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Strahlentherapie und Onkologie
Published in: Strahlentherapie und Onkologie 10/2020

01-10-2020 | Prostate Cancer | Review Article

The role of radiomics in prostate cancer radiotherapy

Authors: Rodrigo Delgadillo, John C. Ford, Matthew C. Abramowitz, Alan Dal Pra, Alan Pollack, Radka Stoyanova

Published in: Strahlentherapie und Onkologie | Issue 10/2020

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Abstract

“Radiomics,” as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.
Literature
1.
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577CrossRefPubMed
2.
Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMedCrossRef
3.
Ahmed HU, El-Shater Bosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389(10071):815–822PubMedCrossRef
4.
Futterer JJ, Briganti A, De Visschere P et al (2015) Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol 68(6):1045–1053PubMedCrossRef
5.
Israel B, Leest MV, Sedelaar M, Padhani AR, Zamecnik P, Barentsz JO (2020) Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 2: interpretation. Eur Urol 77(4):469–480PubMedCrossRef
6.
7.
Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS prostate imaging—Reporting and data system: 2015, version 2. Eur Urol 69(1):16–40CrossRefPubMed
8.
Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76(3):340–351PubMedCrossRef
9.
Stabile A, Giganti F, Kasivisvanathan V et al (2020) Factors influencing variability in the performance of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer: a systematic literature review. Eur Urol Oncol 3(2):145–167PubMedCrossRefPubMedCentral
10.
11.
Osman SOS, Leijenaar RTH, Cole AJ et al (2019) Computed tomography-based radiomics for risk stratification in prostate cancer. Int J Radiat Oncol Biol Phys 105(2):448–456PubMedCrossRef
12.
Tanadini-Lang S, Bogowicz M, Veit-Haibach P et al (2018) Exploratory radiomics in computed tomography perfusion of prostate cancer. Anticancer Res 38(2):685–690PubMed
13.
Fave X, Zhang L, Yang J et al (2016) Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer. Transl Cancer Res 5(4):349–363CrossRef
14.
van Timmeren JE, Leijenaar RTH, van Elmpt W et al (2017) Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol 123(3):363–369PubMedCrossRef
15.
Qin Q, Shi A, Zhang R et al (2020) Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Thorac Cancer 11(4):964–972PubMedPubMedCentralCrossRef
16.
Jones KM, Michel KA, Bankson JA, Fuller CD, Klopp AH, Venkatesan AM (2018) Emerging magnetic resonance imaging technologies for radiation therapy planning and response assessment. Int J Radiat Oncol Biol Phys 101(5):1046–1056PubMedCrossRef
17.
18.
Vargas HA, Akin O, Franiel T et al (2011) Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness. Radiology 259(3):775–784PubMedPubMedCentralCrossRef
19.
Somford DM, Hoeks CM, Hulsbergen-van de Kaa CA et al (2013) Evaluation of diffusion-weighted MR imaging at inclusion in an active surveillance protocol for low-risk prostate cancer. Invest Radiol 48(3:152–157CrossRef
20.
Peng Y, Jiang Y, Yang C et al (2013) Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score—a computer-aided diagnosis development study. Radiology 267(3):787–796PubMedCrossRef
21.
Hegde JV, Mulkern RV, Panych LP et al (2013) Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging 37(5):1035–1054PubMedPubMedCentralCrossRef
22.
Isebaert S, Van den Bergh L, Haustermans K et al (2013) Multiparametric MRI for prostate cancer localization in correlation to whole-mount histopathology. J Magn Reson Imaging 37(6):1392–1401PubMedCrossRef
23.
24.
von Eyben FE, Kiljunen T, Kangasmaki A, Kairemo K, von Eyben R, Joensuu T (2016) Radiotherapy boost for the dominant Intraprostatic cancer lesion—A systematic review and meta-analysis. Clin Genitourin Cancer 14(3):189–197CrossRef
25.
Bauman G, Haider M, Van der Heide UA, Menard C (2013) Boosting imaging defined dominant prostatic tumors: a systematic review. Radiother Oncol 107(3):274–281PubMedCrossRef
26.
Monninkhof EM, van Loon JWL, van Vulpen M et al (2018) Standard whole prostate gland radiotherapy with and without lesion boost in prostate cancer: toxicity in the FLAME randomized controlled trial. Radiother Oncol 127(1):74–80PubMedCrossRef
27.
Wolters T, Montironi R, Mazzucchelli R et al (2012) Comparison of incidentally detected prostate cancer with screen-detected prostate cancer treated by prostatectomy. Prostate 72(1):108–115PubMedCrossRef
