Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning

Abstract

The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pancreatic neuroendocrine tumor (NET). WHO mandates the quantification of Ki67 index by counting at least 500 positive tumor cells in a hotspot. Unfortunately, Ki67 antibody may stain both tumor and non-tumor cells as positive depending on the phase of the cell cycle. Likewise, the counter stain labels both tumor and non-tumor as negative. This non-specific nature of Ki67 stain and counter stain therefore hinders the exact quantification of Ki67 index. To address this problem, we present a deep learning method to automatically differentiate between NET and non-tumor regions based on images of Ki67 stained biopsies. Transfer learning was employed to recognize and apply relevant knowledge from previous learning experiences to differentiate between tumor and non-tumor regions. Transfer learning exploits a rich set of features previously used to successfully categorize non-pathology data into 1,000 classes. The method was trained and validated on a set of whole-slide images including 33 NETs subject to Ki67 immunohistochemical staining using a leave-one-out cross-validation. When applied to 30 high power fields (HPF) and assessed against a gold standard (evaluation by two expert pathologists), the method resulted in a high sensitivity of 97.8% and specificity of 88.8%. The deep learning method developed has the potential to reduce pathologists’ workload by directly identifying tumor boundaries on images of Ki67 stained slides. Moreover, it has the potential to replace sophisticated and expensive imaging methods which are recently developed for identification of tumor boundaries in images of Ki67-stained NETs.

Emerging role of deep learning‐based artificial intelligence in tumor pathology

“…Niazi et al. [65] developed AI algorithms to segment tumor areas from interstitium and normal pancreatic tissue on the unevenly Ki‐67‐immunoreactive WSIs and accordingly calculated the Ki‐67 index more precisely in pancreatic neuroendocrine neoplasms.…”

A modular cGAN classification framework: Application to colorectal tumor detection

“…It is often a key first step to the automated analysis of many diseases, most prevalently cancer. As a result, one of the most recurrent manifestations of this problem is identification of malignant or metastatic tissue 4–6 . This is important, as the extent to which malignant tissue has invaded neighboring tissues or metastasized is critical for long term prognosis as well as treatment options.…”

Artificial intelligence (AI) and big data in cancer and precision oncology

“…Examples of AI-based nuclear morphometric include the identification of tumour nuclei by pathologist’s lamina propria in T1 bladder cancer. Likewise, in breast and pancreatic neuroendocrine tumours, the ratio of Ki67 tumour positive nuclei to total tumour nuclei within hotspots is of pathologists’ interest [61] , [62] , [63] .…”