The Continuing Evolution of Molecular Functional Imaging in Clinical Oncology: The Road to Precision Medicine and Radiogenomics (Part I)

Ultrasound-based radiogenomics: status, applications, and future direction

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Comprehensive Guide to Randomized Controlled Trials in Radiology: Everything You Need to Know

Evidence-based medicine integrates clinical research, personal expertise, and patient values. The most robust forms of clinical evidence, such as randomized controlled trials (RCTs) and prospective studies, provide the strongest support for medical decision-making. RCTs are vital in radiology for evaluating new imaging technologies, contrast agents, and therapeutic procedures, despite challenges in translating preclinical findings to clinical practice. This guide discusses the history, principles, methodologies, and applications of RCTs in radiology, highlighting their role in advancing the field and supporting evidence-based practice.

Injectable hydrogel encapsulating siMMP13 with anti-ROS and anti-apoptotic functions for osteoarthritis treatment

BACKGROUND

Osteoarthritis (OA) is a degenerative joint disease characterized by the progressive degeneration of articular cartilage, leading to pain, stiffness, and loss of joint function. The pathogenesis of OA involves multiple factors, including increased intracellular reactive oxygen species (ROS), enhanced chondrocyte apoptosis, and disturbances in cartilage matrix metabolism. These processes contribute to the breakdown of the extracellular matrix (ECM) and the loss of cartilage integrity, ultimately resulting in joint damage and dysfunction. RNA interference (RNAi) therapy has emerged as a promising approach for the treatment of various diseases, including hATTR and acute hepatic porphyria. By harnessing the natural cellular machinery for gene silencing, RNAi allows for the specific inhibition of target genes involved in disease pathogenesis. In the context of OA, targeting key molecules such as matrix metalloproteinase-13 (MMP13), which plays a critical role in cartilage degradation, holds great therapeutic potential.

RESULTS

In this study, we developed an innovative therapeutic approach for OA using a combination of liposome-encapsulated siMMP13 and NG-Monomethyl-L-arginine Acetate (L-NMMA) to form an injectable hydrogel. The hydrogel served as a delivery vehicle for the siMMP13, allowing for sustained release and targeted delivery to the affected joint. Experiments conducted on destabilization of the medial meniscus (DMM) model mice demonstrated the therapeutic efficacy of this composite hydrogel. Treatment with the hydrogel significantly inhibited the degradation of cartilage matrix, as evidenced by histological analysis showing preserved cartilage structure and reduced loss of proteoglycans. Moreover, the hydrogel effectively suppressed intracellular ROS accumulation in chondrocytes, indicating its anti-oxidative properties. Furthermore, it attenuated chondrocyte apoptosis, as demonstrated by decreased levels of apoptotic markers.

CONCLUSION

In summary, the injectable hydrogel containing siMMP13, endowed with anti-ROS and anti-apoptotic properties, may represent an effective therapeutic strategy for osteoarthritis in the future.

Imaging Recommendations for Diagnosis and Management of Primary Parathyroid Pathologies: A Comprehensive Review

Parathyroid pathologies are suspected based on the biochemical alterations and clinical manifestations, and the predominant roles of imaging in primary hyperparathyroidism are localisation of tumour within parathyroid glands, surgical planning, and to look for any ectopic parathyroid tissue in the setting of recurrent disease. This article provides a comprehensive review of embryology and anatomical variations of parathyroid glands and their clinical relevance, surgical anatomy of parathyroid glands, differentiation between multiglandular parathyroid disease, solitary adenoma, atypical parathyroid tumour, and parathyroid carcinoma. The roles, advantages and limitations of ultrasound, four-dimensional computed tomography (4DCT), radiolabelled technetium-99 (99mTc) sestamibi or dual tracer 99mTc pertechnetate and 99mTc-sestamibi with or without single photon emission computed tomography (SPECT) or SPECT/CT, dynamic enhanced magnetic resonance imaging (4DMRI), and fluoro-choline positron emission tomography (18F-FCH PET) or [11C] Methionine (11C -MET) PET in the management of parathyroid lesions have been extensively discussed in this article. The role of fluorodeoxyglucose PET (FDG-PET) has also been elucidated in this article. Management guidelines for parathyroid carcinoma proposed by the American Society of Clinical Oncology (ASCO) have also been described. An algorithm for management of parathyroid lesions has been provided at the end to serve as a quick reference guide for radiologists, clinicians and surgeons.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

Aim

The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging.

