Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Value of computed tomography radiomics combined with inflammation indices in predicting the efficacy of immunotherapy in patients with locally advanced and metastatic non-small cell lung cancer

Background

Although immunotherapy has revolutionized the treatment landscape of lung cancer and improved the prognosis of this malignancy, many patients with lung cancer still are not able to benefit from it because of many different reasons. The expression of programmed death ligand-1 (PD-L1) in tumor cells has been approved for the prediction of immunotherapy efficacy; however, its clinical application has been limited by the invasiveness of PD-L1 determination and the heterogeneity of tumor cells. As a promising technology, radiomics has made significant progress in the diagnosis and treatment of lung cancer. Thus, we constructed a noninvasive predictive model which based on radiomics to predict the immunotherapy efficacy of lung caner patients.

Methods

Data of 82 patients with stage IIIa/IVb NSCLC who received immunotherapy at the First Affiliated Hospital of Soochow University from December 2019 to January 2023 were retrospectively collected. These patients were followed up for durable clinical benefit (DCB), as defined by whether progression-free survival (PFS) reached 12 months. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen for the radiomic features in the training set, and a radiomics score (Rad-score) was calculated. The clinical baseline data were analyzed, and the peripheral blood inflammation indices were calculated. Univariate and multivariate analyses were performed to identify the applicable indices, which were combined with the Rad-score to create a comprehensive forecasting model (CFM) and nomograms. Internal validation was performed in the validation set.

Results

Up to the last follow-up time, 48 of 82 patients had a PFS of more than 12 months. The area under the receiver operating characteristic (ROC) curve (AUC) of the Rad-score was 0.858 and 0.812, respectively, in the training set and validation set. A systemic immune-inflammation index (SII) score of <500.88 after two cycles of immunotherapy was a protective factor for PFS >12 months [odds ratio (OR) 0.054; P=0.003]. The CFM had an AUC of 0.930 and 0.922, respectively, in the training and validation sets. The calibration curves and decision curve analysis (DCA) demonstrated the reliability and clinical applicability of the model, respectively.

Conclusions

The radiomics model performed well in predicting whether patients with locally advanced or metastatic NSCLC can achieve DCB after receiving immunotherapy. The CFM had good predictive performance and reliability.

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.

Prognostic models for immunotherapy in non-small cell lung cancer: A comprehensive review

The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.

Development and validation of an MRI-Based nomogram to predict the effectiveness of immunotherapy for brain metastasis in patients with non-small cell lung cancer

Introduction

The variability and unpredictability of immune checkpoint inhibitors (ICIs) in treating brain metastases (BMs) in patients with advanced non-small cell lung cancer (NSCLC) is the main concern. We assessed the utility of novel imaging biomarkers (radiomics) for discerning patients with NSCLC and BMs who would derive advantages from ICIs treatment.

Methods

Data clinical outcomes and pretreatment magnetic resonance images (MRI) were collected on patients with NSCLC with BMs treated with ICIs between June 2019 and June 2022 and divided into training and test sets. Metastatic brain lesions were contoured using ITK-SNAP software, and 3748 radiomic features capturing both intra- and peritumoral texture patterns were extracted. A clinical radiomic nomogram (CRN) was built to evaluate intracranial progression-free survival, progression-free survival, and overall survival. The prognostic value of the CRN was assessed by Kaplan-Meier survival analysis and log-rank tests.

Results

In the study, a total of 174 patients were included, and 122 and 52 were allocated to the training and validation sets correspondingly. The intratumoral radiomic signature, peritumoral radiomic signature, clinical signature, and CRN predicted intracranial objective response rate. Kaplan-Meier analyses showed a significantly longer intracranial progression-free survival in the low-CRN group than in the high-CRN group (p < 0.001). The CRN was also significantly associated with progression-free survival (p < 0.001) but not overall survival.

