A new method for segmentation of FDG PET metabolic tumour volume using the peritumoural halo layer and a 10-step colour scale

Summary

Aim: We observed a layer between tumour activity and background on FDG PET/CT with the 10-step colour scale and the window level set properly. We named the layer peritumoral halo layer (PHL). We performed this study to establish the reliability of metabolic tumor volume (MTV) segmentation using PHL (MTV_PHL) in patients with papillary thyroid carcinoma. Patients, methods: Of a total of 140 papillary thyroid carcinoma (PTC) patients, 70 (50.0%) had FDG-avid PTC. In these patients, MTV_PHL, MTV segmented according to fixed 50% SUVmax (MTV_50%), and fixed SUV with 2.5 to 4.0 (MTV_2.5 to MTV_4.0) were compared with pathologic tumour volume (PTV). The absolute percentage difference between MTV_PHL and PTV was compared in micropapillary carcinoma (MPTC) and non-micropapillary carcinoma (non-MPTC) subgroups. The % SUVmax and SUV thresholds of MTV_PHL were compared with tumour SUVmax. Results: Among the MTVs, MTV50% was not correlated with PTV (r = –0.16, p = 0.182) and was not reliable according to the Bland-Altman plot. Although MTV_2.5, MTV_3.0, MTV_3.5, and MTV_4.0 correlated with PTV (r = 0.85, 0.86, 0.87, and 0.87, respectively; p < 0.001), these MTVs were not reliable on Bland- Altman analyses. MTV_PHL was significantly correlated with PTV (r = 0.80, p < 0.001), and the Bland-Altman plot did not show systemic error. The MTV_PHL was more accurate in non-MPTC than in MPTC (p < 0.001), and the absolute % difference was smaller as PTV became larger (σ = –0.65, p < 0.001). The MTV_PHL thresholds had correlations with SUVmax (% SUVmax threshold: σ = –0.87, p < 0.001; SUV threshold: r = 0.88, p < 0.001). Conclusions: MTV_PHL was more reliable than MTV_%SUVmax or MTV_SUV. The reliability of MTV_PHL improved with larger PTVs. The threshold of the MTV_PHL was naturally altered by PHL according to SUVmax.

Zusammenfassung

Peritumoraler Halo, FDG-PET/CT, metabolisches Tumorvolumen, Schilddrüsenkarzinom, Zuverlässigkeitsprüfung Zusammenfassung Ziel: Wir haben anhand einer 10-stufigen Farbskala und entsprechend eingestelltem Fenster in der FDG-PET/CT eine Schicht zwischen Tumoraktivität und Hintergrund beobachtet. Diese Schicht haben wir als peritumoralen Halo (PHL) bezeichnet. Diese Studie wurde durchgeführt, um die Zuverlässigkeit der Segmentierung des metabolischen Tumorvolumens (MTV) anhand des PHL (MTV_PHL) bei Patienten mit papillärem Schilddrüsenkarzinom zu untersuchen. Patienten, Methoden: Von insgesamt 140 Patienten mit papillärem Schilddrüsenkarzinom (PTC) hatten 70 (50,0%) ein FDG-avides PTC. Bei diesen Patienten wurden MTV_PHL, das MTV, segmentiert nach fixem SUVmax von 50% (MTV_50%) und nach fixem SUV von 2,5 bis 4,0 (MTV_2.5 bis MTV_4.0) mit dem pathologischen Tumorvolumen (PTV) verglichen. Der absolute prozentuale Unterschied zwischen MTV_PHL und PTV wurde in den Untergruppen des mikropapillären Karzinoms (MPTC) und des nicht mikropapillären Karzinoms (non-MPTC) verglichen. Die prozentualen Schwellen von SUVmax und SUV des MTV_PHL wurden mit dem Tumor-SUVmax verglichen. Ergebnisse: Bei den MTV korrelierte MTV_50% nicht mit dem PTV (r = –0,16, p = 0,182) und war daher nach dem Bland-Altman-Diagramm nicht zuverlässig. Obgleich MTV_2.5, MTV_3.0, MTV_3.5 und MTV_4.0 mit dem PTV korrelierten (r = 0,85, 0,86, 0,87 bzw. 0,87; p < 0,001), waren diese MTV im Bland-Altman-Diagramm nicht zuverlässig. MTV_PHL korrelierte signifikant mit dem PTV (r = 0,80, p < 0,001), und das Bland-Altman-Diagramm zeigte keinen systemischen Fehler. MTV_PHL war beim non-MPTC genauer als beim MPTC (p < 0,001), und der absolute prozentuale Unterschied wurde mit zunehmendem PTV kleiner (σ = –0,65, p < 0,001). Die MTV_PHL-Schwellen korrelierten mit dem SUVmax (% SUVmax- Schwelle: σ = –0,87, p < 0,001; SUVSchwelle: r = 0,88, p < 0,001). Schlussfolgerungen: MTV_PHL war zuverlässiger als MTV_%SUVmax oder MTV_SUV. MTV_PHL war bei größeren PTV zuverlässiger. Die Schwelle des MTV_PHL wurde durch den PHL entsprechend SUVmax natürlich verändert.

