Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Objective

To develop and validate a radiomics-clinical nomogram for the detection of the acquired mutation in patients with advanced non-small cell lung cancer (NSCLC) with resistance after the duration of first-line epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment.

Materials and Methods

Thoracic CT was collected from 120 advanced NSCLC patients who suffered progression on first- or second-generation TKIs. Radiomics signatures were retrieved from the entire tumor. Pearson correlation and the least absolute shrinkage and selection operator (LASSO) regression method were adopted to choose the most suitable radiomics features. Clinical and radiological factors were assessed using univariate and multivariate analysis. Three Machine Learning (ML) models were constructed according to three classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), and RandomForest (RF), combining clinical and radiomic features. A nomogram combining clinical features and the rad score signature was built. The predictive ability of the nomogram was assessed by the ROC curve, calibration curve, and decision curve analysis (DCA).

Results

Multivariate regression analysis showed that two clinicopathological characteristics and two radiological features were highly correlated with the acquired mutation, including the progression-free survival (PFS) of first-line EGFR TKIs (P = 0.029), the initial EGFR profile (P = 0.01), vascular convergence (P = 0.043), and air bronchogram (P = 0.030). The AUCs of clinical, radiomics, and combined models using RF classifiers for mutation detection were 0.951 (95% confidence interval [CI] 0.911,0.991), 0.917 (95%CI 0.856,0.978), and 0.961 (95%CI 0.927,0.995) in the training cohort, respectively, higher than those of other classifier models.The calibration curve and Hosmer-Lemeshow Test showed good calibration power, and the DCA demonstrated a significant net benefit.

Conclusion

A radiomics-clinical nomogram based on CT radiomics proved valuable in non-invasively and efficiently predicting the acquired mutation in patients who suffered progression on first-line TKIs.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056283623240215102037
2024-01-01
2025-05-29

