Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Tumor Margin Infiltration in Soft Tissue Sarcomas: Prediction Using 3T MRI Texture Analysis

연조직 육종의 종양 가장자리 침윤: 3T 자기공명영상 텍스처 분석을 통한 예측

  • Minji Kim (Department of 1Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Won-Hee Jee (Department of 1Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Youngjun Lee (Department of 1Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Ji Hyun Hong (Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine) ;
  • Chan Kwon Jung (Department of Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Yang-Guk Chung (Department of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • So-Yeon Lee (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea)
  • 김민지 (가톨릭대학교 의과대학 서울성모병원 영상의학과) ;
  • 지원희 (가톨릭대학교 의과대학 서울성모병원 영상의학과) ;
  • 이영준 (가톨릭대학교 의과대학 서울성모병원 영상의학과) ;
  • 홍지현 (한림대학교 의과대학 강동성심병원 영상의학과) ;
  • 정찬권 (가톨릭대학교 의과대학 서울성모병원 병리과) ;
  • 정양국 (가톨릭대학교 의과대학 서울성모병원 정형외과) ;
  • 이소연 (가톨릭대학교 의과대학 서울성모병원 영상의학과)
  • Received : 2021.02.23
  • Accepted : 2021.05.11
  • Published : 2022.01.01
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Abstract

Purpose To determine the value of 3 Tesla (T) MRI texture analysis for predicting tumor margin infiltration in soft tissue sarcomas. Materials and Methods Thirty-one patients who underwent 3T MRI and had a pathologically confirmed diagnosis of soft tissue sarcoma were included in this study. Margin infiltration on pathology was used as the gold standard. Texture analysis of soft tissue sarcomas was performed on axial T1-weighted images (WI) and T2WI, fat-suppressed contrast-enhanced (CE) T1WI, diffusion-weighted images (DWI) with b-value of 800 s/mm2, and apparent diffusion coefficient (ADC) was mapped. Quantitative parameters were compared between sarcomas with infiltrative margins and those with circumscribed margins. Results Among the 31 patients with soft tissue sarcomas, 23 showed tumor margin infiltration on pathology. There were significant differences in kurtosis with the spatial scaling factor (SSF) of 0 and 6 on T1WI, kurtosis (SSF, 0) on CE-T1WI, skewness (SSF, 0) on DWI, and skewness (SSF, 2, 4) on ADC between sarcomas with infiltrative margins and those with circumscribed margins (p ≤ 0.046). The area under the receiver operating characteristic curve based on MR texture features for identification of infiltrative tumor margins was 0.951 (p < 0.001). Conclusion MR texture analysis is reliable and accurate for the prediction of infiltrative margins of soft tissue sarcomas.

목적 연조직 육종의 종양 가장자리 침윤을 예측하기 위한 3T 자기공명영상 텍스처 분석의 가치를 규명한다. 대상과 방법 3T 자기공명영상을 시행하고, 병리학적으로 연조직 육종으로 확인된 31명의 환자를 대상으로 하고, 병리학적인 가장자리 침윤을 표준으로 사용하였다. 연조직 육종에 대한 텍스처 분석은 축상 T1 강조영상, T2 강조영상, 지방억제 조영증강 T1 강조영상, 확산강조영상(b = 800 sec/mm2) 및 현성확산계수 지도 영상에서 이루어졌다. 텍스처 분석에서 얻어진 정량적 변수가 침윤성(infiltrative) 육종과 국한성(circumscribed) 육종에서 차이가 있는지 비교하였다. 결과 총 23명의 연조직 육종에서 병리학적인 가장자리 침윤을 보였다. 침윤성 육종과 국한성 육종은, T1 강조영상 공간 스케일 인자(spatial scaling factor; 이하 SSF) 0, 6에서의 첨도(kurtosis), 조영증강 T1 강조영상(SSF, 0)에서의 첨도, 확산강조영상(SSF, 0)에서의 왜도(skewness), 현성확산계수 지도(SSF 2, 4)에서의 왜도에서 유의한 차이가 있었다(p ≤ 0.046). 자기공명영상 텍스처 소견을 이용한 종양 가장자리 침윤을 예측하는 정확도는 수신자운영특성곡선(receiver operating characteristic; 이하 ROC)의 곡선하 면적(area under the ROC curve) 0.951 (p < 0.001)이었다. 결론 자기공명영상 텍스처 분석은 연조직 육종의 침윤성 가장자리를 예측하는 데 있어 신뢰 할 수 있으며 정확하다.