Discussion

Radiomics biomarkers from pretreatment MRI images were predictive of intracranial response. Pretreatment radiomics may allow the early prediction of benefits.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

Validation of a multiomic model of plasma extracellular vesicle PD-L1 and radiomics for prediction of response to immunotherapy in NSCLC

BACKGROUND

Immune-checkpoint inhibitors (ICIs) have showed unprecedent efficacy in the treatment of patients with advanced non-small cell lung cancer (NSCLC). However, not all patients manifest clinical benefit due to the lack of reliable predictive biomarkers. We showed preliminary data on the predictive role of the combination of radiomics and plasma extracellular vesicle (EV) PD-L1 to predict durable response to ICIs.

MAIN BODY

Here, we validated this model in a prospective cohort of patients receiving ICIs plus chemotherapy and compared it with patients undergoing chemotherapy alone. This multiparametric model showed high sensitivity and specificity at identifying non-responders to ICIs and outperformed tissue PD-L1, being directly correlated with tumor change.

SHORT CONCLUSION

These findings indicate that the combination of radiomics and EV PD-L1 dynamics is a minimally invasive and promising biomarker for the stratification of patients to receive ICIs.

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Artificial intelligence in immunotherapy PET/SPECT imaging

OBJECTIVE

Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients.

METHODS

We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022.

RESULTS

Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation.

CONCLUSION

Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts.

CLINICAL RELEVANCE STATEMENT

This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers.

KEY POINTS

• Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study

PURPOSE

No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.

METHODS

A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.

RESULTS

The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).

CONCLUSION

The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

Cancer immunotherapy efficacy and machine learning

INTRODUCTION

Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making.

AREAS COVERED

Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023).

EXPERT OPINION

An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.

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.

Lung Cancer Staging: Imaging and Potential Pitfalls

Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.

Application of radiomics in diagnosis and treatment of lung cancer

Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.

Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study

BACKGROUND

The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB-IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction.

METHODS

CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further.

RESULTS

CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002).

CONCLUSION

By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.

Efficacy evaluation of neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer: comparison of PET/CT with postoperative pathology

OBJECTIVES

To assess the value of positron emission tomography/computed tomography (PET/CT) in the efficacy evaluation of patients undergoing neoadjuvant immunotherapy plus chemotherapy, and to analyze its correlation with postoperative pathology.

METHODS

The PET/CT metabolic parameters and CT size were retrospectively analyzed before and after neoadjuvant immunotherapy plus chemotherapy in 67 patients with resectable stage II/IIIA non-small-cell lung cancer (NSCLC). CT assessment based on immune response evaluation criteria in solid tumor criteria ((i)RECIST) was compared with PET/CT assessment based on the response criteria in solid tumors (PERCIST). The correlations between PET/CT metabolic parameters and postoperative pathology were analyzed. The value of PET/CT in the efficacy evaluation was assessed.

RESULTS

The PET/CT assessment showed high consistency with postoperative pathological evaluation, yet the CT assessment showed low consistency with postoperative pathological evaluation. The (i)RECIST and PERCIST criteria showed statistically significant differences (p < 0.001). The postoperative pathological response was negatively associated with ΔSUVmax (%) (r =  - 0.812, p < 0.001), ΔSUVmean (%) (r =  - 0.805, p < 0.001), and ΔSUVpeak (%) (r =  - 0.800, p < 0.001). The cut-off values of 75.8 for ΔSUVmax (%), 67.8 for ΔSUVmean (%), and 74.6 for ΔSUVpeak (%) had the highest sensitivity and specificity.

CONCLUSION

The PERCIST criteria are more sensitive and accurate than (i)RECIST criteria to identify more responders when evaluating the response of neoadjuvant immunotherapy plus chemotherapy for NSCLC. PET/CT shows high accuracy in predicting postoperative pathological response. Our study shows the important role PET/CT plays in the efficacy evaluation of NSCLC patients undergoing neoadjuvant immunotherapy plus chemotherapy, as well as in predicting the prognosis and guiding postoperative treatment.

CLINICAL RELEVANCE STATEMENT

Neoadjuvant immunotherapy plus chemotherapy is highly effective in the treatment of non-small-cell lung cancer. And PET/CT played an important role in the efficacy evaluation following neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer.

