Top

Published in:

Open Access 01-12-2023 | osteosarcoma | Critical Review

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging—What can we do better?

Authors: Cyrano Chatziantoniou, Reineke A. Schoot, Roelof van Ewijk, Rick R. van Rijn, Simone A. J. ter Horst, Johannes H. M. Merks, Alexander Leemans, Alberto De Luca

Published in: Insights into Imaging | Issue 1/2023

Abstract

Purpose

Diffusion-weighted MRI is a promising technique to monitor response to treatment in pediatric rhabdomyosarcoma. However, its validation in clinical practice remains challenging. This study aims to investigate how the tumor segmentation strategy can affect the apparent diffusion coefficient (ADC) measured in pediatric rhabdomyosarcoma.

Materials and methods

A literature review was performed in PubMed using search terms relating to MRI and sarcomas to identify commonly applied segmentation strategies. Seventy-six articles were included, and their presented segmentation methods were evaluated. Commonly reported segmentation strategies were then evaluated on diffusion-weighted imaging of five pediatric rhabdomyosarcoma patients to assess their impact on ADC.

Results

We found that studies applied different segmentation strategies to define the shape of the region of interest (ROI)(outline 60%, circular ROI 27%), to define the segmentation volume (2D 44%, multislice 9%, 3D 21%), and to define the segmentation area (excludes edge 7%, excludes other region 19%, specific area 27%, whole tumor 48%). In addition, details of the segmentation strategy are often unreported. When implementing and comparing these strategies on in-house data, we found that excluding necrotic, cystic, and hemorrhagic areas from segmentations resulted in on average 5.6% lower mean ADC. Additionally, the slice location used in 2D segmentation methods could affect ADC by as much as 66%.

Conclusion

Diffusion-weighted MRI studies in pediatric sarcoma currently employ a variety of segmentation methods. Our study shows that different segmentation strategies can result in vastly different ADC measurements, highlighting the importance to further investigate and standardize segmentation.

Available only for authorised users

Schoot RA, McHugh K, van Rijn RR et al (2013) Response assessment in pediatric rhabdomyosarcoma: can response evaluation criteria in solid tumors replace three-dimensional volume assessments? Radiology 269:870–878. https://doi.org/10.1148/RADIOL.13122607CrossRef

van Ewijk R, Vaarwerk B, Breunis WB (2021) The value of early tumor size response to chemotherapy in pediatric rhabdomyosarcoma. Cancers 13:510. https://doi.org/10.3390/CANCERS13030510CrossRef

Suzuki C, Torkzad MR, Jacobsson H et al (2010) Interobserver and intraobserver variability in the response evaluation of cancer therapy according to RECIST and WHO-criteria. Acta Oncol 49:509–514. https://doi.org/10.3109/02841861003705794CrossRef

Vaarwerk B, van der Lee JH, Breunis WB et al (2018) Prognostic relevance of early radiologic response to induction chemotherapy in pediatric rhabdomyosarcoma: a report from the International Society of Pediatric Oncology Malignant Mesenchymal Tumor 95 study. Cancer 124:1016–1024. https://doi.org/10.1002/CNCR.31157CrossRef

Ermoian RP, Breneman J, Walterhouse DO et al (2017) 45 Gy is not sufficient radiotherapy dose for Group III orbital embryonal rhabdomyosarcoma after less than complete response to 12 weeks of ARST0331 chemotherapy: a report from the Soft Tissue Sarcoma Committee of the Children’s Oncology Group. Pediatr Blood Cancer. https://doi.org/10.1002/PBC.26540CrossRef

Rosenberg AR, Anderson JR, Lyden E et al (2014) Early response as assessed by anatomic imaging does not predict failure-free survival among patients with Group III rhabdomyosarcoma: a report from the Children’s Oncology Group. Eur J Cancer 50:816–823. https://doi.org/10.1016/J.EJCA.2013.11.031CrossRef