Keywords

References

  1. Hui JY. Epidemiology and etiology of sarcomas. Surg Clin North Am 2016;96:901-914
  2. Morrison BA. Soft tissue sarcomas of the extremities. Proc (Bayl Univ Med Cent) 2003;16:285-290
  3. Robinson E, Bleakney RR, Ferguson PC, O'Sullivan B. Oncodiagnosis panel: 2007: multidisciplinary management of soft-tissue sarcoma. Radiographics 2008;28:2069-2086
  4. Hong JH, Jee WH, Jung CK, Jung JY, Shin SH, Chung YG. Soft tissue sarcoma: adding diffusion-weighted imaging improves MR imaging evaluation of tumor margin infiltration. Eur Radiol 2019;29:2589-2597
  5. Kandel R, Coakley N, Werier J, Engel J, Ghert M, Verma S; Sarcoma Disease Site Group of Cancer Care Ontario's Program in Evidence-Based Care. Surgical margins and handling of soft-tissue sarcoma in extremities: a clinical practice guideline. Curr Oncol 2013;20:e247-e254
  6. Gerrand CH, Wunder JS, Kandel RA, O'Sullivan B, Catton CN, Bell RS, et al. Classification of positive margins after resection of soft-tissue sarcoma of the limb predicts the risk of local recurrence. J Bone Joint Surg Br 2001;83:1149-1155
  7. Beltran J, Simon DC, Katz W, Weis LD. Increased MR signal intensity in skeletal muscle adjacent to malignant tumors: pathologic correlation and clinical relevance. Radiology 1987;162:251-255
  8. White LM, Wunder JS, Bell RS, O'Sullivan B, Catton C, Ferguson P, et al. Histologic assessment of peritumoral edema in soft tissue sarcoma. Int J Radiat Oncol Biol Phys 2005;61:1439-1445
  9. Ditmer A, Zhang B, Shujaat T, Pavlina A, Luibrand N, Gaskill-Shipley M, et al. Diagnostic accuracy of MRI texture analysis for grading gliomas. J Neurooncol 2018;140:583-589
  10. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-577
  11. Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging 2013;13:140-149
  12. Makanyanga J, Ganeshan B, Rodriguez-Justo M, Bhatnagar G, Groves A, Halligan S, et al. MRI texture analysis (MRTA) of T2-weighted images in Crohn's disease may provide information on histological and MRI disease activity in patients undergoing ileal resection. Eur Radiol 2017;27:589-597
  13. Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 2013;13:400-406
  14. Peeken JC, Spraker MB, Knebel C, Dapper H, Pfeiffer D, Devecka M, et al. Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine 2019;48:332-340
  15. Tagliafico AS, Bignotti B, Rossi F, Valdora F, Martinoli C. Local recurrence of soft tissue sarcoma: a radiomic analysis. Radiol Oncol 2019;53:300-306
  16. Wang H, Chen H, Duan S, Hao D, Liu J. Radiomics and machine learning with multiparametric preoperative MRI may accurately predict the histopathological grades of soft tissue sarcomas. J Magn Reson Imaging 2020;51:791-797
  17. Zhang Y, Zhu Y, Shi X, Tao J, Cui J, Dai Y, et al. Soft tissue sarcomas: preoperative predictive histopathological grading based on radiomics of MRI. Acad Radiol 2019;26:1262-1268
  18. Khoo MM, Tyler PA, Saifuddin A, Padhani AR. Diffusion-weighted imaging (DWI) in musculoskeletal MRI: a critical review. Skeletal Radiol 2011;40:665-681
