Breast cancer intelligent analysis of histopathological data: A systematic review

Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Breast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of these images is tedious, subjective, error-prone and requires domain knowledge. To this end, computer-aided tools are gaining much attention in the recent past as it aids pathologists and save time. Furthermore, advances in computational power have leveraged the usage of computer tools. Yet, usage of computer-aided tools to analyse these images is challenging due to various reasons such as heterogeneity of malignant tumours, colour variations and presence of artefacts. Moreover, these images are captured at high resolutions which pose a major challenge to designing deep learning models as it demands high computational requirements. In this context, the present work proposes a new approach to efficiently and effectively extract features from these high-resolution images. In addition, at 400x magnification, the characteristics and structure of nuclei play a prominent role in the decision of malignancy. In this regard, the study introduces a novel CNN architecture called as CWA-Net that uses a colour channel attention module to enhance the features of the potential regions of interest such as nuclei. The developed model is qualitatively and quantitatively evaluated on private and public datasets and achieved an accuracy of 0.95% and 0.96%, respectively. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art methods on both datasets.