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Annals of Nuclear Medicine
Erschienen in:

05.05.2024 | Original Article

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

verfasst von: Kun-Han Lue, Yu-Hung Chen, Sung-Chao Chu, Chih-Bin Lin, Tso-Fu Wang, Shu-Hsin Liu

Erschienen in: Annals of Nuclear Medicine | Ausgabe 8/2024

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Abstract

Objective

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

Methods

We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

Results

In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

Conclusions

Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.
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Literatur
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.CrossRefPubMed
2.
Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet. 2021;398:535–54.PubMed
3.
4.
Shea M, Costa DB, Rangachari D. Management of advanced non-small cell lung cancers with known mutations or rearrangements: latest evidence and treatment approaches. Ther Adv Respir Dis. 2015;10:113–29.PubMedPubMedCentral
5.
Tan AC, Tan DSW. Targeted therapies for lung cancer patients with oncogenic driver molecular alterations. J Clin Oncol. 2022;40:611–25.PubMed
6.
Soria JC, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med. 2018;378:113–25.PubMed
7.
Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, et al. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N Engl J Med. 2020;382:41–50.PubMed
8.
Kim G, Kim J, Cha H, Park WY, Ahn JS, Ahn MJ, et al. Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations. Sci Rep. 2020;10:13231.PubMedPubMedCentral
9.
Elzakra N, Kim Y. HIF-1alpha metabolic pathways in human Cancer. Adv Exp Med Biol. 2021;1280:243–60.PubMed
10.
Kelloff GJ, Hoffman JM, Johnson B, Scher HI, Siegel BA, Cheng EY, et al. Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clin Cancer Res. 2005;11:2785–808.PubMed
11.
Ng KS, King Sun C, Boom Ting K, Ting Kun AY. Prognostic factors of EGFR-mutated metastatic adenocarcinoma of lung. Eur J Radiol. 2020;123: 108780.PubMed
12.
Aguloglu N, Akyol M, Komek H, Katgi N. The prognostic value of 18F-FDG PET/ct metabolic parameters in predicting treatment response before EGFR TKI treatment in patients with advanced lung adenocarcinoma. Mol Imaging Radionucl Ther. 2022;31:104–13.PubMedPubMedCentral
13.
Afshar P, Mohammadi A, Tyrrell PN, Cheung P, Sigiuk A, Plataniotis KN, et al. [Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer. Sci Rep. 2020;10:12366.PubMedPubMedCentral
14.
Lue KH, Chen YH, Chu SC, Chang BS, Lin CB, Chen YC, et al. A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study. Nucl Med Commun. 2023;44:1094–105.PubMed
15.
Tau N, Stundzia A, Yasufuku K, Hussey D, Metser U. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol. 2020;215:192–7.PubMed
16.
van der Vos CS, Koopman D, Rijnsdorp S, Arends AJ, Boellaard R, van Dalen JA, et al. Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET. Eur J Nucl Med Mol Imaging. 2017;44:4–16.PubMedPubMedCentral
17.
Wagatsuma K, Miwa K, Sakata M, Oda K, Ono H, Kameyama M, et al. Comparison between new-generation SiPM-based and conventional PMT-based TOF-PET/CT. Phys Med. 2017;42:203–10.PubMed
18.
Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (Eighth) Edition of the TNM classification for lung cancer. J Thorac Oncol. 2016;11:39–51.PubMed
19.
Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338:b605.PubMed
20.
Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what, why, how, when and where? Clin Kidney J. 2021;14:49–58.PubMed
21.
Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.PubMed
22.