28.
Wolters T, Roobol MJ, van Leeuwen PJ et al (2010) Should pathologists routinely report prostate tumour volume? The prognostic value of tumour volume in prostate cancer. Eur Urol 57(5):821–829PubMedCrossRef
29.
Klotz L (2013) Active surveillance for prostate cancer: overview and update. Curr Treat Options Oncol 14(1):97–108PubMedCrossRef
30.
31.
Stoyanova R, Takhar M, Tschudi Y et al (2016) Prostate cancer radiomics and the promise of radiogenomics. Transl Cancer Res 5(4):432–447PubMedCrossRef
32.
Stoyanova R, Pollack A, Takhar M et al (2016) Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget 7(33):53362–53376PubMedPubMedCentralCrossRef
33.
Stoyanova R, Chinea F, Kwon D et al (2018) An automated multiparametric MRI quantitative imaging prostate habitat risk scoring system for defining external beam radiation therapy boost volumes. Int J Radiat Oncol Biol Phys 102(4):821–829PubMedPubMedCentralCrossRef
34.
35.
Parra NA, Pollack A, Chinea FM et al (2017) Automatic detection and quantitative DCE-MRI scoring of prostate cancer aggressiveness. Front Oncol 7:259PubMedPubMedCentralCrossRef
36.
37.
Jonsson JH, Karlsson MG, Karlsson M, Nyholm T (2010) Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions. Radiat Oncol 5(1):62PubMedPubMedCentralCrossRef
38.
Rasch C, Barillot I, Remeijer P, Touw A, van Herk M, Lebesque JV (1999) Definition of the prostate in CT and MRI: a multi-observer study. Int J Radiat Oncol Biol Phys 43(1):57–66CrossRefPubMed
39.
Siversson C, Nordström F, Nilsson T et al (2015) Technical Note: MRI only prostate radiotherapy planning using the statistical decomposition algorithm. Med Phys 42(10):6090–6097PubMedCrossRef
40.
Liu Y, Lei Y, Wang Y et al (2019) Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Phys Med Biol 64(20):205022PubMedCrossRefPubMedCentral
41.
Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 8(1):10545PubMedPubMedCentralCrossRef
42.
Yang F, Ford JC, Dogan N et al (2018) Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol 7(3):445–458PubMedPubMedCentralCrossRef
43.
Padgett KR, Swallen A, Pirozzi S et al (2018) Towards a universal MRI atlas of the prostate and prostate zones: comparison of MRI vendor and image acquisition parameters. Strahlenther Onkol 195(2):121–130PubMedPubMedCentralCrossRef
44.
45.
Mecke KR (2000) Additivity, convexity, and beyond: applications of Minkowski functionals in statistical physics. Springer, Berlin, Heidelberg, New York
46.
Johnson P, Young L, Lamichhane N, Patel V, Chinea F, Yang F (2017) Quantitative imaging: correlating image features with the segmentation accuracy of PET based tumor contours in the lung. Radiother Oncol 123(2):257–262PubMedCrossRef
47.
Li BL (2002) Fractal dimensions. Wiley Online Library
48.
Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19(5):1264–1274CrossRef
49.
Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804CrossRef
50.
Loh H, Leu J, Luo RC (1988) The analysis of natural textures using run length features. IEEE Trans Ind Electron 35(2):323–328CrossRef
51.
Thibault G, Fertil B, Navarro C et al (2009) Texture indexes and gray level size zone matrix: application to cell nuclei classification. Pattern Recognition and Information Processing.
52.
Bigun J (1994) Speed, frequency, and orientation tuned 3-d gabor filter banks and their design. In: Proceedings of the 12th IAPR International Conference Pattern Recognition. Conference C: Signal Processing, vol 3: 184–187
53.
Ganeshan B, Miles KA, Young RC, Chatwin CR (2008) Three-dimensional selective-scale texture analysis of computed tomography pulmonary angiograms. Invest Radiol 43(6):382–394PubMedCrossRef
54.
Mallat S (1999) A wavelet tour of signal processing. Academic Press, Cambridge
55.
Varghese B, Chen F, Hwang D et al (2019) Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Sci Rep 9(1):1570PubMedPubMedCentralCrossRef
56.
Cuocolo R, Stanzione A, Ponsiglione A et al (2019) Clinically significant prostate cancer detection on MRI: a radiomic shape features study. Eur J Radiol 116:144–149PubMedCrossRef
57.
Algohary A, Viswanath S, Shiradkar R et al (2018) Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings. J Magn Reson Imaging 48(3):818–828CrossRef
58.
Shiradkar R, Ghose S, Jambor I et al (2018) Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging 48(6):1626–1636PubMedPubMedCentralCrossRef
59.