Burke M, Anderson JR, Kao SC et al (2007) Assessment of response to induction therapy and its influence on 5-year failure-free survival in group III rhabdomyosarcoma: the intergroup rhabdomyosarcoma study-IV experience - A report from the Soft Tissue Sarcoma Committee of the Children’s Oncology Group. J Clin Oncol 25:4909–4913. https://doi.org/10.1200/JCO.2006.10.4257CrossRef

Norman G, Fayter D, Lewis-Light K, McHugh K, Levine D, Phillips B (2014) (2014) Mind the gap: extent of use of diffusion-weighted MRI in children with rhabdomyosarcoma. Pediatr Radiol 455(45):778–781. https://doi.org/10.1007/S00247-014-3120-ZCrossRef

Pourmehdi Lahiji A, Jackson T, Nejadnik H et al (2019) Association of tumor [18F]FDG activity and diffusion restriction with clinical outcomes of rhabdomyosarcomas. Mol Imaging Biol 21:591–598. https://doi.org/10.1007/s11307-018-1272-1CrossRef

10.

Galbán CJ, Hoff BA, Chenevert TL, Ross BD (2017) Diffusion MRI in early cancer therapeutic response assessment. NMR Biomed 30:e3458CrossRef

11.

Humphries PD, Sebire NJ, Siegel MJ, Olsen ØE (2007) Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology 45:848–854. https://doi.org/10.1148/radiol.2452061535CrossRef

12.

Reichardt W, Juettner E, Uhl M, Elverfeldt DV, Kontny U (2009) Diffusion-weighted imaging as predictor of therapy response in an animal model of ewing sarcoma. Invest Radiol 44:298–303. https://doi.org/10.1097/RLI.0b013e31819dcc84CrossRef

13.

Raafat TA, Kaddah RO, Bokhary LM, Sayed HA, Awad AS (2021) The role of diffusion-weighted MRI in assessment of response to chemotherapy in osteosarcoma. Egypt J Radiol Nucl Med. https://doi.org/10.1186/s43055-020-00392-yCrossRef

14.

Ognjanovic S, Linabery AM, Charbonneau B, Ross JA (2009) Trends in childhood rhabdomyosarcoma incidence and survival in the United States, 1975–2005. Cancer 115:4218–4226. https://doi.org/10.1002/CNCR.24465CrossRef

15.

Bisogno G, Jenney M, Bergeron C et al (2018) Addition of dose-intensified doxorubicin to standard chemotherapy for rhabdomyosarcoma (EpSSG RMS 2005): a multicentre, open-label, randomised controlled, phase 3 trial. Lancet Oncol 19:1061–1071. https://doi.org/10.1016/S1470-2045(18)30337-1CrossRef

16.

Bossuyt PM, Reitsma JB, Bruns DE et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277:826–832. https://doi.org/10.1148/radiol.2015151516CrossRef

17.

Granzier RWY, Verbakel NMH, Ibrahim A et al (2020) MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability. Sci Rep. https://doi.org/10.1038/S41598-020-70940-ZCrossRef

18.

Joskowicz L, Cohen D, Caplan N, Sosna J (2018) Automatic segmentation variability estimation with segmentation priors. Med Image Anal 50:54–64. https://doi.org/10.1016/J.MEDIA.2018.08.006CrossRef

19.

Schurink NW, van Kranen SR, Roberti S et al (2021) Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 1:1–11. https://doi.org/10.1007/S00330-021-08251-8/FIGURES/4CrossRef

20.

Guo Y, Tang WJ, Kong Q et al (2019) Can whole-tumor apparent diffusion coefficient histogram analysis be helpful to evaluate breast phyllode tumor grades? Eur J Radiol 114:25–31. https://doi.org/10.1016/J.EJRAD.2019.02.035CrossRef

21.

Singer AD, Pattany PM, Fayad LM, Tresley J, Subhawong TK (2016) Volumetric segmentation of ADC maps and utility of standard deviation as measure of tumor heterogeneity in soft tissue tumors. Clin Imaging 40:386–391. https://doi.org/10.1016/J.CLINIMAG.2015.11.017CrossRef

22.

