Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Issue 7 (7th June 2019)

• Record Type: Journal Article
• Authors: Uthoff, Johanna; Stephens, Matthew J.; Newell, John D.; Hoffman, Eric A.; Larson, Jared; Koehn, Nicholas; De Stefano, Frank A.; Lusk, Chrissy M.; Wenzlaff, Angela S.; Watza, Donovan; Neslund‐Dudas, Christine; Carr, Laurie L.; Lynch, David A.; Schwartz, Ann G.; Sieren, Jessica C.
• Is Part Of: Medical physics. Volume 46:Issue 7(2019)
• Page Start: 3207
• Page End: 3216
• Publication Date: 2019-06-07
• Subjects: artificial intelligence -- computed tomography -- pulmonary nodule -- radiomics -- risk assessment; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13592
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow‐up procedures on the large number of false‐positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. Methods: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k‐medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). Results: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features ( P < 0.01). The best performing ML tool included features derived from nodule diameter‐based surrounding parenchyma tissue quartile bands. We demonstrate similar high‐performance values on the independent validation cohort (AUC‐ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow‐up guidelines demonstrated a theoretical reduction in recommended follow‐up imaging and procedures. Conclusions: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.