Chen YH, Chen YC, Lue KH, Chu SC, Chang BS, Wang LY, et al. Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma. Ann Nucl Med. 2023;37:139–50.PubMed
23.
Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018;59:1321–8.PubMed
24.
Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan M, et al. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med. 2022;63:172–9.PubMedPubMedCentral
25.
Tsutsui Y, Daisaki H, Akamatsu G, Umeda T, Ogawa M, Kajiwara H, et al. Multicentre analysis of PET SUV using vendor-neutral software: the Japanese harmonization technology (J-Hart) study. EJNMMI Res. 2018;8:83.PubMedPubMedCentral
26.
Daisaki H, Kitajima K, Nakajo M, Watabe T, Ito K, Sakamoto F, et al. Usefulness of semi-automatic harmonization strategy of standardized uptake values for multicenter PET studies. Sci Rep. 2021;11:8517.PubMedPubMedCentral
27.
Shao X, Niu R, Shao X, Gao J, Shi Y, Jiang Z, et al. Application of dual-stream 3D convolutional neural network based on (18)F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules. EJNMMI Phys. 2021;8:74.PubMedPubMedCentral
28.
Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep. 2017;7:46479.PubMedPubMedCentral
29.
Chen S, Han X, Tian G, Cao Y, Zheng X, Li X, et al. Using stacked deep learning models based on PET/CT images and clinical data to predict EGFR mutations in lung cancer. Front Med (Lausanne). 2022;9:1041034.PubMed
30.
Yang Y, Zheng B, Li Y, Li Y, Ma X. Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT. Med Phys. 2023;50:152–62.PubMed
31.
Wanichwecharungruang B, Kaothanthong N, Pattanapongpaiboon W, Chantangphol P, Seresirikachorn K, Srisuwanporn C, et al. Deep learning for anterior segment optical coherence tomography to predict the presence of plateau iris. Transl Vis Sci Technol. 2021;10:7.PubMedPubMedCentral
32.
Venugopal VK, Vaidhya K, Murugavel M, Chunduru A, Mahajan V, Vaidya S, et al. Unboxing AI—radiological insights into a deep neural network for lung nodule characterization. Acad Radiol. 2020;27:88–95.PubMed
33.
Huff DT, Weisman AJ, Jeraj R. Interpretation and visualization techniques for deep learning models in medical imaging. Phys Med Biol. 2021;66:04TR01.PubMedPubMedCentral
34.
Zeiler MD, Fergus R (2014) Visualizing and Understanding Convolutional Networks. In Computer Vision—ECCV 2014: 13th European Conference, Proceedings, Part I. Berlin: Springer International Publishing
35.
Roengvoraphoj O, Kasmann L, Eze C, Taugner J, Gjika A, Tufman A, et al. Maximum standardized uptake value of primary tumor (SUVmax_PT) and horizontal range between two most distant PET-positive lymph nodes predict patient outcome in inoperable stage III NSCLC patients after chemoradiotherapy. Transl Lung Cancer Res. 2020;9:541–8.PubMedPubMedCentral
36.
Nair VS, Gevaert O, Davidzon G, Napel S, Graves EE, Hoang CD, et al. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res. 2012;72:3725–34.PubMedPubMedCentral
37.
Wang H, Sun Z, Zhao W. Geng B [S100A10 promotes proliferation and invasion of lung adenocarcinoma cells by activating the Akt-mTOR signaling pathway]. Nan Fang Yi Ke Da Xue Xue Bao. 2023;43:733–40.PubMed
38.
Goodman A, Mahmud W, Buckingham L. Gene variant profiles and tumor metabolic activity as measured by FOXM1 expression and glucose uptake in lung adenocarcinoma. J Pathol Transl Med. 2020;54:237–45.PubMedPubMedCentral
39.
Giatromanolaki A, Koukourakis MI, Sivridis E, Turley H, Talks K, Pezzella F, et al. Relation of hypoxia inducible factor 1 alpha and 2 alpha in operable non-small cell lung cancer to angiogenic/molecular profile of tumours and survival. Br J Cancer. 2001;85:881–90.PubMedPubMedCentral
40.
Torresano L, Nuevo-Tapioles C, Santacatterina F, Cuezva JM. Metabolic reprogramming and disease progression in cancer patients. Biochim Biophys Acta Mol Basis Dis. 2020;1866:165721.PubMed
