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Cancer Informatics

Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.


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ISSN: 1176-9351


Aims and scope:

We live in a time when it is widely recognized that scientific collaborations across traditional disciplines can yield exponential gains through synergy. Cancer research is currently benefiting from advances in many fields, including biology (genomics and proteomics), physical chemistry (mass spectrometry and radio imaging), computer science and biostatistics (machine learning, artificial intelligence), and many others.

There exists a bewildering diversity of scientific journals in which new applications of these advances toward discovery in cancer research is reported. Leveraging these advances into new medicines and medical practices for the early detection, prevention and treatment of cancer is made difficult by the broad diffusion this literature. Bioinformatics and computational biology appear to play a central role at each nexus, in part because novel technologies lead to immense leaps in the amount and granularity of data from patients and patient samples.

A number of important journals exist that focus on a wide breadth of foci within bioinformatics. Medical informatics is a field nearly as wide that includes patient information systems and related critical components of information management in the information age.

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Submissions, excluding editorials, letters to the editor and dedications, will be peer reviewed by two reviewers.  Reviewers are required to provide fair, balanced and constructive reports.  

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Effect of Quantitative Nuclear Image Features on Recurrence of Ductal Carcinoma In Situ (DCIS) of the Breast

Authors: David E. Axelrod, Naomi A. Miller, H. Lavina Lickley, Jin Qian, William A. Christens-Barry, Yan Yuan, Yuejiao Fu and Judith-Anne W. Chapman
Publication Date: 01 Mar 2008
Cancer Informatics 2008:6 99-109

David E. Axelrod1, Naomi A. Miller2, H. Lavina Lickley3, Jin Qian4, William A. Christens-Barry5, Yan Yuan4, Yuejiao Fu6 and Judith-Anne W. Chapman7

1Department of Genetics and Cancer Institute of New Jersey, Rutgers—The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854-8082, U.S.A. 2Department of Pathology, University Health Network and University of Toronto, 610 University Avenue, Toronto, Ontario, Canada. M5G 2M9. 3Henrietta Banting Breast Cancer Centre, Women’s College Hospital, University of Toronto, 76 Grenville Street, 7th floor, Toronto, Ontario, Canada. M5S 1B2. 4Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada. N2L 3G1. 5Equipoise Imaging LLC , 4009 St. Johns Lane, Ellicott City, MD 21042, U.S.A. 6Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario, Canada. M3J 1P3. 7National Cancer Institute of Canada Clinical Trials Group, Queen’s University, 10 Stuart Street, Kingston, Ontario, Canada. K7L 3N6.


Abstract

Background: Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence.

Methods: Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fi ne structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features.

Results: Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient’s nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04).

Conclusion: Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade.



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