Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab

Consolidative radiotherapy in oligometastatic and oligoprogressive NSCLC: A systematic review

Consolidative radiation is increasingly regarded as an effective treatment for oligometastatic and oligoprogressive non-small cell lung cancer (NSCLC). This systematic review examines the clinical evidence on the significance of consolidative radiation in improving outcomes in NSCLC, including progression-free survival and overall survival. Innovations in radiotherapy, including stereotactic body radiotherapy and intensity-modulated radiotherapy, have enhanced the accuracy and effectiveness of local control in oligometastatic disease. This paper analyzes the integration of consolidative radiotherapy with systemic agents, including immunotherapy and targeted therapy, along with the application of biomarkers such circulating tumor DNA for patient selection. Our findings indicate that consolidative radiotherapy could benefit some patients with controlled oligometastatic NSCLC following systemic therapy, emphasizing the importance of proper patient selection. Additional research is necessary to optimize treatment combinations and develop biomarkers for better patient stratification in consolidative radiotherapy.

Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal

Radiomics is a health technology that has the potential to extract clinically meaningful biomarkers from standard of care imaging. Despite a wealth of exploratory analysis performed on scans acquired from patients with lung cancer and existing guidelines describing some of the key steps, no radiomic-based biomarker has been widely accepted. This is primarily due to limitations with methodology, data analysis, and interpretation of the available studies. There is currently a lack of guidance relating to the entire radiomic workflow from study design to critical appraisal. This guide, written with early career lung cancer researchers, describes a more complete radiomic workflow. Lung cancer image analysis is the focus due to some of the unique challenges encountered such as patient movement from breathing. The guide will focus on CT imaging as these are the most common scans performed on patients with lung cancer. The aim of this article is to support the production of high-quality research that has the potential to positively impact outcome of patients with lung cancer.

The evolving landscape of stage III unresectable non-small cell lung cancer "between lights and shadows"

Despite PACIFIC set a new milestone in the clinical management of unresectable stage III non-small cell lung cancer (NSCLC), it left some critical questions pending for clinical research: the efficacy of durvalumab in the real-world setting; the activity of less intensive regimens for frail populations; the role of targeted therapies in oncogene-addicted tumors; the selection of subsequent strategies at immunotherapy failure; the efficacy of novel and intensified treatments; the role of molecular biomarkers for patients' selection. This review aims to describe the evolving landscape of unresectable stage III NSCLC and provides an updated overview of the available evidence, analyzing lights and shadows emerging from recent clinical trials and discussing the most relevant challenges of post-PACIFIC era.

IRF4: A potential prognostic biomarker for immunotherapy in NSCLC

BACKGROUND

Immunotherapy is revolutionizing the management of advanced non-small cell lung cancer (NSCLC). However, sustained responses are observed in only a minority of patients. Reliable biomarkers are required to identify potential beneficiaries. Interferon regulatory factor 4 (IRF4) plays a crucial role in immune regulation, suggesting its potential as a prognostic biomarker in NSCLC immunotherapy. This study aimed to investigate the predictive role of IRF4 expression in patients with NSCLC receiving immunotherapy.

METHODS

Data from three NSCLC cohorts treated with immune checkpoint inhibitors were collected from the Gene Expression Omnibus (GEO) database. The prognostic significance of IRF4 was assessed across these cohorts, and gene set enrichment analysis (GSEA) was performed. IRF4-based nomograms were developed to predict the outcomes of immunotherapy. Correlations among IRF4 expression, immune cell infiltration, and immunotherapy prognosis were evaluated in our cohort.

RESULTS

Elevated IRF4 expression was associated with improved prognosis in patients with NSCLC undergoing immunotherapy, consistent with both GEO dataset and our cohort. IRF4 emerged as an independent predictor for progression-free survival (PFS) and overall survival (OS) in multivariable Cox regression analysis. GSEA analysis highlighted links between IRF4 expression and immune activation pathways such as Chemokine_Signaling_Pathway, Natural_Killer_Cell_Mediated_Cytotoxicity, B_Cell_Receptor_Signaling_Pathway, and T_Cell_Receptor_Signaling_Pathway. In our cohort, immunohistochemistry demonstrated correlations between IRF4 expression and the infiltration of CD8+ T cells, CD20+ B cells, and PD-L1 expression in the tumor microenvironment.