Methods

Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI).

Results

In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model.

Conclusions

Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.

Imaging of Neck Nodes in Head and Neck Cancers - a Comprehensive Update

Cervical lymph node metastases from head and neck squamous cell cancers significantly reduce disease-free survival and worsen overall prognosis and, hence, deserve more aggressive management and follow-up. As per the eighth edition of the American Joint Committee on Cancer staging manual, extranodal extension, especially in human papillomavirus-negative cancers, has been incorporated in staging as it is important in deciding management and significantly impacts the outcome of head and neck squamous cell cancer. Lymph node imaging with various radiological modalities, including ultrasound, computed tomography and magnetic resonance imaging, has been widely used, not only to demonstrate nodal involvement but also for guided histopathological evaluation and therapeutic intervention. Computed tomography and magnetic resonance imaging, together with positron emission tomography, are used widely for the follow-up of treated patients. Finally, there is an emerging role for artificial intelligence in neck node imaging that has shown promising results, increasing the accuracy of detection of nodal involvement, especially normal-appearing nodes. The aim of this review is to provide a comprehensive overview of the diagnosis and management of involved neck nodes with a focus on sentinel node anatomy, pathogenesis, imaging correlates (including radiogenomics and artificial intelligence) and the role of image-guided interventions.

Imaging of brain metastasis in non-small-cell lung cancer: indications, protocols, diagnosis, post-therapy imaging, and implications regarding management

Increased survival (due to the use of targeted therapies based on genomic profiling) has resulted in the increased incidence of brain metastasis during the course of disease, and thus, made it essential to have proper imaging guidelines in place for brain metastasis from non-small-cell lung cancer (NSCLC). Brain parenchymal metastases can have varied imaging appearances, and it is pertinent to be aware of the various molecular risk factors for brain metastasis from NSCLC along with their suggestive imaging appearances, so as to identify them early. Leptomeningeal metastasis requires additional imaging of the spine and an early cerebrospinal fluid (CSF) analysis. Differentiation of post-therapy change from recurrence on imaging has a bearing on the management, hence the need for its awareness. This article will provide in-depth literature review of the epidemiology, aetiopathogenesis, screening, detection, diagnosis, post-therapy imaging, and implications regarding the management of brain metastasis from NSCLC. In addition, we will also briefly highlight the role of artificial intelligence (AI) in brain metastasis screening.

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.

Glioma radiogenomics and artificial intelligence: road to precision cancer medicine

Radiogenomics refers to the study of the relationship between imaging phenotypes and gene expression patterns/molecular characteristics, which might allow improved diagnosis, decision-making, and predicting patient outcomes in the context of multiple diseases. Central nervous system (CNS) tumours contribute to significant cancer-related mortality in the present age. Although historically CNS neoplasms were classified and graded based on microscopic appearance, there was discordance between two histologically similar tumours that showed varying prognosis and behaviour, attributable to their molecular signatures. These led to the incorporation of molecular markers in the classification of CNS neoplasms. Meanwhile, advancements in imaging technology such as diffusion-based imaging (including tractography), perfusion, and spectroscopy in addition to the conventional imaging of glial neoplasms, have opened an avenue for radiogenomics. This review touches upon the schema of the current classification of gliomas, concepts behind molecular markers, and parameters that are used in radiogenomics to characterise gliomas and the role of artificial intelligence for the same. Further, the role of radiomics in the grading of brain tumours, prediction of treatment response and prognosis has been discussed. Use of automated and semi-automated tumour segmentation for radiotherapy planning and follow-up has also been discussed briefly.