  19. Suzuki C, Maeda M, Matsumine A, Matsubara T, Taki W, Maier SE, et al. Apparent diffusion coefficient of subcutaneous epidermal cysts in the head and neck comparison with intracranial epidermoid cysts. Acad Radiol 2007;14:1020-1028
  20. Crombe A, Marcellin PJ, Buy X, Stoeckle E, Brouste V, Italiano A, et al. Soft-tissue sarcomas: assessment of MRI features correlating with histologic grade and patient outcome. Radiology 2019;291:710-721
  21. Chamming's F, Ueno Y, Ferre R, Kao E, Jannot AS, Chong J, et al. Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy. Radiology 2018;286:412-420
  22. Lee SY, Jee WH, Jung JY, Park MY, Kim SK, Jung CK, et al. Differentiation of malignant from benign soft tissue tumours: use of additive qualitative and quantitative diffusion-weighted MR imaging to standard MR imaging at 3.0 T. Eur Radiol 2016;26:743-754
  23. Mandard AM, Petiot JF, Marnay J, Mandard JC, Chasle J, de Ranieri E, et al. Prognostic factors in soft tissue sarcomas. A multivariate analysis of 109 cases. Cancer 1989;63:1437-1451
  24. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-35
  25. Lintz F, Moreau A, Odri GA, Waast D, Maillard O, Gouin F. Critical study of resection margins in adult soft-tissue sarcoma surgery. Orthop Traumatol Surg Res 2012;98:S9-S18
  26. Lang P, Honda G, Roberts T, Vahlensieck M, Johnston JO, Rosenau W, et al. Musculoskeletal neoplasm: perineoplastic edema versus tumor on dynamic postcontrast MR images with spatial mapping of instantaneous enhancement rates. Radiology 1995;197:831-839
  27. Yoon MA, Chee CG, Chung HW, Song JS, Lee JS, Kim W, et al. Added value of diffusion-weighted imaging to conventional MRI for predicting fascial involvement of soft tissue sarcomas. Eur Radiol 2019;29:1863-1873
  28. Zhao F, Ahlawat S, Farahani SJ, Weber KL, Montgomery EA, Carrino JA, et al. Can MR imaging be used to predict tumor grade in soft-tissue sarcoma? Radiology 2014;272:192-201
  29. Just N. Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 2014;111:2205-2213
  30. Weathers SP, Han X, Liu DD, Conrad CA, Gilbert MR, Loghin ME, et al. A randomized phase II trial of standard dose bevacizumab versus low dose bevacizumab plus lomustine (CCNU) in adults with recurrent glioblastoma. J Neurooncol 2016;129:487-494 
  31. Xiang P, Zhang X, Liu D, Wang C, Ding L, Wang F, et al. Distinguishing soft tissue sarcomas of different histologic grades based on quantitative MR assessment of intratumoral heterogeneity. Eur J Radiol 2019;118:194-199
  32. Jin KP, Rao SX, Sheng RF, Zeng MS. Skewness of apparent diffusion coefficient (ADC) histogram helps predict the invasive potential of intraductal papillary neoplasms of the bile ducts (IPNBs). Abdom Radiol (NY) 2019;44:95-103
  33. Maeda M, Matsumine A, Kato H, Kusuzaki K, Maier SE, Uchida A, et al. Soft-tissue tumors evaluated by linescan diffusion-weighted imaging: influence of myxoid matrix on the apparent diffusion coefficient. J Magn Reson Imaging 2007;25:1199-1204
  34. Nagata S, Nishimura H, Uchida M, Sakoda J, Tonan T, Hiraoka K, et al. Diffusion-weighted imaging of soft tissue tumors: usefulness of the apparent diffusion coefficient for differential diagnosis. Radiat Med 2008;26:287-295