Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer

CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation

A benchmark histopathological Hematoxylin and Eosin (H&E) image dataset for Cervical Adenocarcinoma in Situ (CAISHI), containing 2240 histopathological images of Cervical Adenocarcinoma in Situ (AIS), is established to fill the current data gap, of which 1010 are images of normal cervical glands and another 1230 are images of cervical AIS. The sampling method is endoscope biopsy. Pathological sections are obtained by H&E staining from Shengjing Hospital, China Medical University. These images have a magnification of 100 and are captured by the Axio Scope. A1 microscope. The size of the image is 3840 × 2160 pixels, and the format is ".png". The collection of CAISHI is subject to an ethical review by China Medical University with approval number 2022PS841K. These images are analyzed at multiple levels, including classification tasks and image retrieval tasks. A variety of computer vision and machine learning methods are used to evaluate the performance of the data. For classification tasks, a variety of classical machine learning classifiers such as k-means, support vector machines (SVM), and random forests (RF), as well as convolutional neural network classifiers such as Residual Network 50 (ResNet50), Vision Transformer (ViT), Inception version 3 (Inception-V3), and Visual Geometry Group Network 16 (VGG-16), are used. In addition, the Siamese network is used to evaluate few-shot learning tasks. In terms of image retrieval functions, color features, texture features, and deep learning features are extracted, and their performances are tested. CAISHI can help with the early diagnosis and screening of cervical cancer. Researchers can use this dataset to develop new computer-aided diagnostic tools that could improve the accuracy and efficiency of cervical cancer screening and advance the development of automated diagnostic algorithms.

Transferable and Interpretable Treatment Effectiveness Prediction for Ovarian Cancer via Multimodal Deep Learning

Ovarian cancer, a potentially life-threatening disease, is often difficult to treat. There is a critical need for innovations that can assist in improved therapy selection. Although deep learning models are showing promising results, they are employed as a "black-box" and require enormous amounts of data. Therefore, we explore the transferable and interpretable prediction of treatment effectiveness for ovarian cancer patients. Unlike existing works focusing on histopathology images, we propose a multimodal deep learning framework which takes into account not only large histopathology images, but also clinical variables to increase the scope of the data. The results demonstrate that the proposed models achieve high prediction accuracy and interpretability, and can also be transferred to other cancer datasets without significant loss of performance.

Vascular normalization: reshaping the tumor microenvironment and augmenting antitumor immunity for ovarian cancer

Ovarian cancer remains a challenging disease with limited treatment options and poor prognosis. The tumor microenvironment (TME) plays a crucial role in tumor growth, progression, and therapy response. One characteristic feature of the TME is the abnormal tumor vasculature, which is associated with inadequate blood perfusion, hypoxia, and immune evasion. Vascular normalization, a therapeutic strategy aiming to rectify the abnormal tumor vasculature, has emerged as a promising approach to reshape the TME, enhance antitumor immunity, and synergize with immunotherapy in ovarian cancer. This review paper provides a comprehensive overview of vascular normalization and its potential implications in ovarian cancer. In this review, we summarize the intricate interplay between anti-angiogenesis and immune modulation, as well as ICI combined with anti-angiogenesis therapy in ovarian cancer. The compelling evidence discussed in this review contributes to the growing body of knowledge supporting the utilization of combination therapy as a promising treatment paradigm for ovarian cancer, paving the way for further clinical development and optimization of this therapeutic approach.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains.