Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Typ-2-Diabetes und Adipositas Klimawandel und Gesundheit Neues aus dem Markt Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende Patientenratgeber
Erweiterte Suche
Anmelden
nach oben
Japanese Journal of Radiology
Erschienen in:

27.02.2024 | Original Article

Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study

verfasst von: Jingfeng Cheng, Wenzhe Su, Yuzhe Wang, Yang Zhan, Yin Wang, Shuyu Yan, Yuan Yuan, Lingxin Chen, Zixun Wei, Shengjian Zhang, Xin Gao, Zuohua Tang

Erschienen in: Japanese Journal of Radiology | Ausgabe 7/2024

Einloggen, um Zugang zu erhalten

Abstract

Purpose

To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH).

Materials and methods

This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets.

Results

Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets.

Conclusion

The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
2.
3.
King AD, Wong LYS, Law BKH, Bhatia KS, Woo JKS, Ai QY, et al. MR imaging criteria for the detection of nasopharyngeal carcinoma: discrimination of early-stage primary tumors from benign hyperplasia. AJNR Am J Neuroradiol. 2018;39(3):515–23.CrossRefPubMedPubMedCentral
4.
Gao L, Zhou L, Huang X. Identification of novel kinase-transcription factor-mRNA-miRNA regulatory network in nasopharyngeal carcinoma by bioinformatics analysis. Int J Gen Med. 2021;14:7453–69.CrossRefPubMedPubMedCentral
5.
Abdel Khalek Abdel Razek A, King A. MRI and CT of nasopharyngeal carcinoma. AJR Am J Roentgenol. 2012;198(1):11–8.CrossRefPubMed
6.
Wang P, Xiao Z, Tang Z, Wang J. Dual-energy CT in the differentiation of stage T1 nasopharyngeal carcinoma and lymphoid hyperplasia. Eur J Radiol. 2020;124: 108824.CrossRefPubMed
7.
King AD, Woo JKS, Ai QY, Chan JSM, Lam WKJ, Tse IOL, et al. Complementary roles of MRI and endoscopic examination in the early detection of nasopharyngeal carcinoma. Ann Oncol. 2019;30(6):977–82.CrossRefPubMed
8.
Wang ML, Wei XE, Yu MM, Li WB. Value of contrast-enhanced MRI in the differentiation between nasopharyngeal lymphoid hyperplasia and T1 stage nasopharyngeal carcinoma. Radiol Med. 2017;122(10):743–51.CrossRefPubMed
9.
Zhang SX, Jia QJ, Zhang ZP, Liang CH, Chen WB, Qiu QH, et al. Intravoxel incoherent motion MRI: emerging applications for nasopharyngeal carcinoma at the primary site. Eur Radiol. 2014;24(8):1998–2004.CrossRefPubMedPubMedCentral
10.
Ai QY, King AD, Chan JSM, Chen W, Chan KCA, Woo JKS, et al. Distinguishing early-stage nasopharyngeal carcinoma from benign hyperplasia using intravoxel incoherent motion diffusion-weighted MRI. Eur Radiol. 2019;29(10):5627–34.CrossRefPubMed
11.
Yu JY, Zhang D, Huang XL, Ma J, Yang C, Li XJ, et al. Quantitative analysis of DCE-MRI and RESOLVE-DWI for differentiating nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia. J Med Syst. 2020;44(4):75.CrossRefPubMed
12.
Meng T, He H, Liu H, Lv X, Huang C, Zhong L, et al. Investigation of the feasibility of synthetic MRI in the differential diagnosis of non-keratinising nasopharyngeal carcinoma and benign hyperplasia using different contoured methods for delineation of the region of interest. Clin Radiol. 2021;76(3):238 e9-338.CrossRefPubMed
13.
Xiao B, Wang P, Zhao Y, Liu Y, Ye Z. Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia. Medicine (Baltimore). 2021;100(8): e24955.CrossRefPubMed
14.
Ke L, Deng Y, Xia W, Qiang M, Chen X, Liu K, et al. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images. Oral Oncol. 2020;110: 104862.CrossRefPubMed
15.
Deng Y, Li C, Lv X, Xia W, Shen L, Jing B, et al. The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: an exploratory study in large-scale population of endemic area. Comput Methods Programs Biomed. 2022;217: 106702.CrossRefPubMed
16.
Wong LM, King AD, Ai QYH, Lam WKJ, Poon DMC, Ma BBY, et al. Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI. Eur Radiol. 2021;31(6):3856–63.CrossRefPubMed
17.
Wong LM, Ai QYH, Zhang R, Mo F, King AD. Radiomics for discrimination between early-stage nasopharyngeal carcinoma and benign hyperplasia with stable feature selection on MRI. Cancers (Basel). 2022;14(14):3433.CrossRefPubMed
18.
Chan KCA, Woo JKS, King A, Zee BCY, Lam WKJ, Chan SL, et al. Analysis of plasma Epstein-Barr virus DNA to screen for nasopharyngeal cancer. N Engl J Med. 2017;377(6):513–22.CrossRefPubMed
19.
20.
Wang X, Dai S, Wang Q, Chai X, Xian J. Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas. Jpn J Radiol. 2021;39(8):755–62.CrossRefPubMed
21.
Bao D, Liu Z, Geng Y, Li L, Xu H, Zhang Y, et al. Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment. Cancer Imaging. 2022;22(1):10.CrossRefPubMedPubMedCentral
22.
Song Y, Zhang J, Zhang YD, Hou Y, Yan X, Wang Y, et al. FeAture Explorer (FAE): a tool for developing and comparing radiomics models. PLoS ONE. 2020;15(8): e0237587.CrossRefPubMedPubMedCentral
23.
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.CrossRefPubMedPubMedCentral
24.
Yang P, Xu L, Cao Z, Wan Y, Xue Y, Jiang Y, et al. Extracting and selecting robust radiomic features from PET/MR Images in nasopharyngeal carcinoma. Mol Imaging Biol. 2020;22(6):1581–91.CrossRefPubMed
25.
Chen C, Qin Y, Chen H, Cheng J, He B, Wan Y, et al. Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values. Eur Radiol. 2022;32(6):3819–29.CrossRefPubMedPubMedCentral
26.
Yardimci AH, Kocak B, Sel I, Bulut H, Bektas CT, Cin M, et al. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol. 2023;41(1):71–82.CrossRefPubMed
27.
Miranda D, Olivares R, Munoz R, Minonzio JG. Improvement of patient classification using feature selection applied to bidirectional axial transmission. IEEE Trans Ultrason Ferroelectr Freq Control. 2022;69(9):2663–71.CrossRefPubMed
28.
Lin X, Yang F, Zhou L, Yin P, Kong H, Xing W, et al. A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. J Chromatogr B Analyt Technol Biomed Life Sci. 2012;910:149–55.CrossRefPubMed
29.
Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH. Relief-based feature selection: introduction and review. J Biomed Inform. 2018;85:189–203.CrossRefPubMedPubMedCentral
30.
Zheng Y, Zhou D, Liu H, Wen M. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors. Eur Radiol. 2022;32(10):6953–64.CrossRefPubMed
31.
Guang-Wu H, Sunagawa M, Jie-En L, Shimada S, Gang Z, Tokeshi Y, et al. The relationship between microvessel density, the expression of vascular endothelial growth factor (VEGF), and the extension of nasopharyngeal carcinoma. Laryngoscope. 2000;110(12):2066–9.CrossRefPubMed
32.
Bangiyev L, Raz E, Block TK, Hagiwara M, Wu X, Yu E, et al. Evaluation of the orbit using contrast-enhanced radial 3D fat-suppressed T1 weighted gradient echo (Radial-VIBE) sequence. Br J Radiol. 2015;88(1054):20140863.CrossRefPubMedPubMedCentral
33.
King AD, Lam WW, Leung SF, Chan YL, Metreweli C. Comparison of T2 weighted fat suppressed turbo spin echo and contrast enhanced T1 weighted spin echo MRI in nasopharyngeal carcinoma. Br J Radiol. 1997;70(840):1208–14.CrossRefPubMed
34.
Duron L, Heraud A, Charbonneau F, Zmuda M, Savatovsky J, Fournier L, et al. A magnetic resonance imaging radiomics signature to distinguish benign from malignant orbital lesions. Invest Radiol. 2021;56(3):173–80.CrossRefPubMed
35.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.CrossRefPubMed
36.
Dehghani Firouzabadi F, Gopal N, Homayounieh F, Anari PY, Li X, Ball MW, et al. CT radiomics for differentiating oncocytoma from renal cell carcinomas: systematic review and meta-analysis. Clin Imaging. 2023;94:9–17.CrossRefPubMed
Metadaten
Titel
Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study
verfasst von
Jingfeng Cheng
Wenzhe Su
Yuzhe Wang
Yang Zhan
Yin Wang
Shuyu Yan
Yuan Yuan
Lingxin Chen
Zixun Wei
Shengjian Zhang
Xin Gao
Zuohua Tang
Publikationsdatum
27.02.2024
Verlag
Springer Nature Singapore
Erschienen in
Japanese Journal of Radiology / Ausgabe 7/2024
Print ISSN: 1867-1071
Elektronische ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-024-01544-0