  • From This Site

    /content/journals/cmir/10.2174/0115734056283623240215102037
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. SiegelR.L. MillerK.D. FuchsH.E. JemalA. Cancer statistics, 2022.CA Cancer J. Clin.202272173310.3322/caac.2170835020204
     [Google Scholar]
  2. EttingerD.S. WoodD.E. AisnerD.L. AkerleyW. BaumanJ.R. BharatA. BrunoD.S. ChangJ.Y. ChirieacL.R. D’AmicoT.A. DeCampM. DillingT.J. DowellJ. GettingerS. GrotzT.E. GubensM.A. HegdeA. LacknerR.P. LanutiM. LinJ. LooB.W. LovlyC.M. MaldonadoF. MassarelliE. MorgenszternD. NgT. OttersonG.A. PachecoJ.M. PatelS.P. RielyG.J. RiessJ. SchildS.E. ShapiroT.A. SinghA.P. StevensonJ. TamA. TanvetyanonT. YanagawaJ. YangS.C. YauE. GregoryK. HughesM. Non–small cell lung cancer, version 3.2022, NCCN clinical practice guidelines in oncology.J. Natl. Compr. Canc. Netw.202220549753010.6004/jnccn.2022.002535545176
     [Google Scholar]
  3. HanB. TjulandinS. HagiwaraK. NormannoN. WulandariL. LaktionovK. HudoyoA. HeY. ZhangY.P. WangM.Z. LiuC.Y. RatcliffeM. McCormackR. ReckM. EGFR mutation prevalence in asia-pacific and russian patients with advanced NSCLC of adenocarcinoma and non-adenocarcinoma histology: The IGNITE study.Lung Cancer2017113374410.1016/j.lungcan.2017.08.02129110846
     [Google Scholar]
  4. WuS.G. ShihJ.Y. Management of acquired resistance to EGFR TKI–targeted therapy in advanced non-small cell lung cancer.Mol. Cancer20181713810.1186/s12943‑018‑0777‑129455650
     [Google Scholar]
  5. MinguetJ. SmithK.H. BramlageP. Targeted therapies for treatment of non-small cell lung cancer—Recent advances and future perspectives.Int. J. Cancer2016138112549256110.1002/ijc.2991526537995
     [Google Scholar]
  6. LiH. WangJ. ZhangG. LiY. LinL. YangH. ZhouJ. ZhangL. LvD. Detection of plasma T790M mutation after the first generation EGFR-TKI resistance of non-small cell lung cancer in the real world.J. Thorac. Dis.202012355055710.21037/jtd.2019.12.12232274120
     [Google Scholar]
  7. WuY.L. ZhouC. HuC.P. FengJ. LuS. HuangY. LiW. HouM. ShiJ.H. LeeK.Y. XuC.R. MasseyD. KimM. ShiY. GeaterS.L. Afatinib versus cisplatin plus gemcitabine for first-line treatment of Asian patients with advanced non-small-cell lung cancer harbouring EGFR mutations (LUX-Lung 6): An open-label, randomised phase 3 trial.Lancet Oncol.201415221322210.1016/S1470‑2045(13)70604‑124439929
     [Google Scholar]
  8. KoB. PaucarD. HalmosB. EGFR T790M: Revealing the secrets of a gatekeeper.Lung Cancer2017814715910.2147/LCTT.S11794429070957
     [Google Scholar]
  9. HataA.N. NiederstM.J. ArchibaldH.L. Gomez-CaraballoM. SiddiquiF.M. MulveyH.E. MaruvkaY.E. JiF. BhangH.C. Krishnamurthy RadhakrishnaV. SiravegnaG. HuH. RaoofS. LockermanE. KalsyA. LeeD. KeatingC.L. RuddyD.A. DamonL.J. CrystalA.S. CostaC. PiotrowskaZ. BardelliA. IafrateA.J. SadreyevR.I. StegmeierF. GetzG. SequistL.V. FaberA.C. EngelmanJ.A. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition.Nat. Med.201622326226910.1038/nm.404026828195
     [Google Scholar]
  10. Díaz-SerranoA. GellaP. JiménezE. ZugazagoitiaJ. Paz-AresR.L. Targeting EGFR in lung cancer: Current standards and developments.Drugs201878989391110.1007/s40265‑018‑0916‑429915896
     [Google Scholar]
  11. CrossD.A.E. AshtonS.E. GhiorghiuS. EberleinC. NebhanC.A. SpitzlerP.J. OrmeJ.P. FinlayM.R.V. WardR.A. MellorM.J. HughesG. RahiA. JacobsV.N. BrewerM.R. IchiharaE. SunJ. JinH. BallardP. Al-KadhimiK. RowlinsonR. KlinowskaT. RichmondG.H.P. CantariniM. KimD.W. RansonM.R. PaoW. AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer.Cancer Discov.2014491046106110.1158/2159‑8290.CD‑14‑033724893891
     [Google Scholar]
  12. SequistL.V. RolfeL. AllenA.R. Rociletinib in EGFR -mutated non–small-cell lung cancer.N. Engl. J. Med.2015373657857910.1056/NEJMc150683126244318
     [Google Scholar]
  13. LiaoY. ChenY. KouX. XiaoY. YeJ. WuA. Diagnostic test accuracy of droplet digital PCR for the detection of EGFR mutation (T790M) in plasma: Systematic review and meta-analysis.Clin. Chim. Acta202050319019610.1016/j.cca.2019.11.02331805270
     [Google Scholar]
