Xiangxue Wang

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, nu2009=u200970), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (nu2009=u2009119) and Cohort #3 (nu2009=u2009116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratiou2009=u200920.81, 95%xa0CI: 6.42–67.52, Pu2009<u20090.001).

Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer

The local spatial arrangement of nuclei in histopathology image has been shown to have prognostic value in the context of different cancers. In order to capture the nuclear architectural information locally, local cell cluster graph based measurements have been proposed. However, conventional ways of cell graph construction only utilize nuclear spatial proximity, and do not differentiate different cell types while constructing a cell graph. In this paper, we present feature driven local cell graph (FeDeG), a new approach to constructing local cell graphs by simultaneously considering spatial proximity and attributes of the individual nuclei (e.g. shape, size, texture). In addition, we designed a new set of quantitative graph derived metrics to be extracted from FeDeGs, in turn capturing the interplay between different local cell clusters. We evaluated the efficacy of FeDeG features in a digitized H&E stained tissue micro-array (TMA) images cohort consists of 434 early stage non-small cell lung cancer for predicting short-term ( 5 years) survival. Across a 100 runs of 10-fold cross-validation, a linear discriminant classifier in conjunction with the 15 most predictive FeDeG features identified via the Wilcoxon Rank Sum Test (WRST) yielded an average of AUC = 0.68. By comparison, four state-of-the-art pathomic and a deep learning based classifier had a corresponding AUC of 0.56, 0.54, 0.61, 0.62, and 0.55 respectively.

RaPtomics: Integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer related deaths worldwide. The treatment of choice for early stage NSCLC is surgical resection followed by adjuvant chemotherapy for high risk patients. Currently, the decision to offer chemotherapy is primarily dependent on several clinical and visual radiographic factors as there is a lack of a biomarker which can accurately stratify and predict disease risk in these patients. Computer extracted image features from CT scans (radiomic) and (pathomic) from H&E tissue slides have already shown promising results in predicting recurrence free survival (RFS) in lung cancer patients. This paper presents new radiology-pathology fusion approach (RaPtomics) to combine radiomic and pathomic features for predicting recurrence in early stage NSCLC. Radiomic textural features (Gabor, Haralick, Law, Laplace and CoLlAGe) from within and outside lung nodules on CT scans and intranuclear pathology features (Shape, Cell Cluster Graph and Global Graph Features) were extracted from digitized whole slide H&E tissue images on an initial discovery set of 50 patients. The top most predictive radiomic and pathomic features were then combined and in conjunction with machine learning algorithms were used to predict classifier. The performance of the RaPtomic classifier was evaluated on a training set from the Cleveland Clinic (n=50) and independently validated on images from the publicly available cancer genome atlas (TCGA) dataset (n=43). The RaPtomic prognostic model using Linear Discriminant Analysis (LDA) classifier, in conjunction with two radiomic and two pathomic shape features, significantly predicted 5-year recurrence free survival (RFS) (AUC 0.78; p<0.005) as compared to radiomic (AUC 0.74; p<0.01) and pathomic (AUC 0.67; p<0.05) features alone.

A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images.

Automatic detection of lymphocytes could contribute to develop objective measures of the infiltration grade of tumors, which can be used by pathologists for improving the decision making and treatment planning processes. In this article, a simple framework to automatically detect lymphocytes on lung cancer images is presented. This approach starts by automatically segmenting nuclei using a watershed-based approach. Nuclei shape, texture, and color features are then used to classify each candidate nucleus as either lymphocyte or non-lymphocyte by a trained SVM classifier. Validation was carried out using a dataset containing 3420 annotated structures (lymphocytes and non-lymphocytes) from 13 1000 × 1000 fields of view extracted from lung cancer whole slide images. A Deep Learning model was trained as a baseline. Results show an F-score 30% higher with the presented framework than with the Deep Learning approach. The presented strategy is, in addition, more flexible, requires less computational power, and requires much lower training times.

Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers

Early-stage estrogen receptor-positive (ER+) breast cancer (BCa) is the most common type of BCa in the United States. One critical question with these tumors is identifying which patients will receive added benefit from adjuvant chemotherapy. Nuclear pleomorphism (variance in nuclear shape and morphology) is an important constituent of breast grading schemes, and in ER+ cases, the grade is highly correlated with disease outcome. This study aimed to investigate whether quantitative computer-extracted image features of nuclear shape and orientation on digitized images of hematoxylin-stained and eosin-stained tissue of lymph node-negative (LN−), ER+ BCa could help stratify patients into discrete (<10 years short-term vs. >10 years long-term survival) outcome groups independent of standard clinical and pathological parameters. We considered a tissue microarray (TMA) cohort of 276 ER+, LN− patients comprising 150 patients with long-term and 126 patients with short-term overall survival, wherein 177 randomly chosen cases formed the modeling set, and 99 remaining cases the test set. Segmentation of individual nuclei was performed using multiresolution watershed; subsequently, 615 features relating to nuclear shape/texture and orientation disorder were extracted from each TMA spot. The Wilcoxons rank-sum test identified the 15 most prognostic quantitative histomorphometric features within the modeling set. These features were then subsequently combined via a linear discriminant analysis classifier and evaluated on the test set to assign a probability of long-term vs. short-term disease-specific survival. In univariate survival analysis, patients identified by the image classifier as high risk had significantly poorer survival outcome: hazard ratio (95% confident interval)u2009=u20092.91(1.23–6.92), pu2009=u20090.02786. Multivariate analysis controlling for T-stage, histology grade, and nuclear grade showed the classifier to be independently predictive of poorer survival: hazard ratio (95% confident interval)u2009=u20093.17(0.33–30.46), pu2009=u20090.01039. Our results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN− BCa.This study investigated whether quantitative computer-extracted images of tissue of lymph node (LN)-, estrogen receptor (ER)+ breast cancer could help stratify patients into discrete outcome groups. The results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN- breast cancer.

A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images

Tumor-infiltrating lymphocytes occurs when various classes of white blood cells migrate from the blood stream towards the tumor, infiltrating it. The presence of TIL is predictive of the response of the patient to therapy. In this paper, we show how the automatic detection of lymphocytes in digital H and E histopathological images and the quantitative evaluation of the global lymphocyte configuration, evaluated through global features extracted from non-parametric graphs, constructed from the lymphocytes’ detected positions, can be correlated to the patient’s outcome in early-stage non-small cell lung cancer (NSCLC). The method was assessed on a tissue microarray cohort composed of 63 NSCLC cases. From the evaluated graphs, minimum spanning trees and K-nn showed the highest predictive ability, yielding F1 Scores of 0.75 and 0.72 and accuracies of 0.67 and 0.69, respectively. The predictive power of the proposed methodology indicates that graphs may be used to develop objective measures of the infiltration grade of tumors, which can, in turn, be used by pathologists to improve the decision making and treatment planning processes.

Quantifying expert diagnosis variability when grading tumor-infiltrating lymphocytes

Tumor-infiltrating lymphocytes (TILs) have proved to play an important role in predicting prognosis, survival, and response to treatment in patients with a variety of solid tumors. Unfortunately, currently, there are not a standardized methodology to quantify the infiltration grade. The aim of this work is to evaluate variability among the reports of TILs given by a group of pathologists who examined a set of digitized Non-Small Cell Lung Cancer samples (n=60). 28 pathologists answered a different number of histopathological images. The agreement among pathologists was evaluated by computing the Kappa index coefficient and the standard deviation of their estimations. Furthermore, TILs reports were correlated with patient’s prognosis and survival using the Pearson’s correlation coefficient. General results show that the agreement among experts grading TILs in the dataset is low since Kappa values remain below 0.4 and the standard deviation values demonstrate that in none of the images there was a full consensus. Finally, the correlation coefficient for each pathologist also reveals a low association between the pathologists’ predictions and the prognosis/survival data. Results suggest the need of defining standardized, objective, and effective strategies to evaluate TILs, so they could be used as a biomarker in the daily routine.