MSCT Follow-Up in Malignant Lymphoma: Comparison of Manual Linear Measurements with Semi-Automated Lymph Node Analysis for Therapy Response Classification

Abstract

Purpose: Assignment of semi-automated lymph node analysis compared to manual measurements for therapy response classification of malignant lymphoma in MSCT.

Materials and Methods: MSCT scans of 63 malignant lymphoma patients before and after 2 cycles of chemotherapy (307 target lymph nodes) were evaluated. The long axis diameter (LAD), short axis diameter (SAD) and bi-dimensional WHO were determined manually and semi-automatically. The time for manual and semi-automatic segmentation was evaluated. The ref. standard response was defined as the mean relative change across all manual and semi-automatic measurements (mean manual/semi-automatic LAD, SAD, semi-automatic volume). Statistical analysis encompassed t-test and McNemar’s test for clustered data.

Results: Response classification per lymph node revealed semi-automated volumetry and bi-dimensional WHO to be significantly more accurate than manual linear metric measurements. Response classification per patient based on RECIST revealed more patients to be correctly classified by semi-automatic measurements, e. g. 96.0 %/92.9 % (WHO bi-dimensional/volume) compared to 85.7/84.1 % for manual LAD and SAD, respectively (mean reduction in misclassified patients of 9.95 %). Considering the use of correction tools, the time expenditure for lymph node segmentation (29.7 ± 17.4 sec) was the same as with the manual approach (29.1 ± 14.5 sec).

Conclusion: Semi-automatically derived “lymph node volume” and “bi-dimensional WHO” significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO.

Zusammenfassung

Ziel: Beurteilung semi-automatischer Lymphknoten-Messungen im Vergleich zu manuellen Messungen in der Beurteilung des Therapieansprechens beim malignen Lymphom in der MSCT.

Material und Methoden: MSCT-Untersuchungen von 63 Patienten mit malignem Lymphom vor und nach 2 Zyklen Chemotherapie (307 Target-Lymphknoten) wurden ausgewertet. Der Langachsen(LAD)-, der Kurzachsendurchmesser (SAD) und die bi-dimensionale WHO-Fläche wurden sowohl manuell als auch semi-automatisch vermessen. Weiterhin wurde der Zeitaufwand der Messungen erfasst. Als Referenzstandard diente die mittlere relative Änderung aller manuell und semi-automatisch bestimmten Parameter (mittlerer manueller/semi-automatischer LAD, SAD, semi-automatisches Volumen). Die statistische Auswertung umfasste den t-test und den McNemar’s-Test.

Ergebnisse: Bei Beurteilung des Therapieansprechens auf Basis des einzelnen Lymphknotens zeigten sich die semi-automatische Volumetrie und die bi-dimensionale WHO-Fläche signifikant besser als die manuellen metrischen Parameter. Bei Beurteilung des Therapieansprechens auf Basis des Patienten entsprechend RECIST konnte eine höhere Anzahl korrekter Klassifizierungen für die semi-automatischen Messungen nachgewiesen werden, z. B. 96,0 %/92,9 %(WHO bi-dimensional/Volumen) im Vergleich zu 85,7/84,1 % für den manuellen LAD und SAD, bei einer mittleren Reduktion der fehlklassifizierten Patienten von 9,95 %. Bei Verwendung von Korrektur-Tools war der Zeitaufwand für die Lymphknotensegmentierung (29,7 ± 17,4 s) weitgehend vergleichbar zu den manuellen Messungen (29,1 ± 14,5 s).

Schlussfolgerung: Bei Verwendung des semi-automatisch bestimmten Lymphknotenvolumens und der bi-dimensionale WHO-Fläche kommt es zu einer signifikanten Reduktion von Fehlebeurteilungen des Therapieansprechens von mindestens 10 % in CT-Verlaufskontrollen beim malignen Lymphom. Das Lymphknotenvolumen ist der bi-dimensionalen WHO-Fläche jedoch nicht überlegen.