KEY POINTS

• Neoadjuvant immunotherapy plus chemotherapy is highly effective in the treatment of NSCLC. • The PERCIST criteria are more sensitive and accurate than (i)RECIST criteria to identify more responders when evaluating the response of neoadjuvant immunotherapy plus chemotherapy for NSCLC. • PET/CT played an important role in the efficacy evaluation; ΔSUVmax (%), ΔSUVmean (%), and ΔSUVpeak (%) following neoadjuvant immunotherapy plus chemotherapy for NSCLC had high consistency and strong correlations with postoperative pathology.

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.

The prognostic biological markers of immunotherapy for non-small cell lung cancer: current landscape and future perspective

The emergence of immunotherapy, particularly programmed cell death 1 (PD-1) and programmed cell death ligand-1 (PD-L1) produced profound transformations for treating non-small cell lung cancer (NSCLC). Nevertheless, not all NSCLC patients can benefit from immunotherapy in clinical practice. In addition to limited response rates, exorbitant treatment costs, and the substantial threats involved with immune-related adverse events, the intricate interplay between long-term survival outcomes and early disease progression, including early immune hyperprogression, remains unclear. Consequently, there is an urgent imperative to identify robust predictive and prognostic biological markers, which not only possess the potential to accurately forecast the therapeutic efficacy of immunotherapy in NSCLC but also facilitate the identification of patient subgroups amenable to personalized treatment approaches. Furthermore, this advancement in patient stratification based on certain biological markers can also provide invaluable support for the management of immunotherapy in NSCLC patients. Hence, in this review, we comprehensively examine the current landscape of individual biological markers, including PD-L1 expression, tumor mutational burden, hematological biological markers, and gene mutations, while also exploring the potential of combined biological markers encompassing radiological and radiomic markers, as well as prediction models that have the potential to better predict responders to immunotherapy in NSCLC with an emphasis on some directions that warrant further investigation which can also deepen the understanding of clinicians and provide a reference for clinical practice.

PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature

The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy.

MATERIALS AND METHODS

A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302.

RESULTS

Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy.

CONCLUSIONS

Radiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

The artificial intelligence and machine learning in lung cancer immunotherapy

Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.

Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study

BACKGROUND

Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).

METHODS

Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis.

RESULTS

Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype.

CONCLUSIONS

This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.

Can Baseline [18F]FDG PET/CT Predict Response to Immunotherapy After 6 Months and Overall Survival in Patients with Lung Cancer or Malignant Melanoma? A Multicenter Retrospective Study

Aim: To assess the role of baseline 18F-fluorodeoxyglucose ([18F]FDG)-positron emission tomography/computed tomography (PET/CT) in predicting response to immunotherapy after 6 months and overall survival (OS) in patients with lung cancer (LC) or malignant melanoma (MM). Methods: Data from a multicenter, retrospective study conducted between March and November 2021 were analyzed. Patients >18 years old with a confirmed diagnosis of LC or MM, who underwent a baseline [18F]FDG-PET/CT within 1-2 months before starting immunotherapy and had a follow-up of at least 12 months were included. PET scans were examined visually and semiquantitatively by physicians at peripheral centers. The metabolic tumor burden (number of lesions with [18F]FDG-uptake) and other parameters were recorded. Clinical response was assessed at 3 and 6 months after starting immunotherapy, and OS was calculated as the time elapsing between the PET scan and death or latest follow-up. Results: The study concerned 177 patients with LC and 101 with MM. Baseline PET/CT was positive in primary or local recurrent lesions in 78.5% and 9.9% of cases, in local/distant lymph nodes in 71.8% and 36.6%, in distant metastases in 58.8% and 84%, respectively, in LC and in MM patients. Among patients with LC, [18F]FDG-uptake in primary/recurrent lung lesions was more often associated with no clinical response to immunotherapy after 6 months than in cases without any tracer uptake. After a mean 21 months, 46.5% of patients with LC and 37.1% with MM had died. A significant correlation emerged between the site/number of [18F]FDG foci and death among patients with LC, but not among those with MM. Conclusions: In patients with LC who are candidates for immunotherapy, baseline [18F]FDG-PET/CT can help to predict response to this therapy after 6 months, and to identify those with a poor prognosis based on their metabolic parameters. For patients with MM, there was only a weak correlation between baseline PET/CT parameters, response to therapy, and survival.

