[Research progress in the association of peri-implant diseases and metabolic syndrome]

Peri-implant disease, an important group of diseases that cause implant failure, are associated with metabolic abnormality. Metabolic syndrome (MetS) is a common metabolic disorder comprising abdominal obesity, hyperglycemia, systemic hypertension and atherogenic dyslipidemia. Previous studies had reported that MetS and its diversified clinical manifestations might be associated with peri-implant diseases, but the relationship and underlying mechanisms were unclear. This review aims to explore the relationship between MetS and peri-implant disease, in order to provide beneficial reference for the prevention and treatment of peri-implant disease in patients with MetS.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging

BACKGROUND

The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity.

PURPOSE

To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model.

STUDY TYPE

Prospective.

ANIMAL MODEL

A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC.

FIELD STRENGTH/SEQUENCE

A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm² ) and T1-twist DCE acquisition series.

ASSESSMENT

DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists.

STATISTICAL TESTS

The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant.

RESULTS

The apparent diffusion coefficient (ADC), mean diffusivity (MD), K_trans and K_ep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and V_e values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = -0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The K_trans and K_ep values were significantly correlated with the CD31 level (K_trans , r = 0.820; K_ep , r = 0.683).

DATA CONCLUSION

DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 4.

Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase

OBJECTIVE

To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer.

MATERIALS AND METHODS

This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model.

RESULTS

Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant.

CONCLUSION

The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer.

KEY POINTS

• Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. • We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.

Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer

Purpose

To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer.

Methods

In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10^th, 25^th, 50^th, 75^th, and 90^th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests.

Results

A statistically significant difference existed in the 10^th and 25^th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10^th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029).

Conclusions

Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.

Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma

PURPOSE

To evaluate the utility of the whole-lesion histogram apparent diffusion coefficient (ADC) for characterizing the heterogeneity of mucinous breast carcinoma (MBC) and to determine which ADC metrics may help to best differentiate subtypes of MBC.

MATERIALS AND METHODS

This retrospective study involved 52 MBC patients, including 37 pure MBC (PMBC) and 15 mixed MBC (MMBC). The PMBC patients were subtyped into PMBC-A (20 cases) and PMBC-B (17 cases) groups. All patients underwent preoperative diffusion-weighted imaging (DWI) at 1.5T and the whole-lesion ADC assessments were generated. Histogram-derived ADC parameters were compared between PMBC vs. MMBC and PMBC-A vs. PMBC-B, and receiver operating characteristic (ROC) curve analysis was used to determine optimal histogram parameters for differentiating these groups.

RESULTS

The PMBC group exhibited significantly higher ADC values for the mean (P = 0.004), 25^th (P = 0.004), 50^th (P = 0.004), 75^th (P = 0.006), and 90^th percentiles (P = 0.013) and skewness (P = 0.021) than did the MMBC group. The 25^th percentile of ADC values achieved the highest area under the curve (AUC) (0.792), with a cutoff value of 1.345 × 10^-3 mm² /s, in distinguishing PMBC and MMBC. The PMBC-A group showed significantly higher ADC values for the mean (P = 0.049), 25^th (P = 0.015), and 50^th (P = 0.026) percentiles and skewness (P = 0.004) than did the PMBC-B group. The 25^th percentile of the ADC cutoff value (1.476 × 10^-3 mm² /s) demonstrated the best AUC (0.837) among the ADC values for distinguishing PMBC-A and PMBC-B.

CONCLUSION

Whole-lesion ADC histogram analysis enables comprehensive evaluation of an MBC in its entirety and differentiating subtypes of MBC. Thus, it may be a helpful and supportive tool for conventional MRI.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:391-400.

Backfill is a specific sign of axial spondyloarthritis seen on MRI

OBJECTIVES

To summarize the characteristics of backfill in patients with axial spondyloarthritis (SpA) and patients with non-specific back pain (NSBP) and healthy controls, and to assess the value of backfill in diagnosing axial SpA.

METHODS

Three readers blinded recorded backfill seen on T1SE MRI scans from 647 subjects: 297 patients with ankylosing spondylitis (AS), 126 patients with non-radiographic axial SpA (nr-axSpA), 147 patients with NSBP, and 77 healthy controls. The SPARCC SIJ Structural Score (SSS) method was used to assess backfill. The changes of backfill were evaluated by the follow-up MRI scans from 157 patients. We summarized the characteristics of backfill and calculated its sensitivity and specificity for diagnosing axial SpA.

RESULTS

Backfill was recorded in 78.8% AS patients, 11.1% nr-axSpA patients, 1.8% patients with NSBP, and no healthy control. Backfill affected more frequently at ilium bone, lower half of sacroiliac joints in axial SpA (both P<0.05). The SSS score of backfill was much higher in axial SpA than in patients with NSBP (both P<0.01) and it did not correlate with demographics and BASDAI, BASFI, and CRP (all P>0.05). The score of backfill only positively correlated with symptom duration in AS (r=0.251, P<0.01) and in nr-axSpA (r=0.743, P<0.01) patients. Only 8.9% patients had the change of backfill in an average follow-up time of 1.09 years. Backfill had high specificity (0.98) and moderate sensitivity (0.59) for diagnosing axial SpA.

CONCLUSIONS

We summarized the characteristics of backfill and found that backfill is a specific sign of axial SpA seen on T1SE MRI.