Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

Background: Pleural effusion can be caused by a wide range of benign and malignant conditions. Pleural biopsy and effusion cytology represent two key methods of pathological diagnosis. To compare the performance these two methods, a large cohort of matched pleural biopsy and effusion cytology with clinical follow‐up was reviewed.Methods: Pleural biopsies and effusion cytology specimens over a period of 18 years were retrieved. Cytology specimens collected within 7 days of pleural biopsy were matched. Reports were reviewed, and the cause for pleural effusion was determined by hospital disease coding and clinical data.Results: Totally, 3026 cases were included. The leading cause of benign effusion was tuberculosis (n = 650). Malignant pleural effusion (MPE) was more common in older females (p < 0.001) and mostly due to lung cancer (n = 959), breast cancer (n = 64), and mesothelioma (n = 48). The inadequate/insufficient (B1/C1) rate of biopsy was higher than cytology (15.6% vs. 0.3%) but the rates for other diagnostic categories were similar. Biopsy and cytology showed a correlation coefficient of 0.315, improving to 0.449 when inadequate/insufficient (B1/C1) cases were excluded. The ROM for benign cytology (C2) was lower than biopsy (B2) (p < 0.001). Compared with biopsy, the diagnostic accuracy was higher in cytology overall and for metastatic carcinomas (p < 0.001) but lower for hematolymphoid malignancies (p = 0.014) and mesotheliomas (p = 0.002).Conclusions: These results suggest that effusion cytology may be better for confirming benignity and diagnosing carcinomatous MPE. In these cases, pleural biopsy may be withheld to reduce procedural risks. However, for suspected hematolymphoid malignancies and mesothelioma, biopsy should be considered.