Abdel Razek AAK, Nada N (2013) Role of diffusion-weighted MRI in differentiation of masticator space malignancy from infection. Dentomaxillofacial Radiol. https://doi.org/10.1259/dmfr.20120183CrossRef

23.

Aktas E, Arikan SM, Ardıç F et al (2021) The importance of diffusion apparent diffusion coefficient values in the evaluation of soft tissue sarcomas after treatment. Polish J Radiol 86:e291. https://doi.org/10.5114/PJR.2021.106413CrossRef

24.

Albalawi ED, Alkatan HM, Elkhamary SM, Safieh LA, Maktabi AMY (2019) Genetic profiling of rhabdomyosarcoma with clinicopathological and radiological correlation. Can J Ophthalmol 54:247–257. https://doi.org/10.1016/j.jcjo.2018.03.011CrossRef

25.

Alsharief AN, Martinez-Rios C, Hopyan S et al (2019) Usefulness of diffusion-weighted MRI in the initial assessment of osseous sarcomas in children and adolescents. Pediatr Radiol 49:1201–1208. https://doi.org/10.1007/s00247-019-04436-yCrossRef

26.

Ashikyan O, Bradshaw SB, Dettori NJ, Hwang H, Chhabra A (2021) Conventional and advanced MR imaging insights of synovial sarcoma. Clin Imaging 76:149–155. https://doi.org/10.1016/J.CLINIMAG.2021.02.010CrossRef

27.

Asmar K, Saade C, Salman R et al (2020) The value of diffusion weighted imaging and apparent diffusion coefficient in primary Osteogenic and Ewing sarcomas for the monitoring of response to treatment: Initial experience. Eur J Radiol 124:108855. https://doi.org/10.1016/J.EJRAD.2020.108855CrossRef

28.

Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A (2019) Intravoxel incoherent motion (IVIM) for response assessment in patients with osteosarcoma undergoing neoadjuvant chemotherapy. Eur J Radiol. https://doi.org/10.1016/j.ejrad.2019.08.004CrossRef

29.

Bajpai J, Gamanagatti S, Sharma MC et al (2009) Noninvasive imaging surrogate of angiogenesis in osteosarcoma. Pediatr Blood Cancer. https://doi.org/10.1002/pbc.22328CrossRef

30.

Bajpai J, Gamnagatti S, Kumar R et al (2011) Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis. Pediatr Radiol 41:441–450. https://doi.org/10.1007/s00247-010-1876-3CrossRef

31.

Banerjee I, Crawley A, Bhethanabotla M, Daldrup-Link HE, Rubin DL (2018) Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput Med Imaging Graph 65:167–175. https://doi.org/10.1016/j.compmedimag.2017.05.002CrossRef

32.

Baunin C, Schmidt G, Baumstarck K et al (2012) Value of diffusion-weighted images in differentiating mid-course responders to chemotherapy for osteosarcoma compared to the histological response: Preliminary results. Skeletal Radiol 41:1141–1149. https://doi.org/10.1007/s00256-012-1360-2CrossRef

33.

Bi Q, Xiao Z, Lv F, Liu Y, Zou C, Shen Y (2018) Utility of clinical parameters and multiparametric MRI as predictive factors for differentiating uterine sarcoma from atypical leiomyoma. Acad Radiol 25:993–1002. https://doi.org/10.1016/j.acra.2018.01.002CrossRef

34.

Bi Q, Wu K, Lv F, Xiao Z, Xiong Y, Shen Y (2020) The value of clinical parameters combined with magnetic resonance imaging (MRI) features for preoperatively distinguishing different subtypes of uterine sarcomas An observational study (STROBE compliant). Medicine (Baltimore). https://doi.org/10.1097/MD.0000000000019787

35.

Bologna M, Montin E, Corino VDA, Mainardi LT (2017) Stability assessment of first order statistics features computed on ADC maps in soft-tissue sarcoma. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS. Institute of electrical and electronics engineers Inc., pp 612–615

36.

