Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients

Yoshiro Ieko, Noriyuki Kadoya, Yuto Sugai, Shiina Mouri, Mariko Umeda, Shohei Tanaka, Takayuki Kanai, Kei Ichiji, Takaya Yamamoto, Hisanori Ariga, Keiichi Jingu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)28-35
Number of pages8
JournalPhysica Medica
Publication statusPublished - 2022 Sept


  • Lung
  • Machine learning
  • Pulmonary function test
  • Radiomics
  • Radiotherapy
  • Ventilation


Dive into the research topics of 'Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients'. Together they form a unique fingerprint.

Cite this