Patlak-Ki derived from ultra-high sensitivity dynamic total body [18F]FDG PET/CT correlates with the response to induction immuno-chemotherapy in locally advanced non-small cell lung cancer patients.

PURPOSE: This study aimed to investigate the predictive value of metabolic features in response to induction immuno-chemotherapy in patients with locally advanced non-small cell cancer (LA-NSCLC), using ultra-high sensitivity dynamic total body [18F]FDG PET/CT. METHODS: The study analyzed LA-NSCLC patients who received two cycles of induction immuno-chemotherapy and underwent a 60-min dynamic total body [18F]FDG PET/CT scan before treatment. The primary tumors (PTs) were manually delineated, and their metabolic features, including the Patlak-Ki, Patlak-Intercept, maximum SUV (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were evaluated. The overall response rate (ORR) to induction immuno-chemotherapy was evaluated according to RECIST 1.1 criteria. The Patlak-Ki of PTs was calculated from the 20-60 min frames using the Patlak graphical analysis. The best feature was selected using Laplacian feature importance scores, and an unsupervised K-Means method was applied to cluster patients. ROC curve was used to examine the effect of selected metabolic feature in predicting tumor response to treatment. The targeted next generation sequencing on 1021 genes was conducted. The expressions of CD68, CD86, CD163, CD206, CD33, CD34, Ki67 and VEGFA were assayed through immunohistochemistry. The independent samples t test and the Mann-Whitney U test were applied in the intergroup comparison. Statistical significance was considered at P < 0.05. RESULTS: Thirty-seven LA-NSCLC patients were analyzed between September 2020 and November 2021. All patients received two cycles of induction chemotherapy combined with Nivolumab/ Camrelizumab. The Laplacian scores showed that the Patlak-Ki of PTs had the highest importance for patient clustering, and the unsupervised K-Means derived decision boundary of Patlak-Ki was 2.779 ml/min/100 g. Patients were categorized into two groups based on their Patlak-Ki values: high FDG Patlak-Ki (H-FDG-Ki, Patlak-Ki > 2.779 ml/min/100 g) group (n = 23) and low FDG Patlak-Ki (L-FDG-Ki, Patlak-Ki ≤ 2.779 ml/min/100 g) group (n = 14). The ORR to induction immuno-chemotherapy was 67.6% (25/37) in the whole cohort, with 87% (20/23) in H-FDG-Ki group and 35.7% (5/14) in L-FDG-Ki group (P = 0.001). The sensitivity and specificity of Patlak-Ki in predicting the treatment response were 80% and 75%, respectively [AUC = 0.775 (95%CI 0.605-0.945)]. The expression of CD3+/CD8+ T cells and CD86+/CD163+/CD206+ macrophages were higher in the H-FDG-Ki group, while Ki67, CD33+ myeloid cells, CD34+ micro-vessel density (MVD) and tumor mutation burden (TMB) were comparable between the two groups. CONCLUSIONS: The total body [18F]FDG PET/CT scanner performed a dynamic acquisition of the entire body and clustered LA-NSCLC patients into H-FDG-Ki and L-FDG-Ki groups based on the Patlak-Ki. Patients with H-FDG-Ki demonstrated better response to induction immuno-chemotherapy and higher levels of immune cell infiltration in the PTs compared to those with L-FDG-Ki. Further studies with a larger patient cohort are required to validate these findings.

First Total-Body Kinetic Modeling and Parametric Imaging of Dynamic 68Ga-FAPI-04 PET in Pancreatic and Gastric Cancer.

Fibroblast activation protein inhibitor (FAPI) is an ideal diagnostic and therapeutic target in malignant tumors. However, the knowledge of kinetic modeling and parametric imaging of 68Ga-FAPI is limited. Purpose: The purpose of this study was to explore the pharmacokinetics of 68Ga-FAPI-04 PET/CT in pancreatic cancer and gastric cancer and to conduct parametric imaging of dynamic total-body data compared with SUV imaging. Methods: Dynamic total-body 68Ga-FAPI-04 PET/CT was performed on 13 patients. The lesion time-activity curves were fitted by 3-compartment models and multigraphical models. The kinetics parameters derived from the 2-tissue reversible compartment model (2T4K) and multigraphical models were analyzed. Parametric [Formula: see text] imaging was generated using the 2T4K and Logan models, and their performances were evaluated compared with SUV images. Results: 2T4K had the lowest Akaike information criterion value, and its fitting curves matched excellently with the origin time-activity curves. Visual assessment revealed that the [Formula: see text](2T4K) images and [Formula: see text](Logan with spatial constraint [SC]) images both showed less image noise and higher lesion conspicuity compared with SUV images. Objective image quality assessment demonstrated that parametric [Formula: see text](2T4K) images and parametric [Formula: see text](Logan with SC) images had a 5.0-fold and 5.0-fold higher average signal-to-noise ratio and 3.6-fold and 4.1-fold higher average contrast-to-noise ratio compared with conventional SUV images, respectively. In addition, no significant differences in signal-to-noise ratio and contrast-to-noise of pathologic lesions were observed between parametric [Formula: see text](2T4K) images and parametric [Formula: see text](Logan with SC) images (all P > 0.05). Conclusions: The 2T4K model was the preferred compartment model. Total-body parametric imaging of 68Ga-FAPI-04 PET yielded superior quantification beyond SUV with enhanced lesion contrast, which may serve as a promising imaging method to make an early diagnosis, to better reflect tumor characterization, or to allow evaluation of treatment response. [Formula: see text](2T4K) images are comparable in image quality and consistent to [Formula: see text](Logan with SC) images in lesions conspicuity; however, [Formula: see text](Logan with SC) images presented an appealing alternative to [Formula: see text](2T4K) images because of their simplicity.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes.

