MR texture analysis in differentiation of small and very small renal cell carcinoma subtypes.
Magnetic resonance imaging
Renal cell carcinoma
Texture analysis
Journal
Abdominal radiology (New York)
ISSN: 2366-0058
Titre abrégé: Abdom Radiol (NY)
Pays: United States
ID NLM: 101674571
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
30
08
2022
accepted:
23
12
2022
revised:
23
12
2022
pubmed:
18
1
2023
medline:
25
2
2023
entrez:
17
1
2023
Statut:
ppublish
Résumé
To explore the diagnostic efficacy of MR-based texture analysis in differentiation of small (≤ 4 cm) and very small (≤ 2 cm) renal cell carcinoma subtypes. One hundred and eight patients with pT1a (≤ 4 cm) renal cell carcinoma and pretreatment MRI were enrolled in this retrospective study. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from whole-tumor images. Among subtypes, patient age, tumor size, histological grading and texture parameters were compared. Diagnostic model using combination of texture parameters was constructed using logistic regression and validated using fivefold cross-validation. AUC with 95% CI, accuracy, sensitivity and specificity for subtype differentiation are reported. Further we explored the distinguishing ability of texture parameters and diagnostic model in very small (≤ 2 cm) RCC subgroups. Significant texture parameters among RCC subtypes were identified. For small (≤ 4 cm) renal cell carcinoma subtyping, combining models based on texture parameters achieved good AUCs for differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC (0.79, 0.74 and 0.81). Further, in subgroups of very small (≤ 2 cm) RCCs, diagnostic models had better differentiating performances, achieving AUCs of 0.88, 0.99, 0.96 in differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC. MR texture analysis may help to differentiate small (≤ 4 cm) and very small (≤ 2 cm) RCC subtypes. This non-invasive method can potentially provide additional information for localized RCC treatment and surveillance strategy.
Identifiants
pubmed: 36650366
doi: 10.1007/s00261-022-03794-w
pii: 10.1007/s00261-022-03794-w
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1044-1050Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Hsieh JJ, Purdue MP, Signoretti S et al (2017) Renal cell carcinoma. Nat Rev Dis Primers 3:17009
doi: 10.1038/nrdp.2017.9
pubmed: 28276433
pmcid: 5936048
Antonio F, Nofisat I, Bill B et al (2016) Management of Small Renal Masses: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 35:668-680
Campbell S, Uzzo RG, Allaf ME, et al (2017) Renal mass and localized renal cancer: AUA guideline. J Urol 198(3): 520-529
doi: 10.1016/j.juro.2017.04.100
pubmed: 28479239
Ryan DW, Hajime T, Steven CC, Erick MR (2018) AUA Renal Mass and Localized Renal Cancer Guidelines: Imaging Implications. RadioGraphics 38:2021–2033
doi: 10.1148/rg.2018180127
Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM (2016) The 2016 WHO classification of tumours of the urinary system and male genital organs-part a: renal, penile, and testicular tumours. Eur Urol 70:93-105
doi: 10.1016/j.eururo.2016.02.029
pubmed: 26935559
Jung JP, Chan KK (2017) Small (< 4 cm) Renal Tumors With Predominantly Low Signal Intensity on T2-Weighted Images: Differentiation of Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma. Am J Roentgenol 208:124-130
doi: 10.2214/AJR.16.16102
Kohei S, Naoki T (2018) CT and MR imaging for solid renal mass characterization. Eur J Radiol 99:40-54.
doi: 10.1016/j.ejrad.2017.12.008
Young JR, Coy H, Kim HJ et al (2017) Performance of relative enhancement on multiphasic MRI for the differentiation of clear cell renal cell carcinoma (RCC) from papillary and chromophobe RCC subtypes and oncocytoma. Am J Roentgenol 208:812-819
doi: 10.2214/AJR.16.17152
Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400-406
doi: 10.1102/1470-7330.2013.9045
pubmed: 24061266
pmcid: 3781643
Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061-1069
doi: 10.1016/j.crad.2004.07.008
pubmed: 15556588
Ankur G, Abdul R, Devasenathipathy K et al (2019) Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol, 44(10):3336-3349
doi: 10.1007/s00261-019-02122-z
Wei W, KaiMing Cao, ShengMing J et al (2020) Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radio 30:5738-5747
doi: 10.1007/s00330-020-06896-5
Uyen NH, S. Mojdeh M, Osorio M et al (2018) Assessment of multiphasic contrast-enhanced MR textures in differentiating small renal mass subtypes. Abdom Radiol 43:3400-3409
doi: 10.1007/s00261-018-1625-x
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563-577
doi: 10.1148/radiol.2015151169
pubmed: 26579733
Stephan U, Lucian B, Annemarie B et al (2020) Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis. Eur Radio 30:3558-3566
doi: 10.1007/s00330-020-06666-3
Cui E, Li Z, Ma C et al (2020) Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics. Eur Radiol. 30(5):2912-2921
doi: 10.1007/s00330-019-06601-1
pubmed: 32002635
Yao Z, Shuai W, Yan C, Huiqian D (2021) Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study. Abdom Radiol 46:3260-3268
doi: 10.1007/s00261-021-02981-5
Xi IL, Zhao Y, Wang R, et al. (2020) Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res 26(8):1944-1952
doi: 10.1158/1078-0432.CCR-19-0374
pubmed: 31937619
Meghan GL (2020) Radiomics and Artificial Intelligence for Renal Mass Characterization. Radiol Clin North Am 58(5):995-1008
doi: 10.1016/j.rcl.2020.06.001