Differentiation between immune checkpoint inhibitor-related and radiation pneumonitis in lung cancer by CT radiomics and machine learning

Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: a focus on feature reproducibility

OBJECTIVE

This study aims to assess a hybrid framework that combines radiomic features with deep learning and attention mechanisms to improve the accuracy of classifying lung cancer subtypes using CT images.

MATERIALS AND METHODS

A dataset of 2725 lung cancer images was used, covering various subtypes: adenocarcinoma (552 images), SCC (380 images), small cell lung cancer (SCLC) (307 images), large cell carcinoma (215 images), and pulmonary carcinoid tumors (180 images). The images were extracted as 2D slices from 3D CT scans, with tumor-containing slices selected from scans obtained across five healthcare centers. The number of slices per patient varied between 7 and 30, depending on tumor visibility. CT images were preprocessed using standardization, cropping, and Gaussian smoothing to ensure consistency across scans from different imaging instruments used at the centers. Radiomic features, including first-order statistics (FOS), shape-based, and texture-based features, were extracted using the PyRadiomics library. A DeepCNN architecture, integrated with attention mechanisms in the second convolutional block, was used for deep feature extraction, focusing on diagnostically important regions. The dataset was split into training (60%), validation (20%), and testing (20%) sets. Various feature selection techniques, such as Non-negative Matrix Factorization (NMF) and Recursive Feature Elimination (RFE), were used, and multiple machines learning models, including XGBoost and Stacking, were evaluated using accuracy, sensitivity, and AUC metrics. The model's reproducibility was validated using ICC analysis across different imaging conditions.

RESULTS

The hybrid model, which integrates DeepCNN with attention mechanisms, outperformed traditional methods. It achieved a testing accuracy of 92.47%, an AUC of 93.99%, and a sensitivity of 92.11%. XGBoost with NMF showed the best performance across all models, and the combination of radiomic and deep features improved classification further. Attention mechanisms played a key role in enhancing model performance by focusing on relevant tumor areas, reducing misclassification from irrelevant features. This also improved the performance of the 3D Autoencoder, boosting the AUC to 93.89% and accuracy to 93.24%.

CONCLUSIONS

This study shows that combining radiomic features with deep learning-especially when enhanced by attention mechanisms-creates a powerful and accurate framework for classifying lung cancer subtypes. Clinical trial number Not applicable.

Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography

PURPOSE

Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life threatening pneumonitis [PN]), which can cause the discontinuation of treatment. Predicting which patients may develop PN while on ICI would improve both safety and potential efficacy because treatments could be safely administered for longer or discontinued before severe toxicity.

METHODS

Starting from a cohort of 3,351 patients with cancer who received previous ICI therapy at the Vanderbilt University Medical Center, we curated 2,700 contrast chest computed tomography (CT) volumes for 671 patients. Three different pure imaging models predicted the potential for PN using only a single time point before the first ICI dose.

RESULTS

The first model used 109 radiomics features only and achieved an AUC of 0.747 (CI, 0.705 to 0.789) with a positive predictive value (PPV) of 0.244 (CI, 0.211 to 0.276) at a sensitivity of 0.553 (CI, 0.485 to 0.621) using mainly features describing the global lung properties. The second model used a convolutional neural network (CNN) on the raw CTs to improve to an AUC of 0.819 (CI, 0.781 to 0.857) with a PPV of 0.244 (CI, 0.203 to 0.284) at a sensitivity of 0.743 (CI, 0.681 to 0.806). The third model combined both radiomics and deep learning but, with an AUC of 0.829 (CI, 0.797 to 0.862) and a PPV of 0.254 (CI, 0.228 to 0.281) at a sensitivity of 0.780 (CI, 0.721 to 0.840), did not show a significant improvement on the CNN-only model.

CONCLUSION

This new model suggests the utility of deep learning in PN prediction over traditional pure radiomics and promises better management for patients receiving ICI and the ability to better stratify patients in immunotherapy drug trials.

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images

OBJECTIVES

This study utilizes [99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach.

MATERIALS AND METHODS

We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC).

RESULTS

Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set.

CONCLUSIONS

Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.

ContraSurv: Enhancing Prognostic Assessment of Medical Images via Data-Efficient Weakly Supervised Contrastive Learning

Prognostic assessment remains a critical challenge in medical research, often limited by the lack of well-labeled data. In this work, we introduce ContraSurv, a weakly-supervised learning framework based on contrastive learning, designed to enhance prognostic predictions in 3D medical images. ContraSurv utilizes both the self-supervised information inherent in unlabeled data and the weakly-supervised cues present in censored data, refining its capacity to extract prognostic representations. For this purpose, we establish a Vision Transformer architecture optimized for our medical image datasets and introduce novel methodologies for both self-supervised and supervised contrastive learning for prognostic assessment. Additionally, we propose a specialized supervised contrastive loss function and introduce SurvMix, a novel data augmentation technique for survival analysis. Evaluations were conducted across three cancer types and two imaging modalities on three real-world datasets. The results confirmed the enhanced performance of ContraSurv over competing methods, particularly in data with a high censoring rate.