Prognostic and Predictive Biomarkers in the Era of Immunotherapy for Lung Cancer

The therapeutic algorithm of lung cancer has recently been revolutionized by the emergence of immune checkpoint inhibitors. However, an objective and durable response rate remains low with those recent therapies and some patients even experience severe adverse events. Prognostic and predictive biomarkers are therefore needed in order to select patients who will respond. Nowadays, the only validated biomarker is the PD-L1 expression, but its predictive value remains imperfect, and it does not offer any certainty of a sustained response to treatment. With recent progresses in molecular biology, genome sequencing techniques, and the understanding of the immune microenvironment of the tumor and its host, new molecular features have been highlighted. There are evidence in favor of the positive predictive value of the tumor mutational burden, as an example. From the expression of molecular interactions within tumor cells to biomarkers circulating in peripheral blood, many markers have been identified as associated with the response to immunotherapy. In this review, we would like to summarize the latest knowledge about predictive and prognostic biomarkers of immune checkpoint inhibitors efficacy in order to go further in the field of precision immuno-oncology.

Assessment of the Association between Entropy in PET/CT and Response to Anti-PD-1/PD-L1 Monotherapy in Stage III or IV NSCLC

Anti-PD-1/PD-L1 therapy indications are broadened in non-small cell lung cancer (NSCLC) although immune checkpoint inhibitors (ICI) do not provide benefits for the entire population. Texture features based on positron emission tomography/computed tomography (PET/CT), especially entropy (based on a gray-level co-occurrence matrix (GLCM)), could be interesting as predictors in NSCLC. The aim of our retrospective study was to evaluate the association between GLCM-entropy and response to anti-PD-1/PD-L1 monotherapy at the first evaluation in stage III or IV NSCLC, comparing patients with progressive disease (PD) and non-progressive disease (non-PD). In total, 47 patients were included. Response Evaluation Criteria in Solid Tumors (RECIST 1.1) were used to evaluate the response to ICI treatment (nivolumab, pembrolizumab, or atezolizumab). At the first evaluation, 25 patients were PD and 22 were non-PD. GLCM-entropy was not predictive of response at the first evaluation. Furthermore, GLCM-entropy was not associated with progression-free survival (PFS) (p = 0.393) or overall survival (OS) (p = 0.220). Finally, GLCM-entropy measured in PET/CT performed before ICI initiation in stage III or IV NSCLC was not predictive of response at the first evaluation. However, this study demonstrates the feasibility of using texture parameters in routine clinical practice. The interest of measuring PET/CT texture parameters in NSCLC remains to be evaluated in larger prospective studies.

Radiomics of Tumor Heterogeneity in 18F-FDG-PET-CT for Predicting Response to Immune Checkpoint Inhibition in Therapy-Naïve Patients with Advanced Non-Small-Cell Lung Cancer

We aimed to evaluate the predictive and prognostic value of baseline ¹⁸F-FDG-PET-CT (PET-CT) radiomic features (RFs) for immune checkpoint-inhibitor (CKI)-based first-line therapy in advanced non-small-cell lung cancer (NSCLC) patients. In this retrospective study 44 patients were included. Patients were treated with either CKI-monotherapy or combined CKI-based immunotherapy-chemotherapy as first-line treatment. Treatment response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST). After a median follow-up of 6.4 months patients were stratified into "responder" (n = 33) and "non-responder" (n = 11). RFs were extracted from baseline PET and CT data after segmenting PET-positive tumor volume of all lesions. A Radiomics-based model was developed based on a Radiomics signature consisting of reliable RFs that allow classification of response and overall progression using multivariate logistic regression. These RF were additionally tested for their prognostic value in all patients by applying a model-derived threshold. Two independent PET-based RFs differentiated well between responders and non-responders. For predicting response, the area under the curve (AUC) was 0.69 for "PET-Skewness" and 0.75 predicting overall progression for "PET-Median". In terms of progression-free survival analysis, patients with a lower value of PET-Skewness (threshold < 0.2014; hazard ratio (HR) 0.17, 95% CI 0.06-0.46; p < 0.001) and higher value of PET-Median (threshold > 0.5233; HR 0.23, 95% CI 0.11-0.49; p < 0.001) had a significantly lower probability of disease progression or death. Our Radiomics-based model might be able to predict response in advanced NSCLC patients treated with CKI-based first-line therapy.