Key words

lymphoma - segmentation - treatment effects - CT - lymphatic

• References

• 1 Elstrom R, Guan L, Baker G et al. Utility of FDG-PET scanning in lymphoma by WHO classification. Blood 2003; 101: 3875-3876
• 2 Puesken M, Buerke B, Gerss J et al. Prediction of lymph node manifestations in malignant lymphoma: significant role of volumetric compared with established metric lymph node analysis in multislice computed tomography. J Comput Assist Tomogr 2010; 34: 564-569
• 3 Wahl RL, Jacene H, Kasamon Y et al. From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med 2009; 50: 122S-150S
• 4 Therasse P, Arbuck SG, Eisenhauer EA et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 2000; 92: 205-216
• 5 Jaffe TA, Wickersham NW, Sullivan DC. Quantitative imaging in oncology patients: Part 1, radiology practice patterns at major U. S. cancer centers. Am J Roentgenol 2010; 195: 101-106
• 6 Assouline S, Meyer RM, Infante-Rivard C et al. Development of adapted RECIST criteria to assess response in lymphoma and their comparison to the International Workshop Criteria. Leuk Lymphoma 2007; 48: 513-520
• 7 Eisenhauer EA, Therasse P, Bogaerts J et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45: 228-247
• 8 Schwartz LH, Bogaerts J, Ford R et al. Evaluation of lymph nodes with RECIST 1.1. Eur J Cancer 2009; 45: 261-267
• 9 Cheson BD, Horning SJ, Coiffier B et al. Report of an international workshop to standardize response criteria for non-Hodgkin’s lymphomas. NCI Sponsored International Working Group. J Clin Oncol 1999; 17: 1244
• 10 Cheson BD, Pfistner B, Juweid ME et al. Revised response criteria for malignant lymphoma. J Clin Oncol 2007; 25: 579-586
• 11 Keil S, Plumhans C, Behrendt FF et al. Automated measurement of lymph nodes: a phantom study. Eur Radiol 2009; 19: 1079-1086
• 12 Puesken M, Juergens KU, Edenfeld A et al. Einfluss des Vaskularisationsgrades auf die automatische Segmentierung und Messung von Lebertumoren nach RECIST in einer biphasischen Multi-Slice-CT (MSCT). Fortschr Röntgenstr 2009; 181: 67-73
• 13 Marten K, Rummeny EJ, Engelke C. Computerassistierter Nachweis und automatisierte Volumetrie pulmonaler Rundherde in der Multislice-CT: Aktueller Stand und Perspektiven. Fortschr Röntgenstr 2005; 177: 188-196
• 14 Fabel M, von Tengg-Kobligk H, Giesel FL et al. Semi-automated volumetric analysis of lymph node metastases in patients with malignant melanoma stage III/IV – a feasibility study. Eur Radiol 2008; 18: 1114-1122
• 15 Radiological Society of North America Quantitative Imaging Biomarkers Alliance In: Radiological Society of North America Web site; 24.11.2009 www.rsna.org/Research/qiba_intro.cfm
• 16 Redman HC, Glatstein E, Castellino RA et al. Computed tomography as an adjunct in the staging of Hodgkin’s disease and non-Hodgkin’s lymphomas. Radiology 1977; 124: 381-385
• 17 Buerke B, Puesken M, Beyer F et al. Semiautomatic lymph node segmentation in multislice computed tomography: impact of slice thickness on segmentation quality, measurement precision, and interobserver variability. Invest Radiol 2010; 45: 82-88
• 18 Kuhnigk JM, Dicken V, Bornemann L et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 2006; 25: 417-434
• 19 Wormanns D, Kohl G, Klotz E et al. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 2004; 14: 86-92
• 20 James K, Eisenhauer E, Christian M et al. Measuring response in solid tumors: unidimensional versus bidimensional measurement. J Natl Cancer Inst 1999; 91: 523-528
• 21 Buerke B, Puesken M, Muter S et al. Measurement accuracy and reproducibility of semiautomated metric and volumetric lymph node analysis in MDCT. Am J Roentgenol 2010; 195: 979-985
• 22 Zhao B, Schwartz LH, Moskowitz CS et al. Lung cancer: computerized quantification of tumor response – initial results. Radiology 2006; 241: 892-898
• 23 Obuchowski NA. On the comparison of correlated proportions for clustered data. Stat Med 1998; 17: 1495-1507
• 24 Jaffe TA, Wickersham NW, Sullivan DC. Quantitative imaging in oncology patients: Part 2, oncologists’ opinions and expectations at major U. S. cancer centers. Am J Roentgenol 2010; 195: W19-W30
• 25 Juweid ME, Stroobants S, Hoekstra OS et al. Use of positron emission tomography for response assessment of lymphoma: consensus of the Imaging Subcommittee of International Harmonization Project in Lymphoma. J Clin Oncol 2007; 25: 571-578
• 26 Fabel M, Bolte H, von Tengg-Kobligk H et al. Semi-automated volumetric analysis of lymph node metastases during follow-up-initial results. Eur Radiol 2011; 21: 683-692
• 27 Cheson BD. Staging and evaluation of the patient with lymphoma. Hematol Oncol Clin North Am 2008; 22: 825-837
• 28 Pien HH, Fischman AJ, Thrall JH et al. Using imaging biomarkers to accelerate drug development and clinical trials. Drug Discov Today 2005; 10: 259-266
• 29 Frank R, Hargreaves R. Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2003; 2: 566-580
• 30 Clarke LP, Croft BS, Nordstrom R et al. Quantitative imaging for evaluation of response to cancer therapy. Transl Oncol 2009; 2: 195-197