Bone metastases from breast cancer: associations between morphologic CT patterns and glycolytic activity on PET and bone scintigraphy as well as explorative search for influential factors

Abstract

BackgroundThis study aimed to compare the detection of bone metastases from breast cancer on F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and bone scintigraphy (BS). An explorative search for factors influencing the sensitivity or uptake of BS and FDG-PET was also performed.MethodsEighty-eight patients with bone metastases from breast cancer were eligible for this study. Histological confirmation of bone metastases was obtained in 31 patients. The bone metastases were visually classified into four types based on their computed tomography (CT) appearance: osteoblastic, osteolytic, mixed, and negative. The sensitivity of BS and FDG-PET were obtained regarding CT type, adjuvant therapy, and the primary tumor characteristics. The FDG maximum standardized uptake value (SUVmax) was analyzed.ResultsThe sensitivities of the three modalities (CT, BS, and FDG-PET) were 77, 89, and 94%, respectively. The sensitivity of FDG-PET for the osteoblastic type (69%) was significantly lower than that for the other types (P < 0.001), and the sensitivity of BS for the negative type (70%) was significantly lower than that for the others. Regarding tumor characteristics, the sensitivity of FDG-PET significantly differed between nuclear grade (NG)1 and NG2–3 (P = 0.032). The SUVmax of the osteoblastic type was significantly lower than that of the other types (P = 0.009). The SUVmax of NG1 was also significantly lower than that of NG2–3 (P = 0.011). No significant difference in FDG uptake (SUVmax) was detected between different histological types.ConclusionAlthough FDG-PET is superior to BS for the detection of bone metastases from breast cancer, this technique has limitations in depicting osteoblastic bone metastases and NG1.

Diagnosis of bone metastases in breast cancer: Lesion-based sensitivity of dual-time-point FDG-PET/CT compared to low-dose CT and bone scintigraphy

“…In a retrospective Japanese study of 88 breast cancer patients with bone metastasis, they found higher lesion-based sensitivity (94%) for FDG-PET/CT than for CT and BS (77% and 89%, respectively), which were in line with the results of our study. However, they found a relatively lower detection of osteosclerotic lesions for FDG-PET/CT than other lesion types [ 13 ], which was not confirmed by our study. Additional to the results of previous studies regarding the superiority of FDG-PET/CT in detection of bone metastases compared to BS [ 11 , 12 , 15 ], our study showed that LDCT and BS combined could provide sensitivity equal to that of FDG-PET/CT in detection of skeletal metastases.…”

The added value of hybrid 18F-FDG PET/CT over CT in the detection of breast cancer metastatic deposits

“…Our study revealed weak sensitivity for PET just 16.7% while CT showed 100% sensitivity. A study made by Sugihara et al [10] found the sensitivity of PET was 69% (11/16). This difference can be explained by the smaller sample size in our study for the osteoblastic metastases.…”

“…In bony lytic lesions, it is reported that CT has lower sensitivity than PET. A study conducted by Sugihara et al [10] revealed sensitivities of CT and PET were 77.9% and 94% respectively. Our study revealed similar sensitivities where it was for CT 89% and for PET it was 100%.…”

Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by 18F-FDG PET/CT texture analysis

“…Aquiduo PET/CT scanner has the following technical characteristics: detector material, Lu 2 SiO 5 (Ce) (LSO); crystal size, 4.0 × 4.0 × 20 mm 3 ; detector ring diameter, 830 mm; transaxial field of view (FOV), 585 mm; axial FOV, 162 mm, coincidence window, 4.5 ns; energy window, 425–650 keV; maximum ring difference, 27; random correction, delayed; scatter correction, single scatter simulation. Emission data were acquired for 2–3 min per bed position 17 . The PET images were reconstructed using an iterative algorithm (the combination of Fourier rebinning and the ordered subsets expectation–maximization [FORE + 2D-OSEM]; 4 iterations, 14 subsets) with an 8-mm Gaussian filter, a 128 × 128 matrix (3.9 mm/pixel) and 81 slices (2 mm/slice).…”