Bourbonne V, Fournier G, Vallières M et al (2020) External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers 12(4):814PubMedCentralCrossRef
60.
van der Leest M, Israel B, Cornel EB et al (2019) High diagnostic performance of short magnetic resonance imaging protocols for prostate cancer detection in biopsy-naive men: the next step in magnetic resonance imaging accessibility. Eur Urol 76(5):574–581PubMedCrossRef
61.
Abdollahi H, Mofid B, Shiri I et al (2019) Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol Med 124(6):555–567PubMedCrossRef
62.
Pollack A, Chinea FM, Bossart E et al (2020) Phase I trial of MRI-guided prostate cancer lattice extreme ablative dose (LEAD) boost radiation therapy. Int J Radiat Oncol Biol Phys 107(2):305–315PubMedCrossRefPubMedCentral
63.
Chindasombatjaroen J, Kakimoto N, Murakami S, Maeda Y, Furukawa S (2011) Quantitative analysis of metallic artifacts caused by dental metals: comparison of cone-beam and multi-detector row CT scanners. Oral Radiol 27(2):114–120CrossRef
64.
Lechuga L, Weidlich GA (2016) Cone beam CT vs. fan beam CT: a comparison of image quality and dose delivered between two differing CT imaging modalities. Cureus 8(9):e778–e778PubMedPubMedCentral
65.
66.
Nardi C, Borri C, Regini F et al (2015) Metal and motion artifacts by cone beam computed tomography (CBCT) in dental and maxillofacial study. Radiol Med 120(7):618–626PubMedCrossRef
67.
Bagher-Ebadian H, Siddiqui F, Liu C, Movsas B, Chetty IJ (2017) On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers. Med Phys 44(5):1755–1770CrossRefPubMed
68.
Fave X, Mackin D, Yang J et al (2015) Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 42(12):6784–6797PubMedPubMedCentralCrossRef
69.
Hall WA, Paulson ES, van der Heide UA et al (2019) The transformation of radiation oncology using real-time magnetic resonance guidance: a review. Eur J Cancer 122:42–52PubMedCrossRefPubMedCentral
70.
Pathmanathan AU, van As NJ, Kerkmeijer LGW et al (2018) Magnetic resonance imaging-guided adaptive radiation therapy: a “game changer” for prostate treatment? Int J Radiat Oncol Biol Phys 100(2):361–373PubMedCrossRef
71.
Boldrini L, Cusumano D, Chiloiro G et al (2018) Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach. Radiol Med 124(2):145–153PubMedPubMedCentralCrossRef
72.
73.
Renard-Penna R, Cancel-Tassin G, Comperat E et al (2015) Multiparametric magnetic resonance imaging predicts postoperative pathology but misses aggressive prostate cancers as assessed by cell cycle progression score. J Urol 194(6):1617–1623PubMedCrossRef
74.
Varlotto J, Stevenson MA (2005) Anemia, tumor hypoxemia, and the cancer patient. Int J Radiat Oncol Biol Phys 63(1):25–36PubMedCrossRef
75.
Bristow RG, Hill RP (2008) Hypoxia and metabolism. Hypoxia, DNA repair and genetic instability. Nat Rev Cancer 8(3:180–192CrossRef
76.
Vaupel P (2004) Tumor microenvironmental physiology and its implications for radiation oncology. Semin Radiat Oncol 14(3):198–206PubMedCrossRef
77.
Bache M, Kappler M, Said HM, Staab A, Vordermark D (2008) Detection and specific targeting of hypoxic regions within solid tumors: current preclinical and clinical strategies. Curr Med Chem 15(4):322–338PubMedCrossRef
78.
Tatum JL, Kelloff GJ, Gillies RJ et al (2006) Hypoxia: importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy. Int J Radiat Biol 82(10):699–757PubMedCrossRef
79.
Cho H, Ackerstaff E, Carlin S et al (2009) Noninvasive multimodality imaging of the tumor microenvironment: registered dynamic magnetic resonance imaging and positron emission tomography studies of a preclinical tumor model of tumor hypoxia. Neoplasia 11(3):247–259PubMedPubMedCentralCrossRef
80.
Stoyanova R, Huang K, Sandler K et al (2012) Mapping tumor hypoxia in vivo using pattern recognition of dynamic contrast-enhanced MRI data. Transl Oncol 5(6):437–447PubMedPubMedCentralCrossRef
81.
Erho N, Crisan A, Vergara IA et al (2013) Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8(6):e66855PubMedPubMedCentralCrossRef
82.
Den RB, Feng FY, Showalter TN et al (2014) Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy. Int J Radiat Oncol Biol Phys 89(5):1038–1046PubMedPubMedCentralCrossRef
83.
Den RB, Yousefi K, Trabulsi EJ et al (2015) Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol 33(8):944–951PubMedPubMedCentralCrossRef
84.
Klein EA, Yousefi K, Haddad Z et al (2015) A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy. Eur Urol 67(4):778–786PubMedCrossRef
85.
