Literature DB >> 29959421

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

Cheng Lu1,2, David Romo-Bucheli3, Xiangxue Wang2, Andrew Janowczyk2, Shridar Ganesan4, Hannah Gilmore5, David Rimm6, Anant Madabhushi7.   

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 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 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.

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Year:  2018        PMID: 29959421      PMCID: PMC6214731          DOI: 10.1038/s41374-018-0095-7

Source DB:  PubMed          Journal:  Lab Invest        ISSN: 0023-6837            Impact factor:   5.662


  30 in total

1.  An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival.

Authors:  Cheng Lu; James S Lewis; William D Dupont; W Dale Plummer; Andrew Janowczyk; Anant Madabhushi
Journal:  Mod Pathol       Date:  2017-08-04       Impact factor: 7.842

Review 2.  Molecular Testing and the Pathologist's Role in Clinical Trials of Breast Cancer.

Authors:  Hyo Sook Han; Anthony M Magliocco
Journal:  Clin Breast Cancer       Date:  2016-02-12       Impact factor: 3.225

Review 3.  Histopathological image analysis: a review.

Authors:  Metin N Gurcan; Laura E Boucheron; Ali Can; Anant Madabhushi; Nasir M Rajpoot; B Yener
Journal:  IEEE Rev Biomed Eng       Date:  2009-10-30

4.  A robust automatic nuclei segmentation technique for quantitative histopathological image analysis.

Authors:  Cheng Lu; Muhammad Mahmood; Naresh Jha; Mrinal Mandal
Journal:  Anal Quant Cytopathol Histpathol       Date:  2012-12

5.  Diagnostic concordance among pathologists interpreting breast biopsy specimens.

Authors:  Joann G Elmore; Gary M Longton; Patricia A Carney; Berta M Geller; Tracy Onega; Anna N A Tosteson; Heidi D Nelson; Margaret S Pepe; Kimberly H Allison; Stuart J Schnitt; Frances P O'Malley; Donald L Weaver
Journal:  JAMA       Date:  2015-03-17       Impact factor: 56.272

6.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.

Authors:  Bjoern H Menze; B Michael Kelm; Ralf Masuch; Uwe Himmelreich; Peter Bachert; Wolfgang Petrich; Fred A Hamprecht
Journal:  BMC Bioinformatics       Date:  2009-07-10       Impact factor: 3.169

7.  Comprehensive Histologic Scoring to Maximize the Predictability of Pathology-generated Equation of Breast Cancer Oncotype DX Recurrence Score.

Authors:  Thaer Khoury; Xiao Huang; Xiwei Chen; Dan Wang; Song Liu; Mateusz Opyrchal
Journal:  Appl Immunohistochem Mol Morphol       Date:  2016 Nov/Dec

8.  Fractal analysis of nuclear histology integrates tumor and stromal features into a single prognostic factor of the oral cancer microenvironment.

Authors:  Pinaki Bose; Nigel T Brockton; Kelly Guggisberg; Steven C Nakoneshny; Elizabeth Kornaga; Alexander C Klimowicz; Mauro Tambasco; Joseph C Dort
Journal:  BMC Cancer       Date:  2015-05-15       Impact factor: 4.430

9.  Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

Authors:  Mitko Veta; Paul J van Diest; Robert Kornegoor; André Huisman; Max A Viergever; Josien P W Pluim
Journal:  PLoS One       Date:  2013-07-29       Impact factor: 3.240

10.  Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.

Authors:  Cheng Lu; Hongming Xu; Jun Xu; Hannah Gilmore; Mrinal Mandal; Anant Madabhushi
Journal:  Sci Rep       Date:  2016-10-03       Impact factor: 4.379

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  30 in total

Review 1.  Chromatin's physical properties shape the nucleus and its functions.

Authors:  Andrew D Stephens; Edward J Banigan; John F Marko
Journal:  Curr Opin Cell Biol       Date:  2019-03-16       Impact factor: 8.382

2.  Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.

Authors:  Kaustav Bera; Ian Katz; Anant Madabhushi
Journal:  JCO Clin Cancer Inform       Date:  2020-11

Review 3.  Applications of machine learning in drug discovery and development.

Authors:  Jessica Vamathevan; Dominic Clark; Paul Czodrowski; Ian Dunham; Edgardo Ferran; George Lee; Bin Li; Anant Madabhushi; Parantu Shah; Michaela Spitzer; Shanrong Zhao
Journal:  Nat Rev Drug Discov       Date:  2019-06       Impact factor: 84.694

4.  Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis.

Authors:  Jun Xu; Haoda Lu; Haixin Li; Chaoyang Yan; Xiangxue Wang; Min Zang; Dirk G de Rooij; Anant Madabhushi; Eugene Yujun Xu
Journal:  Med Image Anal       Date:  2020-10-10       Impact factor: 8.545

Review 5.  Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Authors:  Stephnie A Harmon; Sena Tuncer; Thomas Sanford; Peter L Choyke; Barış Türkbey
Journal:  Diagn Interv Radiol       Date:  2019-05       Impact factor: 2.630

Review 6.  Understanding and overcoming tumor heterogeneity in metastatic breast cancer treatment.

Authors:  Nida Pasha; Nicholas C Turner
Journal:  Nat Cancer       Date:  2021-07-19

Review 7.  Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.

Authors:  Kaustav Bera; Kurt A Schalper; David L Rimm; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Nat Rev Clin Oncol       Date:  2019-08-09       Impact factor: 66.675

Review 8.  Molecular Biology in the Breast Clinics-Current status and future perspectives.

Authors:  Vani Parmar; Nita S Nair; Purvi Thakkar; Garvit Chitkara
Journal:  Indian J Surg Oncol       Date:  2019-08-10

9.  Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.

Authors:  Cheng Lu; Can Koyuncu; German Corredor; Prateek Prasanna; Patrick Leo; XiangXue Wang; Andrew Janowczyk; Kaustav Bera; James Lewis; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Med Image Anal       Date:  2020-11-16       Impact factor: 8.545

10.  An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.

Authors:  Eliot G Peyster; Sara Arabyarmohammadi; Andrew Janowczyk; Sepideh Azarianpour-Esfahani; Miroslav Sekulic; Clarissa Cassol; Luke Blower; Anil Parwani; Priti Lal; Michael D Feldman; Kenneth B Margulies; Anant Madabhushi
Journal:  Eur Heart J       Date:  2021-06-21       Impact factor: 35.855

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