Advertisement

Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses

Published:October 19, 2017DOI:https://doi.org/10.1016/j.urology.2017.08.056

      Objective

      To implement a platform for colocalization of in vivo quantitative multiparametric magnetic resonance imaging features with ex vivo surgical specimens of patients with renal masses using patient-specific 3-dimensional (3D)-printed tumor molds, which may aid in targeted tissue procurement and radiomics and radiogenomic analyses.

      Materials and Methods

      Volumetric segmentation of 6 renal masses was performed with 3D Slicer (http://www.slicer.org) to create a 3D tumor model. A slicing guide template was created with specialized software, which included notches corresponding to the anatomic locations of the magnetic resonance images. The tumor model was subtracted from the slicing guide to create a depression in the slicing guide corresponding to the exact size and shape of the tumor. A customized, tumor-specific, slicing guide was then printed using a 3D printer. After partial nephrectomy, the surgical specimen was bivalved through the preselected magnetic resonance imaging (MRI) plane. A thick slab of the tumor was obtained, fixed, and processed as a whole-mount slide and was correlated to multiparametric MRI findings.

      Results

      All patients successfully underwent partial nephrectomy and adequate fitting of the tumor specimens within the 3D mold was achieved in all tumors. Distinct in vivo MRI features corresponded to unique pathologic characteristics in the same tumor. The average cost of printing each mold was US$160.7 ± 111.1 (range: US$20.9-$350.7).

