Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI

Radiation Oncology

Volumn 11, Issue 1, 2016, Pages

  Shiradkar, Rakesh ^a   Podder, Tarun K ^b   Algohary, Ahmad ^a   Viswanath, Satish ^a   Ellis, Rodney J ^b   Madabhushi, Anant ^a  

^a Case Western Reserve University   (United States)

^b CASE WESTERN RESERVE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Computer aided diagnosis (CAD); Prostate cancer; Radiomics; Treatment planning

Indexed keywords

ADULT; AGED; ARTICLE; BIOMICS; BRACHYTHERAPY; CANCER RADIOTHERAPY; CLASSIFIER; CLINICAL ARTICLE; CLINICAL DECISION SUPPORT SYSTEM; COHORT ANALYSIS; COMPUTER ASSISTED TOMOGRAPHY; DOSIMETRY; EXTERNAL BEAM RADIOTHERAPY; HUMAN; MACHINE LEARNING; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; ORGANS AT RISK; PROSTATE CANCER; RADIOMICS; RETROSPECTIVE STUDY; TREATMENT PLANNING; TUMOR LOCALIZATION; TUMOR VOLUME; IMAGE GUIDED RADIOTHERAPY; PROCEDURES; PROSTATIC NEOPLASMS; RADIOTHERAPY DOSAGE; RADIOTHERAPY PLANNING SYSTEM;

HUMANS; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; ORGANS AT RISK; PROSTATIC NEOPLASMS; RADIOTHERAPY DOSAGE; RADIOTHERAPY PLANNING, COMPUTER-ASSISTED; RADIOTHERAPY, IMAGE-GUIDED; RETROSPECTIVE STUDIES;

EID: 84998812079     PISSN: None     EISSN: 1748717X     Source Type: Journal    
DOI: 10.1186/s13014-016-0718-3     Document Type: Article

References (39)

