Prejbisz A, Lenders JW, Eisenhofer G, Januszewicz A. Cardiovascular manifestations of phaeochromocytoma. J Hypertens. 2011;29:2049–60.
CAS
PubMed
Google Scholar
Bravo EL, Tagle R. Pheochromocytoma: state-of-the-art and future prospects. Endocr Rev. 2003;24:539–53.
CAS
PubMed
Google Scholar
Young WF Jr. Clinical practice. The incidentally discovered adrenal mass. N Engl J Med. 2007;356:601–10.
CAS
PubMed
Google Scholar
Lam TBL. Optimizing the diagnosis of pelvic lymph node metastasis in bladder cancer using computed tomography and magnetic resonance imaging. Cancer Commun. 2018;38:2.
Google Scholar
Caoili EM, Korobkin M, Francis IR, Cohan RH, Dunnick NR. Delayed enhanced CT of lipid-poor adrenal adenomas. AJR Am J Roentgenol. 2000;175:1411–5.
CAS
PubMed
Google Scholar
Boland GW, Blake MA, Hahn PF, Mayo-Smith WW. Incidental adrenal lesions: principles, techniques, and algorithms for imaging characterization. Radiology. 2008;249:756–75.
PubMed
Google Scholar
Sahdev A, Reznek RH. The indeterminate adrenal mass in patients. Cancer Imaging. 2007;7:S100-109.
PubMed
PubMed Central
Google Scholar
Wang F, Liu J, Zhang R, Bai Y, Li C, Li B, Liu H, Zhang T. CT and MRI of adrenal gland pathologies. Quant Imaging Med Surg. 2018;8:853–75.
PubMed
PubMed Central
Google Scholar
Varghese JC, Hahn PF, Papanicolaou N, Mayo-Smith WW, Gaa JA, Lee MJ. MR differentiation of phaeochromocytoma from other adrenal lesions based on qualitative analysis of T2 relaxation times. Clin Radiol. 1997;52:603–6.
CAS
PubMed
Google Scholar
Lattin GE Jr, Sturgill ED, Tujo CA, Marko J, Sanchez-Maldonado KW, Craig WD, Lack EE. From the radiologic pathology archives: adrenal tumors and tumor-like conditions in the adult: radiologic-pathologic correlation. Radiographics. 2014;34:805–29.
PubMed
Google Scholar
McDermott S, McCarthy CJ, Blake MA. Images of pheochromocytoma in adrenal glands. Gland Surg. 2015;4:350–8.
PubMed
PubMed Central
Google Scholar
Umanodan T, Fukukura Y, Kumagae Y, Shindo T, Nakajo M, Takumi K, Nakajo M, Hakamada H, Umanodan A, Yoshiura T. ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma. J Magn Reson Imaging. 2017;45:1195–203.
PubMed
Google Scholar
Park BK, Kim B, Ko K, Jeong SY, Kwon GY. Adrenal masses falsely diagnosed as adenomas on unenhanced and delayed contrast-enhanced computed tomography: pathological correlation. Eur Radiol. 2006;16:642–7.
PubMed
Google Scholar
Blake MA, Kalra MK, Maher MM, Sahani DV, Sweeney AT, Mueller PR, Hahn PF, Boland GW. Pheochromocytoma: an imaging chameleon. Radiographics. 2004;24(Suppl 1):S87-99.
PubMed
Google Scholar
Mannelli M, Lenders JW, Pacak K, Parenti G, Eisenhofer G. Subclinical phaeochromocytoma. Best Pract Res Clin Endocrinol Metab. 2012;26:507–15.
PubMed
PubMed Central
Google Scholar
Low G, Sahi K. Clinical and imaging overview of functional adrenal neoplasms. Int J Urol. 2012;19:697–708.
PubMed
Google Scholar
Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuze S, Schernberg A, Paragios N, Deutsch E, Ferte C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28:1191–206.
CAS
PubMed
Google Scholar
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
CAS
PubMed
Google Scholar
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.
PubMed
Google Scholar
Khorrami M, Bera K, Thawani R, Rajiah P, Gupta A, Fu P, Linden P, Pennell N, Jacono F, Gilkeson R, et al. Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans. Eur J Cancer. 2021;148:146–58.
CAS
PubMed
PubMed Central
Google Scholar
Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, Wang S, Li XT, Tian J, Sun YS. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23:7253–62.
CAS
PubMed
Google Scholar
Min X, Li M, Dong D, Feng Z, Zhang P, Ke Z, You H, Han F, Ma H, Tian J, Wang L. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method. Eur J Radiol. 2019;115:16–21.
PubMed
Google Scholar
Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res. 2019;25:3538–47.
CAS
PubMed
Google Scholar
Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W, Liu H, Su Y, Huang J, Lin T. A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res. 2017;23:6904–11.
CAS
PubMed
Google Scholar
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30:1323–41.
PubMed
PubMed Central
Google Scholar
Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007;26:5512–28.
PubMed
Google Scholar
Han K, Song K, Choi B. How to develop, validate, and compare clinical prediction models involving radiological parameters: study design and statistical methods. Korean J Radiol. 2016;17:339–50.
PubMed
PubMed Central
Google Scholar
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:7594.
Google Scholar
Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8:53.
PubMed
PubMed Central
Google Scholar
Lafata K, Wang Y, Konkel B, Yin F, Bashir M. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol. 2021. https://doi.org/10.1007/s00261-021-03254-x.
Article
Google Scholar
Aerts H. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2:1636–42.
