Bone Marrow Adipose Tissue Is Increased in Postmenopausal Women With Postsurgical Hypoparathyroidism.

CONTEXT: Suppression of bone turnover, greater trabecular volume, and normal-high normal all-site bone mineral density (BMD) are hallmarks of postsurgical hypoparathyroidism (HypoPT). Impairment in the trabecular microarchitecture with possible higher risk of vertebral fractures (VF) in women with postmenopausal HypoPT has also been described. Currently, no data on bone marrow adipose tissue (BMAT) are available in HypoPT. OBJECTIVE: To assess BMAT by magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy (1H-MRS) in postmenopausal women with chronic postsurgical HypoPT. METHODS: This cross-sectional pilot study, conducted at an ambulatory referral center, included 29 postmenopausal women (mean age 66 ± 8.4 years) with postsurgical HypoPT and 31 healthy postmenopausal women (mean age 63 ± 8.5). Lumbar spine MRI was performed and BMAT was measured by applying PRESS sequences on the L3 body. Lumbar spine, femoral neck, and total hip BMD were measured by dual x-ray absorptiometry (DXA); site-matched spine trabecular bone score (TBS) was calculated by TBS iNsight (Medimaps, Switzerland); VF assessment was performed with lateral thoracic and lumbar spine DXA. RESULTS: Fat content (FC) and saturation level (SL%) were higher (P <.0001 and P <.001), while water content (W) was lower in HypoPT compared to controls (P <.0001). FC significantly correlated with years since menopause and body weight (P <.05) in HypoPT, while TBS negatively correlated with FC and SL% (P <.05) and positively with residual lipids (RL) and W (P <.05). CONCLUSION: We demonstrate for the first time that BMAT is increased in postmenopausal women with postsurgical hypoparathyroidism and negatively associated with trabecular microarchitecture.

Do body composition parameters correlate with response to targeted therapy in ER+/HER2- metastatic breast cancer patients? Role of sarcopenia and obesity.

Purpose: To investigate the association between body composition parameters, sarcopenia, obesity and prognosis in patients with metastatic ER+/HER2- breast cancer under therapy with cyclin-dependent kinase (CDK) 4/6 inhibitors. Methods: 92 patients with biopsy-proven metastatic ER+/HER2- breast cancer, treated with CDK 4/6 inhibitors between 2018 and 2021 at our center, were included in this retrospective analysis. Visceral Adipose Tissue (VAT), Subcutaneous Adipose Tissue (SAT) and Skeletal Muscle Index (SMI) were measured before starting therapy with CDK 4/6 inhibitors (Palbociclib, Abemaciclib or Ribociclib). Measurements were performed on a computed tomography-derived abdominal image at third lumbar vertebra (L3) level by an automatic dedicated software (Quantib body composition®, Rotterdam, Netherlands). Visceral obesity was defined as a VAT area > 130 cm2. Sarcopenia was defined as SMI < 40 cm2/m2. Changes in breast lesion size were evaluated after 6 months of treatment. Response to therapy was assessed according to RECIST 1.1 criteria. Spearman's correlation and χ2 analyses were performed. Results: Out of 92 patients, 30 were included in the evaluation. Of the 30 patients (mean age 53 ± 12 years), 7 patients were sarcopenic, 16 were obese, while 7 patients were neither sarcopenic nor obese. Statistical analyses showed that good response to therapy was correlated to higher SMI values (p < 0.001), higher VAT values (p = 0.008) and obesity (p = 0.007); poor response to therapy was correlated to sarcopenia (p < 0.001). Moreover, there was a significant association between sarcopenia and menopause (p = 0.021) and between sarcopenia and the persistence of axillary lymphadenopathies after treatment (p = 0.003), while the disappearance of axillary lymphadenopathies was associated with obesity (p = 0.028). Conclusions: There is a growing interest in body composition, especially in the field of breast cancer. Our results showed an interesting correlation between sarcopenia and progression of disease, and demonstrated that VAT can positively influence the response to targeted therapy with CDK 4/6 inhibitors. Larger-scale studies are needed to confirm these preliminary results. Clinical Relevance: Sarcopenia and obesity seem to predict negative outcomes in many oncologic entities. Their prevalence and impact in current breast cancer care are promising but still controversial.

MRI as a biomarker for breast cancer diagnosis and prognosis.

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes?

BACKGROUND: Breast cancer (BC) includes different pathological and molecular subtypes. This study aimed to investigate whether multiparametric magnetic resonance imaging (mpMRI) could reliably predict the molecular status of BC, comparing mpMRI features with pathological and immunohistochemical results. METHODS: This retrospective study included 156 patients with an ultrasound-guided biopsy-proven BC, who underwent breast mpMRI (including diffusion-weighted imaging) on a 3-T scanner from 2017 to 2020. Histopathological analyses were performed on the surgical specimens. Kolmogorov-Smirnov Z, χ2, and univariate and multivariate logistic regression analyses were performed. RESULTS: Fifteen patients were affected with ductal carcinoma in situ, 122 by invasive carcinoma of no special type, and 19 with invasive lobular carcinoma. Out of a total of 141 invasive cancers, 45 were luminal A-like, 54 luminal B-like, 5 human epidermal growth factor receptor 2 (HER2) positive, and 37 triple negative. The regression analyses showed that size < 2 cm predicted luminal A-like status (p = 0.025), while rim enhancement (p < 0.001), intralesional necrosis (p = 0.001), peritumoural oedema (p < 0.001), and axillary adenopathies (p = 0.012) were negative predictors. Oppositely, round shape (p = 0.001), rim enhancement (p < 0.001), intralesional necrosis (p < 0.001), and peritumoural oedema (p < 0.001) predicted triple-negative status. CONCLUSIONS: mpMRI has been confirmed to be a valid noninvasive predictor of BC subtypes, especially luminal A and triple negative. Considering the central role of pathology in BC diagnosis and immunohistochemical profiling in the current precision medicine era, a detailed radiologic-pathologic correlation seems vital to properly evaluate BC.