Nanoparticle Surface Functionalization: How to Improve Biocompatibility and Cellular Internalization

The use of nanoparticles (NP) in diagnosis and treatment of many human diseases, including cancer, is of increasing interest. However, cytotoxic effects of NPs on cells and the uptake efficiency significantly limit their use in clinical practice. The physico-chemical properties of NPs including surface composition, superficial charge, size and shape are considered the key factors that affect the biocompatibility and uptake efficiency of these nanoplatforms. Thanks to the possibility of modifying physico-chemical properties of NPs, it is possible to improve their biocompatibility and uptake efficiency through the functionalization of the NP surface. In this review, we summarize some of the most recent studies in which NP surface modification enhances biocompatibility and uptake. Furthermore, the most used techniques used to assess biocompatibility and uptake are also reported.

Standardized uptake value (SUVmax) in 18F-FDG PET/CT is correlated with the total number of main oncogenic anomalies in cancer patients

Cancer diagnosis and therapy is quickly moving from the traditional histology-based approaches to genomic stratification, providing a huge opportunity for radiogenomics, associating imaging features with genomic data. Genome sequencing is time consuming, expensive and invasive whereas ¹⁸F-FDG PET/CT is readily available, fast and noninvasive. The aim of this study was to determine the relationship between the maximum standardized uptake value (SUV_max) and the frequency of 11 common oncogenic anomalies determined by specific common genomic alterations in tissue biopsies from patients with cancer. We retrospectively studied 102 consecutive untreated patients with gastrointestinal, lung, and breast cancer who underwent ¹⁸F-FDG PET/CT imaging, shortly prior to molecular testing by a biopsy for genomic profiling that consisted of 11 common DNA alterations: (1) TP53, (2) DNA repair, (3) EGFR, (4) PI3K/AKT/MTOR (PAM) pathway including PTEN, PIK3CA, AKT, TSC, CCNB1, MTOR, FBXW2, and NF2, (5) MEK, (6) CYCLIN including CCND,CDK, CDKN, and RB, (7) WNT, (8) ALK, (9) MYC, (10) MET, and (11) FGF/FGFR. Higher SUV_max was associated with the presence of TP53 and PAM genomic anomalies (p < .05), but not the other 9 gene groups (p > .05). More importantly, SUV_max was positively correlated with total number of oncogenic anomalies (r = 0.27, p = .005). We propose higher SUV_max as an indicator for total number of common oncogenic anomalies. This finding is a step forward in noninvasive stratification of cancer patients, in terms of the overall load of oncogenic anomalies, based on their SUV_max.

Introduction to Special Issue of Radiology and Imaging of Cancer

The increase in knowledge in oncology and the possibility of creating personalized medicine by selecting a more appropriate therapy related to the different tumor subtypes, as well as the management of patients with cancer within a multidisciplinary team has improved the clinical outcomes [...].

A bird's-eye view of deep learning in bioimage analysis

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

Imaging in oral cancers: A comprehensive review

This review aims at simplifying the relevant imaging anatomy, guiding the optimal imaging method and highlighting the key imaging findings that influence prognosis and management of oral cavity squamous cell carcinoma (OSCC). Early OSCC can be treated with either surgery alone while advanced cancers are treated with a combination of surgery, radiotherapy and/or chemotherapy. Considering the complex anatomy of the oral cavity and its surrounding structures, imaging plays an indispensable role not only in locoregional staging but also in the distant metastatic work-up and post treatment follow-up. Knowledge of the anatomy with understanding of common routes of spread of cancer, allows the radiologist to accurately determine disease extent and augment clinical findings to plan appropriate therapy. This review aims at simplifying the relevant imaging anatomy, guiding the optimal imaging method and highlighting the key imaging findings that influence prognosis and management.

Integrating Small Animal Irradiators withFunctional Imaging for Advanced Preclinical Radiotherapy Research

Translational research aims to provide direct support for advancing novel treatment approaches in oncology towards improving patient outcomes. Preclinical studies have a central role in this process and the ability to accurately model biological and physical aspects of the clinical scenario in radiation oncology is critical to translational success. The use of small animal irradiators with disease relevant mouse models and advanced in vivo imaging approaches offers unique possibilities to interrogate the radiotherapy response of tumors and normal tissues with high potential to translate to improvements in clinical outcomes. The present review highlights the current technology and applications of small animal irradiators, and explores how these can be combined with molecular and functional imaging in advanced preclinical radiotherapy research.