Investigators were able to identify breast cancer immunohistochemistry markers including Ki-67, estrogen receptor, and progesterone receptor status utilizing deep learning–based artificial intelligence algorithms
Deep learning–based artificial intelligence (AI) algorithms were able to accurately identify tumor cell types and nuclear breast cancer immunohistochemistry (IHC) marker statuses, including Ki-67, estrogen receptor (ER), and progesterone receptor (PR) status, according to a poster presented at the 2021 San Antonio Breast Cancer Symposium.
Feasibility data from the study indicated that whole slide image (WSI) scoring via AI analysis algorithms yielded comparable scoring for Ki-67, ER, and PR in terms of accuracy. Additionally, the method surpassed results garnered through manual scoring in terms of reproducibility.
Results from a study assessing the use of deep learning–based artificial intelligence algorithms to identify Ki-67, estrogen receptor, and progesterone receptor status in breast cancer.
“The differences between image analysis and manual read overall concordance rates when compared to the expert ground-truth were all small. In addition, the AI statistic scoring significantly outperformed manual scoring or inter-reader reproducibility. Thus, our feasibility results showed that pathologists using whole slide imaging analysis–assisted scoring was equivalent to manual scoring in an expert panel ground-truth.”
Three AI-based algorithms were developed to conduct WSI analyses of Ki-67-, ER-, and PR-stained slides. The goal was to determine whether the algorithms could address variabilities and give pathologists in different labs the ability to consistently score with the same accuracy.
During the trial, image analysis results were compared with digital reads for every assay utilizing Roche uPath enterprise software plus the given AI algorithm regarding ground truth reference diagnosis; both were performed via 3 reader pathologists. Moreover, positive (PPA), negative (NPA), and overall percent agreements (OPA) were determined for image analysis compared with ground truth, as well as digital reads compared with ground truth.
The study’s co-primary end points were between-method differences of PPA and NPA across the 3 reader pathologists.
Notably, all IHC markers are included as separate algorithms within Roche’s uPath enterprise software. The whole slides are tiled into smaller field of views (FOV) that are subsequently entered into the deep learning model, which then processes every FOV to produce multi-channel probability images. Each channel represents a class of cell type and the probability of being grouped to a class is represented by pixel values. The probability image is further processed in order to determine the locations of cells and to note class labels.
In total, 312 cases of breast cancer were gathered to comprise the benchmark and clinical feasibility validation set. Cases were selected in order to represent factors beyond subtypes, including score, specimen, and grade.
Breast cancer data distribution
“Leveraging the standardization and reproducibility of Roche's end-to-end solution from assay staining, image scanning, and uPath Enterprise pathology platform, these algorithms suggest that the untapped potential of elevating lab-to-lab and reader-to-reader reproducibility of clinical scoring at an expert-level is achievable,” the investigators concluded.
Kapadia M, Khojasteh M, Kouzova M, et al. Artificial intelligence-based whole slide scoring of nuclear breast cancer IHC markers Ki67, ER, and PR matches performance of manual clinical scoring. Poster presented at San Antonio Breast Cancer Symposium; December 7-10, 2021; Virtual. Poster P1-02-17.
Experts Discuss Differences in Radiotherapy Outcomes in BRCA+ Breast Cancer
October 30th 2023Rebecca M. Shulman, MD, and Zachary Kiss, DO, discuss findings from a study evaluating differences in outcomes with radiotherapy and disease characteristics of patients with breast cancer harboring BRCA mutations compared with those without mutated disease.
Profiling Tests May Predict Chemo Efficacy in Early-Stage Breast Cancer
December 8th 2023Future research may focus on the relationship between neoadjuvant chemotherapy regimens, pathologic complete response, and whole transcriptome changes in patients with hormone receptor–positive, HER2-negative early-stage breast cancer.
Oncology On-The-Go Podcast: ASCO 2023 Recap
June 19th 2023Experts from University of California, Los Angeles Health and Mayo Clinic discuss key data presented at the 2023 American Society of Clinical Oncology (ASCO) Annual Meeting in the gynecologic and gastrointestinal cancer spaces and how they may impact patient care.
Dato-DXd Improves PFS Vs Chemo in HR+/HER2– Metastatic Breast Cancer
December 8th 2023Data from the phase 3 TROPION-Breast01 trial support datopotamab deruxtecan as a potential treatment option for patients with endocrine-resistant, hormone receptor–positive, metastatic breast cancer, says Aditya Bardia, MD, MPH.