Bruegel M, Muenzel D, Waldt S, Specht K, Rummeny EJ (2013) Hepatic angiosarcoma: Cross-sectional imaging findings in seven patients with emphasis on dynamic contrast-enhanced and diffusion-weighted MRI. Abdom Imaging 38:745–754. https://doi.org/10.1007/s00261-012-9967-2CrossRef

37.

Byun BH, Kong CB, Lim I et al (2013) Combination of 18F-FDG PET/CT and diffusion-weighted mr imaging as a predictor of histologic response to neoadjuvant chemotherapy: preliminary results in osteosarcoma. J Nucl Med 54:1053–1059. https://doi.org/10.2967/jnumed.112.115964CrossRef

38.

Chhabra A, Ashikyan O, Slepicka C et al (2019) Conventional MR and diffusion-weighted imaging of musculoskeletal soft tissue malignancy: correlation with histologic grading. Eur Radiol 29:4485–4494. https://doi.org/10.1007/s00330-018-5845-9CrossRef

39.

Chodyla M, Demircioglu A, Schaarschmidt BM et al (2021) Evaluation of 18F-FDG PET and DWI datasets for predicting therapy response of soft-tissue sarcomas under neoadjuvant isolated limb perfusion. J Nucl Med 62:348–353. https://doi.org/10.2967/JNUMED.120.248260CrossRef

40.

Chodyla M, Demircioglu A, Schaarschmidt BM et al (2021) Evaluation of the predictive potential of 18F-FDG PET and DWI data sets for relevant prognostic parameters of primary soft-tissue sarcomas. Cancers 13:2753. https://doi.org/10.3390/CANCERS13112753CrossRef

41.

Corino VDA, Montin E, Messina A et al (2018) Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging 47:829–840. https://doi.org/10.1002/jmri.25791CrossRef

42.

Degnan AJ, Chung CY, Shah AJ (2018) Quantitative diffusion-weighted magnetic resonance imaging assessment of chemotherapy treatment response of pediatric osteosarcoma and Ewing sarcoma malignant bone tumors. Clin Imaging 47:9–13. https://doi.org/10.1016/j.clinimag.2017.08.003CrossRef

43.

Del Grande F, Subhawong T, Weber K, Aro M, Mugera C, Fayad LM (2014) Detection of soft-tissue sarcoma recurrence: added value of functional MR imaging techniques at 3.0 T. Radiology 271:499–511. https://doi.org/10.1148/radiol.13130844CrossRef

44.

Dudeck O, Zeile M, Pink D et al (2008) Diffusion-weighted magnetic resonance imaging allows monitoring of anticancer treatment effects in patients with soft-tissue sarcomas. J Magn Reson Imaging 27:1109–1113. https://doi.org/10.1002/jmri.21358CrossRef

45.

Einarsdóttir H, Karlsson M, Wejde J, Bauer HCF (2004) Diffusion-weighted MRI of soft tissue tumours. Eur Radiol 14:959–963. https://doi.org/10.1007/s00330-004-2237-0CrossRef

46.

Gao Y, Han F, Zhou Z et al (2017) Distortion-free diffusion MRI using an MRI-guided Tri-Cobalt 60 radiotherapy system: Sequence verification and preliminary clinical experience. Med Phys 44:5357–5366. https://doi.org/10.1002/mp.12465CrossRef

47.

Gao Y, Ghodrati V, Kalbasi A et al (2021) Prediction of soft tissue sarcoma response to radiotherapy using longitudinal diffusion MRI and a deep neural network with generative adversarial network-based data augmentation. Med Phys 48:3262–3372. https://doi.org/10.1002/MP.14897CrossRef

48.

Gerges L, Popiolek D, Rosenkrantz AB (2018) Explorative investigation of whole-lesion histogram MRI metrics for differentiating uterine leiomyomas and leiomyosarcomas. AJR Am J Roentgenol 210:1172–1177CrossRef

49.

