Automatic cellularity assessment from post‐treated breast surgical specimens

• Published in: Cytometry. Part A 2017-11, Vol.91 (11), p.1078-1087
• Main Authors: Peikari, Mohammad, Salama, Sherine, Nofech‐Mozes, Sharon, Martel, Anne L.
• Format: Article
• Language: English
• Subjects: Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - drug therapy; Breast Neoplasms - pathology; Cancer; Cell Tracking - methods; Clinical trials; Cytometry; Eosine Yellowish-(YS) - pharmacology; Female; Hematoxylin - pharmacology; Humans; Information management; machine learning; Neoadjuvant Therapy; Neoplasm Recurrence, Local - diagnosis; Neoplasm Recurrence, Local - drug therapy; Neoplasm Recurrence, Local - pathology; Neoplasm, Residual - pathology; Nuclear electric power generation; Nuclei; Pathology; pathology image analysis; Prognosis; Reproducibility of Results; Surgery; Therapy
• ISSN: 1552-4922; 1552-4930
• DOI: 10.1002/cyto.a.23244

Neoadjuvant treatment (NAT) of breast cancer (BCa) is an option for patients with the locally advanced disease. It has been compared with standard adjuvant therapy with the aim of improving prognosis and surgical outcome. Moreover, the response of the tumor to the therapy provides useful information for patient management. The pathological examination of the tissue sections after surgery is the gold‐standard to estimate the residual tumor and the assessment of cellularity is an important component of tumor burden assessment. In the current clinical practice, tumor cellularity is manually estimated by pathologists on hematoxylin and eosin (H&E) stained slides, the quality, and reliability of which might be impaired by inter‐observer variability which potentially affects prognostic power assessment in NAT trials. This procedure is also qualitative and time‐consuming. In this paper, we describe a method of automatically assessing cellularity. A pipeline to automatically segment nuclei figures and estimate residual cancer cellularity from within patches and whole slide images (WSIs) of BCa was developed. We have compared the performance of our proposed pipeline in estimating residual cancer cellularity with that of two expert pathologists. We found an intra‐class agreement coefficient (ICC) of 0.89 (95% CI of [0.70, 0.95]) between pathologists, 0.74 (95% CI of [0.70, 0.77]) between pathologist #1 and proposed method, and 0.75 (95% CI of [0.71, 0.79]) between pathologist #2 and proposed method. We have also successfully applied our proposed technique on a WSI to locate areas with high concentration of residual cancer. The main advantage of our approach is that it is fully automatic and can be used to find areas with high cellularity in WSIs. This provides a first step in developing an automatic technique for post‐NAT tumor response assessment from pathology slides. © 2017 International Society for Advancement of Cytometry