CONCLUSION

High IRF4 expression in baseline tumor tissue could serve as a favorable predictor of NSCLC immunotherapy outcomes, aiding in personalized treatment strategies.

Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC

Unresectable stage III NSCLC is now treated with chemoradiation (CRT) followed by immune checkpoint inhibitors (ICI). Pneumonitis, a common CRT complication, has heightened risk with ICI, potentially causing severe outcomes. Currently, there are no biomarkers to predict pneumonitis risk or differentiate between radiation-induced pneumonitis (RTP) and ICI-induced pneumonitis (IIP). This study analyzed 293 patients from two institutions, with 140 experiencing pneumonitis (RTP: 84, IIP: 56). Two models were developed: M1 predicted pneumonitis risk using seven radiomic features, achieving high accuracy across internal and external datasets (AUCs: 0.76 and 0.85). M2 differentiated RTP from IIP, with strong performance (AUCs: 0.86 and 0.81). Gene set enrichment analysis linked high pneumonitis risk to pathways such as ECM-receptor interaction and T-cell signaling, while high IIP risk correlated with MAPK and JAK-STAT pathways. Radiomic models show promise in early pneumonitis risk stratification and distinguishing pneumonitis types, potentially guiding personalized NSCLC treatment.

In situ editing of tumour cell membranes induces aggregation and capture of PD-L1 membrane proteins for enhanced cancer immunotherapy

Immune checkpoint blockade (ICB) therapy has emerged as a new therapeutic paradigm for a variety of advanced cancers, but wide clinical application is hindered by low response rate. Here we use a peptide-based, biomimetic, self-assembly strategy to generate a nanoparticle, TPM1, for binding PD-L1 on tumour cell surface. Upon binding with PD-L1, TPM1 transforms into fibrillar networks in situ to facilitate the aggregation of both bound and unbound PD-L1, thereby resulting in the blockade of the PD-1/PD-L1 pathway. Characterizations of TPM1 manifest a prolonged retention in tumour ( > 7 days) and anti-cancer effects associated with reinvigorating CD8+ T cells in multiple mice tumour models. Our results thus hint TPM1 as a potential strategy for enhancing the ICB efficacy.

Ten challenges and opportunities in computational immuno-oncology

Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation.

Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma

BACKGROUND

Concurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways.

METHODS

Overall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing.

RESULTS

The DL model exhibited favorable predictive performance, with an area under the curve of 0.86 (95% confidence interval [CI] 0.79-0·92) in the training cohort and 0.84 (95% CI 0.75-0.94) in the validation cohort. The DL model (low score vs. high score) was an independent predictive factor; it was significantly associated with progression-free survival and overall survival in both the training (hazard ratio [HR] = 0.54 [0.36-0.80], P = 0.002; 0.44 [0.28-0.68], P < 0.001) and validation cohorts (HR = 0.46 [0.24-0.88], P = 0.008; 0.30 [0.14-0.60], P < 0.001). The DL model was also positively related to the cell adhesion molecules, the P53 signaling pathway, and natural killer cell-mediated cytotoxicity. Single-cell analysis revealed that differentially expressed genes were enriched in different immune cells.

CONCLUSION

The DL model demonstrated a strong predictive ability for determining the response in patients with NSCLC undergoing CCRT. Our findings contribute to understanding the potential biological mechanisms underlying treatment responses in these patients.

Prediction of programmed death-1 expression status in non-small cell lung cancer based on intratumoural and peritumoral computed tomography (CT) radiomics nomogram

AIMS

This study aimed to predict the expression of programmed death-1 (PD-1) in non-small cell lung cancer (NSCLC) using intratumoral and peritumoral computed tomography (CT) radiomics nomogram.