  14. MerkerJ.D. OxnardG.R. ComptonC. DiehnM. HurleyP. LazarA.J. LindemanN. LockwoodC.M. RaiA.J. SchilskyR.L. TsimberidouA.M. VasalosP. BillmanB.L. OliverT.K. BruinoogeS.S. HayesD.F. TurnerN.C. Circulating tumor DNA analysis in patients with cancer: American society of clinical oncology and college of american pathologists joint review.J. Clin. Oncol.201836161631164110.1200/JCO.2017.76.867129504847
     [Google Scholar]
  15. LambinP. Rios-VelazquezE. LeijenaarR. CarvalhoS. van StiphoutR.G.P.M. GrantonP. ZegersC.M.L. GilliesR. BoellardR. DekkerA. AertsH.J.W.L. Radiomics: Extracting more information from medical images using advanced feature analysis.Eur. J. Cancer201248444144610.1016/j.ejca.2011.11.03622257792
     [Google Scholar]
  16. GilliesR.J. KinahanP.E. HricakH. Radiomics: Images are more than pictures, they are data.Radiology2016278256357710.1148/radiol.201515116926579733
     [Google Scholar]
  17. MeiD. LuoY. WangY. GongJ. CT texture analysis of lung adenocarcinoma: Can Radiomic features be surrogate biomarkers for EGFR mutation statuses.Cancer Imaging20181815210.1186/s40644‑018‑0184‑230547844
     [Google Scholar]
  18. SongJ. ShiJ. DongD. FangM. ZhongW. WangK. WuN. HuangY. LiuZ. ChengY. GanY. ZhouY. ZhouP. ChenB. LiangC. LiuZ. LiW. TianJ. A new approach to predict progression-free survival in stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy.Clin. Cancer Res.201824153583359210.1158/1078‑0432.CCR‑17‑250729563137
     [Google Scholar]
  19. LiS. DingC. ZhangH. SongJ. WuL. Radiomics for the prediction of EGFR mutation subtypes in non-small cell lung cancer.Med. Phys.201946104545455210.1002/mp.1374731376283
     [Google Scholar]
  20. RossiG. BarabinoE. FedeliA. FicarraG. CocoS. RussoA. AdamoV. BuemiF. ZulloL. DonoM. De LucaG. LongoL. Dal BelloM.G. TagliamentoM. AlamaA. CittadiniG. PronzatoP. GenovaC. Radiomic detection of EGFR mutations in NSCLC.Cancer Res.202181372473110.1158/0008‑5472.CAN‑20‑099933148663
     [Google Scholar]
  21. YangX. FangC. LiC. GongM. YiX. LinH. LiK. YuX. Can CT radiomics detect acquired T790M mutation and predict prognosis in advanced lung adenocarcinoma with progression after first- or second-generation EGFR TKIs?Front. Oncol.20221290498310.3389/fonc.2022.90498335875167
     [Google Scholar]
  22. YoshidaT. TanakaH. KurodaH. ShimizuJ. HorioY. SakaoY. InabaY. IwataH. HidaT. YatabeY. Standardized uptake value on 18 F-FDG-PET/CT is a predictor of EGFR T790M mutation status in patients with acquired resistance to EGFR-TKIs.Lung Cancer2016100141910.1016/j.lungcan.2016.07.02227597275
     [Google Scholar]
  23. KooH.J. KimM.Y. ParkS. LeeH.N. KimH.J. LeeJ.C. KimS.W. LeeD.H. ChoiC.M. Non–small cell lung cancer with resistance to EGFR-TKI therapy: CT characteristics of T790M mutation–positive cancer.Radiology2018289122723710.1148/radiol.201818007030015588
     [Google Scholar]
  24. OlawaiyeA.B. BakerT.P. WashingtonM.K. MutchD.G. The new (Version 9) American joint committee on cancer tumor, node, metastasis staging for cervical cancer.CA Cancer J. Clin.202171428729810.3322/caac.2166333784415
     [Google Scholar]
  25. EisenhauerE.A. TherasseP. BogaertsJ. SchwartzL.H. SargentD. FordR. DanceyJ. ArbuckS. GwytherS. MooneyM. RubinsteinL. ShankarL. DoddL. KaplanR. LacombeD. VerweijJ. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1).Eur. J. Cancer200945222824710.1016/j.ejca.2008.10.02619097774
     [Google Scholar]
  26. FanL. LiJ. ZhangH. YinH. ZhangR. ZhangJ. ChenX. Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables.Abdom. Radiol.20224741209122210.1007/s00261‑021‑03315‑135089370
     [Google Scholar]
  27. ZhangH. WangS. DengZ. LiY. YangY. HuangH. Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: A retrospective study.PeerJ202311e1455910.7717/peerj.1455936643621
     [Google Scholar]
  28. ChenY. LuoY. HuangW. HuD. ZhengR. CongS. MengF. YangH. LinH. SunY. WangX. WuT. RenJ. PeiS.F. ZhengY. HeY. HuY. YangN. YanH. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.Comput. Biol. Med.201789182310.1016/j.compbiomed.2017.07.01228779596
