Automated in vivo analysis of tumor growth rate as a guide for therapeutic decisions to advance personalized cancer treatment

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Project Summary The goal of this project is to develop a novel analytic software module for tumor growth rate assessment during therapy in advanced lung cancer patients. Tumor growth rate is a novel concept for evaluation of clinical benefit of cancer therapy and is proposed as objective guides for treatment decisions, however is not included in the current standards of tumor response evaluation. The concept of tumor growth rate is especially important in patients with specific mutations in their tumors, such as epidermal growth factor receptor (EGFR) mutations in lung cancer, treated with personalized therapy specifically targeting their mutations. EGFR-mutant patients show initial dramatic response to targeted therapy using EGFR inhibitors; however, their tumors grow back and eventually progress. Current clinical practice lacks objective guidelines about when EGFR inhibitor therapy can be safely continued while tumors are growing back, and the decision is left to treating physicians’ discretions. Similar clinical scenarios are observed during therapy using other targeting agents for various cancers, indicating an increasing clinical demand to fulfil this unmet need for objective guides for treatment decisions in the era of precision cancer therapy. Investigators of this academic-industrial partnership team have developed a method to objectively characterize of tumor growth rate over time using the serial clinical CT imaging data obtained in patients receiving cancer therapy. The method was applied to EGFR-mutant lung cancer patients as a well-studied paradigm, and provided a reproducible reference value that indicates fast versus slow growth. Given the demonstrated feasibility as a clinical investigation, the team proposes to deliver this novel analytic functionality to the clinical setting, by developing an automated analytic tool for tumor growth rate assessment and interpretation, which is essential to make the approach more widely adaptable within the clinical workflow. The academic-industrial team consists of accomplished investigators from Dana-Farber/Brigham and Women’s Cancer Center and industrial scientists from Toshiba Medical Systems Corporation, with expertise in oncology, radiology, biostatistics, and engineering, who have a track record of productive collaboration. The team has started to work together to address the following aims: Aim 1) Develop an analytic software module for tumor growth rate in lung cancer during therapy, which operates on an existing workstation; Aim 2) Optimize the module based on the reproducibility assessment and user feedback in a pilot cohort of 30 EGFR-mutant patients; and Aim 3) Apply the analytic software module for tumor growth rate in lung cancer patients treated in prospective trials. Delivery of the novel analytic module for tumor growth rate to the clinical setting will provide objective guides for treatment decision making during cancer therapy, and help to maximize the benefit of precision therapy for cancer. The demonstrated productivity of academic-industrial partnership with bidirectional research relationship further ensures successful completion of the project goal.