1. Prediction of breast cancer progression using nuclear morphometry
Neil Carleton1, Guangjing Zhu2, Linda Resar3, Lisa Rooper4, Young Kyung Bae5, and Robert W. Veltri2
Department of Biomedical & Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
The James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore MD 21287
Departments of Medicine, Oncology & Institute for Cellular Engineering, The Johns Hopkins University School of Medicine, Baltimore MD, 21205
Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore MD 21287
Department of Pathology, Yeungnam University College of Medicine, Nam-gu, Daegu, South Korea
Scanning and Analysis of Images
The H&E stained nuclei of each image were quantified using “Smart Segmentation” of ImagePro® Premier 9.1 software (Media Cybernetics, Maryland, US). In general, several optimized macros to identify the regions of interest (ROI) for each TMA core were created and collected 56 parameters (features) for each nuclei, which allows for background correction and batch processing of all scanned images under the same condition. Cancer regions of each TMA spot were circumscribed and selected as the ROIs. Quantification data of each nucleus within the ROI were generated using the created macros.
Introduction
Figure 6: Nuclear Morphometry Predicts Aggressiveness and Recurrence of Breast Cancer
Results
The progression of breast cancer (BrCa) involves nuclear morphometric changes, which are used in the pathological diagnosis of breast cancer. Although changes in nuclear morphometry (NM) contribute to histologic alterations observed in breast cancer, an accurate and autonomous quantification of NM changes have remained elusive. Here we created an image analysis macro to quantify changes in nuclear parameters, including size, shape, and DNA content and used these parameters to predict BrCa progression.
1. Nuclear Morphometry Automatic Quantification of Breast Cancer Cores
Selecting the Cancer Region of Interest in ImagePro
(A)
(B)
Recurrence Model
Prediction of breast cancer recurrence measured nuclear morphometry: software and multivariate logistic regression were used to calculate the AUC-ROC = The table shows the significant features that the model used to predict recurrence.
The same model features were used to plot the predicted probability for each case to distinguish between cases that had cancer recurrence versus those that did not have cancer recurrence.
Pr Cutoff: 0.53
Sens = 25.9%
Spec = 96.3%
P<0.0001
Figure 1: Methods Flow Chart
MediaCybernetics ImagePro9.1 Software
Nuclear Quantification
Methods
Tissue Microarray
Three tissue microarrays (TMAs) with 140 BrCa cases, stratified by TNM stage were used for this study. The TMAs were generated using 6 mm cores from primary tumors of a Korean cohort from Yengnam University Hospital, Daegu, South Korea. Specimens were obtained from surgical resection between January 1995 and January Each case was represented by 2 cores on the TMAs. Clinical information were provided by the pathology reports and patients’ medical records. H&E slides were first used for pathological diagnosis of BrCa after which slides were scanned with the Aperio scanner and the image of each core was separated using Aperio ImageScope software. The H&E stained nuclei from the tumor core were quantified using ImagePro® Premier 9.1 software (MediaCybernetics). For each core, data of all regions of interest were pooled and the covariance of each parameter was generated. Data were first analyzed alone and then in combination using multivariate logistic regression (MLR) to predict the aggressive cases or recurrence. For all analysis, p<0.05 was considered statistically significant.
Figure 2: The region of interest was traced by a breast pathologist in the first image. ImagePro identifies this region and subsequently quantifies the cancer nuclei for 56 different parameters according to our created program.
1. Nuclear Morphometry Predicts Aggressiveness of Breast Cancer
Summary
Tissue microarray in conjunction with the ImagePro Premier software significantly increases the efficiency of the quantification of nuclear architecture and morphologic features from H&E staining.
Nuclear morphometry can be used for prediction of breast cancer aggressiveness and recurrence.
Our study of 144 invasive breast cancer samples were evaluated by q-IHC using HMGA1. The results of the analysis:
Model for predicting aggressiveness yielded an AUC-ROC = (Sens: 50.9%, Spec: 84.3%).
A predictive probability plot of the logistic regression solution was significantly different for indolent versus aggressive disease (P<0.0001).
Model for predicting recurrence yielded an AUC-ROC = (Sens: 25.9%, Spec: 96.2%).
A predictive probability plot of the logistic regression solution was significantly different for non-recurrence versus recurrence disease (P<0.0001).
Aggressiveness Model
Prediction of breast cancer aggressiveness by nuclear morphometry: software and multivariate logistic regression were used to calculate the AUC-ROC = The table shows the significant features that the model uses to predict aggressiveness.
The same model features were used to plot the predicted probability for each case to distinguish between indolent (Stage I and IIA) and aggressive (IIB and higher) breast cancer.
(A)
(B)
Figure 1: Quantifying H&E histochemical staining with ImagePro Software. Parameters were reduced from 56 to 20 using Principle component analysis (PCA) to identify the most significant variables contributing the aggressiveness model.
Pr Cutoff: 0.50
Sens = 50.9%
Spec = 84.3%
Table 2 ImagePro Premier 9.1 morphometry quantification parameters
Table 1 Breast cancerTMA demographics
P<0.0001
References
Shah SN, Cope L, Poh W, Belton A, Roy S, et al. (2013) HMGA1: A Master Regulator of Tumor Progression in Triple-Negative Breast Cancer Cells. PLoSONE 8(5): e doi: /journal.pone
Makarov DV, Marlow C, Epstein JI, Miller MC, Landis P, et al. Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent. Prostate. 2008;68:183–9.
Acknowledgments
Patrick C. Walsh Prostate Cancer Research Fund
Patana Fund of the Brady Urological Institute