Predictive value of 18F-FDG PET/CT for acute exacerbation of interstitial lung disease in patients with lung cancer and interstitial lung disease treated with chemotherapy
Open accessAbstract
Background We examined whether fluorine-18 2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18 F-FDG PET/CT) performed before chemotherapy could predict the onset of acute exacerbation of interstitial lung disease (AE-ILD) in patients with lung cancer and ILD treated with chemotherapy. Methods Thirty-three patients with lung cancer and ILD who underwent 18 F-FDG PET/CT and were treated with chemotherapy at Kumamoto University Hospital between April 2006 and March 2018 were retrospectively analyzed. The maximum standardized uptake value (SUV max) of interstitial lesions was measured to quantify the background ILD activity. A prediction model of AE-ILD was developed using logistic regression analyses for the SUV max, and receiver operating characteristic (ROC) curve analyses were conducted. Results Among the 33 patients, 7 experienced AE-ILD. The SUV max of contralateral interstitial lesions was significantly higher in patients with vs. without AE-ILD (median SUV max: 2.220 vs. 1.795, P = 0.025). Univariable logistic regression analyses showed that the SUV max of contralateral interstitial lesions trended towards being significantly associated with the onset of AE-ILD [odds ratio: 8.683, 95% confidence interval (CI) 0.88-85.83, P = 0.064]. The area under the ROC curve of the SUV max for predicting AE-ILD was 0.780 (95% CI 0.579-0.982, P = 0.025). The optimal cut-off value for SUV max was 2.005, with sensitivity and specificity values of 0.857 and 0.769, respectively. Conclusions The SUV max of contralateral interstitial lesions in 18 F-FDG PET/CT images might be useful for predicting the onset of AE-ILD in patients with lung cancer and ILD treated with chemotherapy.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Drug-induced lung injury occurs more frequently in groups with high CRP, SUVmax, or GPS.
“Furthermore, drug‑induced lung injury occurs more frequently in groups with high CRP, SUVmax, or GPS(32‑34).”
From Clinical impact of inflammatory and nutrition index based on metabolic tumor activity in non‑small cell lung cancer treated with immunotherapy. (2024) · cites this paper as [33]
The Abstract reports higher SUVmax in patients who experienced chemotherapy-related acute exacerbation of ILD than in those who did not (2.220 vs. 1.795; P = 0.025). This supports the SUVmax portion for patients with lung cancer and pre-existing ILD, but not the broader claim about drug-induced lung injury; this source does not establish the CRP or GPS portions of the multiply cited sentence. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Raghu, G., Collard, H. R., Egan, J. J., Martinez, F. J., Behr, J., Brown, K. K., Colby, T. V., Cordier, J.-F., Flaherty, K. R., Lasky, J. A., et al. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.2009-040gl · Cited by 7,186 · see citing works
Diagnosis of Idiopathic Pulmonary Fibrosis. An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline
Raghu, G., Remy-Jardin, M., Myers, J. L., Richeldi, L., Ryerson, C. J., Lederer, D. J., Behr, J., Cottin, V., Danoff, S. K., Morell, F., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201807-1255st · Cited by 4,103 · see citing works
Cumulative incidence of and predictive factors for lung cancer in IPF
Ozawa, Y., Suda, T., Naito, T., Enomoto, N., Hashimoto, D., Fujisawa, T., Nakamura, Y., Inui, N., Nakamura, H., & Chida, K. (2009) · Respirology · doi:10.1111/j.1440-1843.2009.01547.x · Cited by 314 · see citing works
The Impact of Lung Cancer on Survival of Idiopathic Pulmonary Fibrosis
Tomassetti, S., Gurioli, C., Ryu, J. H., Decker, P. A., Ravaglia, C., Tantalocco, P., Buccioli, M., Piciucchi, S., Sverzellati, N., Dubini, A., et al. (2014) · CHEST Journal · doi:10.1378/chest.14-0359 · Cited by 312 · see citing works