Habre C, Dabadie A, Loundou AD et al (2021) Diffusion-weighted imaging in differentiating mid-course responders to chemotherapy for long-bone osteosarcoma compared to the histologic response: an update. Pediatr Radiol 51:1714–1723. https://doi.org/10.1007/S00247-021-05037-4/TABLES/4CrossRef

50.

Hao Y, An R, Xue Y et al (2021) Prognostic value of tumoral and peritumoral magnetic resonance parameters in osteosarcoma patients for monitoring chemotherapy response. Eur Radiol 31:3518–3529. https://doi.org/10.1007/S00330-020-07338-Y/FIGURES/5CrossRef

51.

Hélage S, Vandeventer S, Buy JN et al (2021) Uterine sarcomas: are there MRI signs predictive of histopathological diagnosis? A 50-patient case series with pathological correlation. Sarcoma. https://doi.org/10.1155/2021/8880080CrossRef

52.

Hong JH, Jee WH, Jung CK, Jung JY, Shin SH, Chung YG (2019) Soft tissue sarcoma: adding diffusion-weighted imaging improves MR imaging evaluation of tumor margin infiltration. Eur Radiol 29:2589–2597CrossRef

53.

Huang YL, Ueng SH, Chen K et al (2019) Utility of diffusion-weighted and contrast-enhanced magnetic resonance imaging in diagnosing and differentiating between high- and low-grade uterine endometrial stromal sarcoma. Cancer Imaging. https://doi.org/10.1186/s40644-019-0247-zCrossRef

54.

Ioannidis GS, Nikiforaki K, Karantanas A (2019) Statistical and spatial correlation between diffusion and perfusion MR imaging parameters: a study on soft tissue sarcomas. Phys Medica 65:59–66. https://doi.org/10.1016/j.ejmp.2019.08.007CrossRef

55.

Kralik SF, Haider KM, Lobo RR, Supakul N, Calloni SF, Soares BP (2018) Orbital infantile hemangioma and rhabdomyosarcoma in children: differentiation using diffusion-weighted magnetic resonance imaging. J AAPOS 22:27–31. https://doi.org/10.1016/j.jaapos.2017.09.002CrossRef

56.

Lee SK, Jee W-H, Jung CK, Im SA, Chung N-G, Chung Y-G (2020) Prediction of poor responders to neoadjuvant chemotherapy in patients with osteosarcoma: additive value of diffusion-weighted MRI including volumetric analysis to standard MRI at 3T. PLoS One 15:0229983. https://doi.org/10.1371/JOURNAL.PONE.0229983CrossRef

57.

Lee JH, Yoon YC, Seo SW, Choi YL, Kim HS (2020) Soft tissue sarcoma: DWI and DCE-MRI parameters correlate with Ki-67 labeling index. Eur Radiol 30:914–924. https://doi.org/10.1007/s00330-019-06445-9CrossRef

58.

Li HM, Liu J, Qiang JW, Zhang H, Zhang GF, Ma F (2017) Diffusion-weighted imaging for differentiating uterine leiomyosarcoma from degenerated leiomyoma. J Comput Assist Tomogr 41:599–606. https://doi.org/10.1097/RCT.0000000000000565CrossRef

59.

Li HM, Liu J, Qiang JW, Gu WY, Zhang GF, Ma FH (2017) Endometrial stromal sarcoma of the uterus: Magnetic resonance imaging findings including apparent diffusion coefficient value and its correlation with Ki-67 expression. Int J Gynecol Cancer 27:1877–1887. https://doi.org/10.1097/IGC.0000000000001114CrossRef

60.

Li X, Yang L, Wang Q, Tao J, Pan Z, Wang S (2021) Soft tissue sarcomas: IVIM and DKI correlate with the expression of HIF-1α on direct comparison of MRI and pathological slices. Eur Radiol 31:4669–4679. https://doi.org/10.1007/S00330-020-07526-W/FIGURES/6CrossRef

61.

