Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,144
datasets available to search
ShareScore release 0.9.0
Dataset results
6,144 results for “cancer treatment”
A Study of Pertuzumab With High-Dose Trastuzumab for the Treatment of Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Metastatic Breast Cancer (MBC) With Central Nervous System (CNS) Progress
ClinicalTrials.gov study NCT02536339. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Oxaliplatin and Cetuximab in First-line Treatment of Metastatic Colorectal Cancer (mCRC)
ClinicalTrials.gov study NCT00125034. IPD Sharing: Not stated. Countries: 13. Publications: 2.
The LYMPH Trial - Microsurgical Versus Conservative Treatment of Chronic Breast Cancer Associated Lymphedema
ClinicalTrials.gov study NCT05890677. IPD Sharing: YES. Countries: 15. Publications: 1.
Evaluation of a New Vaccine Treatment for Patients With Metastatic Skin Cancer
ClinicalTrials.gov study NCT01149343. IPD Sharing: YES. Countries: 6. Publications: 1.
ESK981 and Nivolumab for the Treatment of Metastatic Castration Resistant Prostate Cancer
ClinicalTrials.gov study NCT04159896. IPD Sharing: NO. Countries: 1. Publications: 1.
Brief Behavioral Treatment for Insomnia in Cancer Survivors
ClinicalTrials.gov study NCT03810365. IPD Sharing: NO. Countries: 1. Publications: 5.
Phase 2 Study of Gemzar, Taxol & Avastin Combination as 1st Line Treatment for Metastatic Breast Cancer
ClinicalTrials.gov study NCT00403130. IPD Sharing: NO. Countries: 1. Publications: 0.
HINC dataset from: Homeopathic treatment as an add-on therapy may improve quality of life and prolong survival in patients with non-small cell lung cancer: A prospective, randomized, placebo-controlled, double-blind, three-arm, multicenter study
Open the record for dataset details and reuse information.
Exploratory mass cytometry analysis reveals immunophenotypes of cancer treatment-related pneumonitis
Open the record for dataset details and reuse information.
Synergistic anticancer activity of resveratrol-loaded polymeric nanoparticles and sunitinib in colorectal cancer treatment
Open the record for dataset details and reuse information.
Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer
Open the record for dataset details and reuse information.
Reshaping the landscape of locoregional treatments for breast cancer liver metastases: A novel, intratumoral, p21-targeted percutaneous therapy increases survival in BALB/c mice inoculated with 4T1 triple negative breast cancer cells in the liver
Open the record for dataset details and reuse information.
Half-Dose Fulvestrant plus Anastrozole as a First-Line Treatment for Hormone Receptor-Positive Metastatic Breast Cancer: A Cost-Effectiveness Analysis
Abstract Objective The S0226 trial demonstrated that the combination of half-dose fulvestrant (FUL) and anastrozole (ANA) (F&A) caused a significant improvement in overall survival (OS) versus ANA monotherapy for first-line treatment of postmenopausal women with hormone receptor-positive metastatic breast cancer (PMW-MBC[HR+]). The objective of this study was to evaluate the cost-effectiveness of F&A in the first-line treatment for PMW-MBC(HR+) in China. Design We constructed a Markov model over a life-time horizon. The clinical outcomes and utility data were obtained from published literature. Cost data were obtained from official Chinese websites. Sensitivity analyses were performed to test result uncertainty. Setting Chinese health care system perspective. Population A hypothetical cohort of adult patients presenting with PMW-MBC(HR+). Interventions F&A compared with full-dose FUL and ANA monotherapy. Main outcome measures The main outcome of this study was the incremental cost-effectiveness ratio (ICER) and quality-adjusted life-years (QALY). Results ANA was estimated to have the lowest cost and minimum life years (LYs). The ICER of F&A versus ANA was $15,665.891/QALY with incremental cost and QALY of $12,401.120 and 0.792, respectively, which was less than the willingness-to-pay (WTP) of $29,383/QALY. Compared with F&A, FUL yielded a higher cost and a shorter lifetime; hence, it was identified as a dominated strategy. The univariate sensitivity analysis indicated the price of FUL was the most influential factor in our study. The probability that F&A was cost-effective at a threshold of $29,383/QALY in China was 86.5%. Conclusion F&A is a cost-effective alternative to FUL and ANA monotherapy for the first-line treatment of PMW-MBC(HR+) in China. F&A is a promising first-line treatment for PMW-MBC(HR+), and more research is needed to evaluate the economy of using F&A in other countries.