Cuzick J, Berney DM, Fisher G et al (2012) Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer 106(6):1095–1099PubMedPubMedCentralCrossRef
86.
Freedland SJ, Gerber L, Reid J et al (2013) Prognostic utility of cell cycle progression score in men with prostate cancer after primary external beam radiation therapy. Int J Radiat Oncol Biol Phys 86(5):848–853PubMedPubMedCentralCrossRef
87.
Cooperberg MR, Simko JP, Cowan JE et al (2013) Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol 31(11):1428–1434PubMedCrossRef
88.
Crawford ED, Scholz MC, Kar AJ et al (2014) Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. Curr Med Res Opin 30(6):1025–1031PubMedCrossRef
89.
Badani KK, Thompson DJ, Brown G et al (2015) Effect of a genomic classifier test on clinical practice decisions for patients with high-risk prostate cancer after surgery. BJU Int 115(3):419–429PubMedCrossRef
90.
Klein EA, Cooperberg MR, Magi-Galluzzi C et al (2014) A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol 66(3):550–560PubMedCrossRef
91.
Knezevic D, Goddard AD, Natraj N et al (2013) Analytical validation of the Oncotype DX prostate cancer assay—A clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics 14:690PubMedPubMedCentralCrossRef
92.
Cullen J, Rosner IL, Brand TC et al (2015) A biopsy-based 17-gene genomic prostate score predicts recurrence after radical prostatectomy and adverse surgical pathology in a racially diverse population of men with clinically low- and intermediate-risk prostate cancer. Eur Urol 68(1):123–131PubMedCrossRef
93.
Badani K, Kemeter M, Febbo PG et al (2015) The impact of a biopsy based 17-gene genomic prostate score on treatment recommendations in men with newly diagnosed clinically prostate cancer who are candidates for active surveillance. Urol Pract 2(4):181–189CrossRef
94.
Hectors SJ, Cherny M, Yadav KK et al (2019) Radiomics features measured with multiparametric magnetic resonance imaging predict prostate cancer aggressiveness. J Urol 202(3):498–505PubMedCrossRef
95.
Zhao SG, Chang SL, Spratt DE et al (2016) Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol 17(11):1612–1620PubMedCrossRef
96.
Woo S, Han S, Kim TH et al (2020) Prognostic value of pretreatment MRI in patients with prostate cancer treated with radiation therapy: a systematic review and meta-analysis. AJR Am J Roentgenol 214(3):597–604PubMedCrossRef
97.
Wibmer A, Hricak H, Gondo T et al (2015) 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 25(10):2840–2850PubMedPubMedCentralCrossRef
98.
Vignati A, Mazzetti S, Giannini V et al (2015) Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol 60(7):2685–2701PubMedCrossRef
99.
Fehr D, Veeraraghavan H, Wibmer A et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A 112(46):E6265–E6273PubMedPubMedCentralCrossRef
100.
Chaddad A, Kucharczyk MJ, Niazi T (2018) Multimodal radiomic features for the predicting gleason score of prostate cancer. Cancers 10(8):249PubMedCentralCrossRef
101.
Chen T, Li M, Gu Y et al (2019) Prostate cancer differentiation and aggressiveness: assessment with a Radiomic-based model vs. PI-RADS v2. J Magn Reson Imaging 49(3):875–884PubMedCrossRef
102.
Min X, Li M, Dong D et al (2019) 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 115:16–21PubMedCrossRef
103.
104.
105.
Li M, Chen T, Zhao W et al (2020) Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI. Quant Imaging Med Surg 10(2):368–379PubMedPubMedCentralCrossRef
106.
Bleker J, Kwee TC, Dierckx RAJO, de Jong IJ, Huisman H, Yakar D (2020) Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer. Eur Radiol 30(3):1313–1324PubMedCrossRef
107.
Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers. Magn Reson Imaging 66:165–175PubMedCrossRef
108.
Tiwari P, Kurhanewicz J, Madabhushi A (2013) Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Med Image Anal 17(2):219–235PubMedCrossRef
109.
Nketiah G, Elschot M, Kim E et al (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 27(7):3050–3059PubMedCrossRef
Metadata
Title
The role of radiomics in prostate cancer radiotherapy
Authors
Rodrigo Delgadillo
John C. Ford
Matthew C. Abramowitz
Alan Dal Pra
Alan Pollack
Radka Stoyanova
Publication date
01-10-2020
Publisher
Springer Berlin Heidelberg
Published in
Strahlentherapie und Onkologie / Issue 10/2020
Print ISSN: 0179-7158
Electronic ISSN: 1439-099X
DOI
https://doi.org/10.1007/s00066-020-01679-9

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