Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients

• Published in: PloS one 2018-11, Vol.13 (11), p.e0207362
• Main Authors: Klaassen, Remy, Larue, Ruben T H M, Mearadji, Banafsche, van der Woude, Stephanie O, Stoker, Jaap, Lambin, Philippe, van Laarhoven, Hanneke W M
• Format: Article
• Language: English
• Subjects: Adult; Aged; Biology and Life Sciences; Cancer; Cancer therapies; Capecitabine - administration & dosage; Chemotherapy; Classification; Computed tomography; Developmental biology; Esophageal cancer; Esophageal Neoplasms - diagnostic imaging; Esophageal Neoplasms - drug therapy; Esophageal Neoplasms - pathology; Esophagus; Feasibility studies; Feature extraction; Female; Gastric cancer; Humans; Lesions; Liver; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - drug therapy; Liver Neoplasms - pathology; Liver Neoplasms - secondary; Lung cancer; Male; Mathematical models; Medical imaging; Medical prognosis; Medicine and Health Sciences; Metastases; Metastasis; Middle Aged; Models, Biological; Neoplasm Metastasis; Nuclear medicine; Oncology; Patients; Physical Sciences; Predictive Value of Tests; Radiomics; Remission; Research and Analysis Methods; Retrospective Studies; Stomach Neoplasms - diagnostic imaging; Stomach Neoplasms - drug therapy; Stomach Neoplasms - pathology; Studies; Tomography, X-Ray Computed; Training
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0207362

In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74-0.83) and 0.65 (95% ci: 0.57-0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83-0.90) and 0.79 (95% ci 0.72-0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings.