Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas

Roles of selenium-containing glutathione peroxidases and thioredoxin reductases in the regulation of processes associated with glioblastoma progression

Glioblastoma remains the most common and aggressive primary tumor of the central nervous system in adults. Current treatment options include standard surgical resection combined with radiation/chemotherapy, but such protocol most likely only delays the inevitable. Therefore, the problem of finding therapeutic targets to prevent the occurrence and development of this severe oncological disease is currently acute. It is known that the functions of selenoproteins in the regulation of carcinogenesis processes are not unambiguous. Either they exhibit cytotoxic activity on cancer cells, or cytoprotective. A special place in the progression of oncological diseases of various etiologies is occupied by proteins of the thioredoxin and glutathione systems. These are two cellular antioxidant systems that regulate redox homeostasis, counteracting the increased production of reactive oxygen species in cells. The review reflects the latest data on the role of key enzymes of these redox systems in the regulation of processes associated with the progression of glioblastoma. A thorough consideration of these issues will expand fundamental knowledge about the functions of selenium-containing thioredoxin reductases and glutathione peroxidases in the therapy of glioblastomas and provide an understanding of the prospects for the treatment of this aggressive oncological disease.

Magnetic resonance imaging-based radiomics for predicting infiltration levels of CD68+ tumor-associated macrophages in glioblastomas

PURPOSE

Tumor-associated macrophages (TAMs) are important biomarkers of tumor invasion and prognosis in patients with glioblastoma. We combined the imaging and radiomics features of preoperative MRI to predict CD68+ macrophage infiltration.

METHODS

Clinical, MRI image, and pathology data of 188 patients with glioblastoma were analyzed. Overall, 143 patients were included in the training (n = 101) and validation (n = 42) sets, whereas 45 patients were included in an independent test set. The optimal cut-off value (14.8%) was based on the minimum p-value formed by the Kaplan-Meier survival analysis and log-rank tests which divided patients into groups with high CD68+ TAMs (≥ 14.8%) and low CD68+ TAMs (< 14.8%). Regions of interest and radiomics features extraction were based on contrast-enhanced T1-weighted images (CE-T1WI) and T2WI. Multi-parameter stepwise regression was used to create the clinical, radiomics, and combined models, each evaluated using the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical applicability of the nomogram.

RESULTS

A clinical model based on the minimum apparent diffusion coefficient (ADCmin) revealed an area under the curve (AUC) of 0.768, 0.764, and 0.624 for the training set, validation set, and test set, respectively. The 2D radiomics model, based on two features, revealed an AUC of 0.783, 0.724, and 0.789 for the training, validation, and test sets, respectively. The 3D radiomics model, based on three features, revealed AUCs of 0.823, 0.811, and 0.787 for the training, validation, and test sets, respectively. The combined model, with ADCmin and radiomics features, showed the best performance, with AUCs of 0.865, 0.822, and 0.776 for the training, validation, and test sets, respectively. The calibration curve of the combined model nomogram showed good agreement between the estimated and actual probabilities.

CONCLUSION

The combined model constructed using ADCmin, a quantitative imaging parameter, combined with five key radiomics features can be used to evaluate the extent of CD68+ macrophages before surgery.

MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma

BACKGROUND

The tumor immune microenvironment can influence the prognosis and treatment response to immunotherapy. We aimed to develop a non-invasive radiomic signature in high-grade glioma (HGG) to predict the absolute density of tumor-associated macrophages (TAMs), the preponderant immune cells in the microenvironment of HGG. We also aimed to evaluate the association between the signature, and tumor immune phenotype as well as response to immunotherapy.

METHODS

In this retrospective setting, total of 379 patients with HGG from three independent cohorts were included to construct a radiomic model named Radiomics Immunological Biomarker (RIB) for predicting the absolute density of M2-like TAM using the mRMR feature ranking method and LASSO classifier. Among them, 145 patients from the TCGA microarray cohort were randomly allocated into a training set (N=101) and an internal validation set (N=44), while the immune-phenotype cohort (N=203) and the immunotherapy-treated cohort (N=31, patients from a prospective clinical trial treated with DC vaccine) recruited from Huashan Hospital were used as two external validation sets. The immunotherapy-treated cohort was also used to evaluate the relationship between RIB and immunotherapy response. Radiogenomic analysis was performed to find functional annotations using RNA sequencing data from TAM cells.

RESULTS

An 11-feature radiomic model for M2-like TAM was developed and validated in four datasets of HGG patients (area under the curve = 0.849, 0.719, 0.674, and 0.671) using MRI images of post contrast enhanced T1-weighted (T1CE). Patients with high RIB scores had a strong inflammatory response. Four hub-genes (SLC7A7, RNASE6, HLA-DRB1 and CD300A) expressed by TAM were identified to be closely related to the RIB, providing important evidence for biological interpretation. Only individuals with a high RIB score were shown to have survival benefits from DC vaccine [DC vaccine vs. Placebo: median progression-free survival (mPFS), 10.0 mos vs. 4.5 mos, HR=0.17, P=0.0056, 95%CI=0.041-0.68; median overall survival (mOS), 15.0 mos vs. 7.0 mos, HR=0.17, P =0.0076, 95%CI=0.04-0.68]. Multivariate analyses also confirmed that treatment by DC vaccine was an independent factor for improved survival in the high RIB score group. However, in the low RIB score group, DC vaccine was not associated with improved survival. Furthermore, a radiomic nomogram based on the RIB score and clinical factors could efficiently predict the 1-, 2-, and 3-year survival rates, as confirmed by ROC curve analysis (AUC for 1-, 2- and 3-year survival: 0.705, 0.729 and 0.684, respectively).

CONCLUSIONS

The radiomic model could allow for non-invasive assessment of the absolute density of TAM from MRI images in HGG patients. Of note, our RIB model is the first immunological radiomic model confirmed to have the ability to predict survival benefits from DC vaccine in gliomas, thereby providing a novel tool to inform treatment decisions and monitor patient treatment course by radiomics.

Normofractionated irradiation and not temozolomide modulates the immunogenic and oncogenic phenotype of human glioblastoma cell lines

PURPOSE

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, with an overall poor prognosis after diagnosis. Conventional treatment includes resection, chemotherapy with temozolomide (TMZ), and concomitant radiotherapy (RT). The recent success of immunotherapy approaches in other tumor entities, particularly with immune checkpoint inhibitors, could not be clinically transferred to GBM treatment so far. Therefore, preclinical analyses of the expression of both immune-suppressive and immune-stimulatory checkpoint molecules following treatment of human glioblastoma cells with RT and/or temozolomide is needed to design feasible radio(chemo)immunotherapy trials for GBM in the future.

METHODS

Five human glioblastoma cell lines (H4, HROG-06, U118, U138, U251) were analyzed regarding their clonogenic survival and cell death forms after chemotherapy (CT) with TMZ and/or normofractionated RT (5 × 2 Gy) via multicolor flow cytometry. Further, the tumor cell surface expression of immune-activating (OX40L, CD137L, CD70, and ICOSL) and immune-suppressive (PD-L1, PD-L2, HVEM) checkpoint molecules and of an oncogenic molecule (EGFR) were measured via multicolor flow cytometry after CT and RT alone or after RCT.

RESULTS

Normofractionated RT and not TMZ was the trigger of induction of predominantly necrosis in the glioblastoma cells. Notably, clonogenicity did not correlate with cell death induction by RT. The basal expression level of immune-suppressive PD-L1, PD-L2, and HVEM varied in the analyzed glioblastoma cells. RT, but not TMZ, resulted in a significant upregulation of PD-L1 and PD-L2 in all tumor cells investigated. Also, the expression of HVEM was increased after RT in most of the GBM cell lines. In contrast, normofractionated RT individually modulated expression of the stimulating immune checkpoint molecules CD70, CD137L, OX40L, and ICOSL1. The oncogenic factor EGFR was significantly increased by irradiation in all examined cell lines, albeit to a different extent. None of the investigated molecules were downregulated after the treatments.

CONCLUSION

Normofractionated radiotherapy modulates the immunogenic as well as the oncogenic phenotype of glioblastoma cells, partly individually. Therefore, not only PD-L1 and PD-L2, but also other immunogenic molecules expressed on the surface of glioblastoma cells could serve as targets for immune checkpoint blockade in combination with RT in the future.