Kenmotsu, H., Naito, T., Kimura, M., Ono, A., Shukuya, T., Nakamura, Y., Tsuya, A., Kaira, K., Murakami, H., Takahashi, T., et al. (2011) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e318216ee6b · Cited by 217 · see citing works
Repeatability of SUV in Oncologic <sup>18</sup>F-FDG PET
Lodge, M. A. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.116.186353 · Cited by 214 · see citing works
van Baardwijk, A. A. W., Dooms, C., van Suylen, R. J., Verbeken, E. K., Hochstenbag, M. M. H., Oberije, C., Rupa, D., Pastoreková, S., Stroobants, S. G., Buell, U., et al. (2007) · European Journal of Cancer · doi:10.1016/j.ejca.2007.03.027 · Cited by 196 · see citing works
Cytokine profiles in idiopathic pulmonary fibrosis suggest an important role for TGF‐β and IL-10
Bergeron, A., Soler, P., Kambouchner, M., Loiseau, P., Milleron, B. J., Valeyre, D., Hance, A. J., & Tazi, A. (2003) · European Respiratory Journal · doi:10.1183/09031936.03.00014703 · Cited by 182 · see citing works
Groves, A. M., Win, T., Screaton, N. J., Berovic, M., Endozo, R., Booth, H., Kayani, I. A., Menezes, L., Dickson, J. C., & Ell, P. J. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.057901 · Cited by 158 · see citing works
Exacerbation of Idiopathic Interstitial Pneumonias Associated with Lung Cancer Therapy
Minegishi, Y., Takenaka, K., Mizutani, H., Sudoh, J., Noro, R., Okano, T., Azuma, A., Yoshimura, A., Ando, M., Tsuboi, E., et al. (2009) · Internal Medicine · doi:10.2169/internalmedicine.48.1650 · Cited by 128 · see citing works
[18F]FDG PET/CT predicts progression-free survival in patients with idiopathic pulmonary fibrosis
Justet, A., Laurent-Bellue, A., Thabut, G., Dieudonné, A., Debray, M., Borie, R., Aubier, M., Lebtahi, R., & Crestani, B. (2017) · Respiratory Research · doi:10.1186/s12931-017-0556-3 · Cited by 103 · see citing works
Andrianifahanana, M., Hernandez, D. M., Yin, X., Kang, J. H., Jung, M., Wang, Y., Yi, E. S., Roden, A. C., LIMPER, A. H., & Leof, E. B. (2016) · The FASEB Journal · doi:10.1096/fj.201600428r · Cited by 75 · see citing works
PET/CT in the Staging of the Non-Small-Cell Lung Cancer
Chao, F., & Zhang, H. (2012) · BioMed Research International · doi:10.1155/2012/783739 · Cited by 72 · see citing works
Glucose Transporter-1 Distribution in Fibrotic Lung Disease
El‐Chemaly, S., Malide, D. A., Yao, J., Nathan, S. D., Rosas, I. O., Gahl, W. A., Moss, J., & Gochuico, B. R. (2012) · CHEST Journal · doi:10.1378/chest.12-1359 · Cited by 67 · see citing works
Enomoto, Y., Inui, N., Kato, T., Baba, T., Karayama, M., Nakamura, Y., Ogura, T., & Suda, T. (2016) · Lung Cancer · doi:10.1016/j.lungcan.2016.03.017 · Cited by 55 · see citing works
Shukuya, T., Ishiwata, T., Hara, M., Muraki, K., Shibayama, R., Koyama, R., & Takahashi, K. (2010) · PubMed · Cited by 54 · see citing works
Kakiuchi, S., Hanibuchi, M., Tezuka, T., Saijo, A., Otsuka, K., Sakaguchi, S., Toyoda, Y., Goto, H., Kawano, H., Azuma, M., et al. (2016) · Respiratory Investigation · doi:10.1016/j.resinv.2016.10.008 · Cited by 26 · see citing works
Flagged first, then by citations
1 of the 18 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02578-9 · Cited by 23 · see citing works
Diagnosis and Treatment of Lung Cancer in the Setting of Interstitial Lung Disease
Fisher, D. A., Murphy, M. C., Montesi, S. B., Hariri, L. P., Hallowell, R. W., Keane, F. K., Lanuti, M. D., Mooradian, M. J., & Fintelmann, F. J. (2022) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2022.06.010 · Cited by 21 · see citing works
Japanese guidelines for the treatment of idiopathic pulmonary fibrosis 2023:Revised edition
Bando, M., Homma, S., Date, H., Kishi, K., Yamauchi, H., Sakamoto, S., Miyamoto, A., Goto, Y., Nakayama, T., Azuma, A., et al. (2024) · Respiratory Investigation · doi:10.1016/j.resinv.2024.02.014 · Cited by 12 · see citing works
Ito, K., Hashimoto, K., Kaira, K., Yamaguchi, O., Mouri, A., Shiono, A., Miura, Y., Kobayashi, K., Imai, H., Kuji, I., et al. (2024) · Oncology Letters · doi:10.3892/ol.2024.14243 · Cited by 11 · see citing works