Preoperative Staging in Breast Cancer: Intraindividual Comparison of Unenhanced MRI Combined With Digital Breast Tomosynthesis and Dynamic Contrast Enhanced-MRI.

OBJECTIVES: To evaluate the accuracy in lesion detection and size assessment of Unenhanced Magnetic Resonance Imaging combined with Digital Breast Tomosynthesis (UE-MRI+DBT) and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), in women with known breast cancer. METHODS: A retrospective analysis was performed on 84 patients with histological diagnosis of breast cancer, who underwent MRI on a 3T scanner and DBT over 2018-2019, in our Institution. Two radiologists, with 15 and 7 years of experience in breast imaging respectively, reviewed DCE-MRI and UE-MRI (including DWI and T2-w) + DBT images in separate reading sections, unaware of the final histological examination. DCE-MRI and UE-MRI+DBT sensitivity, positive predictive value (PPV) and accuracy were calculated, using histology as the gold standard. Spearman correlation and regression analyses were performed to evaluate lesion size agreement between DCE-MRI vs Histology, UE-MRI+DBT vs Histology, and DCE-MRI vs UE-MRI+DBT. Inter-reader agreement was evaluated using Cohen's κ coefficient. McNemar test was used to identify differences in terms of detection rate between the two methodological approaches. Spearman's correlation analysis was also performed to evaluate the correlation between ADC values and histological features. RESULTS: 109 lesions were confirmed on histological examination. DCE-MRI showed high sensitivity (100% Reader 1, 98% Reader 2), good PPV (89% Reader 1, 90% Reader 2) and accuracy (90% for both readers). UE-MRI+DBT showed 97% sensitivity, 91% PPV and 92% accuracy, for both readers. Lesion size Spearman coefficient were 0.94 (Reader 1) and 0.91 (Reader 2) for DCE-MRI vs Histology; 0.91 (Reader 1) and 0.90 (Reader 2) for UE-MRI+DBT vs Histology (p-value <0.001). DCE-MRI vs UE-MRI+DBT regression coefficient was 0.96 for Reader 1 and 0.94 for Reader 2. Inter-reader agreement was 0.79 for DCE-MRI and 0.94 for UE-MRI+DBT. McNemar test did not show a statistically significant difference between DCE-MRI and UE-MRI+DBT (McNemar test p-value >0.05). Spearman analyses showed an inverse correlation between ADC values and histological grade (p-value <0.001). CONCLUSIONS: DCE-MRI was the most sensitive imaging technique in breast cancer preoperative staging. However, UE-MRI+DBT demonstrated good sensitivity and accuracy in lesion detection and tumor size assessment. Thus, UE-MRI could be a valid alternative when patients have already performed DBT.

Imaging technologies in the differential diagnosis and follow-up of brown tumor in primary hyperparathyroidism: Case report and review of the literature.

Brown tumors are osteolytic lesions associated with hyperparathyroidism (HPT). They may involve various skeletal segments, but rarely the cranio-facial bones. We report a case of a young boy with a swelling of the jaw secondary to a brown tumor presenting as the first manifestation of primary HPT (PHPT). He was found to have brown tumor located in the skull, as well. Different imaging technologies were employed for the diagnosis and follow-up after parathyroidectomy. We enclose a review of the literature on the employment of such imaging technologies in the differential diagnosis of osteolytic lesions. A multidisciplinary approach comprising clinical, laboratory and imaging findings is essential for the differential diagnosis of brown tumor in PHPT.

Can MRI Biomarkers Predict Triple-Negative Breast Cancer?

The purpose of this study was to investigate MRI features of triple-negative breast cancer (TNBC) compared with non-TNBC, to predict histopathological results. In the study, 26 patients with TNBC and 24 with non-TNBC who underwent multiparametric MRI of the breast on a 3 T magnet over a 10-months period were retrospectively recruited. MR imaging sets were evaluated by two experienced breast radiologists in consensus and classified according to the 2013 American College of Radiology (ACR) BI-RADS lexicon. The comparison between the two groups was performed using the Chi-square test and followed by logistic regression analyses. We found that 92% of tumors presented as mass enhancements (p = 0.192). 41.7% of TNBC and 86.4% of non-TNBC had irregular shape (p = 0.005); 58.3% of TNBC showed circumscribed margins, compared to 9.1% of non-TNBC masses (p = 0.001); 75% of TNBC and 9.1% of non-TNBC showed rim enhancement (p < 0.001). Intralesional necrosis was significantly associated with TNBC (p = 0.016). Rim enhancement and intralesional necrosis risulted to be positive predictors at univariate analysis (OR = 29.86, and 8.10, respectively) and the multivariate analysis confirmed that rim enhancement is independently associated with TNBC (OR = 33.08). The mean ADC values were significantly higher for TNBC (p = 0.011). In conclusion, TNBC is associated with specific MRI features that can be possible predictors of pathological results, with a consequent prognostic value.