1. Cancer Treatment and Survivorship, Facts and Figures 2014-15. http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-042801.pdf. Accessed 24 Oct 2016.
2. Sanda MG, Dunn RL, Michalski J, Sandler HM, Northouse L, Hembroff L, Lin X, Greenfield TK, Litwin MS, Saigal CS, Mahadevan A, Klein E, Kibel A, Pisters LL, Kuban D, Kaplan I, Wood D, Ciezki J, Shah N, Wei JT. Quality of life and satisfaction with outcome among prostate-cancer survivors. N Engl J Med. 2008; 358(12):1250-1261.
3. Wilt TJ, MacDonald R, Rutks I, Shamliyan TA, Taylor BC, Kane RL. Systematic review: Comparative effectiveness and harms of treatments for clinically localized prostate cancer. Ann Intern Med. 2008; 148(6):435-48.
4. Eggener SE, Scardino PT, Carroll PR, Zelefsky MJ, Sartor O, Hricak H, Wheeler TM, Fine SW, Trachtenberg J, Rubin MA, Ohori M, Kuroiwa K, Rossignol M, Abenhaim L. Focal therapy for localized prostate cancer: A critical appraisal of rationale and modalities. J Urol. 2007; 178(6):2260-267.
5. Karavitakis M, Ahmed HU, Abel PD, Hazell S, Winkler MH. Tumor focality in prostate cancer: implications for focal therapy. Nat Rev Clin Oncol. 2011; 8(1):48-55.
6. Ahmed HU, Hindley RG, Dickinson L, Freeman A, Kirkham AP, Sahu M, Scott R, Allen C, der Meulen JV, Emberton M. Focal therapy for localised unifocal and multifocal prostate cancer: a prospective development study. Lancet Oncol. 2012; 13(6):622-632. doi: 10.1016/S1470-2045(12)70121-3.
7. Radiation Therapy Oncology Group, Clinical Trials. https://www.rtog.org/ClinicalTrials/ProtocolTable.aspx. Accessed 24 Oct 2016.
8. Kirkham AP, Emberton M, Allen C. How good is mri at detecting and characterising cancer within the prostate?Eur Urol. 2006; 50(6):1163-1175.
9. Villeirs GM, De Meerleer GO. Magnetic resonance imaging (mri) anatomy of the prostate and application of mri in radiotherapy planning. Eur J Radiol. 2007; 63(3):361-8.
10. Dirix P, Haustermans K, Vandecaveye V. The value of magnetic resonance imaging for radiotherapy planning. In: Seminars in Radiation Oncology. Elsevier: 2014. p. 151-9, doi: 10.1016/j.semradonc.2014.02.003.
11. Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F. Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: A review. Comput Biol Med. 2015; 60:8-31.
12. Hoeks CM, Hambrock T, Yakar D, Hulsbergen-van de Kaa CA, Feuth T, Witjes JA, Fütterer JJ, Barentsz JO. Transition zone prostate cancer: detection and localization with 3-t multiparametric mr imaging. Radiology. 2013; 266(1):207-17.
13. Ruprecht O, Weisser P, Bodelle B, Ackermann H, Vogl TJ. Mri of the prostate: interobserver agreement compared with histopathologic outcome after radical prostatectomy. Eur J Radiol. 2012; 81(3):456-60.
14. Litjens GJS, Elliott R, Shih NNC, Feldman MD, Kobus T, Hulsbergen-van de Kaa C, Barentsz JO, Huisman HJ, Madabhushi A. Computer-extracted features can distinguish noncancerous confounding disease from prostatic adenocarcinoma at multiparametric mr imaging. Radiology. 2016; 278(1):135-145. doi: 10.1148/radiol.2015142856.
15. Niaf E, Rouvière O, Mège-Lechevallier F, Bratan F, Lartizien C. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric mri. Phys Med Biol. 2012; 57(12):3833.
16. Viswanath SE, Bloch BN, Chappelow J, Toth R, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A. Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo t2-weighted mr imagery. J Magn Reson Imaging. 2012; 36:213-4. doi: 10.1002/jmri.23618.
17. Litjens GJS, Elliott R, Shih N, Feldman M, Barentsz JO, Hulsbergen-van de Kaa CA, Kovacs I, Huisman HJ, Madabhushi A. Distinguishing Prostate Cancer from Benign Confounders Via a Cascaded Classifier on Multi-parametric MRI. In: SPIE Medical Imaging: 2014. p. 903512-0351214, doi: 10.1117/12.2043751. http://dx.doi.org/10.1117/12.2043751.
18. Ginsburg SB, Viswanath SE, Bloch BN, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A. Novel pca-vip scheme for ranking mri protocols and identifying computer-extracted mri measurements associated with central gland and peripheral zone prostate tumors. J Magn Reson Imaging JMRI. 2015; 41(5):1383-93. doi: 10.1002/jmri.24676.
19. Korsager AS, Carl J, Østergaard LR. Mr-ct registration using a ni-ti prostate stent in image-guided radiotherapy of prostate cancer. Med Phys. 2013; 40(6):061907.
20. Zhong H, Wen N, Gordon JJ, Elshaikh MA, Movsas B, Chetty IJ. An adaptive mr-ct registration method for mri-guided prostate cancer radiotherapy. Phys Med Biol. 2015; 60(7):2837.
21. Kessler ML. Image registration and data fusion in radiation therapy. Br J Radiol. 2014; 79(1):99-108. doi: 10.1259/bjr/70617164.
22. Sparks R, Nicolas Bloch B, Feleppa E, Barratt D, Moses D, Ponsky L, Madabhushi A. Multiattribute probabilistic prostate elastic registration (mapper): Application to fusion of ultrasound and magnetic resonance imaging. Med Phys. 2015; 42:1153. doi: 10.1118/1.4905104.
23. Rivest-Hénault D, Dowson N, Greer PB, Fripp J, Dowling JA. Robust inverse-consistent affine ct-mr registration in mri-assisted and mri-alone prostate radiation therapy. Med Image Anal. 2015; 23(1):56-69.
24. Toth R, Traughber B, Ellis R, Kurhanewicz J, Madabhushi A. A domain constrained deformable (docd) model for co-registration of pre-and post-radiated prostate mri. Neurocomputing. 2014; 144:3-12.
25. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging. 1997; 16(2):187-98.
26. Klein S, Staring M, Pluim JP. Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines. IEEE Trans Image Process. 2007; 16(12):2879-890.
27. Ahmed HU, Hindley RG, Dickinson L, Freeman A, Kirkham AP, Sahu M, Scott R, Allen C, Van der Meulen J, Emberton M. Focal therapy for localised unifocal and multifocal prostate cancer: a prospective development study. Lancet Oncol. 2012; 13(6):622-32.
28. Banerjee R, Park SJ, Anderson E, Demanes DJ, Wang J, Kamrava M. From whole gland to hemigland to ultra-focal high-dose-rate prostate brachytherapy: A dosimetric analysis. Brachytherapy. 2015; 14(3):366-72.
29. McPartlin A, Li X, Kershaw L, Heide U, Kerkmeijer L, Lawton C, Mahmood U, Pos F, van As N, van Herk M, et al.Mri-guided prostate adaptive radiotherapy-a systematic review. Radiother Oncol. 2016; 119(3):371-80. doi: 10.1016/j.radonc.2016.04.014.
30. Vovk U, Pernuš F, Likar B. A review of methods for correction of intensity inhomogeneity in mri. IEEE Trans Med Imaging. 2007; 26(3):405-21.
31. Nyul LG, Udupa JK. On standardizing the mr image intensity scale. Magn Reson Med. 1999; 42(6):1072-81. doi: 10.1002/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M.
32. Viswanath SE, Bloch NB, Chappelow JC, Toth R, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A. Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo t2-weighted mr imagery. J Magn Reson Imaging. 2012; 36(1):213-24.
33. Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D, Zheng J, Goldman D, Moskowitz C, Fine SW, et al. Haralick texture analysis of prostate mri: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different gleason scores. Eur Radiol. 2015; 25(10):2840-850.
34. Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, Thoeny HC, Verma S, Weinreb JC. PI-RADS Prostate Imaging-Reporting and Data System: 2015. Version 2. Eur Urol. 2016; 69(1):16-40.
35. Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005; 27(8):1226-1238. doi: 10.1109/TPAMI.2005.159.
36. Pluim JP, Maintz JA, Viergever MA. Image registration by maximization of combined mutual information and gradient information. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2000. Berlin Heidelberg: Springer: 2000. p. 452-61, doi: 10.1007/978-3-540-40899-4_46.
37. MIM Software Inc, Cleveland O. MIM Symphony. http://www.mimsoftware.com/products/symphony/. Accessed 24 Oct 2016.
38. Systems VM. Eclipse Treatment Planning System. https://www.varian.com/oncology/products/software/treatment-planning/eclipse. Accessed 24 Oct 2016.
39. Poggi MM, Gant DA, Sewchand W, Warlick WB. Marker seed migration in prostate localization. Int J Radiat Oncol Biol Phys. 2003; 56(5):1248-1251.