Preoperative 18F-Fdg Pet/CT and CT Radiomics for Identifying Aggressive Histopathological Subtypes in Early Stage Lung Adenocarcinoma

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients

Tertiary Lymphoid Structures as Mediators of Immunotherapy Response

Since its first application in the treatment of cancer during the 1800s, immunotherapy has more recently become the leading edge of novel treatment strategies. Even though the efficacy of these agents can at times be predicted by more traditional metrics and biomarkers, often patient responses are variable. TLS are distinct immunologic structures that have been identified on pathologic review of various malignancies and are emerging as important determinants of patient outcome. Their presence, location, composition, and maturity are critically important in a host’s response to malignancy. Because of their unique immunogenic niche, they are also prime candidates, not only to predict and measure the efficacy of immunotherapy agents, but also to be potentially inducible gatekeepers to increase therapeutic efficacy. Herein, we review the mechanistic underpinnings of TLS formation, the data on its relationship to various malignancies, and the emerging evidence for the role of TLS in immunotherapy function

Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients

Organ-specific heterogeneity in tumor-infiltrating immune cells and cancer antigen expression in primary and autologous metastatic lung adenocarcinoma

Background Tumor immune microenvironment (TIME) and cancer antigen expression, key factors for the development of immunotherapies, are usually based on the data from primary tumors due to availability of tissue for analysis; data from metastatic sites and their concordance with primary tumor are lacking. Although of the same origin from primary tumor, organ-specific differences in the TIME in metastases may contribute to discordant responses to immune checkpoint inhibitor agents. In immunologically ‘cold’ tumors, cancer antigen-targeted chimeric antigen receptor (CAR) T-cell therapy can promote tumor-infiltrating lymphocytes; however, data on distribution and intensity of cancer antigen expression in primary tumor and matched metastases are unavailable.Methods We performed a retrospective review of a prospectively maintained database of patients who had undergone curative resection of pathological stage I–III primary lung adenocarcinoma from January 1995 to December 2012 followed by metastatic recurrence and resection of metastatic tumor (n=87). We investigated the relationship between the primary tumor and metastasis TIME (ie, tumor-infiltrating lymphocytes, tumor-associated macrophages, and programmed death-ligand 1 (PD-L1)) and cancer antigen expression (ie, mesothelin, CA125, and CEACAM6) using multiplex immunofluorescence.Results Brain metastases (n=36) were observed to have fewer tumor-infiltrating lymphocytes and greater PD-L1-negative tumor-associated macrophages compared with the primary tumor (p<0.0001); this relatively inhibitory TIME was not observed in other metastatic sites. In one in three patients, expression of PD-L1 is discordant between primary and metastases. Effector-to-suppressor (E:S) cell ratio, median effector cells (CD20+ and CD3+) to suppressor cells (CD68/CD163+) ratio, in metastases was not significantly different between patients with varying E:S ratios in primary tumors. Cancer antigen distribution was comparable between primary and metastases; among patients with mesothelin, cancer antigen 125, or carcinoembryonic antigen adhesion molecule 6 expression in the primary tumor, the majority (51%–75%) had antigen expression in the metastases; however, antigen-expression intensity was heterogenous.Conclusions In patients with lung adenocarcinoma, brain metastases, but not other sites of metastases, exhibited a relatively immune-suppressive TIME; this should be considered in the context of differential response to immunotherapy in brain metastases. Among patients with cancer antigen expression in the primary tumor, the majority had antigen expression in metastases; these data can inform the selection of antigen-targeted CARs to treat patients with metastatic lung adenocarcinoma