Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer.

Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.

SPECT and PET imaging of EGF receptors with site-specifically labeled EGF and dimeric EGF.

We describe a new generation of tracers for molecular imaging of the cell surface receptors for epidermal growth factor (EGF). These receptors play a key role in the progression of many tumors and are major drug development targets. Our tracers are based on a recombinant human EGF expressed with a cysteine-containing tag that enables facile site-specific radiolabeling with (99m)Tc for single photon emission computed tomography or site-specific conjugation of (64)Cu PEGylated chelators for positron emission tomography. These tracers retain EGF activities in vitro and display selective and highly specific focal uptake in tumors in vivo. We expect that nuclear imaging of EGF receptors with these tracers will be useful for clinical diagnosis, therapeutic monitoring, and development of new drugs and treatment regimens.

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer.

We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non-small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56-2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66-0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67-0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.

Circulating tumor microemboli diagnostics for patients with non-small-cell lung cancer.

INTRODUCTION: Circulating tumor microemboli (CTM) are potentially important cancer biomarkers, but using them for cancer detection in early-stage disease has been assay limited. We examined CTM test performance using a sensitive detection platform to identify stage I non-small-cell lung cancer (NSCLC) patients undergoing imaging evaluation. METHODS: First, we prospectively enrolled patients during 18F-FDG PET-CT imaging evaluation for lung cancer that underwent routine phlebotomy where CTM and circulating tumor cells (CTCs) were identified in blood using nuclear (DAPI), cytokeratin (CK), and CD45 immune-fluorescent antibodies followed by morphologic identification. Second, CTM and CTC data were integrated with patient (age, gender, smoking, and cancer history) and imaging (tumor diameter, location in lung, and maximum standard uptake value [SUVmax]) data to develop and test multiple logistic regression models using a case-control design in a training and test cohort followed by cross-validation in the entire group. RESULTS: We examined 104 patients with NSCLC, and the subgroup of 80 with stage I disease, and compared them to 25 patients with benign disease. Clinical and imaging data alone were moderately discriminating for all comers (Area under the Curve [AUC] = 0.77) and by stage I disease only (AUC = 0.77). However, the presence of CTM combined with clinical and imaging data was significantly discriminating for diagnostic accuracy in all NSCLC patients (AUC = 0.88, p value = 0.001) and for stage I patients alone (AUC = 0.87, p value = 0.002). CONCLUSION: CTM may add utility for lung cancer diagnosis during imaging evaluation using a sensitive detection platform.

Survival and death signals can predict tumor response to therapy after oncogene inactivation.

Cancers can exhibit marked tumor regression after oncogene inhibition through a phenomenon called "oncogene addiction." The ability to predict when a tumor will exhibit oncogene addiction would be useful in the development of targeted therapeutics. Oncogene addiction is likely the consequence of many cellular programs. However, we reasoned that many of these inputs may converge on aggregate survival and death signals. To test this, we examined conditional transgenic models of K-ras(G12D)--or MYC-induced lung tumors and lymphoma combined with quantitative imaging and an in situ analysis of biomarkers of proliferation and apoptotic signaling. We then used computational modeling based on ordinary differential equations (ODEs) to show that oncogene addiction could be modeled as differential changes in survival and death intracellular signals. Our mathematical model could be generalized to different imaging methods (computed tomography and bioluminescence imaging), different oncogenes (K-ras(G12D) and MYC), and several tumor types (lung and lymphoma). Our ODE model could predict the differential dynamics of several putative prosurvival and prodeath signaling factors [phosphorylated extracellular signal-regulated kinase 1 and 2, Akt1, Stat3/5 (signal transducer and activator of transcription 3/5), and p38] that contribute to the aggregate survival and death signals after oncogene inactivation. Furthermore, we could predict the influence of specific genetic lesions (p53⁻/⁻, Stat3-d358L, and myr-Akt1) on tumor regression after oncogene inactivation. Then, using machine learning based on support vector machine, we applied quantitative imaging methods to human patients to predict both their EGFR genotype and their progression-free survival after treatment with the targeted therapeutic erlotinib. Hence, the consequences of oncogene inactivation can be accurately modeled on the basis of a relatively small number of parameters that may predict when targeted therapeutics will elicit oncogene addiction after oncogene inactivation and hence tumor regression.

Progressive Dyspnea After CABG: Complication of Retained Epicardial Pacing Wires.

We report a case of progressive dyspnea and recurrent pneumonia after uneventful coronary artery bypass graft surgery caused by migration of retained epicardial pacing wires into the right upper lobe of the lung. Removal of the wires by open thoracotomy resulted in significant improvement in dyspnea and near complete resolution of the bronchiectasis and consolidation.

Combined Inactivation of MYC and K-Ras oncogenes reverses tumorigenesis in lung adenocarcinomas and lymphomas.

BACKGROUND: Conditional transgenic models have established that tumors require sustained oncogene activation for tumor maintenance, exhibiting the phenomenon known as "oncogene-addiction." However, most cancers are caused by multiple genetic events making it difficult to determine which oncogenes or combination of oncogenes will be the most effective targets for their treatment. METHODOLOGY/PRINCIPAL FINDINGS: To examine how the MYC and K-ras(G12D) oncogenes cooperate for the initiation and maintenance of tumorigenesis, we generated double conditional transgenic tumor models of lung adenocarcinoma and lymphoma. The ability of MYC and K-ras(G12D) to cooperate for tumorigenesis and the ability of the inactivation of these oncogenes to result in tumor regression depended upon the specific tissue context. MYC-, K-ras(G12D)- or MYC/K-ras(G12D)-induced lymphomas exhibited sustained regression upon the inactivation of either or both oncogenes. However, in marked contrast, MYC-induced lung tumors failed to regress completely upon oncogene inactivation; whereas K-ras(G12D)-induced lung tumors regressed completely. Importantly, the combined inactivation of both MYC and K-ras(G12D) resulted more frequently in complete lung tumor regression. To account for the different roles of MYC and K-ras(G12D) in maintenance of lung tumors, we found that the down-stream mediators of K-ras(G12D) signaling, Stat3 and Stat5, are dephosphorylated following conditional K-ras(G12D) but not MYC inactivation. In contrast, Stat3 becomes dephosphorylated in lymphoma cells upon inactivation of MYC and/or K-ras(G12D). Interestingly, MYC-induced lung tumors that failed to regress upon MYC inactivation were found to have persistent Stat3 and Stat5 phosphorylation. CONCLUSIONS/SIGNIFICANCE: Taken together, our findings point to the importance of the K-Ras and associated down-stream Stat effector pathways in the initiation and maintenance of lymphomas and lung tumors. We suggest that combined targeting of oncogenic pathways is more likely to be effective in the treatment of lung cancers and lymphomas.