Aromatase inhibitor adjuvant chemotherapy of breast cancer results in cancer therapy induced bone loss

Osteoporosis, fracture and survival: Application of machine learning in breast cancer prediction models

The risk of osteoporosis in breast cancer patients is higher than that in healthy populations. The fracture and death rates increase after patients are diagnosed with osteoporosis. We aimed to develop machine learning-based models to predict the risk of osteoporosis as well as the relative fracture occurrence and prognosis. We selected 749 breast cancer patients from two independent Chinese centers and applied six different methods of machine learning to develop osteoporosis, fracture and survival risk assessment models. The performance of the models was compared with that of current models, such as FRAX, OSTA and TNM, by applying ROC, DCA curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Three models were developed. The XGB model demonstrated the best discriminatory performance among the models. Internal and external validation revealed that the AUCs of the osteoporosis model were 0.86 and 0.87, compared with the FRAX model (0.84 and 0.72)/OSTA model (0.77 and 0.66), respectively. The fracture model had high AUCs in the internal and external cohorts of 0.93 and 0.92, which were higher than those of the FRAX model (0.89 and 0.86). The survival model was also assessed and showed high reliability via internal and external validation (AUC of 0.96 and 0.95), which was better than that of the TNM model (AUCs of 0.87 and 0.87). Our models offer a solid approach to help improve decision making.

Low Bone Mass Secondary to Antipsychotic Medications

Background and Objective:

Antipsychotic medications are known to cause low bone mass. The objective of this study was to assess the prevalence of osteopenia and osteoporosis secondary to patients taking antipsychotic medications.

Patients and Methods:

This prospective study included 175 patients taking antipsychotic medications and attending the psychiatric clinics at the King Fahd Hospital of the University, Al-Khobar, Saudi Arabia. Demographic data, antipsychotic medications, type, and duration of administration of medication were collected. All patients had bone mass measurement using dual energy X-ray (DXA) absorptiometry. Patients were divided into 5-year groups, from ≤35 to ≥51 years. The data were entered in the database and analyzed using SPSS Inc version 20.

Results:

The average age of patients was 40.75 ± 7.16 years (range: 26–56 years), there were 120 (82.8%) males and 25 (17.2%) females. Our results indicate that the average duration of anti-psychotic medication use was 8.45 ± 5.4 years. DXA of the hip revealed that 25 (14.2%) patients were osteoporotic and 104 (59.42%) were osteopenic, while on the basis of the T-score of the lumbar spine, 77 (44%) patients were osteoporotic and 80 (45.7%) were osteopenic. On the basis of the spinal bone mineral density (BMD), 89.7% had low bone mass.

Conclusion:

Anti-psychotic medications have a strong influence on the reduction of bone mass even in younger populations. The BMD of patients who are prescribed anti-psychotic medication need to be monitored for low bone mass and provided with the appropriate treatment.

Use of MR-based trabecular bone microstructure analysis at the distal radius for osteoporosis diagnostics: a study in post-menopausal women with breast cancer and treated with aromatase inhibitor

PURPOSE

Treatment with aromatase inhibitor (AI) is recommended for post-menopausal women with hormone-receptor positive breast cancer. However, AI therapy is known to induce bone loss leading to osteoporosis with an increased risk for fragility fractures. The purpose of this study was to investigate whether changes of magnetic resonance (MR)-based trabecular bone microstructure parameters as advanced imaging biomarker can already be detected in subjects with AI intake but still without evidence for osteoporosis according to dual energy X-ray absorptiometry (DXA)-based bone mineral density (BMD) measurements as current clinical gold standard.

METHODS

Twenty-one postmenopausal women (62±6 years of age) with hormone-receptor positive breast cancer, ongoing treatment with aromatase inhibitor for 23±15 months, and no evidence for osteoporosis (current DXA T-score greater than -2.5) were recruited for this study. Eight young, healthy women (24±2 years of age) were included as controls. All subjects underwent 3 Tesla magnetic resonance imaging (MRI) of the distal radius to assess the trabecular bone microstructure.

RESULTS

Trabecular bone microstructure parameters were not significantly (p>0.05) different between subjects with AI intake and controls, including apparent bone fraction (0.42±0.03 vs. 0.42±0.05), trabecular number (1.95±0.10 mm(-1) vs 1.89±0.15 mm(-1)), trabecular separation (0.30±0.03 mm vs 0.31±0.06 mm), trabecular thickness (0.21±0.01 mm vs 0.22±0.02 mm), and fractal dimension (1.70±0.02 vs. 1.70±0.03).

CONCLUSION

These findings suggest that the initial deterioration of trabecular bone microstructure as measured by MRI and BMD loss as measured by DXA occur not sequentially but rather simultaneously. Thus, the use of MR-based trabecular bone microstructure assessment is limited as early diagnostic biomarker in this clinical setting.

CYP19A1 Genetic Polymorphisms rs4646 and Osteoporosis in Patients Treated with Aromatase Inhibitor-Based Adjuvant Therapy

OBJECTIVE

Third-generation aromatase inhibitors (AI) are potent suppressors of aromatase activity. The aim of this study was to measure the incidence of adverse effects in breast cancer patients treated with AI-based adjuvant therapy and the relationship with the CYP19A1 genotypes.

MATERIALS AND METHODS

Forty-five postmenopausal breast cancer patients (46-85 yrs) in AI adjuvant treatment were genotyped for the rs4646 polymorphisms of CYP19A1 gene and three variations were identified. Toxicities were registered at each follow-up medical examination, and classified in accord with the Common Terminology Criteria for Adverse Events.

RESULTS

Twenty-four (53.3%) patients presented the GG genotype; 19 (42.2%) the GT, and 2 (4.4%) the TT. The AI treatment was Anastrazole for 35 patients (77.8%) and Letrozole for the others (n=10; 22.2%). Osteoporosis was significantly associated with the GG genotype (p=0.001). Treatment discontinuation (TD) was observed in 6 cases (13.3%). The only parameter able to predict TD was the appearance of severe arthralgia/myalgia (Odds Ratio, OR=23.75; p=0.009), when adjusted for age and AI treatment.

CONCLUSION

Our results suggest that CYP19A1 polymorphic variants may influence susceptibility to develop AI-related side effects. Further prospective studies are needed to confirm the role of the aromatase gene (CYP19A1) polymorphisms in predicting adverse effects to AI-based therapy.