Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients. (September 2022)

• Record Type: Journal Article
• Authors: Ieko, Yoshiro; Kadoya, Noriyuki; Sugai, Yuto; Mouri, Shiina; Umeda, Mariko; Tanaka, Shohei; Kanai, Takayuki; Ichiji, Kei; Yamamoto, Takaya; Ariga, Hisanori; Jingu, Keiichi
• Is Part Of: Physica medica. Volume 101(2022)
• Page Start: 28
• Page End: 35
• Publication Date: 2022-09
• Subjects: Radiomics -- Lung -- Pulmonary function test -- Machine learning -- Radiotherapy -- Ventilation; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2022.07.003
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: We assessed two radiomic approaches based on radiomics features extracted from chest breath-hold CT images for estimating pulmonary function test results. We used two segmentation methods: extraction of lung tissue only (<-250 HU) (APPROACH 1); extraction of small blood vessels and lung tissue (APPROACH 2). Least absolute shrinkage and selection operator (LASSO) regression was used for the radiomics approach. Estimation using radiomics approaches was more accurate than conventional methodology. Abstract: Purpose: We aimed to assess radiomics approaches for estimating three pulmonary function test (PFT) results (forced expiratory volume in one second [FEV1], forced vital capacity [FVC], and the ratio of FEV1 to FVC [FEV1 /FVC]) using data extracted from chest computed tomography (CT) images. Methods: This retrospective study included 85 lung cancer patients (mean age, 75 years ±8; 69 men) who underwent stereotactic body radiotherapy between 2012 and 2020. Their pretreatment chest breath-hold CT and PFT data before radiotherapy were obtained. A total of 107 radiomics features (Shape: 14, Intensity: 18, Texture: 75) were extracted using two methods: extraction of the lung tissue (<-250 HU) (APPROACH 1), and extraction of small blood vessels and lung tissue (APPROACH 2). The PFT results were estimated using the least absolute shrinkage and selection operator regression. Pearson's correlation coefficients (r) were determined for all PFT results, and the area under the curve (AUC) was calculated for FEV1 /FVC (<70 %). Finally, we compared our approaches with the conventional formula (Conventional). Results: For the estimated FEV1 /FVC, the Pearson's r were 0.21 ( P =.06), 0.69 ( P <.01), and 0.73 ( P <.01) for Conventional, APPROACH 1, and APPROACH 2, respectively; the AUCs for FEV1 /FVC (<70 %) were 0.67 (95 % confidence interval [CI]: 0.55, 0.79), 0.82 (CI: 0.72, 0.91; P =.047) and 0.86 (CI: 0.78, 0.94; P =.01), respectively. Conclusions: The radiomics approach performed better than the conventional equation and may be useful for assessing lung function based on CT images.