     [Google Scholar]
  29. TangX. LiY. ShenL.T. YanW.F. QianW.L. YangZ.G. CT radiomics predict EGFR-T790M resistance mutation in advanced non-small cell lung cancer patients after progression on first-line EGFR-TKI.Acad. Radiol.202330112574258710.1016/j.acra.2023.01.04036941156
     [Google Scholar]
  30. KawamuraT. KenmotsuH. OmoriS. NakashimaK. WakudaK. OnoA. NaitoT. MurakamiH. OmaeK. MoriK. TanigawaraY. NakajimaT. OhdeY. EndoM. TakahashiT. Clinical factors predicting detection of T790M mutation in rebiopsy for EGFR-mutant non–small-cell lung cancer.Clin. Lung Cancer2018192e247e25210.1016/j.cllc.2017.07.00228866043
     [Google Scholar]
  31. KoR. KenmotsuH. SerizawaM. KohY. WakudaK. OnoA. TairaT. NaitoT. MurakamiH. IsakaM. EndoM. NakajimaT. OhdeY. YamamotoN. TakahashiK. TakahashiT. Frequency of EGFR T790M mutation and multimutational profiles of rebiopsy samples from non-small cell lung cancer developing acquired resistance to EGFR tyrosine kinase inhibitors in Japanese patients.BMC Cancer201616186410.1186/s12885‑016‑2902‑027821131
     [Google Scholar]
  32. HuangY.H. HsuK.H. TsengJ.S. ChenK.C. HsuC.H. SuK.Y. ChenJ.J.W. ChenH.W. YuS.L. YangT.Y. ChangG.C. The association of acquired T790M mutation with clinical characteristics after resistance to first-line epidermal growth factor receptor tyrosine kinase inhibitor in lung adenocarcinoma.Cancer Res. Treat.20185041294130310.4143/crt.2017.51229334606
     [Google Scholar]
  33. LiangH. PanZ. WangW. GuoC. ChenD. ZhangJ. ZhangY. TangS. HeJ. LiangW. The alteration of T790M between 19 del and L858R in NSCLC in the course of EGFR-TKIs therapy: A literature-based pooled analysis.J. Thorac. Dis.20181042311232010.21037/jtd.2018.03.15029850136
     [Google Scholar]
  34. NosakiK. SatouchiM. KurataT. YoshidaT. OkamotoI. KatakamiN. ImamuraF. TanakaK. YamaneY. YamamotoN. KatoT. KiuraK. SakaH. YoshiokaH. WatanabeK. MizunoK. SetoT. Re-biopsy status among non-small cell lung cancer patients in Japan: A retrospective study.Lung Cancer20161011810.1016/j.lungcan.2016.07.00727794396
     [Google Scholar]
  35. MatsuoN. AzumaK. SakaiK. HattoriS. KawaharaA. IshiiH. TokitoT. KinoshitaT. YamadaK. NishioK. HoshinoT. Association of EGFR exon 19 deletion and EGFR-TKI treatment duration with frequency of T790M mutation in EGFR-mutant lung cancer patients.Sci. Rep.2016613645810.1038/srep3645827811988
     [Google Scholar]
  36. OuyangW. YuJ. HuangZ. ChenG. LiuY. LiaoZ. ZengW. ZhangJ. XieC. Risk factors of acquired T790M mutation in patients with epidermal growth factor receptor-mutated advanced non-small cell lung cancer.J. Cancer20201182060206710.7150/jca.3799132127933
     [Google Scholar]
  37. BalachandranV.P. GonenM. SmithJ.J. DeMatteoR.P. Nomograms in oncology: More than meets the eye.Lancet Oncol.2015164e173e18010.1016/S1470‑2045(14)71116‑725846097
     [Google Scholar]
  38. ZhaoW. WuY. XuY. SunY. GaoP. TanM. MaW. LiC. JinL. HuaY. LiuJ. LiM. The potential of radiomics nomogram in non-invasively prediction of epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma.Front. Oncol.20209148510.3389/fonc.2019.0148531993370
     [Google Scholar]
  39. HouD. LiW. WangS. HuangY. WangJ. TangW. ZhouL. QiL. WuN. ZhaoS. Different clinicopathologic and computed tomography imaging characteristics of primary and acquired EGFR T790M mutations in patients with non-small-cell lung cancer.Cancer Manag. Res.2021136389640110.2147/CMAR.S32397234413682
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056283623240215102037
Loading
A Radiomics-clinical Nomogram based on CT Radiomics to Predict Acquired T790M Mutation Status in Non-small Cell Lung Cancer Patients
Curr. Med. Imaging 20, e15734056283623 (2024); https://doi.org/10.2174/0115734056283623240215102037
/content/journals/cmir/10.2174/0115734056283623240215102037
/content/journals/cmir/10.2174/0115734056283623240215102037
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Acquired T790M mutation; EGFR TKI; Machine learning; Nomogram; Non-small cell lung cancer; Radiomics

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test