In this research, we have two serum SELDI (surface-enhanced laser desorption and ionization) mass spectra (MS) datasets to be used to select features amongst them to identify proteomic cancerous serums from normal serums. Features selection techniques have been applied and classification techniques have been applied as well. Amongst the features selection techniques we have chosen to evaluate the performance of PCA (Principal Component Analysis ) and GA (Genetic algorithm), and amongst the classification techniques we have chosen the LDA (Linear Discriminant Analysis) and Neural networks so as to evaluate the ability of the selected features in identifying the cancerous patterns. Results were obtained for two combinations of features selection techniques and classification techniques, the first one was PCA+(t-test) technique for features selection and LDA for accuracy tracking yielded an accuracy of 93.0233 % , the other one was genetic algorithm and neural network yielded an accuracy of 100%. So, we conclude that GA is more efficient for features selection and hence for cancerous patterns detection than PCA technique.
Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine（纽约大学格罗斯曼医学院维尔切克生物医学科学研究生院）；NYU Langone Health System（纽约大学朗格尼医疗系统）；Merck & Co., Inc（默克公司）；New York University（纽约大学）
The prognosis for patients with epithelial ovarian cancer remains dismal despite improvements in survival for other cancers. Treatment involves multiple lines of chemotherapy and becomes increasingly heterogeneous after first-line therapy. Reinforcement learning with real-world outcomes data has the potential to identify novel treatment strategies to improve overall survival. We design a reinforcement learning environment to model epithelial ovarian cancer treatment trajectories and use model free reinforcement learning to investigate therapeutic regimens for simulated patients.
Accurate and efficient detection of ovarian cancer at early stages is critical to ensure proper treatments for patients. Among the first-line modalities investigated in studies of early diagnosis are features distilled from protein mass spectra. This method, however, considers only a specific subset of spectral responses and ignores the interplay among protein expression levels, which can also contain diagnostic information. We propose a new modality that automatically searches protein mass spectra for discriminatory features by considering the self-similar nature of the spectra. Self-similari…
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Agency（不列颠哥伦比亚省癌症局）；Deeley Research Centre（迪利研究中心）
In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells. In this paper, we propose a self-supervised framework (VOLTA) for cell representation learning in histopathology images using a novel technique that accounts for the cell's mutual relationship with its environment for improved cell representations. We subjected our model to extensive experiments on the data collected from multiple institutions ar…
Khulna University of Engineering & Technology（库尔纳工程技术大学）
Ovarian Cancer (OC) is type of female reproductive malignancy which can be found among young girls and mostly the women in their fertile or reproductive. There are few number of cysts are dangerous and may it cause cancer. So, it is very important to predict and it can be from different types of screening are used for this detection using Transvaginal Ultrasonography (TVUS) screening. In this research, we employed an actual datasets called PLCO with TVUS screening and three machine learning (ML) techniques, respectively Random Forest KNN, and XGBoost within three target variables. We obtained …
Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images
Faculty of Science, University of British Columbia（不列颠哥伦比亚大学理学院）；School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Department of Pathology and Laboratory Medicine, University of British Columbia（不列颠哥伦比亚大学病理学与实验医学系）；Canada’s Michael Smith Genome Sciences Centre, BC Cancer Research Institute（加拿大迈克尔·史密斯基因组科学中心，BC癌症研究所）
In this paper we apply computer learning methods to diagnosing ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass-spectrometry. We are working with a new data set collected over a period of 7 years. Using the level of CA125 and mass-spectrometry peaks, our algorithm gives probability predictions for the disease. To estimate classification accuracy we convert probability predictions into strict predictions. Our algorithm makes fewer errors than almost any linear combination of the CA125 level and one peak's intensity (taken on the log…
Tianjin University（天津大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Shandong University（山东大学）
Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with the development of medical imaging technology, survival prediction approaches based on pathological images have been proposed. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on patho…
Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning
Department of Mathematics, University of Texas at Arlington（德克萨斯大学阿灵顿分校数学系）；Department of Data Science and Mathematics, Embry-Riddle Aeronautical University（埃姆伯里-瑞德航空大学数据科学与数学系）
Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance
School of Health and Life Sciences, Teesside University（提赛德大学健康与生命科学学院）；School of Computing, Engineering and Digital Technologies, Teesside University（提赛德大学计算、工程与数字技术学院）
Optimizing Sparse Generalized Singular Vectors for Feature Selection in Proximal Support Vector Machines with Application to Breast and Ovarian Cancer Detection
