Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions

Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pretreatment Diffuse Optical Spectroscopic-Texture Analysis

Purpose: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pre-treatment DOS functional maps for predicting LABC response to NAC. Methods: LABC patients (n = 37) underwent DOS-breast imaging before starting neoadjuvant chemotherapy. Breast-tissue parametric maps were constructed and texture analyses were performed based on grey level co-occurrence matrices (GLCM) for feature extraction. Ground-truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS-textural features computed on volumetric tumour data before the start of treatment (i.e. “pre-treatment”) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naïve Bayes, and k-nearest neighbour (k-NN) classifiers. Results: Data indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5 and 89.0%, respectively and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn/%Sp = 78.0/81.0% and an accuracy of 79.5%. Conclusions: This study demonstrated that pre-treatment tumour DOS-texture features can predict breast cancer response to NAC and potentially guide treatments

Close or positive resection margins are not associated with an increased risk of chest wall recurrence in women with DCIS treated by mastectomy: a population-based analysis

Abstract Mastectomy is effective treatment for ductal carcinoma in situ (DCIS) but some women will develop chest wall recurrence. Most chest wall recurrences that develop after mastectomy are invasive cancer and are associated with poorer prognosis. Past studies have been unable to identify factors predictive of chest wall recurrence. Therefore, it remains unclear if a subset exists of women with DCIS treated by mastectomy experience a high rate of recurrence in whom more aggressive treatment may be of benefit. We report outcomes of all women in Ontario (N = 1,546) diagnosed with pure DCIS from 1994 to 2003 treated with mastectomy without radiotherapy and evaluate factors associated with the development of chest wall recurrence. Treatments and outcomes were validated by chart review. Proportional differences were compared using Chi square analyses. Survival analyses were used to study the development of chest wall recurrence in relation to patient and tumor characteristics. Median follow-up was 10.1 years. Median age was 57.1 years. 36 patients (2.3%) developed chest wall recurrence. The 10-year actuarial chest wall recurrence-free survival rates and invasive chest wall recurrence-free survival rates were 97.6 and 98.6%, respectively. There was no difference in cumulative 10 year rates of chest wall recurrence by age at diagnosis (50 years = 2.1%; p = 0.19), nuclear grade (high = 3.0%, intermediate = 1.4%, low = 1.0%, unreported = 2.5%; p = 0.41), or among women with close or positive resection margins (positive = 3.0%, 2 mm or less = 1.4%, >2 mm = 1.5%, unreported = 2.8%; p = 0.51). On univariate and multivariable analysis, none of the factors were significantly associated with the development of chest wall recurrence. In this population cohort, individuals treated by mastectomy experienced low rates of chest wall recurrence. We did not identify a subset of patients with a high rate of chest wall recurrence, including those with positive margins