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27.08.2020 | Imaging Informatics and Artificial Intelligence

MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma

verfasst von: Xu Bai, Qingbo Huang, Panli Zuo, Xiaojing Zhang, Jing Yuan, Xu Zhang, Meifeng Wang, Wei Xu, Huiyi Ye, Jinkun Zhao, Haoran Sun, Bin Shao, Haiyi Wang

Erschienen in: European Radiology | Ausgabe 2/2021

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Abstract

Objective

To evaluate the performance of a multiparametric MRI radiomics-based nomogram for the individualised prediction of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC).

Methods

Two-hundred and one patients (training cohort: n = 126; internal validation cohort: n = 39; external validation cohort: n = 36) with ccRCC were retrospectively enrolled between January 2013 and June 2019. In the training cohort, the optimal MRI radiomics features were selected and combined to calculate the radiomics score (Rad-score). Incorporating Rad-score and SDM-related clinicoradiologic characteristics, the radiomics-based nomogram was established by multivariable logistic regression analysis, then the performance of the nomogram (discrimination and clinical usefulness) was evaluated and validated subsequently. Moreover, the prediction efficacy for SDM in ccRCC subgroups of different sizes was also assessed.

Results

Incorporating Rad-score derived from 9 optimal MR radiomics features (age, pseudocapsule and regional lymph node), the radiomics-based nomogram was capable of predicting SDM in the training cohort (area under the ROC curve (AUC) = 0.914) and validated in both the internal and external cohorts (AUC = 0.854 and 0.816, respectively) and also showed a convincing predictive power in ccRCC subgroups of different sizes (≤ 4 cm, AUC = 0.875; 4–7 cm, AUC = 0.891; 7–10 cm, 0.908; > 10 cm, AUC = 0.881). Decision curve analysis indicated that the radiomics-based nomogram is of clinical usefulness.

Conclusions

The multiparametric MRI radiomics-based nomogram could achieve precise individualised prediction of SDM in patients with ccRCC, potentially improving the management of ccRCC.

Key Points

• Radiomics features derived from multiparametric magnetic resonance images showed relevant association with synchronous distant metastasis in clear cell renal cell carcinoma.

• MRI radiomics-based nomogram may serve as a potential tool for the risk prediction of synchronous distant metastasis in clear cell renal cell carcinoma.

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Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424CrossRefPubMed

Patard JJ, Leray E, Rioux-Leclercq N et al (2005) Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience. J Clin Oncol 23:2763–2771CrossRefPubMed

Kammerer-Jacquet SF, Brunot A, Pladys A et al (2017) Synchronous metastatic clear-cell renal cell carcinoma: a distinct morphologic, immunohistochemical, and molecular phenotype. Clin Genitourin Cancer 15:e1–e7CrossRefPubMed

Dabestani S, Thorstenson A, Lindblad P, Harmenberg U, Ljungberg B, Lundstam S (2016) Renal cell carcinoma recurrences and metastases in primary non-metastatic patients: a population-based study. World J Urol 34:1081–1086CrossRefPubMed

Donskov F, Xie W, Overby A et al (2020) Synchronous versus metachronous metastatic disease: impact of time to metastasis on patient outcome-results from the International Metastatic Renal Cell Carcinoma Database Consortium. Eur Urol Oncol. https://doi.org/10.1016/j.euo.2020.01.001

Gill DM, Hahn AW, Hale P, Maughan BL (2018) Overview of current and future first-line systemic therapy for metastatic clear cell renal cell carcinoma. Curr Treat Options Oncol 19:6CrossRefPubMed

Kato S, Demura S, Murakami H, Tsuchiya H (2019) Surgical metastasectomy for renal cell carcinoma: which patients are the real candidates for surgery? Ann Transl Med 7:S273CrossRefPubMedPubMedCentral

Nizam A, Schindelheim JA, Ornstein MC (2020) The role of active surveillance and cytoreductive nephrectomy in metastatic renal cell carcinoma. Cancer Treat Res Commun 23:100169CrossRefPubMed

Ljungberg B, Bensalah K, Canfield S et al (2015) EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol 67:913–924CrossRefPubMed

10.

Kunkle DA, Crispen PL, Li T, Uzzo RG (2007) Tumor size predicts synchronous metastatic renal cell carcinoma: implications for surveillance of small renal masses. J Urol 177:1692–1697CrossRefPubMed

11.

Thompson RH, Hill JR, Babayev Y et al (2009) Metastatic renal cell carcinoma risk according to tumor size. J Urol 182:41–45CrossRefPubMedPubMedCentral

12.

Guðmundsson E, Hellborg H, Lundstam S, Erikson S, Ljungberg B (2011) Metastatic potential in renal cell carcinomas ≤7 cm: Swedish Kidney Cancer Quality Register data. Eur Urol 60:975–982CrossRefPubMed

13.

Diaz de Leon A, Pirasteh A, Costa DN et al (2019) Current challenges in diagnosis and assessment of the response of locally advanced and metastatic renal cell carcinoma. Radiographics 39:998–1016CrossRefPubMed

14.

Rasmussen F (2013) Metastatic renal cell cancer. Cancer Imaging 13:374–380CrossRefPubMedPubMedCentral

15.

Hou TC, Wu CC, Yang CR, Wang J (2010) Synchronous renal cell carcinoma and clear cell hepatocellular carcinoma mimicking metastatic disease. Pathol Res Pract 206:342–345CrossRefPubMed

16.

Lyu HL, Cao JX, Wang HY et al (2018) Differentiation between pancreatic metastases from clear cell renal cell carcinoma and pancreatic neuroendocrine tumor using double-echo chemical shift imaging. Abdom Radiol (NY) 43:2712–2720CrossRef

17.

Yu J, Xu Q, Huang DY et al (2017) Prognostic aspects of dynamic contrast-enhanced magnetic resonance imaging in synchronous distant metastatic rectal cancer. Eur Radiol 27:1840–1847CrossRefPubMed

18.

Mendoza DP, Dagogo-Jack I, Chen T et al (2019) Imaging characteristics of BRAF-mutant non-small cell lung cancer by functional class. Lung Cancer 129:80–84CrossRefPubMed

19.

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006CrossRefPubMed

20.

Shinagare AB, Krajewski KM, Braschi-Amirfarzan M, Ramaiya NH (2017) Advanced renal cell carcinoma: role of the radiologist in the era of precision medicine. Radiology 284:333–351CrossRefPubMed

21.

Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol 12:862–866CrossRefPubMed

22.

Wang W, Cao KM, Jin SM, Zhu XL, Ding JH, Peng WJ (2020) Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radiol. https://doi.org/10.1007/s00330-020-06896-5

23.

Deng Y, Soule E, Samuel A et al (2019) CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 29:6922–6929CrossRefPubMed

24.

Li ZC, Zhai GT, Zhang JH et al (2019) Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur Radiol 29:3996–4007CrossRefPubMed

25.

Bektas CT, Kocak B, Yardimci AH et al (2019) Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol 29:1153–1163CrossRefPubMed

26.

Zhang Y, Zhu Y, Shi X et al (2019) Soft tissue sarcomas: preoperative predictive histopathological grading based on radiomics of MRI. Acad Radiol 26:1262–1268CrossRefPubMed

27.

Shu J, Tang Y, Cui J et al (2018) Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur J Radiol 109:8–12CrossRefPubMed

28.

Kocak B, Ates E, Durmaz ES, Ulusan MB, Kilickesmez O (2019) Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas. Eur Radiol 29:4765–4775CrossRefPubMed

29.

Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative - feature definitions. arXiv:1612.07003

30.

Park JE, Park SY, Kim HJ, Kim HS (2019) Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 20:1124–1137CrossRefPubMedPubMedCentral

31.

Rao SJ (2003) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. J Am Stat Assoc 98:257–258CrossRef

32.

Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26:565–574CrossRefPubMedPubMedCentral

33.

Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16:e173–e180CrossRefPubMedPubMedCentral

34.

Klatte T, Patard JJ, de Martino M et al (2008) Tumor size does not predict risk of metastatic disease or prognosis of small renal cell carcinomas. J Urol 179:1719–1726CrossRefPubMed

35.

Zastrow S, Phuong A, von Bar I, Novotny V, Hakenberg OW, Wirth MP (2014) Primary tumor size in renal cell cancer in relation to the occurrence of synchronous metastatic disease. Urol Int 92:462–467CrossRefPubMed

36.

Wei X, Wang J, Liu L et al (2018) Evaluation of tumor pseudocapsule status and its prognostic significance in renal cell carcinoma. J Urol 199:915–920CrossRef

37.

Karlo CA, Di Paolo PL, Chaim J et al (2014) Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 270:464–471CrossRefPubMed

38.

Zisman A, Wieder JA, Pantuck AJ et al (2003) Renal cell carcinoma with tumor thrombus extension: biology, role of nephrectomy and response to immunotherapy. J Urol 169:909–916CrossRefPubMed

39.

Gershman B, Takahashi N, Moreira DM et al (2016) Radiographic size of retroperitoneal lymph nodes predicts pathological nodal involvement for patients with renal cell carcinoma: development of a risk prediction model. BJU Int 118:742–749CrossRefPubMed

40.

Huhdanpaa H, Hwang D, Cen S et al (2015) CT prediction of the Fuhrman grade of clear cell renal cell carcinoma (RCC): towards the development of computer-assisted diagnostic method. Abdom Imaging 40:3168–3174CrossRefPubMedPubMedCentral

41.

Kierans AS, Rusinek H, Lee A et al (2014) Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma. AJR Am J Roentgenol 203:W637–W644CrossRefPubMed

42.

Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184CrossRefPubMed

43.

Yang Y, Wang WW, Ren Y et al (2019) Computerized texture analysis predicts histological invasiveness within lung adenocarcinoma manifesting as pure ground-glass nodules. Acta Radiol 60:1258–1264CrossRefPubMed

44.

Cui EM, Li ZY, Ma CY et al (2020) Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics. Eur Radiol 30:2912–2921CrossRefPubMed

Titel: MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma
verfasst von: Xu Bai
Qingbo Huang
Panli Zuo
Xiaojing Zhang
Jing Yuan
Xu Zhang
Meifeng Wang
Wei Xu
Huiyi Ye
Jinkun Zhao
Haoran Sun
Bin Shao
Haiyi Wang
Publikationsdatum: 27.08.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 2/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07184-y

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MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma