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

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.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.Author manuscript. Published in final edited form as: Cancer Informatics 2008:4 99–109.The final published version of this article is located at: http://la-press.com/article.php?article_id=583NIH U56 CA113004; to David E. AxelrodThis work was funded by the New Jersey Commission for Cancer Research 1076-CCRS0, the National Institutes of Health U56 CA113004, the Hyde and Watson Foundation, the Busch Memorial Fund, and the E.B. Fish Research Fund.NJ Commission on Cancer Research 1076-CCR-SO; to David E. Axelro

Ductal carcinoma in situ of the breast (DCIS) with heterogeneity of nuclear grade: prognostic effects of quantitative nuclear assessment

<p>Abstract</p> <p>Background</p> <p>Previously, 50% of patients with breast ductal carcinoma <it>in situ (</it>DCIS) had more than one nuclear grade, and neither worst nor predominant nuclear grade was significantly associated with development of invasive carcinoma. Here, we used image analysis in addition to histologic evaluation to determine if quantification of nuclear features could provide additional prognostic information and hence impact prognostic assessments.</p> <p>Methods</p> <p>Nuclear image features were extracted from about 200 nuclei of each of 80 patients with DCIS who underwent lumpectomy alone, and received no adjuvant systemic therapy. Nuclear images were obtained from 20 representative nuclei per duct, from each of a group of 5 ducts, in two separate fields, for 10 ducts. Reproducibility of image analysis features was determined, as was the ability of features to discriminate between nuclear grades. Patient information was available about clinical factors (age and method of DCIS detection), pathologic factors (DCIS size, nuclear grade, margin size, and amount of parenchymal involvement), and 39 image features (morphology, densitometry, and texture). The prognostic effects of these factors and features on the development of invasive breast cancer were examined with Cox step-wise multivariate regression.</p> <p>Results</p> <p>Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. For the pooled assessment with ~200 nuclei per patient, a discriminant function with one densitometric and two texture features was significantly (p < 0.001) associated with nuclear grading, and provided 78.8% correct jackknifed classification of a patient's nuclear grade. In multivariate assessments, image analysis nuclear features had significant prognostic associations (p ≤ 0.05) with the development of invasive breast cancer. Texture (difference entropy, p < 0.001; contrast, p < 0.001; peak transition probability, p = 0.01), densitometry (range density, p = 0.004), and measured margin (p = 0.05) were associated with development of invasive disease for the pooled data across all ducts.</p> <p>Conclusion</p> <p>Image analysis provided reproducible assessments of nuclear features which quantitated differences in nuclear grading for patients. Quantitative nuclear image features indicated prognostically significant differences in DCIS, and may contribute additional information to prognostic assessments of which patients are likely to develop invasive disease.</p

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

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 fine 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

Kaplan-Meier plots for the two image analysis factors significantly (p 0

<p><b>Copyright information:</b></p><p>Taken from "Ductal carcinoma of the breast (DCIS) with heterogeneity of nuclear grade: prognostic effects of quantitative nuclear assessment"</p><p>http://www.biomedcentral.com/1471-2407/7/174</p><p>BMC Cancer 2007;7():174-174.</p><p>Published online 10 Sep 2007</p><p>PMCID:PMC2001197.</p><p></p>001) associated with development of invasive disease in the multivariate assessments: A. Texture (difference entropy) (p < 0.001). B. Texture (contrast) (p < 0.001)