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.

Esophagitis and Pneumonitis Related to Concurrent Chemoradiation ± Durvalumab Consolidation in Unresectable Stage III Non-Small-Cell Lung Cancer: Risk Assessment and Management Recommendations Based on a Modified Delphi Process

The addition of durvalumab consolidation to concurrent chemoradiation therapy (cCRT) has fundamentally changed the standard of care for patients with unresectable stage III non-small-cell lung cancer (NSCLC). Nevertheless, concerns related to esophagitis and pneumonitis potentially impact the broad application of all regimen components. A Canadian expert working group (EWG) was convened to provide guidance to healthcare professionals (HCPs) managing these adverse events (AEs) and to help optimize the patient experience. Integrating literature review findings and real-world clinical experience, the EWG used a modified Delphi process to develop 12 clinical questions, 30 recommendations, and a risk-stratification guide. The recommendations address risk factors associated with developing esophagitis and pneumonitis, approaches to risk mitigation and optimal management, and considerations related to initiation and re-initiation of durvalumab consolidation therapy. For both AEs, the EWG emphasized the importance of upfront risk assessment to inform the treatment approach, integration of preventative measures, and prompt initiation of suitable therapy in alignment with AE grade. The EWG also underscored the need for timely, effective communication between multidisciplinary team members and clarity on responsibilities. These recommendations will help support HCP decision-making related to esophagitis and pneumonitis arising from cCRT ± durvalumab and improve outcomes for patients with unresectable stage III NSCLC.

A two-stage ensemble learning based prediction and grading model for PD-1/PD-L1 inhibitor-related cardiac adverse events: A multicenter retrospective study

BACKGROUND

Immune-related cardiac adverse events (ircAEs) caused by programmed cell death protein-1 (PD-1) and programmed death-ligand-1 (PD-L1) inhibitors can lead to fulminant and even fatal consequences. This study aims to develop a prediction and grading model for ircAEs, enabling graded management of patients.

METHODS

This study utilized medical record systems from two medical institutions to develop a prediction and grading model for ircAEs using ten machine learning algorithms and two variable screening methods. The model was developed based on a two-stage ensemble learning framework. In the first stage, the ircAEs and non-ircAEs cases were classified. In the second stage, ircAEs cases were grouped into grades 1-2 and 3-5. The experiments were evaluated using five-fold cross-validation. The model's prediction performance was assessed using accuracy, precision, recall, F1 value, Brier score, receiver operating characteristic curve area (AUC), and area under the precision-recall curve (AUPR).

RESULTS

615 patients were included in the study. 147 experienced ircAEs, and 44 experienced grade 3-5 ircAEs. The soft voting classifier trained using the variables screened by feature importance ranking performed better than other classifiers in both stages. The average AUC for the first and second stages is 84.18 % and 85.13 %, respectively. In the first stage, the three most important variables are N-terminal B-type natriuretic peptide (NT-proBNP), interleukin-2 (IL-2), and C-reactive protein (CRP). In the second stage, the patient's age, NT-proBNP, and left ventricular ejection fraction (LVEF) are the three most critical variables.

CONCLUSIONS

The prediction and grading model of ircAEs based on two-stage ensemble learning established in this study has good performance and potential clinical application.

Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment

Background

Radiomics extracts specific quantitative data from medical images and explores the characteristics of tumors by analyzing these representations and making predictions. The purpose of this paper is to review computed tomography (CT)-based radiomics articles related to prognostic outcomes in non-small cell lung cancer (NSCLC), assess their scientificity and quality by the latest radiomics quality score (RQS) 2.0 scoring criteria, and provide references for subsequent related studies.

Methods

CT-based radiomics studies on NSCLC prognosis published from 1 November 2012 to 30 November 2022 in English were screened through the databases of the Cochrane Library, Embase, and PubMed. By excluding criteria such as non-original studies, small sample sizes studies, positron emission tomography (PET)/CT only, and methodological studies only, 17 studies in English were included. The RQS proposed in 2017 is a quality evaluation index specific to radiomics following the PRISMA guidelines, and the latest update of RQS 2.0 has improved the scientificity and completeness of the score. Each checkpoint either belongs to handcrafted radiomics (HCR), deep learning, or both.

