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.
This academic-industrial partnership project combines the expertise from oncologists, radiologists, and biostatistician from the academic cancer center and industrial scientists and engineers, to develop a novel analytic software module for tumor growth rate assessment using serial CT scans, which can provide objective guides for therapeutic decisions during personalized cancer therapy. The module will utilize clinical CT images and will operate on an existing DICOM-based workstation used in the clinical setting, which is equipped with validated tumor volume segmentation algorithms and is connected to CT scanners and PACS, enabling smooth translation into the clinical practice. After development and optimization, the module will be applied to advanced lung cancer patients treated in prospective clinical trials of targeted therapy, to demonstrate the functionality and utility in the clinical setting and to validate the clinical significance of the results in correlation with patients' survival, which will lay a foundation to accomplish the long term goal of improving clinical outcome of cancer patients.
|Skoulidis, Ferdinandos; Goldberg, Michael E; Greenawalt, Danielle M et al. (2018) STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer Discov 8:822-835|
|Miller, Ezra R; Putman, Rachel K; Vivero, Marina et al. (2018) Histopathology of Interstitial Lung Abnormalities in the Context of Lung Nodule Resections. Am J Respir Crit Care Med 197:955-958|
|Nishino, Mizuki; Wakai, Satoshi; Hida, Tomoyuki et al. (2018) Automated image analysis tool for tumor volume growth rate to guide precision cancer therapy: EGFR-mutant non-small-cell lung cancer as a paradigm. Eur J Radiol 109:68-76|
|Nishino, Mizuki; Sholl, Lynette M; Awad, Mark M et al. (2018) Sarcoid-Like Granulomatosis of the Lung Related to Immune-Checkpoint Inhibitors: Distinct Clinical and Imaging Features of a Unique Immune-Related Adverse Event. Cancer Immunol Res 6:630-635|
|Nishino, Mizuki; Dahlberg, Suzanne E; Adeni, Anika E et al. (2017) Tumor Response Dynamics of Advanced Non-small Cell Lung Cancer Patients Treated with PD-1 Inhibitors: Imaging Markers for Treatment Outcome. Clin Cancer Res 23:5737-5744|
|Nishino, Mizuki; Hatabu, Hiroto; Sholl, Lynette M et al. (2017) Thoracic Complications of Precision Cancer Therapies: A Practical Guide for Radiologists in the New Era of Cancer Care. Radiographics 37:1371-1387|
|Nishino, Mizuki; Ramaiya, Nikhil H; Hatabu, Hiroto et al. (2017) Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol 14:655-668|