Li X, Liu Y, Tao J et al (2021) Value of intravoxel incoherent motion and diffusion kurtosis imaging in predicting peritumoural infiltration of soft-tissue sarcoma: a prospective study based on MRI–histopathology comparisons. Clin Radiol 76:532–539. https://doi.org/10.1016/J.CRAD.2021.02.014CrossRef

62.

Liu M, Luo C, Wang Y et al (2017) Multiparametric MRI in differentiating pulmonary artery sarcoma and pulmonary thromboembolism: a preliminary experience. Diagnost Interv Radiol 23:15–21. https://doi.org/10.5152/dir.2016.15584CrossRef

63.

Liu C, Xi Y, Li M et al (2019) Monitoring response to neoadjuvant chemotherapy of primary osteosarcoma using diffusion kurtosis magnetic resonance imaging: Initial findings. Korean J Radiol 20:801–811. https://doi.org/10.3348/kjr.2018.0453CrossRef

64.

Manikis GC, Nikiforaki K, Lagoudaki E et al (2021) Differentiating low from high-grade soft tissue sarcomas using post-processed imaging parameters derived from multiple DWI models. Eur J Radiol 138:109660. https://doi.org/10.1016/J.EJRAD.2021.109660CrossRef

65.

Müller U, Kubik-Huch RA, Ares C et al (2016) Is there a role for conventional MRI and MR diffusion-weighted imaging for distinction of skull base chordoma and chondrosarcoma? Acta Radiol 57:225–232. https://doi.org/10.1177/0284185115574156CrossRef

66.

Nakagawa M, Nakaura T, Namimoto T et al (2019) A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18F-FDG PET/CT. Clin Radiol 74:167.e1-167.e7. https://doi.org/10.1016/j.crad.2018.10.010CrossRef

67.

Nakagawa M, Nakaura T, Namimoto T et al (2019) Machine learning to differentiate T2-weighted hyperintense uterine leiomyomas from uterine sarcomas by utilizing multiparametric magnetic resonance quantitative imaging features. Acad Radiol 26:1390–1399. https://doi.org/10.1016/j.acra.2018.11.014CrossRef

68.

Oka K, Yakushiji T, Sato H, Hirai T, Yamashita Y, Mizuta H (2010) The value of diffusion-weighted imaging for monitoring the chemotherapeutic response of osteosarcoma: a comparison between average apparent diffusion coefficient and minimum apparent diffusion coefficient. Skeletal Radiol 39:141–146. https://doi.org/10.1007/s00256-009-0830-7CrossRef

69.

Orsatti G, Zucchetta P, Varotto A et al (2021) Volumetric histograms-based analysis of apparent diffusion coefficients and standard uptake values for the assessment of pediatric sarcoma at staging: preliminary results of a PET/MRI study. Radiol Med 126:878–885. https://doi.org/10.1007/S11547-021-01340-0/FIGURES/4CrossRef

70.

Ozturk M, Polat AV, Tosun FC, Selcuk MB (2021) Does the SUVmax of FDG-PET/CT correlate with the ADC values of DWI in musculoskeletal malignancies? J Belgian Soc Radiol. https://doi.org/10.5334/JBSR.2378CrossRef

71.

Parlak Ş, Ergen FB, Yüksel GY et al (2021) Diffusion-weighted imaging for the differentiation of Ewing sarcoma from osteosarcoma. Skeletal Radiol 50:2023–2030. https://doi.org/10.1007/S00256-021-03741-8/TABLES/3CrossRef

72.

Rio G, Lima M, Gil R, Horta M, Cunha TM (2019) T2 hyperintense myometrial tumors: can MRI features differentiate leiomyomas from leiomyosarcomas? Abdom Radiol (NY) 44:3388–3397. https://doi.org/10.1007/s00261-019-02097-xCrossRef

73.

Sagiyama K, Watanabe Y, Kamei R et al (2017) Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: preliminary results. Eur Radiol 27:5024–5033. https://doi.org/10.1007/s00330-017-4912-yCrossRef

74.