MATERIALS AND METHODS

Two hundred patients pathologically diagnosed with NSCLC from two hospitals were retrospectively analyzed. Of these, 159 NSCLC patients from our hospital were randomly divided into a training cohort (n=96) and an internal validation cohort (n=63) at a ratio of 6:4, while 41 NSCLC patients from another medical institution served as the external validation cohort. The radiomic features of the gross tumor volume (GTV) and peritumoral volume (PTV) were extracted from the CT images. Optimal radiomics features were selected using least absolute shrinkage and selection operator regression analysis. Finally, a CT radiomics nomogram of clinically independent predictors combined with the best rad-score was constructed.

RESULTS

Compared with the 'GTV' and 'PTV' radiomics models, the combined 'GTV + PTV' radiomics model showed better predictive performance, and its area under the curve (AUC) values in the training, internal validation, and external validation cohorts were 0.90 (95% confidence interval [CI]: 0.83-0.97), 0.85 (95% CI: 0.74-0.96) and 0.78 (95% CI: 0.63-0.92). The nomogram constructed by the rad-score of the 'GTV + PTV' radiomics model combined with clinical independent predictors (prealbumin and monocyte) had the best performance, with AUC values in each cohort being 0.92 (95% CI: 0.85-0.98), 0.88 (95% CI: 0.78-0.97), and 0.80 (95% CI: 0.66-0.94), respectively.

CONCLUSION

The intratumoral and peritumoral CT radiomics nomogram may facilitate individualized prediction of PD-1 expression status in patients with NSCLC.

Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy

Background

Definitive chemoradiotherapy (dCRT) is the cornerstone for locally advanced non-small cell lung cancer (LA-NSCLC). The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in LA-NSCLC patients.

Methods

The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. We then constructed a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index).

Results

The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were applied to construct the radiomics model. Multivariable analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radiogenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650).

Conclusions

The radiomics model, genomics model and radiogenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radiogenomics model is superior to the single type model.

SLC25A19 is a novel prognostic biomarker related to immune invasion and ferroptosis in HCC

SLC25A19 is a mitochondrial thiamine pyrophosphate (TPP) carrier that mediates TPP entry into the mitochondria. SLC25A19 has been recognized to play a crucial role in many metabolic diseases, but its role in cancer has not been clearly reported. Based on clinical data from The Cancer Genome Atlas (TCGA), the following parameters were analyzed among HCC patients: SLC25A19 expression, enrichment analyses, immune infiltration, ferroptosis and prognosis analyses. In vitro, the SLC25A19 high expression was validated by qRT-PCR and Immunohistochemistry. Subsequently, a series of cell function experiments, including CCK8, EdU, clone formation, trans-well and scratch assays, were conducted to illustrate the effect of SLC25A19 on the growth and metastasis of cancer cells. Meanwhile, indicators related to ferroptosis were also detected. SCL25A19 is highly expressed in HCC and predicts a poor prognosis. Elevated SLC25A19 expression in HCC patients was markedly associated with T stage, pathological status (PS), tumor status (TS), histologic grade (HG), and AFP. Our results indicate that SLC25A19 has a generally good prognosis predictive and diagnostic ability. The results of gene enrichment analyses showed that SLC25A19 is significantly correlated with immune infiltration, fatty acid metabolism, and ferroptosis marker genes. In vitro experiments have confirmed that silencing SLC25A19 can significantly inhibit the proliferation and migration ability of cancer cells and induce ferroptosis in HCC. In conclusion, these findings indicate that SLC25A19 is novel prognostic biomarker related to immune invasion and ferroptosis in HCC, and it is an excellent candidate for therapeutic target against HCC.

Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study

BACKGROUND

Predicting the response to neoadjuvant chemoradiotherapy (nCRT) before initiating treatment is essential for tailoring therapeutic strategies and monitoring prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to develop and validate radiomic-based models to predict clinical and pathological complete responses (cCR and pCR, respectively) by incorporating the Shapley Additive exPlanations (SHAP) method for model interpretation.

