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European Radiology

, Volume 29, Issue 12, pp 6911–6921 | Cite as

Computed tomography textural analysis for the differentiation of chronic lymphocytic leukemia and diffuse large B cell lymphoma of Richter syndrome

  • C.P. ReinertEmail author
  • B. Federmann
  • J. Hofmann
  • H. Bösmüller
  • S. Wirths
  • J. Fritz
  • M. Horger
Oncology
  • 113 Downloads

Abstract

Objective

To test the hypothesis that both indolent and aggressive chronic lymphocytic leukemia (CLL) can be differentiated from diffuse large B cell lymphoma (DLBCL) of Richter syndrome (RS) by CT texture analysis (CTTA) of involved lymph nodes.

Material and methods

We retrospectively included 52 patients with indolent CLL (26/52), aggressive CLL (8/52), and DLBCL of RS (18/52), who underwent standardized contrast-enhanced CT. In main lymphoma tissue, VOIs were generated from which CTTA features including first-, second-, and higher-order textural features were extracted. CTTA features were compared between the entire CLL group, the indolent CLL subtype, the aggressive CLL subtype, and DLBCL using a Kruskal-Wallis test. All p values were adjusted after the Bonferroni correction. ROC analyses for significant CTTA features were performed to determine cut-off values for differentiation between the groups.

Results

Compared with DLBCL of RS, CTTA of the entire CLL group showed significant differences of entropy heterogeneity (p < 0.001), mean intensity (p < 0.001), mean average (p = 0.02), and number non-uniformity gray-level dependence matrix (NGLDM) (p = 0.03). Indolent CLL significantly differed for entropy (p < 0.001), uniformity of heterogeneity (p = 0.02), mean intensity (p < 0.001), and mean average (p = 0.01). Aggressive CLL showed significant differences in mean intensity (p = 0.04). For differentiation between CLL and DLBCL of RS, cut-off values for mean intensity and entropy of heterogeneity were defined (e.g., 6.63 for entropy heterogeneity [aggressive CLL vs. DLBCL]; sensitivity 0.78; specificity 0.63).

Conclusions

CTTA features of ultrastructure and vascularization significantly differ in CLL compared with that in DLBCL of Richter syndrome, allowing complementary to visual features for noninvasive differentiation by contrast-enhanced CT.

Key Points

• Richter transformation of CLL into DLBCL results in structural changes in lymph node architecture and vascularization that can be detected by CTTA.

• First-order CT textural features including intensity and heterogeneity significantly differ between both indolent CLL and aggressive CLL and DLBCL of Richter syndrome.

CT texture analysis allows for noninvasive detection of Richter syndrome which is of prognostic value.

Keywords

Texture analysis Tomography, X-ray-computed Leukemia, lymphocytic, chronic, B cell Lymphoma, large B cell, diffuse 

Abbreviations

CECT

Contrast-enhanced CT

CLL

Chronic lymphocytic leukemia

CTTA

CT texture analysis

DLBCL

Diffuse large B cell lymphoma

FDG

Fluorodeoxyglucose

LDH

Lactate dehydrogenase

ROI

Region of interest

RS

Richter syndrome

VOI

Volume of interest

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Christian Philipp Reinert.

Conflict of interest

Marius Horger received institutional research funds and speaker’s honorarium from Siemens Healthineers and is a scientific advisor of Siemens Healthcare, Germany. Konstantin Nikolaou received institutional research funds and speaker’s honorarium from Siemens Healthineers and is a scientific advisor of Siemens Healthcare, Germany. Jan Fritz received institutional research support from Siemens Healthcare, USA, DePuy, Zimmer, Microsoft, and BTG International; is a scientific advisor of Siemens Healthcare USA, Alexion Pharmaceuticals, and BTG International; received speaker’s honorarium from Siemens Healthcare, USA; and has shared patents with Siemens Healthcare and Johns Hopkins University. The other authors have declared that no competing interests exist.

Statistics and biometry

Dr. Christian Philipp Reinert has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board (project number 467/2018).

