18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG.

PURPOSE: 18F-Alfatide II has been translated into clinical use and been proven to have good performance in identifying breast cancer. In this study, we investigated 18F-Alfatide II for evaluation of axillary lymph nodes (ALN) in breast cancer patients and compared the performance with 18F-FDG. METHODS: A total of 44 female patients with clinically suspected breast cancer were enrolled and underwent 18F-Alfatide II and 18F-FDG PET/CT within a week. Tracer uptakes in ALN were evaluated by visual analysis, semi-quantitative analysis with maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and SUVmax ratio of target/non-target (T/NT). RESULTS: Among 44 patients, 37 patients were pathologically diagnosed with breast cancer with metastatic (17 cases) or non-metastatic (20 cases) ALN. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of visual analysis were 70.6%, 90%, 81.1%, 85.7%, and 78.3% for 18F-Alfatide II, 64.7%, 90%, 78.4%, 84.6%, and 75% for 18F-FDG, respectively. By combining 18F-Alfatide II and 18F-FDG, the sensitivity significantly increased to 82.4%, the specificity was 85%, the accuracy increased to 83.8%, the PPV was 82.4%, and the NPV significantly increased to 85.0%. Three cases of luminal B subtype were false negative for both 18F-Alfatide II and 18F-FDG. The other 2 false negative cases of 18F-Alfatide II were triple-negative subtype and 3 false negative cases of 18F-FDG were luminal B subtype too. The AUCs of three semi-quantitative parameters (SUVmax, SUVmean, T/NT) for 18F-Alfatide II were between 0.8 and 0.9, whereas those for 18F-FDG were more than 0.9. 18F-Alfatide II T/NT had the highest Youden index (76.5%), specificity (100%), accuracy (89.2%), and PPV (100%) among these semi-quantitative parameters. 18F-Alfatide II uptake as well as 18F-FDG uptake in metastatic axillary lymph nodes (MALN) was significantly higher than that in benign axillary lymph nodes (BALN). Both 18F-Alfatide II and 18F-FDG did not show difference in primary tumor uptake irrespective of ALN status. CONCLUSION: 18F-Alfatide II can be used in breast cancer patients to detect metastatic ALN, however, like 18F-FDG, with high specificity but relatively low sensitivity. The combination of 18F-Alfatide II and 18F-FDG can significantly improve sensitivity and NPV. 18F-Alfatide II T/NT may serve as the most important semi-quantitative parameter to evaluate ALN.

Combined 18F-FDG and 18F-Alfatide II PET May Predict Luminal B (HER2 Negative) Subtype and Nonluminal Subtype of Invasive Breast Cancer.

Noninvasive PET molecular imaging using radiopharmaceuticals is important to classify breast cancer in the clinic. The aim of this study was to investigate the combination of 18F-FDG and 18F-Alfatide II for predicting molecular subtypes of invasive breast cancer. Forty-four female patients with clinically suspected breast cancer were recruited and underwent 18F-FDG and 18F-Alfatide II PET/CT within a week. Tracer uptake in breast lesions was assessed using the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and SUVmax ratio of 18F-FDG to 18F-Alfatide II (FAR). Invasive breast cancer lesions were further classified as luminal A subtype, luminal B subtype, human epidermal growth factor receptor-2 (HER2) overexpressing subtype, and triple negative subtype according to the expression of the estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67. Among 44 patients, 35 patients were pathologically diagnosed with invasive breast cancer. The SUVmax and SUVmean of 18F-FDG were significantly higher in the ER-negative group than those in the ER-positive group, as well as in the PR-negative group than those in the PR-positive group. However, the SUVmax and SUVmean of 18F-Alfatide II were higher in the ER-positive group and the PR-positive group. By combining 18F-FDG and 18F-Alfatide II, the FAR was lower in the ER-positive group and the PR-positive group. The HER2 overexpressing subtype showed the highest SUVmax and SUVmean for 18F-FDG while the luminal B (HER2 negative) subtype revealed the lowest values. The luminal B (HER2 negative) subtype showed the highest 18F-Alfatide II SUVmax, while the triple negative subtype showed the lowest 18F-Alfatide II SUVmax. The FAR was the lowest in the luminal B (HER2 negative) subtype and much higher in the HER2 overexpressing and triple negative subtypes. FAR less than 1 predicted the luminal B (HER2 negative) subtype with high specificity (93.1%) and NPV (90%). FAR greater than 3 predicted the HER2 overexpressing subtype and triple negative subtype (namely, the nonluminal subtype) with very high specificity (100%) and PPV (100%). In summary, FAR, the combined PET parameter of 18F-FDG and 18F-Alfatide II, can be used to predict molecular subtypes of invasive breast cancer, especially for the luminal B (HER2 negative) subtype and the nonluminal subtype.