METHODS

A total of 285 patients with complete pretreatment clinical characteristics and T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) at 3 centers were retrospectively recruited. The features of tumor lesions were extracted by PyRadiomics and selected using least absolute shrinkage and selection operator (LASSO) algorithm. The selected features were used to build multilayer perceptron (MLP) models alone or combined with clinical features. Area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were applied to evaluate performance of models. The SHAP method was adopted to explain the prediction models.

RESULTS

The radiomic-based models all showed better performances than clinical models. The clinical-radiomic models showed the best differentiation on cCR and pCR with mean AUCs of 0.718 and 0.810 in the validation set, respectively. The decision curves of the clinical-radiomic models showed its values in clinical application. The SHAP method powerfully interpreted the prediction models both at a holistic and individual levels.

CONCLUSIONS

Our study highlights that the radiomic-based prediction models have more excellent abilities than clinical models and can effectively predict treatment response and optimize therapeutic strategies for patients with LARCs.

Towards a survival risk prediction model for metastatic NSCLC patients on durvalumab using whole-lung CT radiomics

Background

Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting 'less-than-median-survival risk' in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet.

Methods

To ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features.

Results

The classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively.

Conclusion

Machine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

Moving from conventional to adaptive risk stratification for oropharyngeal cancer

Oropharyngeal cancer (OPC) poses a complex therapeutic dilemma for patients and oncologists alike, made worse by the epidemic increase in new cases associated with the oncogenic human papillomavirus (HPV). In a counterintuitive manner, the very thing which gives patients hope, the high response rate of HPV-associated OPC to conventional chemo-radiation strategies, has become one of the biggest challenges for the field as a whole. It has now become clear that for ~30-40% of patients, treatment intensity could be reduced without losing therapeutic efficacy, yet substantially diminishing the acute and lifelong morbidity resulting from conventional chemotherapy and radiation. At the same time, conventional approaches to de-escalation at a population (selected or unselected) level are hampered by a simple fact: we lack patient-specific information from individual tumors that can predict responsiveness. This results in a problematic tradeoff between the deleterious impact of de-escalation on patients with aggressive, treatment-refractory disease and the beneficial reduction in treatment-related morbidity for patients with treatment-responsive disease. True precision oncology approaches require a constant, iterative interrogation of solid tumors prior to and especially during cancer treatment in order to tailor treatment intensity to tumor biology. Whereas this approach can be deployed in hematologic diseases with some success, our ability to extend it to solid cancers with regional metastasis has been extremely limited in the curative intent setting. New developments in metabolic imaging and quantitative interrogation of circulating DNA, tumor exosomes and whole circulating tumor cells, however, provide renewed opportunities to adapt and individualize even conventional chemo-radiation strategies to diseases with highly variable biology such as OPC. In this review, we discuss opportunities to deploy developing technologies in the context of institutional and cooperative group clinical trials over the coming decade.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Noninvasive radiomic biomarkers for predicting pseudoprogression and hyperprogression in patients with non-small cell lung cancer treated with immune checkpoint inhibition

This study aimed to develop a computed tomography (CT)-based radiomics model capable of precisely predicting hyperprogression and pseudoprogression (PP) in patients with non-small cell lung cancer (NSCLC) treated with immunotherapy. We retrospectively analyzed 105 patients with NSCLC, from three institutions, treated with immune checkpoint inhibitors (ICIs) and categorized them into training and independent testing set. Subsequently, we processed CT scans with a series of image-preprocessing techniques, and 6008 radiomic features capturing intra- and peritumoral texture patterns were extracted. We used the least absolute shrinkage and selection operator logistic regression model to select radiomic features and construct machine learning models. To further differentiate between progressive disease (PD) and hyperprogressive disease (HPD), we developed a new radiomics model. The logistic regression (LR) model showed optimal performance in distinguishing PP from HPD, with areas under the receiver operating characteristic curve (AUC) of 0.95 (95% confidence interval [CI]: 0.91-0.99) and 0.88 (95% CI: 0.66-1) in the training and testing sets, respectively. Additionally, the support vector machine model showed optimal performance in distinguishing PD from HPD, with AUC of 0.97 (95% CI: 0.93-1) and 0.87 (95% CI: 0.72-1) in the training and testing sets, respectively. Kaplan‒Meier survival curves showed clear stratification between PP predicted by the radiomics model and true progression (HPD and PD) (hazard ratio = 0.337, 95% CI: 0.200-0.568, p < 0.01) in overall survival. Our study demonstrates that radiomic features extracted from baseline CT scans are effective in predicting PP and HPD in patients with NSCLC treated with ICIs.

