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

Abstract

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 eosinstained 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 Wilcoxon’s 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) = 2.91(1.23–6.92), p = 0.02786. 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) = 3.17(0.33–30.46), p = 0.01039. Our results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN− BCa.

Computational analysis of morphological and molecular features in gastric cancer tissues

“…Quantification of nuclear components represented the following characteristics: cell nuclei were smaller, with lower chromatin in noncancerous tissues, while cancer cells exhibited greater heterogeneity, probably due to genetic or epigenetic alterations, together with high nuclear density related to differentiation states (Figure ). By microscopic diagnosis, it has been shown that nuclear abnormality is a fundamental hallmark of tumor cells and an indicator of patient outcomes in many cancer types . Observations of tissue structural atypia are essential for pathological diagnosis.…”

“…By microscopic diagnosis, it has been shown that nuclear abnormality is a fundamental hallmark of tumor cells and an indicator of patient outcomes in many cancer types. 46,47 Observations of tissue structural atypia are essential for pathological diagnosis. However, our findings supported that nuclear shape may also be related to the differentiation grades of cancers.…”

Chromatin’s physical properties shape the nucleus and its functions

“…Abnormal nuclear shape is a pathognomonic trait that has been used as a diagnostic indicator of human disease for nearly a century, albeit without an understanding of the mechanisms of nucleus deformations and their effects on function. Cancer diagnostic assays examine cell nuclei for unusual sizes and shapes via Pap smear (cervical) [16], nuclear herniations termed "blebs" that correlate with Gleason score (prostate) [17], and aberrant shapes and orientations via quantitative histomorphometrics (breast) [18,19]. Aberrantly shaped nuclei also occur in mechanically demanding environments, such as muscle cells in heart disease (cardiomyopathy) associated with progeria and aging and muscular dystrophy [20].…”

Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study

“…Many recent researches focus on mining quantitative morphological features from diagnostic pathology slides, which has been proved to be an effective way to alleviate the intra-observer and inter-observer variability, by analyzing digital pathology images in context of cancer grading [17,18], risk stratification [19][20][21][22], and tumor outcomes prediction [20,21,[23][24][25][26]. Wang et al [19] presented an image classifier using nuclear orientation, texture, shape and tumor architecture to predict disease recurrence in early stage non-small cell lung cancer from digitized H&E images.…”