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Background: Early detection of gastrointestinal stromal tumor (GIST) liver metastases is crucial for the management and prognosis. In our experience, GIST liver metastases can display hypermetabolism on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and marked enhancement on magnetic resonance imaging (MRI), which are uncommon in other tumors before treatment. Most literature focus on the imaging evaluation, prognosis after treatment and less is known about imaging features on both imaging methods before treatment. This study analyzes the imaging features of newly diagnosed GIST liver metastases on 18F-FDG PET/CT and MRI, with goal of improving diagnostic accuracy. Methods: This retrospective study included 55 patients with pathological or radiographical confirmed GIST liver metastases who underwent PET/CT (n=29), MRI (n=22), or both methods (n=4). PET/CT and MRI interpretation including lesion's morphologic features, number, density or signal intensity, hemorrhage, cystic changes or necrosis, maximum standardized uptake value (SUVmax) of liver metastases and liver background on PET imaging, degree and pattern of enhancement on MRI were obtained by two experienced nuclear medicine physicians and two radiologists respectively. Data are presented as numbers, percentages, means ± standard deviations or median (interquartile range). The correlation between diameter and SUVmax of metastases, and primary tumor SUVmax and synchronous liver metastases SUVmax were analyzed by Spearman's rank test. Results: On PET/CT visual analysis, 38.9%, 23.9%, and 37.2% of lesions showed significant hypermetabolism, slightly higher metabolism, and equal or lower metabolism than liver, respectively. There was a weak correlation between the diameter and SUVmax of liver metastases (rs =0.370, P<0.001), and a moderate correlation between SUVmax of synchronous liver metastases and the primary tumors (rs =0.492, P<0.001). On contrast-enhanced MRI, 90.8% of lesions showed heterogeneous enhancement in the arterial phase with the variable presentation, and 74.3% had different enhancement patterns between margins and intratumoral parenchyma. Conclusions: Liver lesions in GIST displaying significant, slight hypermetabolism on 18F-FDG PET/CT, marked or heterogeneous gradual enhancement within the intratumoral parenchyma with ring-like enhancement on MRI may denote the diagnosis of liver metastasis. However, GIST liver metastases may also display equal or lower metabolism than liver parenchyma on PET, making small lesions more difficult to diagnose.
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Background: Malignant primary gastric gastrointestinal stromal tumors (gGISTs) without treatment with imatinib are prone to bleeding and peritoneum implantation during operation. Therefore, preoperative assessment of the malignant potential of gGIST is essential. The use of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) combined with computed tomography (PET/CT) as a non-invasive tool for diagnosis, staging and prognosis evaluation in oncology, may also be useful for gGISTs. In the present study, we analyzed the value of 18F-FDG PET-CT in assessing the malignant potential of gGISTs before treatment. Methods: Patients who were diagnosed with gGIST by pathology and underwent 18F-FDG PET/CT at the same time were collected. The clinicopathological features of 26 patients with gGISTs were retrospectively analyzed at last. The gGIST risk classification was graded according to the US National Institutes of Health (NIH) GIST risk classification criteria [2008]. Lesions were classified as malignant group (moderate- or high-risk category) and benign group (low- or very low-risk category) according to pathology. The relationship between the maximal standard uptake value (SUVmax) and GIST risk category, tumor diameter, Ki-67 index, and mitotic count was analyzed. The cut-off level of SUVmax for the diagnosis of malignant gGIST with the highest sensitivity was calculated based on the receiver-operating characteristic (ROC) curve. Results: The SUVmax, tumor diameter, Ki-67 index, and mitotic count of the 26 gGIST patients were 5.90±4.49, 7.40±4.92 cm, 7.62%±11.76%, (5.96±3.19)/50 high-power field (HPF), respectively. SUVmax was significantly correlated with GIST risk category, Ki-67 index, and mitotic count (r=0.855, 0.860, and 0.690, all P<0.01) but not with tumor diameter (r=0.383, P=0.054). The SUVmax of gGIST was 7.00±4.57 in the malignant group (moderate or high NIH risk category in 20 patients), which was significantly different from that (2.25±0.77) in the benign group (low or extremely low NIH risk category in 6 patients) (t=4.566, P<0.01). ROC curve analysis showed that a SUVmax cut-off of 2.60 was most sensitive for predicting malignant gGIST. When the area under the curve was 0.967, the sensitivity was 100% and the specificity was 83.3%. Conclusions: SUVmax may be used as a complementary indicator for predicting the malignant potential of gGISTs before treatment.