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

BACKGROUND

The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC).

METHODS

Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis.

RESULTS

A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001).

CONCLUSION

Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.

Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis

Immune checkpoint inhibitors (ICIs) modulate the body's immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.

Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning

BACKGROUND AND PURPOSE

Immunotherapy is a standard treatment for many tumor types. However, only a small proportion of patients derive clinical benefit and reliable predictive biomarkers of immunotherapy response are lacking. Although deep learning has made substantial progress in improving cancer detection and diagnosis, there is limited success on the prediction of treatment response. Here, we aim to predict immunotherapy response of gastric cancer patients using routinely available clinical and image data.

MATERIALS AND METHODS

We present a multi-modal deep learning radiomics approach to predict immunotherapy response using both clinical data and computed tomography images. The model was trained using 168 advanced gastric cancer patients treated with immunotherapy. To overcome limitations of small training data, we leverage an additional dataset of 2,029 patients who did not receive immunotherapy in a semi-supervised framework to learn intrinsic imaging phenotypes of the disease. We evaluated model performance in two independent cohorts of 81 patients treated with immunotherapy.

RESULTS

The deep learning model achieved area under receiver operating characteristics curve (AUC) of 0.791 (95% CI 0.633-0.950) and 0.812 (95% CI 0.669-0.956) for predicting immunotherapy response in the internal and external validation cohorts. When combined with PD-L1 expression, the integrative model further improved the AUC by 4-7% in absolute terms.

CONCLUSION

The deep learning model achieved promising performance for predicting immunotherapy response from routine clinical and image data. The proposed multi-modal approach is general and can incorporate other relevant information to further improve prediction of immunotherapy response.

Novel tools for early diagnosis and precision treatment based on artificial intelligence

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis. However, medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis, and physician heterogeneity. The rapid development of artificial intelligence (AI) has brought a whole novel opportunity for medical task processing, demonstrating the potential for clinical application in lung cancer diagnosis and treatment. AI technologies, including machine learning and deep learning, have been deployed extensively for lung nodule detection, benign and malignant classification, and subtype identification based on CT images. Furthermore, AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen, and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients, enabling precision medicine to become a reality. Meanwhile, histology-based AI models assist pathologists in typing, molecular characterization, and prognosis prediction to enhance the efficiency of diagnosis and treatment. However, the leap to extensive clinical application still faces various challenges, such as data sharing, standardized label acquisition, clinical application regulation, and multimodal integration. Nevertheless, AI holds promising potential in the field of lung cancer to improve cancer care.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Radiation combined with immune checkpoint inhibitors for unresectable locally advanced non-small cell lung cancer: synergistic mechanisms, current state, challenges, and orientations

Until the advent of immune checkpoint inhibitors (ICIs), definitive radiotherapy (RT) concurrently with chemotherapy was recommended for unresectable, locally advanced non-small cell lung cancer (LA-NSCLC). The trimodality paradigm with consolidation ICIs following definitive concurrent chemoradiotherapy has been the standard of care since the PACIFIC trial. Preclinical evidence has demonstrated the role of RT in the cancer-immune cycle and the synergistic effect of RT combined with ICIs (iRT). However, RT exerts a double-edged effect on immunity and the combination strategy still could be optimized in many areas. In the context of LA-NSCLC, optimized RT modality, choice, timing, and duration of ICIs, care for oncogenic addicted tumors, patient selection, and novel combination strategies require further investigation. Targeting these blind spots, novel approaches are being investigated to cross the borders of PACIFIC. We discussed the development history of iRT and summarized the updated rationale for the synergistic effect. We then summarized the available research data on the efficacy and toxicity of iRT in LA-NSCLC for cross-trial comparisons to eliminate barriers. Progression during and after ICIs consolidation therapy has been regarded as a distinct resistance scenario from primary or secondary resistance to ICIs, the subsequent management of which has also been discussed. Finally, based on unmet needs, we probed into the challenges, strategies, and auspicious orientations to optimize iRT in LA-NSCLC. In this review, we focus on the underlying mechanisms and recent advances of iRT with an emphasis on future challenges and directions that warrant further investigation. Taken together, iRT is a proven and potential strategy in LA-NSCLC, with multiple promising approaches to further improve the efficacy. Video Abstract.

