In patients with lymphomas and other cancers, quantitative evaluation of the extent of tumor burden is im- portant for staging, restaging, and assessment of therapeutic response or relapse; yet measurement of overall tumor burden is challenging with current tools, particularly when lymph nodes are confluent or difficult to fully differentiate from surrounding structures. Precision medicine and novel therapeutics are emphasizing the need to introduce a risk-adapted approach to tailor appropriate treatment strategies for cancer patients. The ability to quantitatively assess cancer phenotypes with functional and anatomical imaging that could efficiently and ac- curately map patients to gene expression profiling, clinical information, matching cohorts, and novel treatment regimens could potentially result in more optimal management of patients with cancer. This Academic-Industry Partnership aims to translate recently developed technologies for semi- automated image segmentation and quantification of lymph nodes into robust tools and integrate them into an existing cloud-based system for management of multicenter oncology clinical trials. The ability to semi- automatically segment lymph node pathology with computed tomography (CT), as well as quantify nodal me- tabolism with positron emission tomography (PET) will enable comprehensive tracking of morphological and functional changes related to disease progression and treatment response. Since 2004, the Dana-Farber/Harvard Cancer Center's (DF/HCC) Tumor Imaging Metrics Core (TIMC) has developed the Precision Imaging Metrics, LLC (PIM) platform to manage clinical trial image assessment workflows. Currently, there are nearly 50,000 consistently measured lymph node measurements in the TIMC database. The PIM system is used to make over 20,000 time point imaging assessments per year at eight NCI- designated Cancer Centers and aims to grow quickly by transitioning to a fully cloud-hosted system. Given sufficient training data, state-of-the-art machine learning and artificial intelligence (AI) technolo- gies can meet or even exceed human performance on specific imaging analysis tasks. Recent studies have indicated that AI-based lymph node segmentation from CT scans is nearing human performance levels, and we will extend and translate this work into a commercial tool. Specifically, our aim is to translate recent ad- vancements in AI-based segmentation into deployable services, and integrate these services into the clinical trial workflow. The proposed system will be designed to incorporate expert feedback provided by image ana- lysts and radiologists back into the ground truth dataset, allowing for continuous improvement in accuracy and clinical acceptance. We will extend our semi-automatic CT segmentation technologies to quantify lymph node metabolism in PET/CT, using lymphoma as the model disease. Integration of these technologies with PIM will provide an ongoing source of consistently measured quantitative data across a network of cancer centers.

Public Health Relevance

Advanced quantitative imaging is underutilized in oncologic clinical practice and research because the time investment entailed in manual lesion segmentation remains prohibitive. This partnership proposes to integrate machine learning based quantitative imaging tools into the existing Precision Imaging Metrics, LLC clinical trial image management system. We will use data from the centralized Tumor Imaging Metrics Core at the Dana- Farber/Harvard Cancer Center to produce ground truth training data and develop semi-automated multimodali- ty lesion analysis for patients with cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Redmond, George O
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Brigham and Women's Hospital
United States
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