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Purpose: To determine whether the addition of metabolic parameters from fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans to clinical factors could improve risk prediction models for radiotherapy-related esophageal fistula (EF) in esophageal squamous cell carcinoma (ESCC). Methods and Materials: Anonymized data from 185 ESCC patients (20 radiotherapy-related EF-positive cases) were collected, including pre-therapy PET/CT scans and EF status. In total, 29 clinical features and 15 metabolic parameters from PET/CT were included in the analysis, and a least absolute shrinkage and selection operator logistic regression model was used to construct a risk score (RS) system. The predictive capabilities of the models were compared using receiver operating characteristic (ROC) curves. Results: In univariate analysis, metabolic tumor volume (MTV)_40% was a risk factor for radiotherapy (RT)-related EF, with an odds ratio (OR) of 1.036 [95% confidence interval (CI): 1.009-1.063, p = 0.007]. However, it was excluded from the predictive model using multivariate logistic regression. Predictive models were built based on the clinical features in the training cohort. The model included diabetes, tumor length and thickness, adjuvant chemotherapy, eosinophil count, and monocyte-to-lymphocyte ratio. The RS was defined as follows: 0.2832 - (7.1369 × diabetes) + (1.4304 × tumor length) + (2.1409 × tumor thickness) - [8.3967 × adjuvant chemotherapy (ACT)] - (28.7671 × eosinophils) + (8.2213 × MLR). The cutoff of RS was set at -1.415, with an area under the curve (AUC) of 0.977 (95% CI: 0.9536-1), a specificity of 0.929, and a sensitivity of 1. Analysis in the testing cohort showed a lower AUC of 0.795 (95% CI: 0.577-1), a specificity of 0.925, and a sensitivity of 0.714. Delong's test for two correlated ROC curves showed no significant difference between the training and testing sets (p = 0.109). Conclusions: MTV_40% was a risk factor for RT-related EF in univariate analysis and was screened out using multivariate logistic regression. A model with clinical features can predict RT-related EF.
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A 62-year-old postmenopausal woman was diagnosed with breast cancer in her left breast and received modified radical mastectomy (molecular type: hormone-receptor-positive human epidermal growth factor receptor-2 negative). After that, she received postoperative chemotherapy and radiotherapy. After 2 years of tamoxifen adjuvant endocrine treatment, the patient inccurred recurrence with metastasis. PET-CT scanning showed metastasis in the left thoracic wall, the sixth left rib, and the right lower lobe of the lung. Multiple lymph node metastases were observed throughout the body. Palbociclib in combination with an aromatase inhibitor (AI) was used, but the metastatic lesion at the sixth left rib increased than before. Subsequently, the lesion shrunk and the clinical symptoms were relieved, which was considered as a pseudoprogression. Herein, we reported a pseudoprogression in the breast cancer patient after treatment with palbociclib plus AI.
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Radiation lung injury is a common side-effect of pulmonary radiotherapy. The aim of this study was to quantitatively assess early changes in lung perfusion single photon emission computed tomography (SPECT) scanning and pulmonary function testing (PFT) prior to and after intensity modulated radiotherapy (IMRT) for patients suffering from locally advanced non-small cell lung cancer (LANSCLC). Twenty patients with LANSCLC received lung perfusion SPECT scanning and PFT prior to IMRT and immediately after IMRT. Lung perfusion index (LPI) was calculated after the quantification of perfusion SPECT images. The LPI of the two groups was analyzed by matched t-test. The radioactive count of each layer of single lung was added to obtain the sum of the irradiated area. The percentage of the irradiated area of single lung was calculated. Linear correlation analysis was carried out between the percentage of the irradiated area and LPI in order to verify the validity of LPI. In this study, LPI and the percentage of the irradiated area of single lung exhibited an excellent correlation either prior to or after IMRT (r=0.820 and r=0.823, respectively; p<0.001). There was no statistically significant difference between pre-IMRT LPI and post-IMRT LPI (p=0.135). LPI in the group receiving a radical dose had no statistically significant difference (p=0.993), however, it showed a statistically significant difference in the group receiving a non-radical dose (p=0.025). In the non-radical dose group, the post-IMRT LPI was larger compared to pre-IMRT. None of the parameters of PFT exhibited a statistically significant difference prior to and after IMRT (p>0.05). The quantitative method of lung perfusion SPECT scanning can be used to evaluate changes in perfusion early in patients receiving a non-radical dose (BED ≤126,500 cGy) IMRT. Evaluating early changes in global lung function using the current method of PFT is difficult, since time can be a contributing factor for radiation-induced lung injury.