A Prediction Model for Deciphering Intratumoral Heterogeneity Derived from the Microglia/Macrophages of Glioma Using Non-Invasive Radiogenomics

Microglia and macrophages play a major role in glioma immune responses within the glioma microenvironment. We aimed to construct a prognostic prediction model for glioma based on microglia/macrophage-correlated genes. Additionally, we sought to develop a non-invasive radiogenomics approach for risk stratification evaluation. Microglia/macrophage-correlated genes were identified from four single-cell datasets. Hub genes were selected via lasso-Cox regression, and risk scores were calculated. The immunological characteristics of different risk stratifications were assessed, and radiomics models were constructed using corresponding MRI imaging to predict risk stratification. We identified eight hub genes and developed a relevant risk score formula. The risk score emerged as a significant prognostic predictor correlated with immune checkpoints, and a relevant nomogram was drawn. High-risk groups displayed an active microenvironment associated with microglia/macrophages. Furthermore, differences in somatic mutation rates, such as IDH1 missense variant and TP53 missense variant, were observed between high- and low-risk groups. Lastly, a radiogenomics model utilizing five features from magnetic resonance imaging (MRI) T2 fluid-attenuated inversion recovery (Flair) effectively predicted the risk groups under a random forest model. Our findings demonstrate that risk stratification based on microglia/macrophages can effectively predict prognosis and immune functions in glioma. Moreover, we have shown that risk stratification can be non-invasively predicted using an MRI-T2 Flair-based radiogenomics model.

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

Radiomics for characterization of the glioma immune microenvironment

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

The role of nanotherapy in head and neck squamous cell carcinoma by targeting tumor microenvironment

Head and neck squamous cell carcinomas (HNSCCs) refers to a group of highly malignant and pathogenically complex tumors. Traditional treatment methods include surgery, radiotherapy, and chemotherapy. However, with advancements in genetics, molecular medicine, and nanotherapy, more effective and safer treatments have been developed. Nanotherapy, in particular, has the potential to be an alternative therapeutic option for HNSCC patients, given its advantageous targeting capabilities, low toxicity and modifiability. Recent research has highlighted the important role of the tumor microenvironment (TME) in the development of HNSCC. The TME is composed of various cellular components, such as fibroblasts, vascular endothelial cells, and immune cells, as well as non-cellular agents such as cytokines, chemokines, growth factors, extracellular matrix (ECM), and extracellular vesicles (EVs). These components greatly influence the prognosis and therapeutic efficacy of HNSCC, making the TME a potential target for treatment using nanotherapy. By regulating angiogenesis, immune response, tumor metastasis and other factors, nanotherapy can potentially alleviate HNSCC symptoms. This review aims to summarize and discuss the application of nanotherapy that targets HNSCC's TME. We highlight the therapeutic value of nanotherapy for HNSCC patients.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.

The various functions and phenotypes of macrophages are also reflected in their responses to irradiation: A current overview

Macrophages are a vital part of the innate immune system that are involved in healthy biological processes but also in disease modulation and response to therapy. Ionizing radiation is commonly used in the treatment of cancer and, in a lower dose range, as additive therapy for inflammatory diseases. In general, lower doses of ionizing radiation are known to induce rather anti-inflammatory responses, while higher doses are utilized in cancer treatment where they result, next to tumor control, in rather inflammatory responses. Most experiments that have been carried out in ex vivo on macrophages find this to be true, however in vivo, tumor-associated macrophages, for example, show a contradictory response to the respective dose-range. While some knowledge in radiation-induced modulations of macrophages has been collected, many of the underlying mechanisms remain unclear. Due to their pivotal role in the human body, however, they are a great target in therapy and could potentially aid in better treatment outcome. We therefore summarized the current knowledge of macrophage mediated radiation responses.

Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study

The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.

Machine Learning for Computed Tomography Radiomics: Prediction of Tumor-Infiltrating Lymphocytes in Patients With Pancreatic Ductal Adenocarcinoma

OBJECTIVES

The aims of the study were to develop and validate a machine learning classifier for preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this retrospective study of 183 PDAC patients who underwent multidetector computed tomography and surgical resection, CD4 + , CD8 + , and CD20 + expression was evaluated using immunohistochemistry, and TIL scores were calculated using the Cox regression model. The patients were divided into TIL-low and TIL-high groups. An extreme gradient boosting (XGBoost) classifier was developed using a training set consisting of 136 consecutive patients, and the model was validated in 47 consecutive patients. The discriminative ability, calibration, and clinical utility of the XGBoost classifier were evaluated.

RESULTS

The prediction model showed good discrimination in the training (area under the curve, 0.93; 95% confidence interval, 0.89-0.97) and validation (area under the curve, 0.79; 95% confidence interval, 0.65-0.92) sets with good calibration. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 0.93, 0.85, 0.90, 0.89, and 0.91, respectively, while those for the validation set were 0.63, 0.91, 0.77, 0.88, and 0.70, respectively.

