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Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT‐based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

Objectives We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT).Materials and Methods This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT. Ninety-six radiomic features, including intensity, morphological and second- and high-order texture features were extracted from segmented 3D volumes from T2W MRI. All features were harmonized using ComBat algorithm. Max-Relevance-Min-Redundancy (MRMR) algorithm was used as feature selector and k-nearest neighbors (KNN), Naive Bayes (NB), Random forests (RF), and eXtreme Gradient Boosting (XGB) algorithms were used as classifiers. The evaluation was performed using the area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, specificity and accuracy.Results In univariate analysis, the highest AUC in pre-, post-, and delta-radiomic features were 0.78, 0.70, and 0.71, for GLCM_IMC1, shape (surface area and volume) and GLSZM_GLNU features, respectively. In multivariate analysis, RF and KNN achieved the highest AUC (0.85 +/- 0.04 and 0.81 +/- 0.14, respectively) among pre- and post-treatment features. The highest AUC was achieved for the delta-radiomic-based RF model (0.96 +/- 0.01) followed by NB (0.96 +/- 0.04). Overall. Delta-radiomics model, outperformed both pre- and post-treatment features (P-valueConclusion Multivariate analysis of delta-radiomic T2W MRI features using machine learning algorithms could potentially be used for response prediction in LARC patients undergoing nCRT. We also observed that multivariate analysis of delta-radiomic features using RF classifiers can be used as powerful biomarkers for response prediction in LARC.

ObjectiveIn this large multi-institutional study, we aimed to analyze the prognostic power of computed tomography (CT)-based radiomics models in COVID-19 patients.MethodsCT images of 14,339 COVID-19 patients with overall survival outcome were collected from 19 medical centers. Whole lung segmentations were performed automatically using a previously validated deep learning-based model, and regions of interest were further evaluated and modified by a human observer. All images were resampled to an isotropic voxel size, intensities were discretized into 64-binning size, and 105 radiomics features, including shape, intensity, and texture features were extracted from the lung mask. Radiomics features were normalized using Z-score normalization. High-correlated features using Pearson (R2>0.99) were eliminated. We applied the Synthetic Minority Oversampling Technique (SMOT) algorithm in only the training set for different models to overcome unbalance classes. We used 4 feature selection algorithms, namely Analysis of Variance (ANOVA), Kruskal- Wallis (KW), Recursive Feature Elimination (RFE), and Relief. For the classification task, we used seven classifiers, including Logistic Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost (AB), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The models were built and evaluated using training and testing sets, respectively. Specifically, we evaluated the models using 10 different splitting and cross-validation strategies, including different types of test datasets (e.g. non-harmonized vs. ComBat-harmonized datasets). The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were reported for models evaluation.ResultsIn the test dataset (4301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83±0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + RF classifier. In RT-PCR-only positive test sets (3644), similar results were achieved, and there was no statistically significant difference. In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in highest performance of AUC, reaching 0.83±0.01 (CI95%: 0.81-0.85), with sensitivity and specificity of 0.77 and 0.74, respectively. At the same time, ComBat harmonization did not depict statistically significant improvement relevant to non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and LR classifier resulted in the highest performance of AUC (0.80±0.084) with sensitivity and specificity of 0.77 ± 0.11 and 0.76 ± 0.075, respectively.ConclusionLung CT radiomics features can be used towards robust prognostic modeling of COVID-19 in large heterogeneous datasets gathered from multiple centers. As such, CT radiomics-based model has significant potential for use in prospective clinical settings towards improved management of COVID-19 patients.

Cancer is one of the most important health issues, which is the second leading cause of death in the world. Conventional cancer therapy methods encounter with serious drawbacks and cannot often provide satisfactory outcomes. It has been demonstrated that the effect of various treatment modalities can be enhanced when combined with nanomaterials. Moreover, a new trend is being emerged in clinical oncology to shift from monotherapy toward combination therapy in the presence of useful nanomaterials. This way, the synergistic interactions occur among different treatments, which result in much stronger therapeutic effect than separate use of corresponding monotherapies. Recently, gold nanoparticles (AuNPs) have been widely implemented as one of the leading nanomaterials for combinatorial cancer therapy. In this review article, the importance and benefits of AuNPs in the field of combinatorial cancer therapy are firstly explained. Then, the applications of AuNPs in the most clinically established cancer therapy modalities, including chemotherapy, radiotherapy and a variety of hyperthermia methods will be described. Recent progresses in the application of AuNPs in dual- and triple-modality cancer therapies will be reviewed. Also, a brief summary of the role of AuNPs in photodynamic therapy and gene therapy will be provided. Finally, the opportunities and challenges of the entry of AuNPs into the clinics for use in cancer therapy methods are discussed.