Fluorrine-18-fluorodeoxyglucose positron emission tomography/computed tomography of adult liver Langerhans cell histiocytosis.

OBJECTIVE: Adult liver Langerhans cell histiocytosis (LCH) is an extremely rare desease. This paper reports a 40 years old male patient who was diagnosed as liver LCH though ultrasound-guided liver biopsy. The initial Fluorrine-18- fluorodeoxyglucose positron emission tomography/ computed tomography (18F-FDG PET/CT) showed multiple nodular low-density lesions in liver without obvious elevated 18F-FDG uptake. Four years later, the follow-up 18F-FDG PET/CT showed the liver multiple lesions with slightly elevated 18F-FDG uptake. CONCLUSION: We describe this case, to highlight the importance of 18F-FDG PET/CT in differential diagnosis for the primary disease and the multiple liver nodules.

18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma.

BACKGROUND: To investigate whether radiomic features from (18F)-fluorodeoxyglucose positron emission tomography/computed tomography [(18F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma. METHODS: One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent (18F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model's performance. RESULTS: Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03). CONCLUSIONS: Radiomic features based on (18F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.

Value of 18F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations.

OBJECTIVES: To investigate the development and validation of a radiomics nomogram based on PET/CT for guiding personalized targeted therapy in patients with lung adenocarcinoma mutation(s) in the EGFR gene. METHODS: A cohort of 109 (77/32 in training/validation cohort) consecutive lung adenocarcinoma patients with an EGFR mutation was enrolled in this study. A total of 1672 radiomic features were extracted from PET and CT images, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the radiomic features and construct the radiomics nomogram for the estimation of overall survival (OS), which was then assessed with respect to calibration and clinical usefulness. Patients with an EGFR mutation were divided into high- and low- risk groups according to their nomogram score. The treatment strategy for high- and low-risk groups was analyzed using Kaplan-Meier analysis and a log-rank test. RESULTS: The C-index of the radiomics nomogram for the prediction of OS in lung adenocarcinoma in patients with an EGFR mutation was 0.840 and 0.803 in the training and validation cohorts, respectively. Distant metastasis [(Hazard ratio, HR),1.80], metabolic tumor volume (MTV, HR, 1.62), and rad score (HR, 17.23) were the independent risk factors for patients with an EGFR mutation. The calibration curve showed that the predicted survival time was remarkably close to the actual time. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Targeted therapy for patients with high-risk EGFR mutations attained a greater benefit than other therapies (p < 0.0001), whereas the prognoses of the two therapies were similar in the low-risk group (p = 0.85). CONCLUSIONS: Development and validation of a radiomics nomogram based on PET/CT radiomic features combined with clinicopathological factors may guide targeted therapy for patients with lung adenocarcinoma with EGFR mutations. This is conducive to the advancement of precision medicine.

Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer.