41.
Gasmi A, Peana M, Arshad M, Butnariu M, Menzel A, Bjorklund G. Krebs cycle: activators, inhibitors and their roles in the modulation of carcinogenesis. Arch Toxicol. 2021;95:1161–78.PubMed
42.
Chen YH, Chu SC, Wang LY, Wang TF, Lue KH, Lin CB, et al. Prognostic value of combing primary tumor and nodal glycolytic-volumetric parameters of 18F-FDG PET in patients with non-small cell lung cancer and regional lymph node metastasis. Diagnostics (Basel). 2021;11:1065.PubMed
43.
Pellegrino S, Fonti R, Pulcrano A, Del Vecchio S. PET-based volumetric biomarkers for risk stratification of non-small cell lung cancer patients. Diagnostics (Basel). 2021;11:210.PubMed
44.
Mahmoud HA, Oteify W, Elkhayat H, Zaher AM, Mohran TZ, Mekkawy N. Volumetric parameters of the primary tumor and whole-body tumor burden derived from baseline (18)F-FDG PET/CT can predict overall survival in non-small cell lung cancer patients: initial results from a single institution. Eur J Hybrid Imaging. 2022;6:37.PubMedPubMedCentral
45.
Park SY, Yoon J-K, Park KJ, Lee SJ. Prediction of occult lymph node metastasis using volume-based PET parameters in small-sized peripheral non-small cell lung cancer. Cancer Imaging. 2015;15:21.PubMedPubMedCentral
46.
Zhang Y, Sheng J, Kang S, Fang W, Yan Y, Hu Z, et al. Patients with exon 19 deletion were associated with longer progression-free survival compared to those with L858R mutation after first-line EGFR-TKIs for advanced non-small cell lung cancer: a meta-analysis. PLoS ONE. 2014;9:e107161.PubMedPubMedCentral
47.
Koyama N, Watanabe Y, Iwai Y, Kawamura R, Miwa C, Nagai Y, et al. Distinct benefit of overall survival between patients with non-small-cell lung cancer harboring EGFR Exon 19 deletion and exon 21 L858R substitution. Chemotherapy. 2017;62:151–8.PubMed
48.
Masago K, Kuroda H, Fujita S, Sasaki E, Takahashi Y, Shinohara S, et al. Biological difference between L858R and Exon 19 deletion contributes to recurrence-free survival of resected non-small cell lung cancer. Oncology. 2023;101:117–25.PubMed
49.
Bi JH, Tuo JY, Xiao YX, Tang DD, Zhou XH, Jiang YF, et al. Observed and relative survival trends of lung cancer: a systematic review of population-based cancer registration data. Thorac Cancer. 2024;15:142–51.PubMed
50.
51.
Zeng L, Xiao L, Jiang W, Yang H, Hu D, Xia C, et al. Investigation of efficacy and acquired resistance for EGFR-TKI plus bevacizumab as first-line treatment in patients with EGFR sensitive mutant non-small cell lung cancer in a Real world population. Lung Cancer. 2020;141:82–8.PubMed
52.
Xu Z, Hao X, Lin L, Li J, Xing P. Concurrent chemotherapy and first-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) with or without an antiangiogenic agent as first-line treatment in advanced lung adenocarcinoma harboring an EGFR mutation. Thorac Cancer. 2021;12:2233–40.PubMedPubMedCentral
53.
Hosomi Y, Morita S, Sugawara S, Kato T, Fukuhara T, Gemma A, et al. Gefitinib alone versus gefitinib plus chemotherapy for non-small-cell lung cancer with mutated epidermal growth factor receptor: NEJ009 study. J Clin Oncol. 2020;38:115–23.PubMed
54.
Wallis D, Soussan M, Lacroix M, Akl P, Duboucher C, Buvat I. An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging. 2022;49:881–8.PubMed
Metadaten
Titel
Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study
verfasst von
Kun-Han Lue
Yu-Hung Chen
Sung-Chao Chu
Chih-Bin Lin
Tso-Fu Wang
Shu-Hsin Liu
Publikationsdatum
05.05.2024
Verlag
Springer Nature Singapore
Erschienen in
Annals of Nuclear Medicine / Ausgabe 8/2024
Print ISSN: 0914-7187
Elektronische ISSN: 1864-6433
DOI
https://doi.org/10.1007/s12149-024-01936-2

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