content was significantly increased in the osteoporotic group ((65.60 +/- 7.68)%, P < 0.001) and the osteopenic group ((57.68 +/- 6.45)%, P < 0.001), when compared with the normal bone density group ((51.67 +/- 3.27)%). ADC values were significantly decreased in the osteoporotic group ((0.39 +/- 0.03) x 10(-3)mm(2)/s, P < 0.001) and in the osteopenic group ((0.42 +/- 0.02) x 10(-3)mm(2)/s, P < 0.001), when compared with the normal bone density group ((0.47 +/- 0.03) x 10(-3)mm(2)/s). The marrow fat content negatively correlated with both bone density (r = -0.731, P < 0.001) and marrow ADC (r = -0.572, P < 0.001). The bone density positively correlated with the ADC values (r = 0.802, P < 0.001). CONCLUSIONS: Postmenopausal women experience a corresponding increase in vertebral marrow fat content as the bone density decreases. Marrow fat content and ADC correlate to the bone density. MRS and DWI may indirectly assess the early bone marrow changes in postmenopausal women.