Keywords

Peritumoural halo layer - FDG PET/CT - metabolic tumour volume - thyroid cancer - reliability test

Schlüsselwörter

Peritumoraler Halo - FDG-PET/CT - metabolisches Tumorvolumen - Schilddrüsenkarzinom - Zuverlässigkeitsprüfung

• References

• 1 Ahuja V, Coleman RE, Herndon J. et al. The prognostic significance of fluorodeoxyglucose positron emission tomography imaging for patients with nonsmall cell lung carcinoma. Cancer 1998; 83: 918-924.
• 2 Ashamalla H, Rafla S, Parikh K. et al. The contribution of integrated PET/CT to the evolving definition of treatment volumes in radiation treatment planning in lung cancer. Int J Radiat Oncol Biol Phys 2005; 63: 1016-1023.
• 3 Boellaard R, Delgado-Bolton R, Oyen WJ. et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015; 42: 328-354.
• 4 Bowden P, Fisher R, Mac Manus M. et al. Measurement of lung tumor volumes using three-dimensional computer planning software. Int J Radiat Oncol Biol Phys 2002; 53: 566-573.
• 5 Byun BH, Jeong UG, Hong SP. et al. Prediction of central lymph node metastasis from papillary thyroid microcarcinoma by ¹⁸F-fluorodeoxyglucose PET/CT and ultrasonography. Ann Nucl Med 2012; 26: 471-477.
• 6 Chung HH, Kim JW, Han KH. et al. Prognostic value of metabolic tumor volume measured by FDG-PET/CT in patients with cervical cancer. Gynecol Oncol 2011; 120: 270-274.
• 7 Cibas ES, Ali SZ. The Bethesda System For Reporting Thyroid Cytopathology. Am J Clin Pathol 2009; 132: 658-665.
• 8 Daisne JF, Sibomana M, Bol A. et al. Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms. Radiother Oncol 2003; 69: 247-250.
• 9 Davison J, Mercier G, Russo G. et al. PET-based primary tumor volumetric parameters and survival of patients with non-small cell lung carcinoma. AJR Am J Roentgenol 2013; 200: 635-640.
• 10 Dibble EH, Alvarez AC, Truong MT. et al. ¹⁸F-FDG metabolic tumor volume and total glycolytic activity of oral cavity and oropharyngeal squamous cell cancer: adding value to clinical staging. J Nucl Med 2012; 53: 709-715.
• 11 Geets X, Lee JA, Bol A. et al. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging 2007; 34: 1427-1438.
• 12 Han D, Yu J, Yu Y. et al. Comparison of ¹⁸F-fluorothymidine and ¹⁸F-fluorodeoxyglucose PET/CT in delineating gross tumor volume by optimal threshold in patients with squamous cell carcinoma of thoracic esophagus. Int J Radiat Oncol Biol Phys 2010; 76: 1235-1241.
• 13 Hatt M, Cheze-le Rest C, van Baardwijk A. et al. Impact of tumor size and tracer uptake heterogeneity in ¹⁸F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med 2011; 52: 1690-1697.
• 14 Hwang SO, Lee SW, Kang JK. et al. Clinical value of visually identifiable ¹⁸F-fluorodeoxyglucose uptake in primary papillary thyroid microcarcinoma. Otolaryngol Head Neck Surg 2014; 151: 415-420.
• 15 Hyun SH, Choi JY, Shim YM. et al. Prognostic value of metabolic tumor volume measured by ¹⁸F-fluorodeoxyglucose positron emission tomography in patients with esophageal carcinoma. Ann Surg Oncol 2010; 17: 115-122.
• 16 Kim BS, Kim SJ, Kim IJ. et al. Factors associated with positive F-18 flurodeoxyglucose positron emission tomography before thyroidectomy in patients with papillary thyroid carcinoma. Thyroid 2012; 22: 725-729.
• 17 Kim H, Na KJ, Choi JH. et al. Feasibility of FDG-PET/CT for the initial diagnosis of papillary thyroid cancer. Eur Arch Otorhinolaryngol. 2015
