Locally advanced breast cancer treated with neoadjuvant chemotherapy and adjuvant radiotherapy: a retrospective cohort analysis.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is increasingly used to treat locally advanced breast cancer (LABC). Improved response to NAC correlates with better survival outcomes. The dual purpose of this study is to report recurrence and survival outcomes for LABC patients treated with NAC, surgery and adjuvant radiotherapy and to correlate these outcomes with tumour response after NAC using multiple response assessment methods. METHODS: All LABC patients treated for curative intent with NAC, surgery, and adjuvant radiotherapy at our institute between January 2009 and December 2014 were included for analysis. NAC was mostly anthracycline and taxane-based; radiotherapy consisted of 50 Gy to the breast/chest wall and regional lymph nodes. Response to NAC was categorized using synoptic pathology reports, modified-RECIST and Chevallier scores. Survival curves were generated by the Kaplan-Meier method and compared using the log-rank test. RESULTS: The cohort included 103 patients nearly equally divided between Stage II (n = 53) and Stage III (n = 50). Rates of locoregional control (LRC), recurrence-free survival (RFS), and overall survival (OS) were 99, 98, and 100% at 1 year and 89, 69 and 77% at 5 years, respectively. Responses to NAC did not correlate with LRC (p > 0.05) but did correlate with RFS and OS (p < 0.05), except that the Chevallier score did not predict RFS (p = 0.06). Using bivariate Cox modeling tumour size before (p = 0.003) and after (p < 0.001) NAC, stage group (p = 0.05), and response assessed by synoptic pathology (p = 0.05), modified-RECIST (p = 0.001), and Chevallier score (p = 0.015) all predicted for RFS. No factors predicted for LRC. CONCLUSION: Pathologic response by all tested methods correlated with improved survival but were not associated with decreased LRC.

A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks.

We demonstrate the clinical utility of combining quantitative ultrasound (QUS) imaging of the breast with an artificial neural network (ANN) classifier to predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) administration prior to the start of treatment. Using a 6 MHz ultrasound system, radiofrequency (RF) ultrasound data were acquired from 100 patients with biopsy-confirmed locally advanced breast cancer prior to the start of NAC. Quantitative ultrasound mean parameter intensity and texture features were computed from the tumour core and margin, and were compared to the clinical/pathological response and 5-year recurrence-free survival (RFS) of patients. A multi-parametric QUS model in conjunction with an ANN classifier predicted patient response with 96 ± 6% accuracy, and a 0.96 ± 0.08 area under the receiver operating characteristic curve (AUC), compared to 65 ± 10 % accuracy and 0.67 ± 0.14 AUC achieved using a K-Nearest Neighbour (KNN) algorithm. A separate ANN model predicted patient RFS with 85 ± 7% accuracy, and a 0.89 ± 0.11 AUC, whereas the KNN methodology achieved a 58 ± 6 % accuracy and a 0.64 ± 0.09 AUC. The application of ANN for classifying patient response based on tumour QUS features performs well in terms of predicting response to chemotherapy. The findings here provide a framework for developing personalized a priori chemotherapy selection for patients that are candidates for NAC, potentially resulting in improved patient treatment outcomes and prognosis.

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach.

PURPOSE: This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC). METHODS: Using a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival. RESULTS: Multi-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively). CONCLUSION: This study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.