Saleh MM, Abdelrahman TM, Madney Y, Mohamed G, Shokry AM, Moustafa AF (2020) Multiparametric MRI with diffusion-weighted imaging in predicting response to chemotherapy in cases of osteosarcoma and Ewing’s sarcoma. Br J Radiol 93:20200257. https://doi.org/10.1259/bjr.20200257CrossRef

75.

Schnapauff D, Zeile M, Ben NM et al (2009) Diffusion-weighted echo-planar magnetic resonance imaging for the assessment of tumor cellularity in patients with soft-tissue sarcomas. J Magn Reson Imaging 29:1355–1359. https://doi.org/10.1002/jmri.21755CrossRef

76.

Soldatos T, Ahlawat S, Montgomery E, Chalian M, Jacobs MA, Fayad LM (2016) Multiparametric assessment of treatment response in high-grade soft-tissue sarcomas with anatomic and functional MR imaging sequences. Radiology 278:831–840. https://doi.org/10.1148/radiol.2015142463CrossRef

77.

Sumi A, Terasaki H, Sanada S et al (2015) Assessment of mr imaging as a tool to differentiate between the major histological types of uterine sarcomas. Magn Reson Med Sci 14:295–304. https://doi.org/10.2463/mrms.2014-0023CrossRef

78.

Teo KY, Daescu O, Cederberg K, Sengupta A, Leavey PJ (2022) Correlation of histopathology and multi-modal magnetic resonance imaging in childhood osteosarcoma: predicting tumor response to chemotherapy. PLoS One 17:e0259564. https://doi.org/10.1371/JOURNAL.PONE.0259564CrossRef

79.

Tian S, Niu M, Xie L, Song Q, Liu A (2021) Diffusion-tensor imaging for differentiating uterine sarcoma from degenerative uterine fibroids. Clin Radiol 76:313.e27-313.e32. https://doi.org/10.1016/J.CRAD.2020.11.115CrossRef

80.

Tong A, Kang SK, Huang C, Huang K, Slevin A, Hindman N (2019) MRI screening for uterine leiomyosarcoma. J Magn Reson Imaging 49:e282–e294. https://doi.org/10.1002/jmri.26630CrossRef

81.

Uhl M, Saueressig U, Van Buiren M et al (2006) Osteosarcoma: Preliminary results of in vivo assessment of tumor necrosis after chemotherapy with diffusion- and perfusion-weighted magnetic resonance imaging. Invest Radiol 41:618–623. https://doi.org/10.1097/01.rli.0000225398.17315.68CrossRef

82.

Uhl M, Saueressig U, Koehler G et al (2006) Evaluation of tumour necrosis during chemotherapy with diffusion-weighted MR imaging: preliminary results in osteosarcomas. Pediatr Radiol 36:1306–1311. https://doi.org/10.1007/s00247-006-0324-xCrossRef

83.

Valdes-Devesa V, del Mar Jimenez M, Sanz-Rosa D, Vaquero ME, Moreno EA, de la Cuesta Abbad RS (2019) Preoperative diagnosis of atypical pelvic leiomyoma and sarcoma: the potential role of diffusion-weighted imaging. J Obstet Gynaecol 39:98–104. https://doi.org/10.1080/01443615.2018.1466110CrossRef

84.

Vossen JA, Kamel IR, Buijs M et al (2008) Role of functional magnetic resonance imaging in assessing metastatic leiomyosarcoma response to chemoembolization. J Comput Assist Tomogr 32:347–352. https://doi.org/10.1097/RCT.0b013e318134ecd6CrossRef

85.

Wang CS, Du LJ, Si MJ et al (2013) Noninvasive assessment of response to neoadjuvant chemotherapy in osteosarcoma of long bones with diffusion-weighted imaging: an initial in vivo study. PLoS One. https://doi.org/10.1371/journal.pone.0072679CrossRef

86.