      Conclusion

      MRI-based preoperative 3D printing of tumor-specific molds allow for accurate sectioning of the tumor after surgical resection and colocalization of in vivo imaging features with tissue-based analysis in radiomics and radiogenomic studies.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Urology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Segal E.
        • Sirlin C.B.
        • Ooi C.
        • et al.
        Decoding global gene expression programs in liver cancer by noninvasive imaging.
        Nat Biotechnol. 2007; 25: 675-680
        • Gillies R.J.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Gerlinger M.
        • Rowan A.J.
        • Horswell S.
        • et al.
        Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.
        N Engl J Med. 2012; 366: 883-892
        • Shinagare A.B.
        • Vikram R.
        • Jaffe C.
        • et al.
        Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group.
        Abdom Imaging. 2015; 40: 1684-1692
        • Jamshidi N.
        • Jonasch E.
        • Zapala M.
        • et al.
        The radiogenomic risk score: construction of a prognostic quantitative, noninvasive image-based molecular assay for renal cell carcinoma.
        Radiology. 2015; 277: 114-123
        • Sun M.
        • Thuret R.
        • Abdollah F.
        • et al.
        Age-adjusted incidence, mortality, and survival rates of stage-specific renal cell carcinoma in North America: a trend analysis.
        Eur Urol. 2011; 59: 135-141
        • Bosniak M.A.
        • Megibow A.J.
        • Hulnick D.H.
        • Horii S.
        • Raghavendra B.N.
        CT diagnosis of renal angiomyolipoma: the importance of detecting small amounts of fat.
        AJR Am J Roentgenol. 1988; 151: 497-501
        • Hindman N.
        • Ngo L.
        • Genega E.M.
        • et al.
        Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques?.
        Radiology. 2012; 265: 468-477
        • de Leon A.D.
        • Costa D.
        • Pedrosa I.
        Role of multiparametric MR imaging in malignancies of the urogenital tract.
        Magn Reson Imaging Clin N Am. 2016; 24: 187-204
        • Canvasser N.E.
        • Kay F.U.
        • Xi Y.
        • et al.
        Diagnostic accuracy of multiparametric magnetic resonance imaging to identify clear cell renal cell carcinoma in cT1a renal masses.
        J Urol. 2017;
        • Chandarana H.
        • Rosenkrantz A.B.
        • Mussi T.C.
        • et al.
        Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer.
        Radiology. 2012; 265: 790-798
        • Pedrosa I.
        • Sun M.R.
        • Spencer M.
        • et al.
        MR imaging of renal masses: correlation with findings at surgery and pathologic analysis.
        Radiographics. 2008; 28: 985-1003
        • Yuan Q.
        • Kapur P.
        • Zhang Y.
        • et al.
        Intratumor heterogeneity of perfusion and diffusion in clear-cell renal cell carcinoma: correlation with tumor cellularity.
        Clin Genitourin Cancer. 2016; 14: e585-e594
        • Zhang Y.
        • Kapur P.
        • Yuan Q.
        • et al.
        Tumor vascularity in renal masses: correlation of arterial spin-labeled and dynamic contrast-enhanced magnetic resonance imaging assessments.
        Clin Genitourin Cancer. 2016; 14: e25-e36
        • Lanzman R.S.
        • Robson P.M.
        • Sun M.R.
        • et al.
        Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings.
        Radiology. 2012; 265: 799-808
        • Silberstein J.L.
        • Maddox M.M.
        • Dorsey P.
        • Feibus A.
        • Thomas R.
        • Lee B.R.
        Physical models of renal malignancies using standard cross-sectional imaging and 3-dimensional printers: a pilot study.
        Urology. 2014; 84: 268-272
        • Mehra P.
        • Miner J.
        • D'Innocenzo R.
        • Nadershah M.
        Use of 3-d stereolithographic models in oral and maxillofacial surgery.
        J Maxillofac Oral Surg. 2011; 10: 6-13
        • Sodian R.
        • Schmauss D.
        • Markert M.
        • et al.
        Three-dimensional printing creates models for surgical planning of aortic valve replacement after previous coronary bypass grafting.
        Ann Thorac Surg. 2008; 85: 2105-2108
        • McGurk M.
        • Amis A.A.
        • Potamianos P.
        • Goodger N.M.
        Rapid prototyping techniques for anatomical modelling in medicine.
        Ann R Coll Surg Engl. 1997; 79: 169-174
        • Lim K.H.
        • Loo Z.Y.
        • Goldie S.J.
        • Adams J.W.
        • McMenamin P.G.
        Use of 3D printed models in medical education: a randomized control trial comparing 3D prints versus cadaveric materials for learning external cardiac anatomy.
        Anat Sci Educ. 2016; 9: 213-221
        • Naftulin J.S.
        • Kimchi E.Y.
        • Cash S.S.
        Streamlined, inexpensive 3D printing of the brain and skull.
        PLoS ONE. 2015; 10 (e0136198)
        • D'Urso P.S.
        • Barker T.M.
        • Earwaker W.J.
        • et al.
        Stereolithographic biomodelling in cranio-maxillofacial surgery: a prospective trial.
        J Craniomaxillofac Surg. 1999; 27: 30-37
        • Zerr J.
        • Chatzinoff Y.
        • Chopra R.
        • Estrera K.
        • Chhabra A.
        Three-dimensional printing for preoperative planning of total hip arthroplasty revision: case report.
        Skeletal Radiol. 2016; 45: 1431-1435
        • Trivedi H.
        • Turkbey B.
        • Rastinehad A.R.
        • et al.
        Use of patient-specific MRI-based prostate mold for validation of multiparametric MRI in localization of prostate cancer.
        Urology. 2012; 79: 233-239
        • Shah V.
        • Pohida T.
        • Turkbey B.
        • et al.
        A method for correlating in vivo prostate magnetic resonance imaging and histopathology using individualized magnetic resonance-based molds.
        Rev Sci Instrum. 2009; 80: 104301
        • 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: 1323-1341
        • Kiessling F.
        • Le-Huu M.
        • Kunert T.
        • et al.
        Improved correlation of histological data with DCE MRI parameter maps by 3D reconstruction, reslicing and parameterization of the histological images.
        Eur Radiol. 2005; 15: 1079-1086
        • Costa D.N.
        • Chatzinoff Y.
        • Passoni N.M.
        • et al.
        Improved magnetic resonance imaging-pathology correlation with imaging-derived, 3D-printed, patient-specific whole-mount molds of the prostate.
        Invest Radiol. 2017;
        • Xu J.
        • Luo X.
        • Wang G.
        • Gilmore H.
        • Madabhushi A.
        A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images.
        Neurocomputing. 2016; 191: 214-223
        • Juntu J.
        • Sijbers J.
        • De Backer S.
        • Rajan J.
        • Van Dyck D.
        Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
        J Magn Reson Imaging. 2010; 31: 680-689