PURPOSE: This study aimed to quantitatively assess [18F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [18F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC. METHODS: The 60-min dynamic total-body [18F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry. RESULTS: A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUVmean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUVmean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3+/CD8+) and macrophages (CD68+/CD163+) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups. CONCLUSION: The dynamic total-body [18F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUVmean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [18F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Machine Learning-Based Noninvasive Quantification of Single-Imaging Session Dual-Tracer 18F-FDG and 68Ga-DOTATATE Dynamic PET-CT in Oncology.

68Ga-DOTATATE PET-CT is routinely used for imaging neuroendocrine tumor (NET) somatostatin receptor subtype 2 (SSTR2) density in patients, and is complementary to FDG PET-CT for improving the accuracy of NET detection, characterization, grading, staging, and predicting/monitoring NET responses to treatment. Performing sequential 18F-FDG and 68Ga-DOTATATE PET scans would require 2 or more days and can delay patient care. To align temporal and spatial measurements of 18F-FDG and 68Ga-DOTATATE PET, and to reduce scan time and CT radiation exposure to patients, we propose a single-imaging session dual-tracer dynamic PET acquisition protocol in the study. A recurrent extreme gradient boosting (rXGBoost) machine learning algorithm was proposed to separate the mixed 18F-FDG and 68Ga-DOTATATE time activity curves (TACs) for the region of interest (ROI) based quantification with tracer kinetic modeling. A conventional parallel multi-tracer compartment modeling method was also implemented for reference. Single-scan dual-tracer dynamic PET was simulated from 12 NET patient studies with 18F-FDG and 68Ga-DOTATATE 45-min dynamic PET scans separately obtained within 2 days. Our experimental results suggested an 18F-FDG injection first followed by 68Ga-DOTATATE with a minimum 5 min delayed injection protocol for the separation of mixed 18F-FDG and 68Ga-DOTATATE TACs using rXGBoost algorithm followed by tracer kinetic modeling is highly feasible.

18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients.

PURPOSE: Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) has unique value in tumor staging, predicting prognosis, and evaluating treatment response. Our aim was to determine if we could identify radiomic predictors from PET/CT in breast cancer patient therapeutic efficacy prior to NAC. METHODS: This retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage. CONCLUSION: Radiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

18F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia.

18F-FDG PET / CT is used clinically for the detection of extramedullary lesions in patients with relapsed acute leukemia (AL). However, the visual analysis of 18F-FDG diffuse bone marrow uptake in detecting bone marrow involvement (BMI) in routine clinical practice is still challenging. This study aims to improve the diagnostic performance of 18F-FDG PET/CT in detecting BMI for patients with suspected relapsed AL. Methods: Forty-one patients (35 in training group and 6 in independent validation group) with suspected relapsed AL were retrospectively included in this study. All patients underwent both bone marrow biopsy (BMB) and 18F-FDG PET/CT within one week. The BMB results were used as the gold standard or real "truth" for BMI. The bone marrow 18F-FDG uptake was visually diagnosed as positive and negative by three nuclear medicine physicians. The skeletal volumes of interest were manually drawn on PET/CT images. A total of 781 PET and 1045 CT radiomic features were automatically extracted to provide a more comprehensive understanding of the embedded pattern. To select the most important and predictive features, an unsupervised consensus clustering method was first performed to analyze the feature correlations and then used to guide a random forest supervised machine learning model for feature importance analysis. Cross-validation and independent validation were conducted to justify the performance of our model. Results: The training group involved 16 BMB positive and 19 BMB negative patients. Based on the visual analysis of 18F-FDG PET, 3 patients had focal uptake, 8 patients had normal uptake, and 24 patients had diffuse uptake. The sensitivity, specificity, and accuracy of visual analysis for BMI diagnosis were 62.5%, 73.7%, and 68.6%, respectively. With the cross-validation on the training group, the machine learning model correctly predicted 31 patients in BMI. The sensitivity, specificity, and accuracy of the machine learning model in BMI detection were 87.5%, 89.5%, and 88.6%, respectively, significantly higher than the ones in visual analysis (P < 0.05). The evaluation on the independent validation group showed that the machine learning model could achieve 83.3% accuracy. Conclusions:18F-FDG PET/CT radiomic analysis with machine learning model provided a quantitative, objective and efficient mechanism for identifying BMI in the patients with suspected relapsed AL. It is suggested in particular for the diagnosis of BMI in the patients with 18F-FDG diffuse uptake patterns.

A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.