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.

Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians

Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives

Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods

A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results

Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion

AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

Objectives

The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.

Methods

After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.

Results

Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.

Conclusions

Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.

Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study

Background

This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction.

Methods

274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data.

Findings

MPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58–0.86) and 0.72 (95% CI: 0.58–0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64–0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62–0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment.

Interpretation

The proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy.

Funding

This study was supported by National Key Research and Development Program of China, China (2017YFA0205200); National Natural Science Foundation of China, China (91959126, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 9195910169, 62176013, 8210071009); Beijing Natural Science Foundation, China (L182061); Strategic Priority Research Program of Chinese Academy of Sciences, China (XDB38040200); Chinese Academy of Sciences, China (GJJSTD20170004, QYZDJ-SSW-JSC005); Shanghai Hospital Development Center, China (SHDC2020CR3047B); and Science and Technology Commission of Shanghai Municipality, China (21YF1438200).

Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

Purpose: We aimed to assess the ability of radiomics features extracted from baseline (PET/CT0) and follow-up PET/CT scans, as well as their evolution (delta-radiomics), to predict clinical outcome (durable clinical benefit (DCB), progression, response to therapy, OS and PFS) in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. Methods: 83 NSCLC patients treated with immunotherapy who underwent a baseline PET/CT were retrospectively included. Response was assessed at 6−8 weeks (PET/CT1) using PERCIST criteria and at 3 months with iPERCIST (PET/CT2) or RECIST 1.1 criteria using CT. The predictive performance of clinical parameters (CP), standard PET metrics (SUV, Metabolic Tumor volume, Total Lesion Glycolysis), delta-radiomics and PET and CT radiomics features extracted at baseline and during follow-up were studied. Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. Model validation was performed on the remaining patients (25%). Overall and progression-free survival was also performed by Kaplan−Meier survival analysis. Results: Numerous radiomics and delta-radiomics parameters had a high individual predictive value of patient outcome with areas under receiver operating characteristics curves (AUCs) >0.80. Their performance was superior to that of CP and standard PET metrics. Several multivariate models were also promising, especially for the prediction of progression (AUCs of 1 and 0.96 for the training and testing subsets with the PET-CT model (PET/CT0)) or DCB (AUCs of 0.85 and 0.83 with the PET-CT-CP model (PET/CT0)). Conclusions: Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.

Tumor metabolic and secondary lymphoid organ metabolic markers on 18F-fludeoxyglucose positron emission tomography predict prognosis of immune checkpoint inhibitors in advanced lung cancer

Background

The purpose of this study was to investigate the predictive value of tumor metabolic parameters in combination with secondary lymphoid metabolic parameters on positron emission tomography (PET)/computed tomography (CT) for immune checkpoint inhibitor (ICI) prognosis in advanced lung cancer.

Methods

This study retrospectively included 125 patients who underwent 18F-fludeoxyglucose (FDG) PET/CT before ICI therapy, including 41 patients who underwent a second PET/CT scan during ICI treatment. The measured PET/CT parameters included tumor metabolism parameters [maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), total lesion glycolysis (TLG), and total metabolic tumor volume (TMTV)] and secondary lymphoid organ metabolism parameters [spleen-to-liver SUVmax ratio (SLR) and bone marrow-to-liver SUVmax ratio (BLR)]. The correlation of PET/CT metabolic parameters with early ICI treatment response, progression-free survival (PFS), and overall survival (OS) was analyzed.

Results

Within a median follow-up of 28.7 months, there were 44 responders and 81 non-responders. The median PFS was 8.6 months (95% confidence interval (CI): 5.872–11.328), and the median OS was 20.4 months (95% CI: 15.526–25.274). Pretreatment tumor metabolic parameters were not associated with early treatment responses. The high bone marrow metabolism (BLR &gt;1.03) was significantly associated with a shorter PFS (p = 0.008). Patients with a high TMTV (&gt;168 mL) and high spleen metabolism (SLR &gt;1.08) had poor OS (p = 0.019 and p = 0.018, respectively). Among the 41 patients who underwent a second PET/CT scan, the ΔSUVmax was significantly lower (p = 0.01) and the SLR was significantly higher (p = 0.0086) in the responders. Populations with low-risk characteristics (low TMTV, low SLR, and ΔSLR &gt; 0) had the longest survival times.

