Advanced biomedical imaging for accurate discrimination and prognostication of mediastinal masses.
artificial intelligence
iodine
mediastinal neoplasm
mediastinum
multidetector computed tomography
thymoma
Journal
European journal of clinical investigation
ISSN: 1365-2362
Titre abrégé: Eur J Clin Invest
Pays: England
ID NLM: 0245331
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
revised:
06
07
2023
received:
20
06
2023
accepted:
25
07
2023
medline:
22
11
2023
pubmed:
12
8
2023
entrez:
12
8
2023
Statut:
ppublish
Résumé
To investigate the potential of radiomic features and dual-source dual-energy CT (DECT) parameters in differentiating between benign and malignant mediastinal masses and predicting patient outcomes. In this retrospective study, we analysed data from 90 patients (38 females, mean age 51 ± 25 years) with confirmed mediastinal masses who underwent contrast-enhanced DECT. Attenuation, radiomic features and DECT-derived imaging parameters were evaluated by two experienced readers. We performed analysis of variance (ANOVA) and Chi-square statistic tests for data comparison. Receiver operating characteristic curve analysis and Cox regression tests were used to differentiate between mediastinal masses. Of the 90 mediastinal masses, 49 (54%) were benign, including cases of thymic hyperplasia/thymic rebound (n = 10), mediastinitis (n = 16) and thymoma (n = 23). The remaining 41 (46%) lesions were classified as malignant, consisting of lymphoma (n = 28), mediastinal tumour (n = 4) and thymic carcinoma (n = 9). Significant differences were observed between benign and malignant mediastinal masses in all DECT-derived parameters (p ≤ .001) and 38 radiomic features (p ≤ .044) obtained from contrast-enhanced DECT. The combination of these methods achieved an area under the curve of .98 (95% CI, .893-1.000; p < .001) to differentiate between benign and malignant masses, with 100% sensitivity and 91% specificity. Throughout a follow-up of 1800 days, a multiparametric model incorporating radiomic features, DECT parameters and gender showed promising prognostic power in predicting all-cause mortality (c-index = .8 [95% CI, .702-.890], p < .001). A multiparametric approach combining radiomic features and DECT-derived imaging biomarkers allows for accurate and noninvasive differentiation between benign and malignant masses in the anterior mediastinum.
Sections du résumé
BACKGROUND
BACKGROUND
To investigate the potential of radiomic features and dual-source dual-energy CT (DECT) parameters in differentiating between benign and malignant mediastinal masses and predicting patient outcomes.
METHODS
METHODS
In this retrospective study, we analysed data from 90 patients (38 females, mean age 51 ± 25 years) with confirmed mediastinal masses who underwent contrast-enhanced DECT. Attenuation, radiomic features and DECT-derived imaging parameters were evaluated by two experienced readers. We performed analysis of variance (ANOVA) and Chi-square statistic tests for data comparison. Receiver operating characteristic curve analysis and Cox regression tests were used to differentiate between mediastinal masses.
RESULTS
RESULTS
Of the 90 mediastinal masses, 49 (54%) were benign, including cases of thymic hyperplasia/thymic rebound (n = 10), mediastinitis (n = 16) and thymoma (n = 23). The remaining 41 (46%) lesions were classified as malignant, consisting of lymphoma (n = 28), mediastinal tumour (n = 4) and thymic carcinoma (n = 9). Significant differences were observed between benign and malignant mediastinal masses in all DECT-derived parameters (p ≤ .001) and 38 radiomic features (p ≤ .044) obtained from contrast-enhanced DECT. The combination of these methods achieved an area under the curve of .98 (95% CI, .893-1.000; p < .001) to differentiate between benign and malignant masses, with 100% sensitivity and 91% specificity. Throughout a follow-up of 1800 days, a multiparametric model incorporating radiomic features, DECT parameters and gender showed promising prognostic power in predicting all-cause mortality (c-index = .8 [95% CI, .702-.890], p < .001).
CONCLUSIONS
CONCLUSIONS
A multiparametric approach combining radiomic features and DECT-derived imaging biomarkers allows for accurate and noninvasive differentiation between benign and malignant masses in the anterior mediastinum.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e14075Informations de copyright
© 2023 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.
Références
Biondi A, Rausei S, Cananzi FCM, Zoccali M, D'Ugo S, Persiani R. Surgical anatomy of the anterior mediastinum. Ann Ital Chir. 2007;78(5):351-353.
