Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images

Role of artificial intelligence in digital pathology for gynecological cancers

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

Deep Learning for Grading Endometrial Cancer

Endometrial cancer is the fourth most common cancer in women in the United States, with a lifetime risk of approximately 2.8%. Precise histologic evaluation and molecular classification of endometrial cancer are important for effective patient management and determining the best treatment options. This study introduces EndoNet, which uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides into high- and low-grade cases. The model was trained on 929 digitized hematoxylin and eosin-stained whole-slide images of endometrial cancer from hysterectomy cases at Dartmouth-Health. It classifies these slides into low-grade (endometrioid grades 1 and 2) and high-grade (endometrioid carcinoma International Federation of Gynecology and Obstetrics grade 3, uterine serous carcinoma, or carcinosarcoma) categories. EndoNet was evaluated on an internal test set of 110 patients and an external test set of 100 patients from The Cancer Genome Atlas database. The model achieved a weighted average F1 score of 0.91 (95% CI, 0.86 to 0.95) and an area under the curve of 0.95 (95% CI, 0.89 to 0.99) on the internal test, and 0.86 (95% CI, 0.80 to 0.94) for F1 score and 0.86 (95% CI, 0.75 to 0.93) for area under the curve on the external test. Pending further validation, EndoNet has the potential to support pathologists without the need of manual annotations in classifying the grades of gynecologic pathology tumors.

Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.

Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer

Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directions

Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorithms can accurately label them as cancerous or non-cancerous. Although many studies have investigated the application of deep learning for diagnosing various diseases, there is a lack of research focusing on the evolution, limitations, and gaps of intelligent algorithms in conjunction with WSI for cervical cancer. This paper provides a comprehensive overview of the state-of-the-art deep learning algorithms used for the timely and precise analysis of cervical WSI images. A total of 115 relevant papers were reviewed, and 37 were selected after screening with specific inclusion and exclusion criteria. Methodological aspects including deep learning techniques, data sources, architectures, and classification techniques employed by the selected studies were analyzed. The review presents the most popular techniques and current trends in deep learning-based cervical classification systems, and categorizes the evolution of the domain based on deep learning techniques, citing an in-depth analysis of various models developed over time. The paper advocates for the implementation of transfer supervised learning when utilizing deep learning models such as ResNet, VGG19, and EfficientNet, and builds a solid foundation for applying relevant techniques in different fields. Although some progress has been made in developing novel models for the diagnosis of cervical cancer, substantial work remains to be done in creating standardized benchmark databases of WSI images for the research community. This paper serves as a comprehensive guide for understanding the fundamental concepts, benefits, and challenges related to various deep learning models on WSI, including their application for cervical system classification. Additionally, it provides valuable insights into future research directions in this area.

Investigating novel biomarkers in uterine corpus endometrial carcinoma: in silico analysis and clinical specimens validation via RT-qPCR and immunohistochemistry

The rising incidence and mortality rate of Uterine Corpus Endometrial Carcinoma (UCEC) pose significant health concerns. CC and CXC chemokines have been linked to tumorigenesis and cancer progression. Recognizing the growing significance of CC and CXC chemokines' diagnostic and prognostic significance in diverse cancer types, our objective was to comprehensively analyze the diagnostic and prognostic values of hub genes from the CC and CXC chemokines in UCEC, utilizing both in silico and clinical samples and cell lines-based approaches. In silico analyses include STRING, Cytoscape, Cytohubba, The Cancer Genome Atlas (TCGA) datasets analysis via the UALCAN, GEPIA, OncoDB, and MuTarget, SurvivalGenie, MEXPRESS, cBioPoratal, TIMER, ENCORI, and DrugBank. Meanwhile, clinical samples and cell lines based analyses include Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), targeted bisulfite sequencing (bisulfite-seq) analysis, and immunohistochemistry (IHC). Through present study, we identified CCL25 (CC motif chemokine ligand 25), CXCL10 (C-X-C motif chemokine ligand 10), CXCL12 (C-X-C motif chemokine ligand 12), and CXCL16 (C-X-C motif chemokine ligand 16) as crucial hub genes among the CC and CXC chemokines. Analyzing the expression data from TCGA, we observed a significant up-regulation of CCL25, CXCL10, and CXCL16 in UCEC samples compared to controls. In contrast, we noted a significant down-regulation of CXCL12 expression in UCEC samples. On clinical UCEC samples and cell lines analysis, the significant higher expression of CCL25, CXCL10, and CXCL16 and significant lower expression of CXCL12 were also denoted in UCEC samples than the controls via RT-qPCR and IHC analyses. Moreover, in silico analysis also confirmed the abnormal promoter methylation levels of the hub genes in TCGA UCEC samples, which was later validated by the clinical samples using targeted based bisulfite-seq analysis. In addition, various additional aspects of the CCL25, CXCL10, CXCL12, and CXCL16 have also been uncovered in UCEC during the present study. Our findings offer novel insights that contribute to the clinical utility of CCL25, CXCL10, CXCL12, and CXCL16 chemokines as potential diagnostic and prognostic biomarkers in UCEC.

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with "malignant" and "other or benign" areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign". Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either "malignant", "other or benign" or "insufficient". The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists' workload.