Wang J, Sun M, Liu D et al (2017) Correlation between apparent diffusion coefficient and histopathology subtypes of osteosarcoma after neoadjuvant chemotherapy. Acta Radiol 58:971–976. https://doi.org/10.1177/0284185116678276CrossRef

87.

Welzel T, Meyerhof E, Uhl M et al (2018) Diagnostic accuracy of DW MR imaging in the differentiation of chordomas and chondrosarcomas of the skull base: a 3.0-T MRI study of 105 cases. Eur J Radiol 105:119–124. https://doi.org/10.1016/j.ejrad.2018.05.026CrossRef

88.

Wu G, Liu X, Xiong Y, Ran J, Li X (2018) Intravoxel incoherent motion and diffusion kurtosis imaging for discriminating soft tissue sarcoma from vascular anomalies. Medicine (Baltimore). https://doi.org/10.1097/MD.0000000000013641CrossRef

89.

Xie H, Zhang X, Ma S, Liu Y, Wang Y (2019) Preoperative differentiation of uterine sarcoma from leiomyoma: comparison of three models based on different segmentation volumes using radiomics. Mol Imaging Biol 21:1157–1164. https://doi.org/10.1007/s11307-019-01332-7CrossRef

90.

Xing S, Freeman CR, Jung S, Turcotte R, Levesque IR (2018) Probabilistic classification of tumour habitats in soft tissue sarcoma. NMR Biomed. https://doi.org/10.1002/nbm.4000CrossRef

91.

Yakushiji T, Oka K, Sato H et al (2009) Characterization of chondroblastic osteosarcoma: gadolinium-enhanced versus diffusion-weighted MR Imaging. J Magn Reson Imaging 29:895–900. https://doi.org/10.1002/jmri.21703CrossRef

92.

Yang Y, Cao M, Sheng K et al (2016) Longitudinal diffusion MRI for treatment response assessment: preliminary experience using an MRI-guided tri-cobalt 60 radiotherapy system. Med Phys 43:1369–1373. https://doi.org/10.1118/1.4942381CrossRef

93.

Yu B, Huang C, Liu S et al (2021) Application of first-order feature analysis of DWI-ADC in rare malignant mesenchymal tumours of the maxillofacial region. BMC Oral Health 21:1–7. https://doi.org/10.1186/S12903-021-01835-2/FIGURES/2CrossRef

94.

Zeitoun R, Shokry AM, Ahmed Khaleel S, Mogahed SM (2018) Osteosarcoma subtypes: magnetic resonance and quantitative diffusion weighted imaging criteria. J Egypt Natl Canc Inst 30:39–44. https://doi.org/10.1016/j.jnci.2018.01.006CrossRef

95.

Zhang K, Dai Y, Liu Y et al (2022) Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices. Eur Radiol 2022:1–10. https://doi.org/10.1007/S00330-022-08646-1CrossRef

Title: Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging—What can we do better?
Authors: Cyrano Chatziantoniou
Reineke A. Schoot
Roelof van Ewijk
Rick R. van Rijn
Simone A. J. ter Horst
Johannes H. M. Merks
Alexander Leemans
Alberto De Luca
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: osteosarcoma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Rhabdomyosarcoma
Uterine Sarcoma
Osteosarcoma
Sarcoma
Rhabdomyosarcoma in Children
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-022-01351-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging—What can we do better?

Abstract

Purpose

Materials and methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Materials and methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2023

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

Optimal combination periprosthetic vasculature visualization and metal artifact reduction by spectral computed tomography using virtual monoenergetic images in total hip arthroplasty

Photon-counting CT allows better visualization of temporal bone structures in comparison with current generation multi-detector CT

Evaluation of imaging findings in gastrointestinal anisakiasis in emergency CT and ultrasound

Undergraduate radiology education in Europe in 2022: a survey from the European Society of Radiology (ESR)

Diagnostic reference level quantities for adult chest and abdomen-pelvis CT examinations: correlation with organ doses