Cancer immune exclusion: breaking the barricade for a successful immunotherapy

Immunotherapy has changed the course of cancer treatment. The initial steps were made through tumor-specific antibodies that guided the setup of an antitumor immune response. A new and successful generation of antibodies are designed to target immune checkpoint molecules aimed to reinvigorate the antitumor immune response. The cellular counterpart is the adoptive cell therapy, where specific immune cells are expanded or engineered to target cancer cells. In all cases, the key for achieving positive clinical resolutions rests upon the access of immune cells to the tumor. In this review, we focus on how the tumor microenvironment architecture, including stromal cells, immunosuppressive cells and extracellular matrix, protects tumor cells from an immune attack leading to immunotherapy resistance, and on the available strategies to tackle immune evasion.

Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis

BACKGROUND

To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis.

METHODS

This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis.

RESULTS

All models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957-1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969-0.995, 95% CI) in the training cohort, respectively.

CONCLUSION

The MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.

Five years after PACIFIC: update on multimodal treatment efficacy based on real-world reports

INTRODUCTION

The growing body of real-life data on maintenance treatment with durvalumab suggests that immunological markers of the cancer host interplay may have significant effects on the efficacy of multimodal therapy in patients with unresectable stage III NSCLC.

AREAS COVERED

We summarize real-world clinical data regarding this new tri-modal approach and report on potential biomarker landscape.

EXPERT OPINION

The obvious question posed in this context of a very heterogeneous inoperable stage III NSCLC disease is: How can we augment an ability to predict checkpoint inhibition success or failure? Which tools and biomarkers, which clinical metadata and genetic background are relevant and feasible? No single biomarker will ever fully dominate the unresectable stage III NSCLC space, so we advocate multilevel and multivariate analysis of biomarkers. In this particular opinion piece, we explore the impact of PD-L1 expression on tumor cells, neutrophil-to-lymphocyte ratio, EGFR and STK11 mutational status, interferon-gamma signature, and tumor-infiltrating lymphocytes among others.

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.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

Purpose

Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.

Methods

This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.

Results

Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049).

Conclusions

The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

Expression Analysis of Five Different Long Non-Coding Ribonucleic Acids in Nonsmall-Cell Lung Carcinoma Tumor and Tumor-Derived Exosomes

Long non-coding ribonucleic acids (LncRNAs) are recently known for their role in regulating gene expression and the development of cancer. Controversial results indicate a correlation between the tissue expression of LncRNA and LncRNA content of extracellular vesicles. The present study aimed to evaluate the expression of different LncRNAs in non-small cell lung cancer (NSCLC) patients in tumor tissue, adjacent non-cancerous tissue (ANCT), and exosome-mediated lncRNA. Tumor and ANCT, as well as serum samples of 168 patient with NSCLC, were collected. The GHSROS, HNF1A-AS1, HOTAIR, HMlincRNA717, and LINCRNA-p21 relative expressions in tumor tissue, ANCT, and serum exosomes were evaluated in NSCLC patients. Among 168 NSCLC samples, the expressions of GHSROS (REx = 3.64, p = 0.028), HNF1A-AS1 (REx = 2.97, p = 0.041), and HOTAIR (REx = 2.9, p = 0.0389) were upregulated, and the expressions of HMlincRNA717 (REx = −4.56, p = 0.0012) and LINCRNA-p21 (REx = −5.14, p = 0.00334) were downregulated in tumor tissue in contrast to ANCT. Moreover, similar statistical differences were seen in the exosome-derived RNA of tumor tissues in contrast to ANCT samples. A panel of the five lncRNAs demonstrated that the area under the curve (AUC) for exosome and tumor was 0.937 (standard error: 0.012, p value < 0.0001). LncRNAs GHSROS, HNF1A-AS1, and HOTAIR showed high expression in tumor tissue and exosome content in NSCLC, and a panel that consisted of all five lncRNAs improved diagnosis of NSCLC.