Purpose: In this study, we developed and validated a radiomics nomogram by combining the radiomic features extracted from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images and clinicopathological factors to evaluate the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). Patients and Methods: A total of 315 consecutive patients with NSCLC (221 in the training cohort and 94 in the validation cohort) were enrolled in this study. A total of 840 radiomic features were extracted from the CT and PET images. Three radiomic scores (rad-scores) were calculated using the least absolute shrinkage and selection operator (LASSO) Cox regression based on subsets of CT, PET, and PET/CT radiomic features. A multivariate Cox regression analysis was performed for each rad-score combined with clinicopathological factors to determine the independent risk factors. The OS nomogram was constructed based on the PET/CT rad-score and independent clinicopathological factors. Validation and calibration were conducted to evaluate the performance of the model in the training and validation cohorts, respectively. Results: A total of 144 (45.71%) women and 171 (54.29%) men with NSCLC were enrolled in this study. The PET/CT rad-score combined with the clinical model had the best C-index (0.776 and 0.789 for the training and validation cohorts, respectively). Distant metastasis, stage, carcinoembryonic antigen (CEA), and targeted therapy were independent risk factors for patients with NSCLC. The validation curve showed that the OS nomogram had a strong predictive power in patients' survival. The calibration curve showed that the predicted survival time was significantly close to the observed one. Conclusion: A radiomic nomogram based on 18F-FDG PET/CT rad-score and clinicopathological factors had good predictive performance for the survival outcome, offering feasible, and practical guidance for individualized management of patients with NSCLC.

Correlation Study of 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Pathological Subtypes of Invasive Lung Adenocarcinoma and Prognosis.

Purpose: To investigate the correlation between 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters and clinicopathological factors in pathological subtypes of invasive lung adenocarcinoma and prognosis. Patients and Methods: Metabolic parameters and clinicopathological factors from 176 consecutive patients with invasive lung adenocarcinoma between August 2008 and August 2016 who underwent 18F-FDG PET/CT examination were retrospectively analyzed. Invasive lung adenocarcinoma was divided into five pathological subtypes:lepidic predominant adenocarcinoma (LPA), acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), solid predominant adenocarcinoma (SPA), and micropapillary predominant adenocarcinoma (MPA). The differences in metabolic parameters [maximal standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV)] and tumor diameter for different pathological subtypes were analyzed. Patients were divided into two groups according to their prognosis: good prognosis group (LPA, APA, PPA) and poor prognosis group (SPA, MPA). Logistic regression was used to filter predictors and construct a predictive model, and areas under the receiver operating curve (AUC) were calculated. Cox regression analysis was performed on prognostic factors. Results: 82 (46.6%) females and 94 (53.4%) males of patients with invasive lung adenocarcinoma were enrolled in this study. Metabolic parameters and tumor diameter of different pathological subtype had statistically significant (P < 0.05). The predictive model constructed using independent predictors (Distant metastasis, Ki-67, and SUVmax) had good classification performance for both groups. The AUC for SUVmax was 0.694 and combined with clinicopathological factors were 0.745. Cox regression analysis revealed that Stage, TTF-1, MTV, and pathological subtype were independent risk factors for patient prognosis. The hazard ratio (HR) of the poor prognosis group was 1.948 (95% CI 1.042-3.641) times the good prognosis group. The mean survival times of good and poor prognosis group were 50.2621 (95% CI 47.818-52.706) and 35.8214 (95% CI 27.483-44.159) months, respectively, while the median survival time was 47.00 (95% CI 45.000-50.000) and 31.50 (95% CI 23.000-49.000) months, respectively. Conclusion: PET/CT metabolic parameters combined with clinicopathological factors had good classification performance for the different pathological subtypes, which may provide a reference for treatment strategies and prognosis evaluation of patients.

Unexpected detection of brain metastases by 18F-NaF PET/CT in a patient with lung cancer.

In recent years, the inconsistent supply of (99m)Tc and the increasingly widespread use of PET/CT have led to a renewed interest in PET/CT bone scans using (18)F-NaF. Recently, a 64-year-old man with biopsy-proven lung cancer underwent an (18)F-NaF PET/CT bone scan due to a shortage of (99m)Tc. Unexpectedly, multiple nodular foci of increased tracer uptake were present in the brain, whereas there were no definitive bone metastases detected. Subsequently, brain MRI confirmed the presence of brain metastases.