Prognostic performance of radiomic data to predict distant metastases and overall survival in patients with early stage (I/II) non-small cell lung cancer (NSCLC)

• Applicant: Dr. Jakob Weiss
• Subject Area: Nuclear Medicine, Radiotherapy, Radiobiology
• Term: from 2018 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 408360143

Lung cancer is the leading cause of cancer deaths in the world with non-small cell lung cancer (NSCLC) being the most commonly observed subtype. Despite improvements in diagnosis and treatment, prognosis of NSCLC remains poor. Only patients with early-stage disease are treated with curative intent based on surgical resection. However, even after complete and successful resection of the same tumor stage, a wide spectrum of survival periods is observed. In this context, it has become increasingly evident that each tumor is associated with distinct phenotypic characteristics, which are responsible for different treatment responses. This has led to concept of personalized medicine with the aim to tailor treatment strategies towards the individual needs of the patient. Thus, the individual tumor phenotype may serve as a decision-making tool for estimating the individual response to different treatment approaches with the aim to improve overall survival and quality of life. Lung cancer is routinely diagnosed using medical imaging, most commonly computed tomography (CT). Diagnosis is based on the subjective image interpretation of the radiologist according to distinct imaging features with the risk for false diagnosis due to inter-observer variability. Therefore, biopsy of tumor tissue is necessary to establish a final diagnosis. However, biopsy only allows for acquiring small samples of tissue, which may fail to comprehensively capture the molecular variations within tumors, thus posing the risk for underestimating tumor aggressiveness. Therefore, quantitative imaging analysis has gained increasing importance with the aim to non-invasively extract objective image features to describe the tumor phenotype based on mathematical algorithms. This new way of analyzing imaging data has become known as radiomics. There is growing evidence that radiomics may have the potential to serve as a cost-effective and non- invasive biomarker for personalized medicine by providing prognostic information regarding tumor stage, metastatic potential, treatment response and overall survival. Patients with early-stage NSCLC are particularly suitable to evaluate the prognostic impact of radiomics for non-invasive tumor profiling, as CT images and histopathological analysis are routinely available for these patients. Moreover, as mentioned above, despite successful surgical resection of the same disease stage it is well known but not predictable so far, that a certain subpopulation of these patients will experience recurrent disease indicating the need for precision medicine. Therefore, the purpose of the proposed study is to investigate the prognostic performance of radiomic data in patients with early-stage NSCLC with the aim to identify predictive image features to estimate the metastatic potential and overall survival. This is of great importance not only for personalized medicine but also from an interdisciplinary and socioeconomic point of view.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Hugo Aerts, Ph.D.