Case no. 1: Risk Stratification: The Breast Cancer Risk Assessment Tool is an interactive tool designed by scientists at the National Cancer Institute (NCI) and the National Surgical Adjuvant Breast and Bowel Project (NSABP) to estimate a woman's risk of developing invasive breast cancer. It has been used widely along with other such tools to predict risk of breast cancer. Breast cancer risk assessment tools are helpful in determining the risk group a patient is in. However, their accuracy for individual women is only modest, partly because not all important risks have been identified and partly because accurate risk stratification requires strong risk factors and most risk factors for breast cancer are relatively weak and common in the population.
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2: Diagnosis and Treatment Stratification: Today, if a woman or her doctor finds a lump, she gets a mammogram and then a biopsy for molecular analysis, which can take two weeks or up to a month. If we can predict the cancer is triple-negative, we can fast track the patient for biopsy and treatment. Especially in cases with triple-negative cancer, two to four weeks saved can be crucial. The new data will help researchers more accurately stratify breast cancer by clinically relevant degrees of risk and potentially have an impact on breast cancer treatment. Moreover, armed with this information, women will be able to better understand the implications for their health based on their breast cancer …show more content…
1. Accelerating genomic annotation, allowing more cancers to be sequenced across a broader range of patients, producing better knowledge on clinically actionable variants.
2. Raising the quality of interpretation of variant data by ensuring clinical data are used in genomic pipelines which offers the potential to accelerate the drug discovery process.
3. Facilitating informed, multi-agency decision making by creating an integrated and patient-specific view of the disease and treatment programme, as opposed to an organisational view.
4. Delivering and monitoring the performance of modelling algorithms that predict the length of time a patient is likely to survive based on the population's longitudinal data.
At Aridhia, a group have been working with one of the Academic Health Science Centres in NHS England to establish a data lake for renal cancer. This data is aggregated from various silos, linked and transformed into a standard dataset model, to support a multi-disciplinary team review of the patient population and the cancer
2: Diagnosis and Treatment Stratification: Today, if a woman or her doctor finds a lump, she gets a mammogram and then a biopsy for molecular analysis, which can take two weeks or up to a month. If we can predict the cancer is triple-negative, we can fast track the patient for biopsy and treatment. Especially in cases with triple-negative cancer, two to four weeks saved can be crucial. The new data will help researchers more accurately stratify breast cancer by clinically relevant degrees of risk and potentially have an impact on breast cancer treatment. Moreover, armed with this information, women will be able to better understand the implications for their health based on their breast cancer …show more content…
1. Accelerating genomic annotation, allowing more cancers to be sequenced across a broader range of patients, producing better knowledge on clinically actionable variants.
2. Raising the quality of interpretation of variant data by ensuring clinical data are used in genomic pipelines which offers the potential to accelerate the drug discovery process.
3. Facilitating informed, multi-agency decision making by creating an integrated and patient-specific view of the disease and treatment programme, as opposed to an organisational view.
4. Delivering and monitoring the performance of modelling algorithms that predict the length of time a patient is likely to survive based on the population's longitudinal data.
At Aridhia, a group have been working with one of the Academic Health Science Centres in NHS England to establish a data lake for renal cancer. This data is aggregated from various silos, linked and transformed into a standard dataset model, to support a multi-disciplinary team review of the patient population and the cancer