CONCLUSIONS

The XGBoost-based model could predict PDAC TILs and may facilitate clinical decision making for immune therapy.

Head and neck tumor cells treated with hypofractionated irradiation die via apoptosis and are better taken up by M1-like macrophages

Purpose

The incidence of head and neck squamous cell carcinomas (HNSCC) is increasing worldwide, especially when triggered by the human papilloma virus (HPV). Radiotherapy has immune-modulatory properties, but the role of macrophages present in HNSCC and having contact with irradiated tumor cells remains unclear. The influence of irradiated (2 × 5Gy) HNSCC cells on the (re-)polarization and phagocytosis of human macrophages, either non-polarized or with a more M1 or M2 phenotype, was therefore investigated.

Methods

Human monocytes were differentiated with the hematopoietic growth factors M‑CSF (m) or GM-CSF (g) and additionally pre-polarized with either interleukin (IL)-4 and IL-10 or interferon (IFN)-γ and lipopolysaccharides (LPS), respectively. Subsequently, they were added to previously irradiated (2 × 5Gy) and mock-treated HPV-positive (UD-SCC-2) and HPV-negative (Cal33) HNSCC cells including their supernatants.

Results

The HNSCC cells treated with hypofractionated irradiation died via apoptosis and were strongly phagocytosed by M0m and M2 macrophages. M0g and M1 macrophages phagocytosed the tumor cells to a lesser extent. Irradiated HNSCC cells were better phagocytosed by M1 macrophages compared to mock-treated controls. The polarization status of the macrophages was not significantly changed, except for the expression of CD206 on M2 macrophages, which was reduced after phagocytosis of irradiated HPV-negative cells. Further, a significant increase in the uptake of irradiated HPV-positive cells by M0g macrophages when compared to HPV-negative cells was observed.

Conclusion

HNSCC cells treated with hypofractionated irradiation foster phagocytosis by anti-tumorigenic M1 macrophages. The data provide the first evidence on the impact of the HPV status of HNSCC cells on the modulation of the macrophage response to irradiated tumor cells.

Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma

OBJECTIVE

To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use.

RESULTS

The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79-0.93) and validation sets (AUC 0.79; 95% CI 0.64-0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier.

CONCLUSION

The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.

A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8+ cells in locally advanced rectal cancer

BACKGROUND AND PURPOSE

Regarding the altered tumor immune status following cytotoxic treatment, this study aims to develop a radiomic signature to predict CD8⁺ tumor-infiltrating lymphocyte (TIL) density changes in chemoradiotherapy (CRT) of rectal cancer.

MATERIALS AND METHODS

We used the magnetic resonance imaging (MRI) and immunohistochemistry data before and after neoadjuvant CRT. The discovery datasets consisted of pre-CRT dataset A1 (n = 113), post-CRT datasets A2 (n = 32; predominance of tumor) and A3 (n = 20; pure fibrosis). The developed model was validated in dataset B (n = 28). Thirty-eight radiomic features from T2-weighted MRI scans were incorporated into the least absolute shrinkage and selection operator method.

RESULTS

In pre-CRT dataset A1, the area under the receiver operating characteristic curve (AUC) values of radiomic score for predicting CD8⁺ TILs were 0.760 and 0.729 for training and validation subsets, respectively. A significant correlation was observed between the signature and CD8⁺ TIL density in the post-CRT dataset A2 (Pearson's R = -0.372, P = 0.036), whereas no association was found in dataset A3 (Pearson's R = -0.069, P = 0.77). The association was also observed in the validation dataset B (Pearson's R = -0.374, P = 0.049). In dataset A2, the radiomic score difference predicted changes in CD8⁺ TIL density (AUC = 0.824).

CONCLUSION

We established the MRI-derived radiomic signature for predicting CRT-induced alterations in CD8⁺ TILs. This study suggests the clinical utility of radiomics-immunophenotype modeling to evaluate tumor immune status following neoadjuvant chemoradiation in rectal cancer.

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma

OBJECTIVES

This study constructed and validated a machine learning model to predict CD8+ tumor-infiltrating lymphocyte expression levels in patients with pancreatic ductal adenocarcinoma (PDAC) using computed tomography (CT) radiomic features.

