Journal Article

Defining the biological basis of radiomic phenotypes in lung cancer

Haibe-Kains, Benjamin, Aerts, Hugo JWL, Rios Velazquez, Emmanuel, Grossmann, Patrick, Parmar, Chintan, Gillies, Robert J, Bui, Marilyn M, Lambin, Philippe, Stringfield, Olya, Leijenaar, Ralph TH, El-Hachem, Nehme

2017

Overview

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images. Medical imaging covers a wide range of techniques that are used to look inside the body, including X-rays, MRI scans and ultrasound. A process called radiomics uses computer algorithms to process the data collected by these techniques to identify and precisely measure a large number of features that would not otherwise be quantifiable by human experts. By doing so, radiomics can automatically measure the radiographic characteristics of a tumor. For example, radiomics can establish the size, shape and texture of a tumor to help to diagnose cancer and guide its treatment. Research has suggested that radiomics can predict certain clinical characteristics of cancer, such as how far through the body the cancer has spread, how likely it is to respond to treatment, and how likely a patient is to survive. However, these radiomic characteristics have not yet been precisely linked to the biological processes that drive how cancer develops and spreads. Cancers develop as a result of genetic changes that activate “molecular pathways” in the cells and trigger processes such as cell division and inflammation. To work out exactly which changes are behind a particular tumor, a sample of the tumor from biopsy or surgery is analyzed using genomics techniques. Linking radiomics features to the molecular processes active in a tumor can generate further information that can complement the molecular data. Images are routinely collected on all cancer patients yet molecular data is not. Hence, in some cases, the images can be used to infer the molecular underpinnings of cancer in individual patients. Grossmann et al. have now analyzed radiomic, genomic and clinical data collected from approximately 350 patients with lung cancer. The analysis revealed links between biological processes normally detected by genomics – in particular, inflammatory responses – and radiomics features. Furthermore, these features could also be associated with clinical characteristics, such as tumor type and patient survival rates. These results were further validated by using a technique called immunohistochemical staining on tumor tissue obtained by surgery. Further investigation revealed that certain radiomics features can predict the state of molecular pathways that are key to cancer development (such as the inflammatory response). Furthermore, Grossmann et al. found that combining data from radiomics, genomics and clinical parameters predicts how the cancer will progress better than any of these parameters can predict on their own. These results demonstrate the complementary value of radiomic data to genomic and clinical data. There are many different algorithms that can be used to process images for radiomics. Before radiomics can be used clinically to assess the biological processes underlying the tumors of patients, a specific algorithm needs to be decided upon and then tested in prospective clinical trials.

• Publisher: eLife Science Publications, Ltd,eLife Sciences Publications Ltd,eLife Sciences Publications, Ltd
• Subject: Adenocarcinoma - diagnostic imaging / Adenocarcinoma - pathology / Adenocarcinoma - radiotherapy / Antigens / Biology / Biomarkers / Biomarkers, Tumor - metabolism / Biopsy / Cancer Biology / Cancer therapies / Carcinoma, Squamous Cell - diagnostic imaging / Carcinoma, Squamous Cell - pathology / Carcinoma, Squamous Cell - radiotherapy / Care and treatment / Clinical Decision-Making / Computational and Systems Biology / computational biology / Decision making / Development and progression / Diagnostic Imaging - methods / DNA-directed RNA polymerase / Female / Genetic aspects / genomics / Health aspects / Histology / Humans / imaging / Immune response / Immunohistochemistry / Lung cancer / Lung Neoplasms - diagnostic imaging / Lung Neoplasms - pathology / Lung Neoplasms - radiotherapy / Male / Medical imaging / Medical prognosis / Oncology / Phenotype / Phenotypes / Prognosis / Questioning / Radiometry - methods / Radiomics / Research centers / Tomography, X-Ray Computed - methods / Transcription / Tumors / Ubiquitin / Ubiquitin-protein ligase