Weitere Artikel der Ausgabe 7/2024

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

  • Open Access
  • Original Article

Significance of definitive concurrent chemoradiotherapy for vulvar cancer: a Japanese Gynecologic Oncology Group nationwide survey study

  • Open Access
  • Original Article

A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning

  • Original Article

In reply to RMED-D-24-00123

  • Letter to the Editor

Performance of dual-energy CT with iodine quantification in differentiating for benign versus malignant lymph nodes in patients with lung adenocarcinoma

  • Letter to the Editor

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions

  • Open Access
  • Original Article

Neu im Fachgebiet Radiologie

KI-gestütztes Mammografiescreening überzeugt im Praxistest

Mit dem Einsatz künstlicher Intelligenz lässt sich die Detektionsrate im Mammografiescreening offenbar deutlich steigern. Mehr unnötige Zusatzuntersuchungen sind laut der Studie aus Deutschland nicht zu befürchten.

Stumme Schlaganfälle − ein häufiger Nebenbefund im Kopf-CT?

In 4% der in der Notfallambulanz initiierten zerebralen Bildgebung sind „alte“ Schlaganfälle zu erkennen. Gar nicht so selten handelt es sich laut einer aktuellen Studie dabei um unbemerkte Insulte. Bietet sich hier womöglich die Chance auf ein effektives opportunistisches Screening?

Die elektronische Patientenakte kommt: Das sollten Sie jetzt wissen

Am 15. Januar geht die „ePA für alle“ zunächst in den Modellregionen an den Start. Doch schon bald soll sie in allen Praxen zum Einsatz kommen. Was ist jetzt zu tun? Was müssen Sie wissen? Wir geben in einem FAQ Antworten auf 21 Fragen.

Stören weiße Wände und viel Licht die Bildqualitätskontrolle?

Wenn es darum geht, die technische Qualität eines Mammogramms zu beurteilen, könnten graue Wandfarbe und reduzierte Beleuchtung im Bildgebungsraum von Vorteil sein. Darauf deuten zumindest Ergebnisse einer kleinen Studie hin. 

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen
Bildnachweise