Tufts University（塔夫茨大学）；Colorado State University（科罗拉多州立大学）
Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家卫生研究院）；ICube（ICube研究中心）；UMR7357；Fondazione Policlinico Universitario A. Gemelli IRCCS（A. Gemelli IRCCS大学医院基金会）；Università Cattolica del Sacro Cuore（圣心大学）；IRCAD（消化道癌症研究研究所）；Research Institute against Digestive Cancer（消化道癌症研究研究所）；Institute of Image-Guided Surgery（影像引导手术研究所）；IHU Strasbourg（斯特拉斯堡IHU）
针对医学图像中密集标注困难的问题，提出从稀疏点标签学习关键点定位，通过Crag and Tail损失函数有效利用正标签并减少假阴性影响，实现晚期卵巢癌腹腔镜视频中癌病关键点的准确密集定位。
Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer
AIRLab, Politecnico di Milano（米兰理工学院AIR实验室）；Politecnico di Milano（米兰理工学院）；NEARLab, Politecnico di Milano（米兰理工学院NEAR实验室）；Istituto Europeo di Oncologia（欧洲肿瘤研究所）；Università degli Studi dell’Insubria（意大利北部大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Cancer is responsible for millions of deaths worldwide every year. Although significant progress has been achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy. Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, as cancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In this study, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic health records (EHRs) and genetic test reports for a collection of cancer patients, we developed a system…
Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in signigicant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked use an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield…
The objectives of this "perspective" paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented, and is applied to predict the time to tumor recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open proble…
Gwangju Institute of Science and Technology（光州科学技术院）
Personalized medicine is expected to maximize the intended drug effects and minimize side effects by treating patients based on their genetic profiles. Thus, it is important to generate drugs based on the genetic profiles of diseases, especially in anticancer drug discovery. However, this is challenging because the vast chemical space and variations in cancer properties require a huge time resource to search for proper molecules. Therefore, an efficient and fast search method considering genetic profiles is required for de novo molecular design of anticancer drugs. Here, we propose a faster mo…
Cancer is responsible for millions of deaths worldwide every year. Although significant progress hasbeen achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy.Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, ascancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In thisstudy, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic healthrecords (EHRs) and genetic test reports for a collection of cancer patients, we evaluated variousgraph …
Diagnosis of breast cancer has been well studied in the past. Multiple linear programming models have been devised to approximate the relationship between cell features and tumour malignancy. However, these models are less capable in handling non-linear correlations. Neural networks instead are powerful in processing complex non-linear correlations. It is thus certainly beneficial to approach this cancer diagnosis problem with a model based on neural network. Particularly, introducing bias to neural network training process is deemed as an important means to increase training efficiency. Out o…
Dana Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard Medical School（哈佛医学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）；ETH Zurich（苏黎世联邦理工学院）；SIB Swiss Institute of Bioinformatics（瑞士生物信息学研究所）
Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find t…
Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Universitat Politècnica de Catalunya - BarcelonaTech（加泰罗尼亚理工大学-巴塞罗那理工）
Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development. In this paper, we explore a recently released dataset of chromosome rearrangements in 2,586 cancer patients, where different sorts of alterations have been detected. Using a Random Forest classifier, we evaluate the relevance of several features (some directly available in the original data, some engineered by us) related to chromosome rearrangements. This evaluation results in a set of potential tumour genetic markers, some of which are validated in the bibliography,…
Osaka University（大阪大学）；University of Alberta（阿尔伯塔大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven the process of subtyping away from teaching signals. Moreover, cancer genetic expression profiles are high-dimensional, scarce, and have complicated dependence, thereby posing a serious challenge to existing subtyping models for outputting sensible clustering. In this study, we propose a novel clustering method for exploiting genetic expression profiles and distinguishing subtypes in an unsupervised manner. The propo…
Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of severa…
University of Science and Technology of China（中国科学技术大学）；The Fourth Affiliated Hospital of Anhui Medical University（安徽医科大学第四附属医院）；Genmab Inc.（Genmab公司）
Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainstream strategy for predicting genetic alterations using WSIs although bag-based models along with self-attention mechanism-based algorithms have been proposed for other digital pathology applications. In this paper, we proposed a novel Attention-based Multiple Instance Mutation Learning (AMIML) model for predicting gene mutations. AMIML was comprised of successive 1-D convolutional layers, a decoder, and a residual we…
