Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis
Tóm tắt
(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies. In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed. Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93–0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27–9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC. Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures. • Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.
Tài liệu tham khảo
Sullivan DC, Obuchowski NA, Kessler LG et al (2015) Metrology standards for quantitative imaging biomarkers. Radiology 277:813–825
Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503
Miles KA (2016) How to use CT texture analysis for prognostication of non-small cell lung cancer. Cancer Imaging 16:10
Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359–E386
Znaor A, Lortet-Tieulent J, Laversanne M, Jemal A, Bray F (2015) International variations and trends in renal cell carcinoma incidence and mortality. Eur Urol 67:519–530
Pierorazio PM, Hyams ES, Mullins JK, Allaf ME (2012) Active surveillance for small renal masses. Rev Urol 14:13–19
Richard PO, Lavallée LT, Pouliot F et al (2018) Is routine use of renal tumor biopsy associated with lower rates of benign histology following nephrectomy for small renal masses? J Urol. https://doi.org/10.1016/j.juro.2018.04.015
Defortescu G, Cornu J-N, Béjar S et al (2017) Diagnostic performance of contrast-enhanced ultrasonography and magnetic resonance imaging for the assessment of complex renal cysts: a prospective study. Int J Urol 24:184–189
Karlo CA, Di Paolo PL, Donati OF et al (2013) Renal cell carcinoma: role of MR imaging in the assessment of muscular venous branch invasion. Radiology 267:454–459
Hindman N, Ngo L, Genega EM et al (2012) Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques? Radiology 265:468–477
O’Connor JPB, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186
McInnes MDF, Moher D, Thombs BD et al (2018) Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies. JAMA 319:388
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
Whiting PF, Rutjes AWS, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529
R Core Team (2016) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available via http://www.r-project.org/. Accessed 31 Oct 2016
Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36:1–48
Marasini D, Quatto P, Ripamonti E (2016) Assessing the inter-rater agreement for ordinal data through weighted indexes. Stat Methods Med Res 25:2611–2633
Wang HY, Su ZH, Xu X et al (2016) Dynamic contrast-enhanced MR imaging in renal cell carcinoma: reproducibility of histogram analysis on pharmacokinetic parameters. Sci Rep 6. https://doi.org/10.1038/srep29146
Sanduleanu S, Woodruff HC, de Jong EECC et al (2018) Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol 127:349–360
Kim JY, Kim JK, Kim N, Cho K-S (2008) CT histogram analysis: differentiation of angiomyolipoma without visible fat from renal cell carcinoma at CT imaging. Radiology 246:472–479
Catalano OA, Samir AE, Sahani DV, Hahn PF (2008) Pixel distribution analysis: can it be used to distinguish clear cell carcinomas from angiomyolipomas with minimal fat? Radiology 247:738–746
Jethanandani A, Lin TA, Volpe S et al (2018) Exploring applications of radiomics in magnetic resonance imaging of head and neck cancer: a systematic review. Front Oncol 8:131
Park JE, Kim D, Kim HS, et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomicsquality score and TRIPOD statement. Eur Radiol 30(1):523–536.https://doi.org/10.1007/s00330-019-06360-z
Shafiq-ul-Hassan M, Zhang GG, Latifi K et al (2017) Intrinsic dependencies of CT radiomics features on voxel size and number of gray levels. Med Phys 44:1050–1062
Berenguer R, Pastor-Juan M d R, Canales-Vázquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:172361
Zhao B, Tan Y, Tsai WY, Schwartz LH, Lu L (2014) Exploring variability in CT characterization of tumors: a preliminary phantom study. Transl Oncol 7:88–93
Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892
Turajlic S, Xu H, Litchfield K et al (2018) Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 173:581–594
Okegawa T, Morimoto M, Nishizawa S et al (2017) Intratumor heterogeneity in primary kidney cancer revealed by metabolic profiling of multiple spatially separated samples within tumors. EBioMedicine 19:31–38