Results

The 17 included studies covered most treatments for NSCLC, including radiotherapy, chemotherapy, surgery, radiofrequency ablation, immunotherapy, and targeted therapy, and predicted outcomes such as overall survival (OS), progression-free survival (PFS), distant metastases, and disease-free survival (DFS). The median score rate for the included studies was 28%, with a range of 12% to 44%. The quality of studies in HCR is not high, and only 4 studies have been validated with independent cohorts.

Conclusions

The value of radiomics studies needs to be increased, such that clinical application will be possible, and the field of radiomics still has much room for growth. To make prediction models more reliable and stable in forecasting the prognosis of NSCLC and advancing the individualized treatment of NSCLC patients, more clinicians must participate in their development and clinical testing.

Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis

PURPOSE

This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models.

METHODS

We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed.

RESULTS

Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models.

CONCLUSIONS

Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life.

PROTOCOL REGISTRATION

The protocol of this study was registered on PROSPERO (CRD42023426565).

Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis

BACKGROUND

Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns.

METHODS

In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists.

RESULTS

Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6).

CONCLUSION

Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.

Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis

Purpose

With the rapid development of immunotherapy, cancer treatment has entered a new phase. Medical imaging, as a primary diagnostic method, is closely related to cancer immunotherapy. However, until now, there has been no systematic bibliometric analysis of the state of this field. Therefore, the main purpose of this article is to clarify the past research trajectory, summarize current research hotspots, reveal dynamic scientific developments, and explore future research directions.

Patients and Methods

A comprehensive search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to immunotherapy specifically for the medical imaging of carcinoma. The search spanned the period from the year 2003 to 2023. Several analytical tools were employed. These included CiteSpace (6.2.4), and the Microsoft Office Excel (2016).

Results

By searching the database, a total of 704 English articles published between 2003 and 2023 were obtained. We have observed a rapid increase in the number of publications since 2018. The two most active countries are the United States (n=265) and China (n=170). Pittock, Sean J and Abu-sbeih, Hamzah are very concerned about the relationship between cancer immunotherapy and medical images and have published more academic papers (n = 5; n = 4). Among the top 10 co-cited authors, Topalian Sl (n=43) cited ranked first, followed by Graus F (n=40) cited. According to clustering, timeline, and burst word analysis, the results show that the current research focus is on "MRI", "deep learning", "tumor microenvironment" and so on.

Conclusion

Medical imaging and cancer immunotherapy are hot topics. The United States is the country with the most publications and the greatest influence in this field, followed by China. "MRI", "PET/PET-CT", "deep learning", "immune-related adverse events" and "tumor microenvironment" are currently hot research topics and potential targets.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Radiological assessment of immunotherapy effects and immune checkpoint-related pneumonitis for lung cancer

Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

OBJECTIVES

Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction.

MATERIALS AND METHODS

Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose.

CONCLUSIONS

Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy

PURPOSE

This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy.

MATERIALS AND METHODS

This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting.

RESULTS

HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts.

CONCLUSIONS

In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

BACKGROUND

Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.

PURPOSE

This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM.

METHODS

We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC).

RESULTS

The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making.

CONCLUSION

The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

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.

Assessment of Response to Immunotherapy in Patients with Hodgkin Lymphoma: Towards Quantifying Changes in Tumor Burden Using FDG-PET/CT

Immune checkpoint inhibitors are currently the standard of care for many advanced solid tumors, and they have been recently approved for the treatment of relapsed/refractory Hodgkin lymphoma and primary mediastinal B cell lymphoma. Assessments of the response to immunotherapy may be complicated by the occurrence of the flare/pseudoprogression phenomenon, consisting of initial tumor enlargement and even the appearance of new lesions, followed by a response, which may initially be indistinguishable from true progression. There have been efforts to characterize and capture the new patterns of response observed during immunotherapy, namely, pseudoprogression and delayed response, and several immune-related response criteria have been proposed. Confirming progression on a subsequent scan and measuring the total tumor burden are both common in immune-related criteria. Due to the peculiarity of hematologic malignancies, lymphoma-specific immune-related criteria have been developed (LYRIC), and they have been evaluated in research studies in comparison to the Lugano Classification. In this review work, we illustrate the evolution of the response criteria in lymphomas from the first CT-based criteria to the development of the PET-based Lugano Classification, further refined to take into account the flare phenomenon encountered during immunotherapy. We also describe the additional contribution of PET-derived volumetric parameters to the interpretation of responses during immunotherapy.

Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors

INTRODUCTION

Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy.

METHODS

Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and spheroidal/cubical regions surrounding the inflammation) were examined to extract the most predictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibration and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements.

RESULTS

A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 patients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio = 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements.

CONCLUSION

Radiomic biomarkers applied to computed tomography imaging may support clinicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive.

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

Background

Machine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future.