The aim of this work was to use sonographic image features as biomarker to assess the malignancy of thyroid nodules in patients recommended to FNA according to ACR TI-RADS guideline. Two hundred and ten patients with FNA test report were included in this study. Eighty Different quantitative radiomic features were extracted from sonographic images. Minimum Redundancy Maximum Relevance (MRMR) and logistic regression (LR) algorithms were used as feature selector and classifier, respectively. The evaluation of the models was performed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). AUC of MRMR feature selection algorithm and LR classifier was 0.87 (with accuracy of 0.74, sensitivity of 0.85 and specificity of 0.60). In the validation dataset, the AUC was 0.92 (with accuracy of 0.70, sensitivity of 0.81 and specificity of 0.58). The proposed model could be potentially used as alternative to FNA as noninvasive tools in clinical setting.

In this study, we assess the power of MRI radiomic features for prediction of locally advanced rectal cancer (LARC) patients' response to neoadjuvant chemoradiation. T2-Weighted MR images acquired 2 weeks before and 4 weeks after treatment of 50 patients were used. The tumor volume was delineated by an experienced radiologist on T2-weighted MR images followed by the extraction of radiomics features, including morphology, first-order, histogram, and texture from volumes of interest (VOI). First, univariate analysis was applied on features to identify predictive power of features. To build a predictive model, we used Random Forest (RF) algorithm along with Max-Relevance-Min-Redundancy (MRMR) feature selection algorithm for reducing complexity and improving generalization. Finally, the model was evaluated through the area under the receiver operator characteristic (ROC) curve (AVC), sensitivity, specificity and accuracy metrics. In univariate analysis, delta radiomics of LAE and LALGLE features from GLSZM had the highest predictive performance (AUC=0.67). In multivariate analysis, the highest predictive performance for response prediction in LARC patients was achieved using delta-radiomic features with AUC of 0.92 and 0.88 in training and validation datasets, respectively. The achieved results were promising to move towards personalized treatment for LARC patients.

Nonspecificity and high toxicity limit the treatment efficacy and safety of chemoradiation therapy. Effective tumor targeting of anticancer drugs and radiosensitizing agents is highly desirable to amplify the efficacy of this standard cancer therapy approach. To achieve this goal, we exploited the synergy of cisplatin and gold nanoparticles (AuNPs) co-loaded into alginate hydrogel network, forming so-called ACA nanocomplex, and X-ray radiation. Cisplatin is a commonly used anticancer agent, and at the same time, along with AuNPs could function as radiosensitizers to enhance the radiation-induced damages through various pathways. The ACA nanocomplex improved the therapeutic efficiency of standard chemotherapy and yielded 79% growth inhibition in CT26 colon adenocarcinoma tumor after 28 days, which was significantly higher than that of 9% for free cisplatin administration. Moreover, the combination of ACA nanocomplex with 6 MV X-ray dramatically suppressed tumor growth up to 95%, showing 51% enhancement in antitumor activity compared to standard chemoradiation. The nanocomplex developed herein holds the promise to promote the efficiency of standard chemoradiation while maintaining the patient's safety through reducing the clinically administered doses of anticancer drug and X-ray. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B:2658-2663, 2019.

Background and Purpose: It has been well-known both gold nanoparticles (AuNPs) and cisplatin are potential radiosensitizers for radiotherapy of cancer. In this in vitro study, we investigated the chemoradiotherapeutic effects of alginate nanogel co-loaded with AuNPs and cisplatin (ACA) on U87-MG human glioblastoma cells. Methods: Based on the accomplished pilot studies, U87-MG cells were incubated with ACA and cisplatin at 10% inhibitory concentration (IC10) for 4h. Then, the cells were irradiated to different doses of 6MV X-rays (2 and 10 Gy). MTT assay was performed to evaluate the cell survival rate. Apoptosis was determined by flow cytometry using an annexinV–fluorescein isothiocyanate/propidium iodide apoptosis detection kit. Results: The results showed that ACA at the concentration of 4 µg/ml (per cisplatin) and free cisplatin at concentration of 15 μg/ml have the same effects on U87-MG cells (survival rate: 90%). The combination of ACA with radiation resulted in a significant decrease in cell viability (survival rate: 30%). The flow cytometry assay also showed that such a combination therapy induces more apoptosis than necrosis. Conclusion: It may be concluded that co-delivery of AuNPs and cisplatin with a single nanoplatform like ACA nanocomplex enhances the therapeutic ratio of human glioblastoma radiation therapy.