Yamazaki, M., Watanabe, S., Tominaga, M., Yagi, T., Goto, Y., Yanagimura, N., Arita, M., Ohtsubo, A., Tanaka, T., Nozaki, K., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.08.043 · Cited by 10 · see citing works
Dahooiyeh, A. B., Das, J. P., Prendergast, C. M., Beizavi, Z., Hong, Y., Jaber, M. Y., & Capaccione, K. M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05379-1 · Cited by 8 · see citing works
Wang, Z., Bai, J., Liu, Y., Li, P., & Jiao, G. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1250688 · Cited by 7 · see citing works
Perrotta, F., Chino, V., Allocca, V., D’Agnano, V., Bortolotto, C., Bianco, A., Corsico, A. G., & Stella, G. M. (2022) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2022.2145948 · Cited by 7 · see citing works
Oishi, H., Sakurada, A., Notsuda, H., Tanaka, R., Takanami, K., Saito, R., Eba, S., Noda, M., & Okada, Y. (2020) · Respiratory Investigation · doi:10.1016/j.resinv.2020.08.007 · Cited by 6 · see citing works
Akaike, K., Saruwatari, K., Matsushima, R., Fujino, K., Morinaga, J., Oda, S., Takahashi, H., Shiraishi, S., Okabayashi, H., Hamada, S., et al. (2022) · Journal of Thoracic Disease · doi:10.21037/jtd-22-604 · Cited by 3 · see citing works
Self-citationOkuzumi, S., Suzuki, H., Morinaga, S., Tamura, M., & Minematsu, N. (2023) · Internal Medicine · doi:10.2169/internalmedicine.2540-23 · Cited by 3 · see citing works
Yin, L., Xu, R., Liu, X., Fu, L., & Qiang, G. (2024) · BMC Pulmonary Medicine · doi:10.1186/s12890-024-03177-5 · Cited by 3 · see citing works
Ishida, Y., Watanabe, S., Sakakibara-Konishi, J., Ikezawa, Y., Kikuchi, H., Kawai, Y., Kimura, H., Nakakubo, S., Hirata, K., Kudo, K., et al. (2025) · Japanese Journal of Radiology · doi:10.1007/s11604-025-01869-4 · Cited by 1 · see citing works
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047904
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047913
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047931
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047919
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047910
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047907
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047925
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047922
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047928
Liang, J., Cao, H., Liu, Y., Ye, B., Sun, Y., Ke, Y., He, Y., Xu, B., & Lin, J. (2021) · Figshare · doi:10.6084/m9.figshare.15047916
Furukawa, T., Akaike, K., Saruwatari, K., Oda, S., Shiraishi, S., Imamura, K., Fukushima, K., Okabayashi, H., Hamada, S., Sakata, S., et al. (2026) · Respiratory Investigation · doi:10.1016/j.resinv.2026.101461
Self-citation
- Authors
- Akaike, K., Saruwatari, K., Oda, S., Shiraishi, S., Takahashi, H., Hamada, S., Iyama, S., Horio, Y., Tomita, Y., Saeki, S., Okamoto, S., Ichiyasu, H., Fujii, K., & Sakagami, T.
- Affiliations
- Kumamoto University Hospital (JP)
- Kumamoto University (JP)
- Corresponding author
- Saruwatari, Koichi
- ORCID
- Venue
- International Journal of Clinical Oncology
- Published
- 2019-11-28
- Volume
- 25
- Issue
- 4
- Pages
- 681-690
- ISSN
- 1341-9625 (ISSN-L)
- 1437-7772
- OpenAlex
- W2991077970
- Cited by
- 23
- References
- 18
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 2 · 2022: 3 · 2021: 10 · 2020: 1
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- International Journal of Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Kumamoto University Repository (Kumamoto University) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- acute exacerbation of interstitial lung disease, interstitial lung disease, lung cancer, chemotherapy-induced lung injury, 18F-FDG PET/CT, standardized uptake value, predictive biomarkers
- MeSH terms
- Positron Emission Tomography Computed Tomography, Aged, Female, Humans, Lung Neoplasms, Male, Middle Aged, Retrospective Studies, ROC Curve, Sensitivity and Specificity, Lung Diseases, Interstitial, Fluorodeoxyglucose F18
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review