Shahrzad M, Le TSM, Silva M, Bankier AA, Eisenberg RL. Anterior mediastinal masses. AJR Am J Roentgenol. 2014;203(2):W128-W138. doi:10.2214/AJR.13.11998
Carter BW, Benveniste MF, Madan R, et al. ITMIG classification of mediastinal compartments and multidisciplinary approach to mediastinal masses. Radiographics. 2017;37(2):413-436. doi:10.1148/rg.2017160095
Kirienko M, Ninatti G, Cozzi L, et al. Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Radiol Med. 2020;125(10):951-960. doi:10.1007/s11547-020-01188-w
El-Zaatari ZM, Ro JY. Mediastinal germ cell tumors: a review and update on pathologic, clinical, and molecular features. Adv Anat Pathol. 2021;28(5):335-350. doi:10.1097/PAP.0000000000000304
Johnson PWM. IV. Masses in the mediastinum: primary mediastinal lymphoma and intermediate types. Hematol Oncol. 2015;33(1):29-32. doi:10.1002/hon.2212
Kluge J. Acute and chronic mediastinitis. Chirurg. 2016;87(6):469-477. doi:10.1007/s00104-016-0172-7
Duwe BV, Sterman DH, Musani AI. Tumors of the mediastinum. Chest. 2005;128(4):2893-2909. doi:10.1378/chest.128.4.2893
Takahashi K, Al-Janabi NJ. Computed tomography and magnetic resonance imaging of mediastinal tumors. J Magn Reson Imaging. 2010;32(6):1325-1339. doi:10.1002/JMRI.22377
Odisio EG, Truong MT, Duran C, de Groot PM, Godoy MC. Role of dual-energy computed tomography in thoracic oncology. Radiol Clin North Am. 2018;56(4):535-548. doi:10.1016/j.rcl.2018.03.011
Vulasala SSR, Wynn GC, Hernandez M, et al. Dual-energy imaging of the chest. Semin Ultrasound CT MR. 2022;43(4):311-319. doi:10.1053/j.sult.2022.03.007
Otrakji A, Digumarthy SR, Lo Gullo R, Flores EJ, Shepard JAO, Kalra MK. Dual-energy CT: spectrum of thoracic abnormalities. Radiographics. 2016;36(1):38-52. doi:10.1148/rg.2016150081
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563-577. doi:10.1148/radiol.2015151169
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-446. doi:10.1016/j.ejca.2011.11.036
Gillies RJ, Schabath MB. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomarkers Prev. 2020;29(12):2556-2567. doi:10.1158/1055-9965.EPI-20-0075
Carter BW, Marom EM, Detterbeck FC. Approaching the patient with an anterior mediastinal mass: a guide for clinicians. J Thorac Oncol. 2014;9(2):S102-S109. doi:10.1097/JTO.0000000000000294
Carter BW, Okumura M, Detterbeck FC, Marom EM. Approaching the patient with an anterior mediastinal mass: a guide for radiologists. J Thorac Oncol. 2014;9(2):S110-S118. doi:10.1097/JTO.0000000000000295
Mahmoudi S, Gruenewald LD, Eichler K, et al. Multiparametric evaluation of radiomics features and dual-energy CT iodine maps for discrimination and outcome prediction of thymic masses. Acad Radiol. 2023;25. doi:10.1016/j.acra.2023.03.034
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104-e107. doi:10.1158/0008-5472.CAN-17-0339
Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30(9):1323-1341. doi:10.1016/j.mri.2012.05.001
Kocak B, Baessler B, Bakas S, et al. CheckList for evaluation of radiomics research (clear): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging. 2023;14(1):75. doi:10.1186/s13244-023-01415-8
Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163. doi:10.1016/j.jcm.2016.02.012
Almeida PT, Heller D. Anterior Mediastinal Mass. StatPearls; 2023.
Johnson TRC. Dual-energy CT: general principles. AJR Am J Roentgenol. 2012;199(5):S3-S8. doi:10.2214/AJR.12.9116
De Cecco CN, Darnell A, Rengo M, et al. Dual-energy CT: oncologic applications. AJR Am J Roentgenol. 2012;199(5):S98-S105. doi:10.2214/AJR.12.9207
Goo HW, Goo JM. Dual-energy CT: new horizon in medical imaging. Korean J Radiol. 2017;18(4):555-569. doi:10.3348/kjr.2017.18.4.555
Araki T, Hammer M, Sodickson A. Fat content quantification using dual-energy CT for differentiation of anterior mediastinal lesions from normal or hyperplastic thymus. Curr Probl Diagn Radiol. 2022;51(3):334-339. doi:10.1067/j.cpradiol.2021.06.007
Yu CH, Zhang RP, Yang XT, et al. Dual-energy CT perfusion imaging for differentiating invasive thymomas, thymic carcinomas, and lymphomas in adults. Clin Radiol. 2022;77(6):e417-e424. doi:10.1016/j.crad.2022.02.012
Zhou Z, Qu Y, Zhou Y, Wang B, Hu W, Cao Y. Development and validation of a CT-based radiomics nomogram in patients with anterior mediastinal mass: individualized options for preoperative patients. Front Oncol. 2022;12:869253. doi:10.3389/fonc.2022.869253
Mayoral M, Pagano AM, Araujo-Filho JAB, et al. Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses. Lung Cancer. 2023;178:206-212. doi:10.1016/j.lungcan.2023.02.014
Koch V, Weitzer N, dos Santos D, et al. Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics. Cancer Imaging. 2023;23(1):38. doi:10.1186/s40644-023-00549-8
Beckett KR, Moriarity AK, Langer JM. Safe use of contrast media: what the radiologist needs to know. Radiographics. 2015;35(6):1738-1750. doi:10.1148/rg.2015150033