Metastasis detection from whole slide images using local features and random forests

Abstract

Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model. The features extracted from the whole slide images include several local descriptors related to image texture, spatial structure, and distribution of nuclei. The method was evaluated in breast cancer metastasis detection from lymph node samples. Our results show that the method detects metastatic areas with high accuracy (AUC = 0.97-0.98 for tumor detection within whole image area, AUC = 0.84-0.91 for tumor vs. normal tissue detection) and that the method generalizes well for images from more than one laboratory. Further, the method outputs an interpretable classification model, enabling the linking of individual features to differences between tissue types. © 2017 International Society for Advancement of Cytometry.

Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning

“…Here we show for the first time that deep convolutional neural networks can identify NPC, a cancer with little differentiation and many admixed inflammatory cells. Compared to previous studies identifying metastatic breast cancer in lymph nodes [10,[12][13][14]16,19], identification of NPC with AI is certainly a more difficult task. The tumor cells of NPC are mostly undifferentiated or poorly differentiated, resulting in more morphologic similarities to germinal center cells.…”

“…To compare the ROC curves of our original and final patch-level models, we repeated the testing inference process 30 times. In each testing run, the two models predicted the same randomly taken 16,000 testing patches. The mean and variance of AUC of the two models were calculated.…”

“…With tremendous success in recognition of natural images, convolutional neural networks have been quickly adopted for medical image analysis [7]. Therefore, analysis of pathology images using artificial intelligence (AI) is becoming more feasible recently [8][9][10][11][12][13][14][15][16][17][18][19]. Of note, a few articles described the success of AI in identifying metastatic breast cancer in lymph nodes [10,[12][13][14]16,19].…”

Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients

“…For example, pixel-wise detection of cytological features such as epithelial cell nuclei, epithelial cell cytoplasm, and the lumen were used for the higher-level tasks of gland segmentation and prediction of tumor grade on the Gleason grading scheme in prostate cancer [ 20 , 21 ]. Another study showed that local descriptors such as the distribution of cell nuclei was one of the most significant features used by a random forest model to detect metastasis from digital pathology images [ 22 ].Therefore, we investigated if we could further improve the performance of baseline CNN models by providing multiple segmentation channels of the input images with pixel-wise histological annotations of such features. Each of these segmentation channels can be extracted by U-net, a CNN model designed for semantic segmentation of biomedical images [ 23 ], which can then be integrated onto the original images depth-wise prior to input into the baseline model.…”

Double Transfer Learning for Breast Cancer Histopathologic Image Classification

“…Peikari et al [13] used a cascade of SVMs for breast cancer classification. Valkonen et al [14] segmented whole slide images (WSI) of breast tissue using a Random Forest classifier. Balazsi et al [15] also studied the segmentation of WSI using a Random Forest and tessellation.…”