MATERIALS AND METHODS

In this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8+ T-high and -low groups using X-tile plots. A total of 1409 radiomics features were extracted from the segmentation of regions of interest, based on preoperative CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. The extreme gradient boosting classifier (XGBoost) was developed using a training set consisting of 137 consecutive patients admitted between January 2017 and December 2017. The model was validated in 47 consecutive patients admitted between January 2018 and April 2018. The performance of the XGBoost classifier was determined by its discriminative ability, calibration, and clinical usefulness.

RESULTS

The cut-off value of the CD8+ T-cell level was 18.69%, as determined by the X-tile program. A Kaplan-Meier analysis indicated a correlation between higher CD8+ T-cell levels and better overall survival (p = 0.001). The XGBoost classifier showed good discrimination in the training set (area under curve [AUC], 0.75; 95% confidence interval [CI]: 0.67-0.83) and validation set (AUC, 0.67; 95% CI: 0.51-0.83). Moreover, it showed a good calibration. The sensitivity, specificity, accuracy, positive and negative predictive values were 80.65%, 60.00%, 0.69, 0.63, and 0.79, respectively, for the training set, and 80.95%, 57.69%, 0.68, 0.61, and 0.79, respectively, for the validation set.

CONCLUSIONS

We developed a CT-based XGBoost classifier to extrapolate the infiltration levels of CD8+ T-cells in patients with PDAC. This method could be useful in identifying potential patients who can benefit from immunotherapies.

Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI

BACKGROUND

To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC.

PATIENTS AND METHODS

This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software. The correlation between TIL levels and clinical features and radiomic features was evaluated. The clinical features model, radiomic signature model, and combined prediction model were constructed and compared. Predictive performance was assessed by receiver operating characteristic analysis and clinical utility by implementing a nomogram.

RESULTS

Seven radiomic features were selected as the best discriminators to construct the radiomic signature model, the performance of which was good in both the training and validation data sets, with an area under the curve (AUC) of 0.742 (95% confidence interval [CI], 0.642-0.843) and 0.718 (95% CI, 0.558-0.878), respectively. Estrogen receptor status and tumor diameter were confirmed to be significant features for building the clinical feature model, which had an AUC of 0.739 (95% CI, 0.632-0.846) and 0.824 (95% CI, 0.692-0.957), respectively. The combined prediction model had an AUC of 0.800 (95% CI, 0.709-0.892) and 0.842 (95% CI, 0.730-0.954), respectively.

CONCLUSION

The radiomic signature could be an important predictor of the TIL level in BC, which, when validated, could be useful in identifying BC patients who can benefit from immunotherapies. The nomogram may help clinicians make decisions.

The Prognostic Role of Glutathione Peroxidase 1 and Immune Infiltrates in Glioma Investigated Using Public Datasets

BACKGROUND Glutathione peroxidase 1 (GPX1) is an essential component of the intracellular antioxidant enzyme system, but little is known about the role of GPX1 in the progression of malignancy in gliomas. Using public datasets, this study investigated the prognostic role of GPX1 and immune infiltrates in glioma. MATERIAL AND METHODS We investigated GPX1 expression levels in different cancers using the ONCOMINE and Tumor Immune Estimation Resource (TIMER) datasets. We also explored the prognostic landscape of GPX1 in gliomas based on The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Some significant pathways were identified by function enrichment analysis. We then explored the association between GPX1 expression and levels of tumor-infiltrating immune cells based on TIMER and Gene Expression Profiling Interactive Analysis (GEPIA) datasets. RESULTS Expression of GPX1 in brain and central nervous system cancers is at a much high level than in normal tissues, and it is higher in glioblastoma (GBM) than in lower-grade glioma (LGG). We found GPX1 expression to be positively correlated with the malignant clinicopathologic characteristics of gliomas. Univariate analysis and multivariate analysis revealed that overexpression of GPX1 was correlated with a worse prognosis in patients, and a nomogram indicated that GPX1 expression can predict clinical prognosis of glioma. Function enrichment analysis showed that some important pathways are related to glioma malignancy. Expression of GPX1 was positively associated with infiltrating levels of 6 types of immune cells and most of their gene markers in GBM and LGG. CONCLUSIONS These results indicate that GPX1 is an independent prognostic factor and a novel biomarker for predicting the progression of malignancy in gliomas, which is associated with immune infiltration.

Radiomics in radiation oncology-basics, methods, and limitations

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.