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Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

ABSTRACT: BACKGROUND:Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. METHODS:In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. RESULTS:The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC?=?0.83), 2) Low ODx vs. High ODx (AUC?=?0.72), 3) Low ODx vs. Intermediate and High ODx (AUC?=?0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC?=?0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. CONCLUSION:Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

SUBMITTER: Whitney J

PROVIDER: S-EPMC5977541 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

Whitney Jon J Corredor German G Janowczyk Andrew A Ganesan Shridar S Doyle Scott S Tomaszewski John J Feldman Michael M Gilmore Hannah H Madabhushi Anant A

BMC cancer 20180530 1

<h4>Background</h4>Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive.<h4>Methods</h4>In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of ...[more]

PMID: 29848291

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Project description:BACKGROUND:Oncotype DX (ODx) is a 12-gene assay assessing the recurrence risk (high, intermediate, and low) of ductal carcinoma in situ (pre-invasive breast cancer), which guides clinicians regarding prescription of radiotherapy. However, ODx is expensive, time-consuming, and tissue-destructive. In addition, the actual prognostic meaning for the intermediate ODx risk category remains unclear. METHODS:In this work, we evaluated the ability of quantitative nuclear histomorphometric features extracted from hematoxylin and eosin-stained slide images of 62 ductal carcinoma in situ (DCIS) patients to distinguish between the corresponding ODx risk categories. The prognostic value of the identified image signature was further evaluated on an independent validation set of 30 DCIS patients in its ability to distinguish those DCIS patients who progressed to invasive carcinoma versus those who did not. Following nuclear segmentation and feature extraction, feature ranking strategies were employed to identify the most discriminating features between individual ODx risk categories. The selected features were then combined with machine learning classifiers to establish models to predict ODx risk categories. The model performance was evaluated using the average area under the receiver operating characteristic curve (AUC) using cross validation. In addition, an unsupervised clustering approach was also implemented to evaluate the ability of nuclear histomorphometric features to discriminate between the ODx risk categories. RESULTS:Features relating to spatial distribution, orientation disorder, and texture of nuclei were identified as most discriminating between the high ODx and the intermediate, low ODx risk categories. Additionally, the AUC of the most discriminating set of features for the different classification tasks was as follows: (1) high vs low ODx (0.68), (2) high vs. intermediate ODx (0.67), (3) intermediate vs. low ODx (0.57), (4) high and intermediate vs. low ODx (0.63), (5) high vs. low and intermediate ODx (0.66). Additionally, the unsupervised clustering resulted in intermediate ODx risk category patients being co-clustered with low ODx patients compared to high ODx. CONCLUSION:Our results appear to suggest that nuclear histomorphometric features can distinguish high from low and intermediate ODx risk category patients. Additionally, our findings suggest that histomorphometric features for intermediate ODx were more similar to low ODx compared to high ODx risk category.

Project description:Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson's r = 0.909) and between users (Pearson's r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.

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Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

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Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

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