Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology
Abstract Background The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estim...
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2024-12-01
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| Series: | Respiratory Research |
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| Online Access: | https://doi.org/10.1186/s12931-024-03075-8 |
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| author | Michihiro Uyama Tomohiro Handa Ryuji Uozumi Seishu Hashimoto Yoshio Taguchi Kohei Ikezoe Kiminobu Tanizawa Naoya Tanabe Tsuyoshi Oguma Atsushi Matsunashi Takafumi Niwamoto Hiroshi Shima Ryobu Mori Tomoki Maetani Yusuke Shiraishi Tomomi W. Nobashi Ryo Sakamoto Takeshi Kubo Akihiko Yoshizawa Kazuhiro Terada Yuji Nakamoto Toyohiro Hirai |
| author_facet | Michihiro Uyama Tomohiro Handa Ryuji Uozumi Seishu Hashimoto Yoshio Taguchi Kohei Ikezoe Kiminobu Tanizawa Naoya Tanabe Tsuyoshi Oguma Atsushi Matsunashi Takafumi Niwamoto Hiroshi Shima Ryobu Mori Tomoki Maetani Yusuke Shiraishi Tomomi W. Nobashi Ryo Sakamoto Takeshi Kubo Akihiko Yoshizawa Kazuhiro Terada Yuji Nakamoto Toyohiro Hirai |
| author_sort | Michihiro Uyama |
| collection | DOAJ |
| description | Abstract Background The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF. The aim of this study was to develop AIQCT-derived CPI formulas to quantify CT abnormalities in IPF and to investigate the associations of these CPI formulas with survival. Methods The first cohort included 116 patients with IPF. In this cohort, ILD, bronchial, and hyperlucent volumes on CT were quantified using AIQCT. New CPI formulas were developed based on PFTs to estimate the volume fraction of ILD (ILD-CPI), the sum of the ILD and bronchial volume fractions (ILDB-CPI), and the sum of the ILD, bronchial and hyperlucent volume fractions (ILDBH-CPI). The associations of the original CPI, the CALIPER-CPI and the AIQCT-derived CPIs with survival were analyzed in the first cohort and in a second cohort of patients with IPF (n = 72). Results In the first cohort, over a median observation time of 92.8 months, 79 patients (68.1%) died, and one patient (0.9%) underwent living-donor lung transplantation. The original CPI, the CALIPER-CPI, and all AIQCT-derived CPIs were associated with overall survival (hazard ratios: 1.07–1.22). The C-index of the ILDB-CPI (0.759) was the highest among all AIQCT-derived CPIs and was comparable to that of the original CPI (0.765) and the CALIPER-CPI (0.749). The C-index of the ILDBH-CPI (0.729) was lower than that of the other CPI variables. The second cohort yielded similar C-indices as the first cohort for the original CPI (0.738), CALIPER-CPI (0.757) and ILDB-CPI (0.749). Conclusions The ILDB-CPI can predict the outcomes of IPF patients with a similar performance to that of the original CPI and the CALIPER-CPI. Adding the hyperlucent volume to the CPI formula did not improve its predictive accuracy for mortality. Trial registration None (no health care interventions were performed). |
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| institution | DOAJ |
| issn | 1465-993X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj-art-b16db321de7942368c08412937a170fa2025-08-20T02:39:38ZengBMCRespiratory Research1465-993X2024-12-0125111010.1186/s12931-024-03075-8Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technologyMichihiro Uyama0Tomohiro Handa1Ryuji Uozumi2Seishu Hashimoto3Yoshio Taguchi4Kohei Ikezoe5Kiminobu Tanizawa6Naoya Tanabe7Tsuyoshi Oguma8Atsushi Matsunashi9Takafumi Niwamoto10Hiroshi Shima11Ryobu Mori12Tomoki Maetani13Yusuke Shiraishi14Tomomi W. Nobashi15Ryo Sakamoto16Takeshi Kubo17Akihiko Yoshizawa18Kazuhiro Terada19Yuji Nakamoto20Toyohiro Hirai21Department of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Industrial Engineering and Economics, Tokyo Institute of TechnologyDepartment of Respiratory Medicine, Tenri HospitalDepartment of Respiratory Medicine, Tenri HospitalDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Kyoto City HospitalDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Radiology, Tenri HospitalDepartment of Diagnostic Pathology, Nara Medical UniversityDepartment of Diagnostic Pathology, Graduate School of Medicine, Kyoto UniversityDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto UniversityAbstract Background The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF. The aim of this study was to develop AIQCT-derived CPI formulas to quantify CT abnormalities in IPF and to investigate the associations of these CPI formulas with survival. Methods The first cohort included 116 patients with IPF. In this cohort, ILD, bronchial, and hyperlucent volumes on CT were quantified using AIQCT. New CPI formulas were developed based on PFTs to estimate the volume fraction of ILD (ILD-CPI), the sum of the ILD and bronchial volume fractions (ILDB-CPI), and the sum of the ILD, bronchial and hyperlucent volume fractions (ILDBH-CPI). The associations of the original CPI, the CALIPER-CPI and the AIQCT-derived CPIs with survival were analyzed in the first cohort and in a second cohort of patients with IPF (n = 72). Results In the first cohort, over a median observation time of 92.8 months, 79 patients (68.1%) died, and one patient (0.9%) underwent living-donor lung transplantation. The original CPI, the CALIPER-CPI, and all AIQCT-derived CPIs were associated with overall survival (hazard ratios: 1.07–1.22). The C-index of the ILDB-CPI (0.759) was the highest among all AIQCT-derived CPIs and was comparable to that of the original CPI (0.765) and the CALIPER-CPI (0.749). The C-index of the ILDBH-CPI (0.729) was lower than that of the other CPI variables. The second cohort yielded similar C-indices as the first cohort for the original CPI (0.738), CALIPER-CPI (0.757) and ILDB-CPI (0.749). Conclusions The ILDB-CPI can predict the outcomes of IPF patients with a similar performance to that of the original CPI and the CALIPER-CPI. Adding the hyperlucent volume to the CPI formula did not improve its predictive accuracy for mortality. Trial registration None (no health care interventions were performed).https://doi.org/10.1186/s12931-024-03075-8Artificial intelligence-based quantitative computed tomographic image analysis softwareComposite physiologic indexIdiopathic pulmonary fibrosisInterstitial lung diseaseBronchial volumeHyperlucent volume |
| spellingShingle | Michihiro Uyama Tomohiro Handa Ryuji Uozumi Seishu Hashimoto Yoshio Taguchi Kohei Ikezoe Kiminobu Tanizawa Naoya Tanabe Tsuyoshi Oguma Atsushi Matsunashi Takafumi Niwamoto Hiroshi Shima Ryobu Mori Tomoki Maetani Yusuke Shiraishi Tomomi W. Nobashi Ryo Sakamoto Takeshi Kubo Akihiko Yoshizawa Kazuhiro Terada Yuji Nakamoto Toyohiro Hirai Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology Respiratory Research Artificial intelligence-based quantitative computed tomographic image analysis software Composite physiologic index Idiopathic pulmonary fibrosis Interstitial lung disease Bronchial volume Hyperlucent volume |
| title | Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology |
| title_full | Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology |
| title_fullStr | Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology |
| title_full_unstemmed | Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology |
| title_short | Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology |
| title_sort | prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology |
| topic | Artificial intelligence-based quantitative computed tomographic image analysis software Composite physiologic index Idiopathic pulmonary fibrosis Interstitial lung disease Bronchial volume Hyperlucent volume |
| url | https://doi.org/10.1186/s12931-024-03075-8 |
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