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

Supplementary material

330_2019_6291_MOESM1_ESM.docx (6.3 mb)
ESM 1 (DOCX 6494 kb)

References

  1. 1.
    Lynch RC, Gratzinger D, Advani RH (2017) Clinical impact of the 2016 update to the WHO lymphoma classification. Curr Treat Options Oncol 18(45)Google Scholar
  2. 2.
    (1990) Effects of chlorambucil and therapeutic decision in initial forms of chronic lymphocytic leukemia (stage A): results of a randomized clinical trial on 612 patients. The French Cooperative Group on Chronic Lymphocytic Leukemia. Blood 75:1414–1421Google Scholar
  3. 3.
    Hallek M (2017) Chronic lymphocytic leukemia: 2017 update on diagnosis, risk stratification, and treatment. Am J Hematol 92:946–965CrossRefGoogle Scholar
  4. 4.
    Tsimberidou AM, O'Brien S, Kantarjian HM et al (2006) Hodgkin transformation of chronic lymphocytic leukemia: the M. D. Anderson Cancer Center experience. Cancer 107:1294–1302CrossRefGoogle Scholar
  5. 5.
    Vitale C, Ferrajoli A (2016) Richter syndrome in chronic lymphocytic leukemia. Curr Hematol Malig Rep 11:43–51CrossRefGoogle Scholar
  6. 6.
    Jain P, O'Brien S (2012) Richter’s transformation in chronic lymphocytic leukemia. Oncology (Williston Park) 26:1146–1152Google Scholar
  7. 7.
    Niemann CU, Polliack A, Hutchings M (2014) Suspected Richter transformation: positron emission tomography/computed tomography tells us who should have a biopsy and where. Leuk Lymphoma 55:233–234CrossRefGoogle Scholar
  8. 8.
    Grozinger G, Adam P, Horger M (2014) HSV lymphadenitis in chronic lymphocytic leukemia -- a rare but difficult differential diagnosis. Rofo 186:79–80PubMedGoogle Scholar
  9. 9.
    Liu Y (2011) Demonstrations of AIDS-associated malignancies and infections at FDG PET-CT. Ann Nucl Med 25:536–546CrossRefGoogle Scholar
  10. 10.
    Mhlanga JC, Durand D, Tsai HL et al (2014) Differentiation of HIV-associated lymphoma from HIV-associated reactive adenopathy using quantitative FDG PET and symmetry. Eur J Nucl Med Mol Imaging 41:596–604CrossRefGoogle Scholar
  11. 11.
    Bruzzi JF, Macapinlac H, Tsimberidou AM et al (2006) Detection of Richter’s transformation of chronic lymphocytic leukemia by PET/CT. J Nucl Med 47:1267–1273PubMedGoogle Scholar
  12. 12.
    Papajík T, Mysliveček M, Urbanová R et al (2014) 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography examination in patients with chronic lymphocytic leukemia may reveal Richter transformation. Leuk Lymphoma 55:314–319CrossRefGoogle Scholar
  13. 13.
    Ansell SM, Armitage JO (2012) Positron emission tomographic scans in lymphoma: convention and controversy. Mayo Clin Proc 87:571–580CrossRefGoogle Scholar
  14. 14.
    Dubreuil J, Salles G, Bozzetto J et al (2017) Usual and unusual pitfalls of 18F-FDG-PET/CT in lymphoma after treatment: a pictorial review. Nucl Med Commun 38:563–576CrossRefGoogle Scholar
  15. 15.
    Song MK, Chung JS, Shin DY et al (2017) Tumor necrosis could reflect advanced disease status in patients with diffuse large B cell lymphoma treated with R-CHOP therapy. Ann Hematol 96:17–23CrossRefGoogle Scholar
  16. 16.
    Soilleux EJ, Wotherspoon A, Eyre TA, Clifford R, Cabes M, Schuh AH (2016) Diagnostic dilemmas of high-grade transformation (Richter's syndrome) of chronic lymphocytic leukaemia: results of the phase II National Cancer Research Institute CHOP-OR clinical trial specialist haemato-pathology central review. Histopathology 69:1066–1076CrossRefGoogle Scholar
  17. 17.
    Swerdlow SH, Campo E, Harris NL et al (2017) WHO classification of tumours of haematopoietic and lymphoid tissues. International Agency for Research on CancerGoogle Scholar
  18. 18.
    Rossi D, Spina V, Deambrogi C et al (2011) The genetics of Richter syndrome reveals disease heterogeneity and predicts survival after transformation. Blood 117:3391–3401CrossRefGoogle Scholar
  19. 19.
    Pathania K (2009) Richter’s syndrome. Med J Armed Forces India 65:375–377CrossRefGoogle Scholar
  20. 20.
    Saito A, Takashima S, Takayama F, Kawakami S, Momose M, Matsushita T (2001) Spontaneous extensive necrosis in non-Hodgkin lymphoma: prevalence and clinical significance. J Comput Assist Tomogr 25:482–486CrossRefGoogle Scholar
  21. 21.
    Adams HJA, de Klerk JMH, Fijnheer R et al (2016) Tumor necrosis at FDG-PET is an independent predictor of outcome in diffuse large B-cell lymphoma. Eur J Radiol 85:304–309CrossRefGoogle Scholar
