RESUMEN
BACKGROUND: Disease-modifying therapies (DMTs) for Alzheimer's disease (AD) have early evidence of efficacy. Widespread delivery of DMTs will require major service reconfiguration. Treatment pathways will need to include triaging for eligibility, regular infusions and baseline and follow-up MRI scanning. A critical step in planning is provision of real-world estimates of patients likely to be eligible for triaging, but these are challenging to obtain. METHODS: We performed a retrospective service evaluation of patients attending five memory services across North and East London and a national specialist cognitive disorders service. We examined the likely proportion of patients who would (1) be referred for triaging for DMTs and (2) potentially be suitable for treatments. RESULTS: Data from a total of 1017 patients were included, 517 of whom were seen in community memory services and 500 in a specialist clinic. In the memory services, 367/517 (71%) were diagnosed with possible AD. After exclusions of those in whom cognitive and frailty scores, MRI contraindications or anticoagulant use indicated they would be unlikely to be suitable, an estimated 32% would be eligible for triaging. In the specialist cognitive clinic, where additional investigations are available, 14% of those seen (70/500) would be potentially eligible for treatment. CONCLUSIONS: While a sizeable proportion of patients attending memory clinics may be referred for triaging for DMTs for AD, only a minority are likely to be suitable for these, as demonstrated in patients seen in specialist cognitive services. This will need to be considered when designing pathways for DMT delivery.
RESUMEN
BACKGROUND AND PURPOSE: Magee Equations have been developed as accurate tools for predicting response and clinical outcomes in breast cancer patients treated with adjuvant systemic therapy using basic clinicopathological parameters. This study aims to evaluate the alternative application of Magee Equation 2 score in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in hormone receptor (HR)-positive, HER2-negative breast cancer. PATIENTS AND METHODS: Patients with HR-positive, HER2-negative breast cancer who received NAC from January 2010 to May 2018 at Siriraj Hospital, Mahidol University, Thailand, were recruited. Pre-treatment status of HR and HER2 was used to calculate the Magee Equation 2 scores. The pCR rates among different clinicopathological parameters were analyzed. Survival analysis was performed by Log-rank test. Kaplan-Meier survival curves were analyzed. RESULTS: A total of 215 patients were eligible. The pCR rates for low, intermediate, and high scores were 4.8%, 3.6%, and 23.8%, respectively. Patients with high scores had significantly higher size reduction and pCR rates compared to those with intermediate or low scores (p<0.001). Those with high scores had higher rates of locoregional recurrence and death. The patients with high score had significantly lower overall survival (p=0.034). CONCLUSION: Among patients with HR-positive and HER2-negative breast cancer treated with NAC, Magee Equation 2 might be used as a tool for predicting the pCR and clinical outcome.
RESUMEN
BACKGROUND AND PURPOSE: Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients. PATIENTS AND METHODS: A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis. RESULTS: For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p<0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p<0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p<0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65-0.82) and 0.75 (95% CI; 0.68-0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71-0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71-0.84). CONCLUSION: CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.
RESUMEN
BACKGROUND: Axillary staging is a significant prognostic factor often used to determine the treatment course for breast cancer. One-step nucleic acid amplification (OSNA) is now the most accepted method for intra-operative assessment of sentinel lymph nodes (SLN) as it can semi-quantitatively determine the tumor burden in these SLN. Axillary lymph node dissection (ALND) may be omitted in patients with limited disease in the axilla. The objective was to create nomogram for prediction of non-sentinel lymph node (NSLN) status using OSNA to avoid unnecessary ALND. PATIENTS AND METHODS: Patients with invasive breast cancer T1-T3 and clinically negative axillary lymph nodes underwent SLN biopsy assessed by OSNA. The patients with positive SLN underwent ALND. Correlations between total tumor load (TTL), clinicopathological parameters, and NSLN status were analyzed by Chi square statistic and logistic regression. Model discrimination was evaluated using receiver-operating characteristic (ROC) analysis. RESULTS: The total number of patients who underwent SLN biopsies was 278. There were 89 patients with positive SLN. NSLNs were positive in 40 patients. Larger tumor size, presence of lymphovascular invasion (LVI) and higher log TTL were independent factors that predicted positive NSLN. TTL can discriminate NSLN status with area under the ROC curve of 0.789 (95% CI 0.686-0.892). Two nomograms using different parameters obtained pre- and post-operatively can predict NSLN involvement with better area under the ROC curve (0.801, 95% CI 0.702-0.900 and 0.849, 95% CI 0.766-0.932, respectively). CONCLUSIONS: Nomograms using results obtained via OSNA can predict NSLN status, as well as aid in deciding to omit the use of ALND.