TY - JOUR
T1 - Machine learning for lung texture analysis on thin-section CT
T2 - Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations
AU - Ohno, Yoshiharu
AU - Aoyagi, Kota
AU - Takenaka, Daisuke
AU - Yoshikawa, Takeshi
AU - Fujisawa, Yasuko
AU - Sugihara, Naoki
AU - Hamabuchi, Nayu
AU - Hanamatsu, Satomu
AU - Obama, Yuki
AU - Ueda, Takahiro
AU - Hattori, Hidekazu
AU - Murayama, Kazuhiro
AU - Toyama, Hiroshi
N1 - Funding Information:
This work was financially or technically supported by Canon Medical Systems Corporation, the Smoking Research Foundation, and Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology (JSTS.KAKEN; No. 18K07675 and No. 20K08037).
Funding Information:
The authors thank Shinichiro Seki (Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine); Yuji Kishida (Department of Radiology, Kobe University Graduate School of Medicine); Shintaro Tokunaga, Shuya Hori, Motoko Tachihara, Kazuyuki Kobayashi, Yoshihiro Nishimura (all from the Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine); Wakiko Tani, Noriyuki Negi, and Takamichi Murakami (all from the Center for Radiology and Radiation Oncology, Kobe University Hospital) for their valuable contributions to this study. The author(s) received the following financial support for the research, authorship, and/or publication of this article: This work was financially or technically supported by Canon Medical Systems Corporation, the Smoking Research Foundation, and Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology (JSTS.KAKEN; No. 18K07675 and No. 20K08037).
Publisher Copyright:
© The Foundation Acta Radiologica 2021.
PY - 2022/10
Y1 - 2022/10
N2 - Background: The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). Purpose: To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. Material and Methods: A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as “Stable” (n = 188), “Worse” (n = 98) and “Improved” (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. Results: Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses (P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). Conclusion: ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.
AB - Background: The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). Purpose: To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. Material and Methods: A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as “Stable” (n = 188), “Worse” (n = 98) and “Improved” (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. Results: Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses (P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). Conclusion: ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.
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U2 - 10.1177/02841851211044973
DO - 10.1177/02841851211044973
M3 - Article
C2 - 34636644
AN - SCOPUS:85116862757
SN - 0284-1851
VL - 63
SP - 1363
EP - 1373
JO - Acta Radiologica
JF - Acta Radiologica
IS - 10
ER -