  22. 22.
    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503CrossRefGoogle Scholar
  23. 23.
    Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13:140–149CrossRefGoogle Scholar
  24. 24.
    Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184CrossRefGoogle Scholar
  25. 25.
    Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67:157–164CrossRefGoogle Scholar
  26. 26.
    Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802CrossRefGoogle Scholar
  27. 27.
    Zhang H, Graham CM, Elci O et al (2013) Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology 269:801–809CrossRefGoogle Scholar
  28. 28.
    Leng S, Takahashi N, Gomez Cardona D et al (2017) Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT. Abdom Radiol (NY) 42:1485–1492CrossRefGoogle Scholar
  29. 29.
    Binet JL, Auquier A, Dighiero G et al (1981) A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis. Cancer 48:198–206CrossRefGoogle Scholar
  30. 30.
    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRefGoogle Scholar
  31. 31.
    Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406CrossRefGoogle Scholar
  32. 32.
    Mao Z, Quintanilla-Martinez L, Raffeld M et al (2007) IgVH mutational status and clonality analysis of Richter’s transformation: diffuse large B-cell lymphoma and Hodgkin lymphoma in association with B-cell chronic lymphocytic leukemia (B-CLL) represent 2 different pathways of disease evolution. Am J Surg Pathol 31:1605–1614CrossRefGoogle Scholar
  33. 33.
    Spira D, Adam P, Linder C et al (2012) Perfusion and flow extraction product as potential discriminators in untreated follicular and diffuse large B cell lymphomas using volume perfusion CT with attempt at histopathologic explanation. AJR Am J Roentgenol 198:1239–1246CrossRefGoogle Scholar
  34. 34.
    Ruan J, Hajjar K, Rafii S, Leonard JP (2009) Angiogenesis and antiangiogenic therapy in non-Hodgkin's lymphoma. Ann Oncol 20:413–424CrossRefGoogle Scholar
  35. 35.
    Cardesa-Salzmann TM, Colomo L, Gutierrez G et al (2011) High microvessel density determines a poor outcome in patients with diffuse large B-cell lymphoma treated with rituximab plus chemotherapy. Haematologica 96:996–1001CrossRefGoogle Scholar
  36. 36.
    Yip C, Davnall F, Kozarski R et al (2015) Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. Dis Esophagus 28:172–179CrossRefGoogle Scholar
  37. 37.
    Ganeshan B, Miles KA, Babikir S et al (2017) CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin’s and aggressive non-hodgkin’s lymphomas. Eur Radiol 27:1012–1020CrossRefGoogle Scholar
  38. 38.
    Durot C, Mulé S, Soyer P, Marchal A, Grange F, Hoeffel C (2019) Metastatic melanoma: pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with pembrolizumab. Eur Radiol 29:3183–3191.  https://doi.org/10.1007/s00330-018-5933-x
  39. 39.
    Feng C, Lu F, Shen Y et al (2018) Tumor heterogeneity in gastrointestinal stromal tumors of the small bowel: volumetric CT texture analysis as a potential biomarker for risk stratification. Cancer Imaging 18:46CrossRefGoogle Scholar
  40. 40.
    Starkov P, Aguilera TA, Golden DI et al (2019) The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy. Br J Radiol 92:20180228CrossRefGoogle Scholar
  41. 41.
    Lee SJ, Zea R, Kim DH, Lubner MG, Deming DA, Pickhardt PJ (2018) CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer. Eur Radiol 28:1520–1528CrossRefGoogle Scholar
  42. 42.
    Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA (2011) Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261:165–171CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • C.P. Reinert
    • 1
    Email author
  • B. Federmann
    • 2
  • J. Hofmann
    • 1
  • H. Bösmüller
    • 2
  • S. Wirths
    • 3
  • J. Fritz
    • 4
  • M. Horger
    • 1
  1. 1.Department of Diagnostic and Interventional RadiologyUniversity Hospital TübingenTübingenGermany
  2. 2.Department of Pathology and NeuropathologyUniversity Hospital TübingenTübingenGermany
  3. 3.Department of Hematology and OncologyUniversity Hospital TübingenTübingenGermany
  4. 4.Russell H. Morgan Department of Radiology and RadiologicalJohns Hopkins University School of MedicineBaltimoreUSA

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