Early recurrence factors in patients with stage III non-small cell lung cancer treated with concurrent chemoradiotherapy

BACKGROUND

The clinical characteristics and risk factors for cancer recurrence have not been well evaluated regarding early recurrence in patients with unresectable locally advanced non-small cell lung cancer (LA-NSCLC) who receive concurrent chemoradiotherapy (CRT). The aim of this study was to determine the clinical characteristics and risk factors of patients with stage III unresectable LA-NSCLC treated with CRT who developed early recurrence.

METHODS

We retrospectively reviewed the clinical records of 46 patients diagnosed with stage III unresectable LA-NSCLC treated with CRT at our center between July 2012 and July 2021. A tumor proportion score (TPS) < 50% was defined as "low expression" and a TPS > 50% was defined as "high expression."

RESULTS

A total of 17 (37.0%) patients had a confirmed recurrence within 1 year of treatment. More patients had a lower body mass index in the early recurrence group than in the later recurrence group (p = 0.038). A higher number of patients in the late recurrence group underwent surgery after CRT (p = 0.036). Patients with a higher TPS were more likely to experience late recurrence than early recurrence (p = 0.001), whereas more patients with stage N3 disease were in the early recurrence group (p = 0.011). Multivariate analysis identified lower TPS expression as an independent risk factor for early recurrence after CRT. Overall survival was prolonged in the late recurrence group (p < 0.001).

CONCLUSIONS

A lower TPS may be a predictor of early recurrence after CRT in patients with LA-NSCLC. These patients should be closely monitored for post-treatment recurrence.

A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors

Tumor vasculature is a key component of the tumor microenvironment that can influence tumor behavior and therapeutic resistance. We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitor (ICI) therapies. A total of 507 cases were used to evaluate different aspects of the QVT biomarkers. QVT features were extracted from computed tomography imaging of patients before and after ICI therapy to capture the tortuosity, curvature, density, and branching statistics of the nodule vasculature. Our results showed that QVT features were prognostic of OS (HR = 3.14, 0.95% CI = 1.2 to 9.68, P = 0.0006, C-index = 0.61) and could predict ICI response with AUCs of 0.66, 0.61, and 0.67 on three validation sets. Our study shows that QVT imaging biomarker could potentially aid in predicting and monitoring response to ICI in patients with NSCLC.

What does radiomics do in PD-L1 blockade therapy of NSCLC patients?

With the in‐depth understanding of programmed cell death 1 ligand 1 (PD‐L1) in non‐small cell lung cancer (NSCLC), PD‐L1 has become a vital immunotherapy target and a significant biomarker. The clinical utility of detecting PD‐L1 by immunohistochemistry or next‐generation sequencing has been written into guidelines. However, the application of these methods is limited in some circumstances where the biopsy size is small or not accessible, or a dynamic monitor is needed. Radiomics can noninvasively, in real‐time, and quantitatively analyze medical images to reflect deeper information about diseases. Since radiomics was proposed in 2012, it has been widely used in disease diagnosis and differential diagnosis, tumor staging and grading, gene and protein phenotype prediction, treatment plan decision‐making, efficacy evaluation, and prognosis prediction. To explore the feasibility of the clinical application of radiomics in predicting PD‐L1 expression, immunotherapy response, and long‐term prognosis, we comprehensively reviewed and summarized recently published works in NSCLC. In conclusion, radiomics is expected to be a companion to the whole immunotherapy process.