Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks

• In early-stage breast cancer, the Nottingham Histologic Grading (NHG) is a strong prognostic factor. It is made up of nuclear pleomorphism, tubular formation, and mitotic count evaluation. Major grade disagreement is low (1.5%), but inter-observer agreement in grading among pathologists is moderate. Grading errors or inconsistencies caused by a variety of factors may jeopardize patient care and overall survival. It has been demonstrated that the assessment of the NHG is comparable to light microscopy and Whole Slide Images (WSI), which are digitized images of histopathologic slides. Because AI-based breast cancer grading is a new area of pathology, there are inherent difficulties in training AI models. We mitigate the high computational cost associated with the dimensions of WSIs by using a patch-based approach, and we mitigate the problems associated with the availability of training data by carefully annotating and labeling these patches. This chapter describes a fully automated computer-aided patch-based system that employs deep learning (DL) methods. Nuclear pleomorphism, tubular formation, and mitotic count are all graded using the proposed method. In addition, to train and test the DL methods in the proposed approach, we created an in-house individual dataset for pleomorphism, tubule detection, nuclei, and mitosis detection, which consists of 23.283, 10.117, 2.993, and 9.816 annotated patches extracted from WSIs of breast tissue with varying hematoxylin and eosin stains, respectively. These WSIs were obtained from a variety of patients who had been diagnosed with invasive ductal carcinoma. Four different difficult tasks are solved using the proposed computer-aided DL patch-based system. Semantic segmentation is used for tubular formation, object detection is used for nuclei detection, and image classification is used for mitotic count and nuclear pleomorphism. To obtain the results, we fine-tuned pre-trained (on ImageNet) DL architectures such as EfficientNet backbone U-Net, Scaled-Yolov4, DenseNet-161, and VGG-11 with our dataset for tubule segmentation, nuclei detection, and mitosis and nuclear pleomorphism classification tasks. We demonstrate that data augmentation is critical for improving the accuracy of patch-based DL models, which serve as the foundation of our WSI grading system. The proposed method resulted in reproducible histologic scores with F1- values of 94%, 94.1%, and 50.7% for nuclear pleomorphism classification, tubule formation segmentation, and mitotic classification, respectively. The results of the experiments presented in this chapter show promise for clinical translation of the DL algorithms described. Using the proposed approach to perform histological grading of WSIs will reduce the subjectivity associated with pathologist-assigned grades. © 2023 Elsevier Inc. All rights reserved.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

AI

breast cancer

classification

Deep learning

detection

histologic grade

pathology

segmentation

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