Cancer Data
Early-stage cervical cancer is characterized by morphological changes in cells, which are effectively identified through biopsy techniques with high diagnostic accuracy. However, biopsies can be expensive and occasionally painful, with diagnosis reports often taking days or weeks to generate. These delays and costs create significant barriers for underprivileged women with limited access to timely and affordable healthcare. Our Cervi- ImagingDiag framework and app provide a painless, cost-efficient, and accessible solution, delivering diagnostic reports within seconds.
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Brain tumors are among the most severe and life-threatening conditions affecting both children and adults. They constitute approximately 85-90% of all primary Central Nervous System (CNS) tumors, with an estimated 11,700 new cases diagnosed annually. The 5-year survival rate for individuals with malignant brain or CNS tumors is alarmingly low, at 34% for men and 36% for women. Brain tumors are categorized into various types, including benign, malignant, and pituitary tumors.
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The table contains the standard symbols for genes, with each symbol corresponding to a specific gene. The GENE_title column provides the full names of the genes. The subsequent numerical values represent experimental data related to the genes, with each column indicating the gene expression levels (e.g., FPKM values from RNA-Seq data) under different experimental conditions, samples, or states. The magnitude of these values reflects variations in gene expression across the corresponding samples or conditions.
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As a retrospective study, 586 NSCLC patients are included, where patients have received 18F-FDG PET/CT one month prior to treatment at the First Hospital of Chongqing Medical University from January 2015 to December 2020. The retrospective study is approved by the Ethics Committee of the Centre and waives the requirement of informed consent. CT/PET scanning can be an important tool for preoperative diagnosis, treatment guidance and efficacy assessment in lung cancer patients.
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Biopsy information and protein Prostate-Specific Antigen (PSA) levels are the most robust information available to oncologists worldwide to diagnose and decide therapies for prostate cancer patients. However, prostate cancer presents a high risk of recurrence, and the technologies used to evaluate it demand more complex resources. This paper aims to predict Biochemical Recurrence (BCR) based on Whole Slide Images (WSI) of biopsies, Gleason scores, and PSA levels.
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The dataset contains 560 different observations each having 1049 absorption data points for cancerous and non-cancerous skin cells. The reflection absorption data were obtained from terahertz metamaterials on top of which the cells are placed. The 560 observations made were for varying size tissue thickness and polarization and incident wave angle
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Studies indicate the occurrence and development of lung adenocarcinoma (LUAD) is regulated by ferroptosis and long non-coding RNA (lncRNA). While the role of ferroptosis-related lncRNA signature on the prognosis of LUAD is unclear. This study aimed to identify ferroptosis-related lncRNA signature for predicting the prognosis of LUAD. RNA expression profile and clinical data of LUAD patients were downloaded from public databases. The cox regression model was used to construct a multi-lncRNA signature.
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CT RECIST response, as measured by the change of tumor diameter, can accurately reflect objective response rate for advanced NSCLC patients. However, there exists obvious discordant between CT RECIST response and prognostic indicators. Thus, our study aimed to identify a new CT RECIST response indicator at the early treatment stage to reflect the prognosis more accurately.We studied 916 tumor lesions obtained through deep learning and found that the shape of the lesions was irregular.
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The prognostic survival dataset, Pancreatic Cancer Survival based on Preoperative Features (PCSPF), was constructed to explore the impact of key preoperative features on prognosis based on the follow-up data of patients with pancreatic cancer at Changhai Hospital, Shanghai, China.
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The data included here within is the associated model training results from the correlated paper "Distribution-Driven Augmentation of Real-World Datasets for Improved Cancer Diagnostics With Machine Learning". This paper focuses on using kernel density estimators to curate datasets by balancing classes and filling missing null values though synthetically generated data. Additionally, this manuscript proposes a technique for joining distinct datasets to train a model with necessary features from multiple different datasets as a type of transfer-learning.
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