Cost-effectiveness analysis of cetuximab combined with chemotherapy as a first-line treatment for RAS wild-type metastatic colorectal cancer patients based on the TAILOR trial
<p><b>Objectives</b> Cetuximab plus leucovorin, fluorouracil, and oxaliplatin (FOLFOX-4) is superior to FOLFOX-4 alone as a first-line treatment for patients with RAS wild-type metastatic colorectal cancer (wt mCRC), with significantly improved survival benefit by TAILOR, an open-label, randomized, multicentre, phase III trial. Nevertheless, the cost-effectiveness of these two regimens remains uncertain. The following study aims to determine whether cetuximab combined with FOLFOX-4 is a cost-effective strategy for specific RAS wt mCRC patients in China.</p> <p><b>Design</b> A combined decision tree and Markov model with three health states (stable, progressive and dead) was constructed to simulate a hypothetical cohort of patients with RAS wt mCRC. The health outcomes and utility scores were derived from the TAILOR trial and previously published sources, respectively. Costs were calculated with reference to the Chinese societal perspective. A lifetime horizon was used. Univariate and probabilistic sensitivity analyses were carried out to test the robustness of the model results.</p> <p><b>Participants</b> The included patients were newly diagnosed Chinese patients with fully RAS wt mCRC. <b>Interventions</b> Either cetuximab plus FOLFOX-4 or FOLFOX-4 alone as a first-line treatment.</p> <p><b>Main outcome measures</b> The primary outcomes are costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs).</p> <p><b>Results</b> Baseline analysis showed that the addition of cetuximab increased the QALYs by 0.383, while an increase of $62,947 was observed in relation to FOLFOX-4 chemotherapy. This led to an incremental cost-effectiveness ratio (ICER) of $164,044/QALY. Sensitivity analysis showed that across the wide variation in parameters, the ICER exceeded the willingness-to-pay threshold of $28,106/QALY, which was three times the per capita GDP in China.</p> <p><b>Conclusions</b> Despite the survival benefit, cetuximab combined with FOLFOX-4 is not a cost-effective treatment for the first line treatment of patients with RAS wt mCRC in China.</p>
Data from: Outcome of breast cancer in Moroccan young women correlated to clinic-pathological features, risk factors and treatment: a comparative study of 716 cases in a single institution
Background: Breast cancer in young women is quite uncommon and shows more aggressive characteristics with major disparities between worldwide populations. Prognosis and outcome of breast cancer in young patients are widely studied, but still no consensus is available. Methods: We retrospectively included 716 cases of breast cancer women diagnosed in 2009 at the National Institute of Oncology of Rabat. Patients were divided into two groups according to their age: women aged ≤40 years (Group 1) and women aged >40 years (Group 2). Data were recorded from patients' medical files and analyzed using SPSS 13.0 software (IBM). Results: Young patients represent 24.9% of all patients with breast cancer. The comparison between the two groups displayed significant differences regarding nulliparity (p = 0.001) and progesterone receptor negativity (p = 0.01). Moreover, more progression (Metastases/Relapse) was registered in young women as compared to older women with breast cancer (p = 0.03). The estimated median follow-up period was 31 months. The 5-years Event-Free Survival (EFS) of patients with local disease was 64.6% in young women and 71.5% in older women with breast cancer (p = 0.04). Multivariate analysis in young women showed that nulliparity (HR: 7.2; 95%CI: 1.16–44.54; p = 0.03), T3 tumors (HR: 17.39; 95%CI: 1.74–173.34; p = 0.01) and negative PgR status (HR: 19.85; 95%CI: 1.07–366.54; p = 0.04) can be considered as risk factors for poorer event free survival while hormone therapy was associated with better EFS (HR: 0.11; 95%CI: 0.00–0.75; p = 0.03). In Group 2, multivariate analysis showed that patients with inflammatory breast cancer, N+ status, absence of radiotherapy, absence of chemotherapy, and absence of hormone therapy are at increased risk of recurrence. Conclusions: In Morocco, breast cancer is more frequent in young women as compared to western countries. Breast cancer in young women is more aggressive and is diagnosed late, leading to an intensive treatment. Moreover, the main factors associated with breast cancer development in young women would be hormonal and reproductive status. Analysis of other genetic biomarkers is needed to explain the high prevalence of breast cancer in young women to improve breast cancer management in Morocco
Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
Objective: A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient's features. Method: We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the Lynn Sage Data Set (LSDS). We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis. Results: In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788). Discussion: Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations.
Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
Flowchart of the manuscript "Total Mesorectal Excision after Rectal Sparing Approach in Locally Advanced Rectal Cancer Patients after neoadjuvant treatment: a high volume center experience"
<p>I uploade the original figure of the manuscript "Total Mesorectal Excision after Rectal Sparing Approach in<br>Locally Advanced Rectal Cancer Patients after neoadjuvant treatment: a high volume center experience."</p>
Decorated ROS-generating CeO2 nanoparticles for cancer treatment
<p>NFFA proposal ID677</p>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.