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Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration. Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration. Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22-85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group. Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

The tumor microenvironment affects the outcome of radiotherapy against head and neck squamous cell carcinoma (HNSCC). We recently found that tolerogenic myeloid cells accumulate in the circulation of HNSCC patients undergoing radiotherapy. Here, we analyzed tumor-containing lymph node biopsies collected from these patients. After 2 weeks of radiotherapy, we found an increase in tumor-associated macrophages (TAMs) with activated STAT3, while CD[8.sup.+] T cells were reduced as detected using multiplex IHC. Gene expression profiling indicated upregulation of M2 macrophage-related genes (CD163, CD206), immunosuppressive mediators (ARG1, LIF, TGFB1), and Th2 cytokines (IL4, IL5) in irradiated tumors. We next validated STAT3 as a potential target in human HNSCC-associated TAMs, using UM-SCC1 xenotransplants in humanized mice. Local injections of myeloid cell-targeted STAT3 antisense oligonucleotide (CpG-STAT3ASO) activated human DCs/macrophages and promoted CD[8.sup.+] T cell recruitment, thereby arresting UM-SCC1 tumor growth. Furthermore, CpG-STAT3ASO synergized with tumor irradiation against syngeneic HP[V.sup.+] mEERL and HP[V.sup.-] MOC2 HNSCC tumors in mice, triggering tumor regression and/or extending animal survival. The antitumor immune responses were CD[8.sup.+] and CD[4.sup.+] T cell dependent and associated with the activation of antigen-presenting cells (DCs/M1 macrophages) and increased CD[8.sup.+] to regulatory T cell ratio. Our observations suggest that targeted inhibition of STAT3 in tumor-associated myeloid cells augments the efficacy of radiotherapy against HNSCC., Introduction Head and neck squamous cell carcinoma (HNSCC) is the sixth leading malignancy by incidence worldwide, with less than half of patients surviving for 5 years (1). HPV infection is [...]

Salivary gland tumors (SGTs) are heterogeneous tumors that range from benign masses to aggressive high-grade carcinomas with distant metastatic potential and limited response to chemotherapy. Mucoepidermoid carcinoma (MEC) accounts for 10% of SGTs and has a poor prognosis. In this research report, we describe two cases of metastatic high-grade MECs with prolonged response to immune checkpoint inhibitor pembrolizumab. Case 1 presented with a left neck mass, and biopsy of the parotid mass revealed MEC. The patient underwent surgical resection and adjuvant chemoradiation therapy for stage IVB disease. Post-treatment, she was found to have brain and spinal metastases and was placed on pembrolizumab. Case 2 presented with a left neck mass, and biopsy of the right parotid gland revealed MEC. Further staging demonstrated metastatic disease in the lungs, and he was placed on pembrolizumab. Both cases of MEC demonstrated prolonged extracranial responses to pembrolizumab. Although both cases reported little to no PD-L1 expression, these results demonstrate immunotherapy efficacy in advanced/metastatic MEC. [ABSTRACT FROM AUTHOR]