• 18 La TH, Filion EJ, Turnbull BB. et al. Metabolic tumor volume predicts for recurrence and death in head-and-neck cancer. Int J Radiat Oncol Biol Phys 2009; 74: 1335-1341.
• 19 Lee HS, Kim HO, Hong YS. et al. Prognostic value of metabolic parameters in patients with synchronous colorectal cancer liver metastasis following curative-intent colorectal and hepatic surgery. J Nucl Med 2014; 55: 582-589.
• 20 Murphy JD, Chisholm KM, Daly ME. et al. Correlation between metabolic tumor volume and pathologic tumor volume in squamous cell carcinoma of the oral cavity. Radiother Oncol 2011; 101: 356-361.
• 21 Nestle U, Kremp S, Schaefer-Schuler A. et al. Comparison of different methods for delineation of ¹⁸F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 2005; 46: 1342-1348.
• 22 Romesser PB, Qureshi MM, Shah BA. et al. Superior prognostic utility of gross and metabolic tumor volume compared to standardized uptake value using PET/CT in head and neck squamous cell carcinoma patients treated with intensity-modulated radiotherapy. Ann Nucl Med 2012; 26: 527-534.
• 23 Satoh Y, Onishi H, Nambu A. et al. Volume-based parameters measured by using FDG PET/CT in patients with stage I NSCLC treated with stereotactic body radiation therapy: prognostic value. Radiology 2014; 270: 275-281.
• 24 Schwartz DL, Harris J, Yao M. et al. Metabolic tumor volume as a prognostic imaging-based biomarker for head-and-neck cancer: pilot results from Radiation Therapy Oncology Group protocol 0522. Int J Radiat Oncol Biol Phys 2015; 91: 721-729.
• 25 Seol YM, Kwon BR, Song MK. et al. Measurement of tumor volume by PET to evaluate prognosis in patients with head and neck cancer treated by chemo-radiation therapy. Acta Oncol 2010; 49: 201-208.
• 26 Sridhar P, Mercier G, Tan J. et al. FDG PET metabolic tumor volume segmentation and pathologic volume of primary human solid tumors. AJR Am J Roentgenol 2014; 202: 1114-1119.
• 27 Subramaniam RM, Truong M, Peller P. et al. Fluorodeoxyglucose-positron-emission tomography imaging of head and neck squamous cell cancer. AJNR Am J Neuroradiol 2010; 31: 598-604.
• 28 Sureshbabu W, Mawlawi O. PET/CT imaging artifacts. J Nucl Med Technol 2005; 33: 156-161.
• 29 Van Baardwijk A, Bosmans G, Boersma L. et al. PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 2007; 68: 771-778.
• 30 Van Dalen JA, Hoffmann AL, Dicken V. et al. A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl Med Commun 2007; 28: 485-493.
• 31 Werner-Wasik M, Nelson AD, Choi W. et al. What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys 2012; 82: 1164-1171.
• 32 Wilcox BE, Subramaniam RM, Peller PJ. et al. Utility of integrated computed tomography-positron emission tomography for selection of operable malignant pleural mesothelioma. Clin Lung Cancer 2009; 10: 244-248.
• 33 Yu J, Li X, Xing L. et al. Comparison of tumor volumes as determined by pathologic examination and FDG-PET/CT images of non-small-cell lung cancer: a pilot study. Int J Radiat Oncol Biol Phys 2009; 75: 1468-1474.
• 34 Yun M, Noh TW, Cho A. et al. Visually discernible [¹⁸F]fluorodeoxyglucose uptake in papillary thyroid microcarcinoma: a potential new risk factor. J Clin Endocrinol Metab 2010; 95: 3182-3188.
• 35 Zhong X, Yu J, Zhang B. et al. Using ¹⁸F-fluorodeoxyglucose positron emission tomography to estimate the length of gross tumor in patients with squamous cell carcinoma of the esophagus. Int J Radiat Oncol Biol Phys 2009; 73: 136-141.