Methods

The involved researches were obtained from the Web of Science Core Collection database (WoSCC). We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis.

Results

We found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, “radiomics” was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC.

Results

The research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.

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

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

Radiation Recall Pneumonitis Anticipates Bilateral Immune-Induced Pneumonitis in Non-Small Cell Lung Cancer

Radiation recall pneumonitis (RRP) is a rare inflammatory reaction that occurs in previously irradiated fields, and it may be caused by various triggering agents. Immunotherapy has been reported to potentially be one of these triggers. However, precise mechanisms and specific treatments have not been explored yet due to a lack of data in this setting. Here, we report a case of a patient who received radiation therapy and immune checkpoint inhibitor therapy for non-small cell lung cancer. He developed first radiation recall pneumonitis and subsequently immune-checkpoint inhibitor-induced pneumonitis (IIP). After presenting the case, we discuss the currently available literature on RRP and the challenges of differential diagnosis between RRP, IIP, and other forms of pneumonitis. We believe that this case is of particular clinical value since it highlights the importance of including RRP in a differential diagnosis of lung consolidation during immunotherapy. Furthermore, it suggests that RRP might anticipate more extensive ICI-induced pneumonitis.

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

AIM

To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

MATERIALS AND METHODS

Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression.

RESULTS

CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability.

CONCLUSION

The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.

Advances in CT features and radiomics of checkpoint inhibitor-related pneumonitis: A short review

Checkpoint inhibitor-related pneumonitis (CIP) is a complication of immunotherapy for malignant tumors that severely limits the treatment cycles as well as endangers patients' health. The chest CT imaging features or typing of CIP and the application of radiomics will contribute to the precise prevention, early diagnosis and instant treatment of CIP. This article reviews the advances in the CT features and the application of radiomics in CIP.

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.

Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.

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.

Immunotherapy in unresectable stage III non-small-cell lung cancer: state of the art and novel therapeutic approaches

The standard of care for patients with stage III non-small-cell lung cancer (NSCLC) is concurrent chemoradiotherapy (CCRT) followed by 1 year of adjuvant durvalumab. Despite the survival benefit granted by immunotherapy in this setting, only 1/3 of patients are alive and disease free at 5 years. Novel treatment strategies are under development to improve patient outcomes in this setting: different anti-programmed cell death protein 1/programmed death-ligand 1 [anti-PD-(L)1] antibodies after CCRT, consolidation immunotherapy after sequential chemoradiotherapy, induction immunotherapy before CCRT and immunotherapy concurrent with CCRT and/or sequential chemoradiotherapy. Cross-trial comparison is particularly challenging in this setting due to the different timing of immunotherapy delivery and different patients' inclusion and exclusion criteria. In this review, we present the results of clinical trials investigating immune therapy in unresectable stage III NSCLC and discuss in-depth their biological rationale, their pitfalls and potential benefits. Particular emphasis is placed on the potential mechanisms of synergism between chemotherapy, radiation therapy and different monoclonal antibodies, and how this affects the tumor immune microenvironment. The designs and questions tackled by ongoing clinical trials are also discussed. Last, we address open questions and unmet clinical needs, such as the necessity for predictive biomarkers (e.g. radiomics and circulating tumor DNA). Identifying distinct subsets of patients to tailor anticancer treatment is a priority, especially in a heterogeneous disease such as stage III NSCLC.

Assessing Outcomes in NSCLC: Radiomic analysis, kinetic analysis and circulating tumor DNA

Current radiographic methods of measuring treatment response for patients with nonsmall cell lung cancer have significant limitations. Recently, new modalities using standard of care images or minimally invasive blood-based DNA tests have gained interest as methods of evaluating treatment response. This article highlights three emerging modalities: radiomic analysis, kinetic analysis and serum-based measurement of circulating tumor DNA, with a focus on the clinical evidence supporting these methods. Additionally, we discuss the possibility of combining these modalities to develop a robust biomarker with strong correlation to clinically meaningful outcomes that could impact clinical trial design and patient care. At Last, we focus on how these methods specifically apply to a Veteran population.

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer

Background

The combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advance-stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by pretreatment CT radiomics and clinical or radiological parameters.

Methods

A total of 126 advance-stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into the training dataset (n =88) and the validation dataset (n = 38). A total of 837 radiomics features were extracted from regions of interest based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator. A logistic regression was applied to develop a radiomics nomogram. Receiver operating characteristics curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.

Results

There was no significant difference between the training and the validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes (p < 0.001) and sharp border (p = 0.001) were associated with the identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and in the validation dataset (AUC = 0.947 and 0.936).

Conclusions

CT-based radiomics features have potential values for differentiating between patients with CIP and patients with RP. The addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.