Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface

The interplay between metal ions and immune cells in glioma: pathways to immune escape

This review explores the intricate roles of metal ions-iron, copper, zinc, and selenium-in glioma pathogenesis and immune evasion. Dysregulated metal ion metabolism significantly contributes to glioma progression by inducing oxidative stress, promoting angiogenesis, and modulating immune cell functions. Iron accumulation enhances oxidative DNA damage, copper activates hypoxia-inducible factors to stimulate angiogenesis, zinc influences cell proliferation and apoptosis, and selenium modulates the tumor microenvironment through its antioxidant properties. These metal ions also facilitate immune escape by upregulating immune checkpoints and secreting immunosuppressive cytokines. Targeting metal ion pathways with therapeutic strategies such as chelating agents and metalloproteinase inhibitors, particularly in combination with conventional treatments like chemotherapy and immunotherapy, shows promise in improving treatment efficacy and overcoming resistance. Future research should leverage advanced bioinformatics and integrative methodologies to deepen the understanding of metal ion-immune interactions, ultimately identifying novel biomarkers and therapeutic targets to enhance glioma management and patient outcomes.

Delta radiomics: an updated systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.

MRI radiomics predicts the efficacy of EGFR-TKI in EGFR-mutant non-small-cell lung cancer with brain metastasis

AIM

To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases.

MATERIALS AND METHODS

117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models.

RESULTS

Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820-0.901, 95%CI) and 0.843 (0.783-0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751-0.923, 95%CI) and 0.850 (0.763-0.937, 95%CI).

CONCLUSION

The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms.

MRI radiomics for predicting intracranial progression in non-small-cell lung cancer patients with brain metastases treated with epidermal growth factor receptor tyrosine kinase inhibitors

AIM

To identify clinical and magnetic resonance imaging (MRI) radiomics predictors specialised for intracranial progression (IP) after first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment in non-small-cell lung cancer (NSCLC) patients with brain metastases (BMs).

MATERIALS AND METHODS

Seventy EGFR-mutated NSCLC patients with a total of 212 BMs who received first-line EGFR-TKI therapy were enrolled. Radiomics features were extracted from the BM regions on the pretreatment contrast-enhanced T1-weighted images, and the radiomics score (rad-score) of each BM was established based on the selected features. Furthermore, the mean rad-score derived from the average rad-score of all included BMs in each patient was calculated. Univariate and multivariate logistic regression analyses were performed to identify potential predictors of IP. Prediction models based on different predictors and their combinations were constructed, and nomogram based on the optimal prediction model was evaluated.

RESULTS

Thirty-three (47.1 %) patients developed IP, and the remaining 37 (52.9 %) patients were IP-free. EGFR-19del mutation (OR 0.19, 95 % CI 0.05-0.69), third-generation TKI treatment (OR 0.33, 95 % CI 0.16-0.67) and mean rad-score (OR 5.71, 95 % CI 1.65-19.68) were found to be independent predictive factors. Models based on these three predictors alone and in combination (combined model) achieved AUCs of 0.64, 0.64, 0.74, and 0.86 and 0.64, 0.64, 0.75, and 0.84 in the training and validation sets, respectively, and the combined model demonstrated optimal performance for predicting IP.

CONCLUSIONS

The model integrating EGFR-19del mutation, third-generation TKI treatment and mean rad-score had good predictive value for IP after EGFR-TKI treatment in NSCLC patients with BM.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.

An overview on multimodal imaging for the diagnostic workup of pleural mesothelioma

Pleural mesothelioma (PM) is an aggressive disease that has a strong causal relationship with asbestos exposure and represents a major challenge from both a diagnostic and therapeutic viewpoint. Despite recent improvements in patient care, PM typically carries a poor outcome, especially in advanced stages. Therefore, a timely and effective diagnosis taking advantage of currently available imaging techniques is essential to perform an accurate staging and dictate the most appropriate treatment strategy. Our aim is to provide a brief, but exhaustive and up-to-date overview of the role of multimodal medical imaging in the management of PM.

CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy

BACKGROUND

To evaluate the value of computed tomography (CT) radiomics in predicting the risk of developing epidermal growth factor receptor (EGFR) T790M resistance mutation for metastatic non-small lung cancer (NSCLC) patients before first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy.

METHODS

A total of 162 metastatic NSCLC patients were recruited and split into training and testing cohort. Radiomics features were extracted from tumor lesions on nonenhanced CT (NECT) and contrast-enhanced CT (CECT). Radiomics score (rad-score) of two CT scans was calculated respectively. A nomogram combining two CT scans was developed to evaluate T790M resistance within up to 14 months. Patients were followed up to calculate the time of T790M occurrence. Models were evaluated by area under the curve at receiver operating characteristic analysis (ROC-AUC), calibration curve, and decision curve analysis (DCA). The association of the nomogram with the time of T790M occurrence was evaluated by Kaplan-Meier survival analysis.

RESULTS

The nomogram constructed with the rad-score of NECT and CECT for predicting T790M resistance within 14 months achieved the highest ROC-AUCs of 0.828 and 0.853 in training and testing cohorts, respectively. The DCA showed that the nomogram was clinically useful. The Kaplan-Meier analysis showed that the occurrence time of T790M difference between the high- and low-risk groups distinguished by the rad-score was significant (p < 0.001).

CONCLUSIONS

The CT-based radiomics signature may provide prognostic information and improve pretreatment risk stratification in EGFR NSCLC patients before EGFR-TKIs therapy. The multimodal radiomics nomogram further improved the capability.

RELEVANCE STATEMENT

Radiomics based on NECT and CECT images can effectively identify and stratify the risk of T790M resistance before the first-line TKIs treatment in metastatic non-small cell lung cancer patients.

KEY POINTS

• Early identification of the risk of T790M resistance before TKIs treatment is clinically relevant. • Multimodel radiomics nomogram holds potential to be a diagnostic tool. • It provided an imaging surrogate for identifying the pretreatment risk of T790M.

Radiomics in gastrointestinal stromal tumours: an up-to-date review

Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics

Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool

PURPOSE

The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision^® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM).

METHODS

GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features.

RESULTS

A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT.

CONCLUSIONS

AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.

Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer

AIM

To build and externally validate an [¹⁸F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [¹⁸F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index).

RESULTS

In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82.

CONCLUSION

Although assessed in two small but independent cohorts, an [¹⁸F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.

Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review

Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.

Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments

The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.

Gender Medicine in Clinical Radiology Practice

Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

Radiomics in Lung Metastases: A Systematic Review

Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.