PubMed
Google Scholar
Raja A, Leung K, Stamm M, Girgis S, Low G. Multimodality imaging findings of pheochromocytoma with associated clinical and biochemical features in 53 patients with histologically confirmed tumors. AJR Am J Roentgenol. 2013;201:825–33.
PubMed
Google Scholar
Fishbein L, Leshchiner I, Walter V, Danilova L, Robertson AG, Johnson AR, Lichtenberg TM, Murray BA, Ghayee HK, Else T, et al. Comprehensive molecular characterization of pheochromocytoma and paraganglioma. Cancer Cell. 2017;31:181–93.
CAS
PubMed
PubMed Central
Google Scholar
Fassnacht M, Arlt W, Bancos I, Dralle H, Newell-Price J, Sahdev A, Tabarin A, Terzolo M, Tsagarakis S, Dekkers OM. Management of adrenal incidentalomas: European Society of Endocrinology Clinical Practice Guideline in collaboration with the European Network for the Study of Adrenal Tumors. Eur J Endocrinol. 2016;175:G1–34.
CAS
PubMed
Google Scholar
Kannan S, Purysko A, Faiman C, Remer EM, Shah L, Bena J, Siperstein A, Berber E, Fergany A, Bravo E, Hamrahian AH. Biochemical and radiological relationships in patients with pheochromocytoma: lessons from a case control study. Clin Endocrinol. 2014;80:790–6.
CAS
Google Scholar
Motta-Ramirez GA, Remer EM, Herts BR, Gill IS, Hamrahian AH. Comparison of CT findings in symptomatic and incidentally discovered pheochromocytomas. AJR Am J Roentgenol. 2005;185:684–8.
PubMed
Google Scholar
Darr R, Kuhn M, Bode C, Bornstein SR, Pacak K, Lenders JWM, Eisenhofer G. Accuracy of recommended sampling and assay methods for the determination of plasma-free and urinary fractionated metanephrines in the diagnosis of pheochromocytoma and paraganglioma: a systematic review. Endocrine. 2017;56:495–503.
PubMed
PubMed Central
Google Scholar
Lenders JW, Duh QY, Eisenhofer G, Gimenez-Roqueplo AP, Grebe SK, Murad MH, Naruse M, Pacak K, Young WF Jr, Endocrine S. Pheochromocytoma and paraganglioma: an endocrine society clinical practice guideline. J Clin Endocrinol Metab. 2014;99:1915–42.
CAS
PubMed
Google Scholar
Buitenwerf E, Korteweg T, Visser A, Haag C, Feelders RA, Timmers H, Canu L, Haak HR, Bisschop P, Eekhoff EMW, et al. Unenhanced CT imaging is highly sensitive to exclude pheochromocytoma: a multicenter study. Eur J Endocrinol. 2018;178:431–7.
CAS
PubMed
Google Scholar
Lenders JW, Willemsen JJ, Eisenhofer G, Ross HA, Pacak K, Timmers HJ, Sweep CG. Is supine rest necessary before blood sampling for plasma metanephrines? Clin Chem. 2007;53:352–4.
CAS
PubMed
Google Scholar
Schieda N, Alrashed A, Flood TA, Samji K, Shabana W, McInnes MD. Comparison of quantitative MRI and CT washout analysis for differentiation of adrenal pheochromocytoma from adrenal adenoma. AJR Am J Roentgenol. 2016;206:1141–8.
PubMed
Google Scholar
Northcutt BG, Raman SP, Long C, Oshmyansky AR, Siegelman SS, Fishman EK, Johnson PT. MDCT of adrenal masses: can dual-phase enhancement patterns be used to differentiate adenoma and pheochromocytoma? AJR Am J Roentgenol. 2013;201:834–9.
PubMed
Google Scholar
Sadowski S, Millo C, Cottle-Delisle C, Merkel R, Yang L, Herscovitch P, Pacak K, Simonds W, Marx S, Kebebew E. Results of (68)Gallium-DOTATATE PET/CT scanning in patients with multiple endocrine neoplasia type 1. J Am Coll Surg. 2015;221:509–17.
PubMed
PubMed Central
Google Scholar
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69:127–57.
PubMed
PubMed Central
Google Scholar
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.
PubMed
Google Scholar
Xie T, Wang X, Zhang Z, Zhou Z. CT-based radiomics analysis for preoperative diagnosis of pancreatic mucinous cystic neoplasm and atypical serous cystadenomas. Front Oncol. 2021;11:621520.
PubMed
PubMed Central
Google Scholar
O’Connor J, Aboagye E, Adams J, Aerts H, Barrington S, Beer A, Boellaard R, Bohndiek S, Brady M, Brown G, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86.
CAS
PubMed
Google Scholar
Gao Y, Kikinis R, Bouix S, Shenton M, Tannenbaum A. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. Med Image Anal. 2012;16:1216–27.
PubMed
PubMed Central
Google Scholar
Land S, Ritter M, Costantino J, Julian T, Cronin W, Haile S, Wolmark N, Ganz P. Compliance with patient-reported outcomes in multicenter clinical trials: methodologic and practical approaches. J Clin Oncol. 2007;25:5113–20.
PubMed
Google Scholar
Wang S, Li C, Wang R, Liu Z, Wang M, Tan H, Wu Y, Liu X, Sun H, Yang R, et al. Annotation-efficient deep learning for automatic medical image segmentation. Nat Commun. 2021;12:5915.
CAS
PubMed
PubMed Central
Google Scholar