Indonesian
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Journal of nuclear medicine : official publication, Society of Nuclear Medicine 2012-Sep

Role of O-(2-(18)F-fluoroethyl)-L-tyrosine PET for differentiation of local recurrent brain metastasis from radiation necrosis.

Hanya pengguna terdaftar yang dapat menerjemahkan artikel
Masuk daftar
Tautan disimpan ke clipboard
Norbert Galldiks
Gabriele Stoffels
Christian P Filss
Marc D Piroth
Michael Sabel
Maximilian I Ruge
Hans Herzog
Nadim J Shah
Gereon R Fink
Heinz H Coenen

Kata kunci

Abstrak

The aim of this study was to investigate the potential of O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) PET for differentiating local recurrent brain metastasis from radiation necrosis after radiation therapy because the use of contrast-enhanced MRI for this issue is often difficult.

METHODS

Thirty-one patients (mean age ± SD, 53 ± 11 y) with single or multiple contrast-enhancing brain lesions (n = 40) on MRI after radiation therapy of brain metastases were investigated with dynamic (18)F-FET PET. Maximum and mean tumor-to-brain ratios (TBR(max) and TBR(mean), respectively; 20-40 min after injection) of (18)F-FET uptake were determined. Time-activity curves were generated, and the time to peak (TTP) was calculated. Furthermore, time-activity curves of each lesion were assigned to one of the following curve patterns: (I) constantly increasing (18)F-FET uptake, (II) (18)F-FET uptake peaking early (TTP ≤ 20 min) followed by a plateau, and (III) (18)F-FET uptake peaking early (TTP ≤ 20 min) followed by a constant descent. The diagnostic accuracy of the TBR(max) and TBR(mean) of (18)F-FET uptake and the curve patterns for the correct identification of recurrent brain metastasis were evaluated by receiver-operating-characteristic analyses or Fisher exact test for 2 × 2 contingency tables using subsequent histologic analysis (11 lesions in 11 patients) or clinical course and MRI findings (29 lesions in 20 patients) as reference.

RESULTS

Both TBR(max) and TBR(mean) were significantly higher in patients with recurrent metastasis (n = 19) than in patients with radiation necrosis (n = 21) (TBR(max), 3.2 ± 0.9 vs. 2.3 ± 0.5, P < 0.001; TBR(mean), 2.1 ± 0.4 vs. 1.8 ± 0.2, P < 0.001). The diagnostic accuracy of (18)F-FET PET for the correct identification of recurrent brain metastases reached 78% using TBR(max) (area under the ROC curve [AUC], 0.822 ± 0.07; sensitivity, 79%; specificity, 76%; cutoff, 2.55; P = 0.001), 83% using TBR(mean) (AUC, 0.851 ± 0.07; sensitivity, 74%; specificity, 90%; cutoff, 1.95; P < 0.001), and 92% for curve patterns II and III versus curve pattern I (sensitivity, 84%; specificity, 100%; P < 0.0001). The highest accuracy (93%) to diagnose local recurrent metastasis was obtained when both a TBR(mean) greater than 1.9 and curve pattern II or III were present (AUC, 0.959 ± 0.03; sensitivity, 95%; specificity, 91%; P < 0.001).

CONCLUSIONS

Our findings suggest that the combined evaluation of the TBR(mean) of (18)F-FET uptake and the pattern of the time-activity curve can differentiate local brain metastasis recurrence from radionecrosis with high accuracy. (18)F-FET PET may thus contribute significantly to the management of patients with brain metastases.

Bergabunglah dengan
halaman facebook kami

Database tanaman obat terlengkap yang didukung oleh sains

  • Bekerja dalam 55 bahasa
  • Pengobatan herbal didukung oleh sains
  • Pengenalan herbal melalui gambar
  • Peta GPS interaktif - beri tag herba di lokasi (segera hadir)
  • Baca publikasi ilmiah yang terkait dengan pencarian Anda
  • Cari tanaman obat berdasarkan efeknya
  • Atur minat Anda dan ikuti perkembangan berita, uji klinis, dan paten

Ketikkan gejala atau penyakit dan baca tentang jamu yang mungkin membantu, ketik jamu dan lihat penyakit dan gejala yang digunakan untuk melawannya.
* Semua informasi didasarkan pada penelitian ilmiah yang dipublikasikan

Google Play badgeApp Store badge