Conclusion

High pretreatment TMTV and SLR are associated with poor OS, and increased spleen metabolism after ICI therapy predicts treatment benefit. This indicates that the combination of tumor and spleen metabolic parameters is a valuable prognostic strategy.

PET/CT molecular imaging in the era of immune-checkpoint inhibitors therapy

Cancer immunotherapy, especially immune-checkpoint inhibitors (ICIs), has paved a new way for the treatment of many types of malignancies, particularly advanced-stage cancers. Accumulating evidence suggests that as a molecular imaging modality, positron emission tomography/computed tomography (PET/CT) can play a vital role in the management of ICIs therapy by using different molecular probes and metabolic parameters. In this review, we will provide a comprehensive overview of the clinical data to support the importance of 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) imaging in the treatment of ICIs, including the evaluation of the tumor microenvironment, discovery of immune-related adverse events, evaluation of therapeutic efficacy, and prediction of therapeutic prognosis. We also discuss perspectives on the development direction of 18F-FDG PET/CT imaging, with a particular emphasis on possible challenges in the future. In addition, we summarize the researches on novel PET molecular probes that are expected to potentially promote the precise application of ICIs.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.

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.

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

Background

Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.

Methods

Studies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.

Results

The systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).

Conclusion

The models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.

Systematic Review Registration

https://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.

PET/CT for Target Delineation of Lung Cancer Before Radiation Therapy

In clinical routine of patients suffering from lung cancer, radiotherapy/radiation oncology represents one of the therapeutic hallmarks in the multimodal treatment besides or in combination with other local treatments such as surgery, but also systemic treatments such as chemotherapy, tyrosine kinase, and immune check-point inhibitors. Conventional morphological imagings such as CT or MR are commonly used for staging, response assessment, but also for radiotherapy planning. However, advanced imaging techniques such as PET do continuously get increasing access to clinical routine overcoming limitations of standard imaging techniques by visualizing and quantifying molecular processes such as glucose metabolism, which is also of relevance for radiotherapy planning. This review article summarizes the current place of radiotherapy within the treatment regimens of patients with lung cancer and elucidates current concepts of standard morphological imaging for staging and radiotherapy planning. Moreover, the place of PET-based radiotherapy planning in a clinical context is presented and current methodological/technical advances that do comprise a potential role for radiotherapy planning in lung cancer patients are discussed.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

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

Extracellular vesicle PD-L1 dynamics predict durable response to immune-checkpoint inhibitors and survival in patients with non-small cell lung cancer

BACKGROUND

Immune-checkpoint inhibitors (ICIs) changed the therapeutic landscape of patients with lung cancer. However, only a subset of them derived clinical benefit and evidenced the need to identify reliable predictive biomarkers. Liquid biopsy is the non-invasive and repeatable analysis of biological material in body fluids and a promising tool for cancer biomarkers discovery. In particular, there is growing evidence that extracellular vesicles (EVs) play an important role in tumor progression and in tumor-immune interactions. Thus, we evaluated whether extracellular vesicle PD-L1 expression could be used as a biomarker for prediction of durable treatment response and survival in patients with non-small cell lung cancer (NSCLC) undergoing treatment with ICIs.

METHODS

Dynamic changes in EV PD-L1 were analyzed in plasma samples collected before and at 9 ± 1 weeks during treatment in a retrospective and a prospective independent cohorts of 33 and 39 patients, respectively.

RESULTS

As a result, an increase in EV PD-L1 was observed in non-responders in comparison to responders and was an independent biomarker for shorter progression-free survival and overall survival. To the contrary, tissue PD-L1 expression, the commonly used biomarker, was not predictive neither for durable response nor survival.

CONCLUSION

These findings indicate that EV PD-L1 dynamics could be used to stratify patients with advanced NSCLC who would experience durable benefit from ICIs.