Clinical Oncology Research Labs are at the forefront of cancer care and classification research, continually advancing the field through innovative studies and clinical trials. Faced with the challenge of analyzing vast oncology data spread across multiple institutions, these labs require a solution that allows AI model training and deep learning on large, decentralized datasets without compromising the security of sensitive clinical data.
Accessing and utilizing large-scale oncology data from multiple medical centers introduces significant privacy and security challenges. Pathology images, and other Protected Health Information (PHI), cannot be freely shared among organizations, necessitating a secure method of collaborative analysis. Today, data regulations are largely addressed by manual checkpoints that satisfy compliance, but at great cost to time and data quality. This is particularly important in fields like breast cancer, lung cancer, prostate cancer, and colorectal cancer research, where such sensitive data is essential for cancer diagnosis and treatment decisions.
The platform is designed for ease of use, enabling cancer detection and research without requiring extensive technical knowledge from participants.
Advanced encryption techniques prevent potential adversaries from intercepting or recreating the model from network traffic.
The platform supports various models and scales according to collaborative needs, making it suitable for diverse areas such as precision oncology, drug discovery, and digital pathology.
No direct data sharing occurs between institutions, safeguarding clinical oncology practices and maintaining the integrity of tissue samples and high-resolution medical device data.
Easily deploy in any environment (AWS, GCP, Azure) to minimize data movement while maximizing data security and privacy for cancer treatment and early detection research.
“Ongoing precision medicine studies can immediately benefit from these capabilities by enabling secure collaboration across clinical institutions without requiring complex data sharing agreements or compromising individual-level privacy. This technology can also empower patients to participate in research studies directly and receive personalized results knowing that their individual data will not be exposed.”
The secure federated learning study conducted by the Dana Farber Cancer Institute in collaboration with Duality Technologies highlights significant operational benefits for cancer research. This approach has proven pivotal for multi-institutional collaborative oncology research, offering agility, flexibility, and privacy protection for sensitive data for all participants.
By leveraging machine learning and artificial intelligence securely, researchers maintain full control over sensitive medical data, eliminating the need for data transfer.
This method benefits precision medicine, cancer screening, and radiation oncology, where protecting cancer patients’ data is necessary.
The study also underscores the importance of collaboration in advancing treatment decisions and improving patient care and overall clinical decision-making.
Maximize the value of sensitive, regulated, or confidential data.
