Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET/CT radiomics

Pericardial Fat and Primary Tumor Radiomics for Predicting Occult N2 Disease and Survival in Clinical Stage I Non-Small Cell Lung Cancer: Multicenter Study With Biologic Correlation

Background: Occult N2 disease significantly affects clinical stage I non-small cell lung cancer (NSCLC) prognosis. Pericardial fat characteristics also have prognostic associations. Objective: To develop and test a model incorporating pericardial fat and tumor radiomic features on CT for detecting occult N2 disease in clinical stage I NSCLC, explore the model's prognostic role, and investigate its biologic basis through radiogenomics analyses. Methods: This retrospective study included patients who underwent clinical stage I NSCLC resection at three hospitals [center 1 (January 2016 to December 2022), stratified randomly by 6:2:2 ratio into training, tuning, and internal test sets; centers 2 and 3 (January 2019 to December 2023), serving as external test sets]. Pericardial fat and primary tumors were segmented on preoperative CT to extract radiomic features and generate tumor and fat rad-scores, respectively. Multivariable analysis was performed to create a hybrid model for predicting occult N2 disease at surgery. Performance was evaluated in external test sets. Associations with recurrence-free survival (RFS) and overall survival (OS) were evaluated using log-rank tests in the internal test set; follow-up data were unavailable in external test sets. Biologic mechanisms were explored through RNA and gene expression analysis in a separate set of patients with NSCLC obtained from a public radiogenomics database. Results: From the three centers, 1662 patients (mean age, 58.6 years; 663 men, 999 women) were included. Following multivariable analysis, the hybrid model included nodule density, fat rad-score, and tumor rad-score. The model had AUC, accuracy, sensitivity, and specificity for occult N2 disease of 0.921, 89.7%, 59.3%, and 93.1%, and 0.913, 91.8%, 56.2%, and 95.5% in external test sets 1 and 2, respectively. High-risk compared with low-risk patients, applying the model in the internal test set, showed worse RFS (p<.001) and OS (p<.001). In 122 patients in radiogenomics analysis, high-risk status was associated with activation of molecular pathways and increased activated dendritic cell and mast cell infiltration. Conclusion: A model incorporating tumor and pericardial fat radiomics showed good performance in predicting occult N2 disease as well as associations with survival and with RNA and gene expression. Clinical Impact: The model could help guide NSCLC management.

Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer

OBJECTIVES

This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis.

METHODS

Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability.

RESULTS

Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes.

CONCLUSIONS

Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary.

CLINICAL RELEVANCE STATEMENT

Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies.

RESEARCH REGISTRATION UNIQUE IDENTIFYING NUMBER (UIN)

International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer

Currently, the second most commonly diagnosed cancer in the world is lung cancer, and 85% of cases are non-small cell lung cancer (NSCLC). With growing knowledge of oncogene drivers and cancer immunology, several novel therapeutics have emerged to improve the prognostic outcomes of NSCLC. However, treatment outcomes remain diverse, and an accurate tool to achieve precision medicine is an unmet need. Radiomics, a method of extracting medical imaging features, is promising for precision medicine. Among all radiomic tools, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)-based radiomics provides distinct information on glycolytic activity and heterogeneity. In this review, we collected relevant literature from PubMed and summarized the various applications of 18F-FDG PET-derived radiomics in improving the detection of metastasis, subtyping histopathologies, characterizing driver mutations, assessing treatment response, and evaluating survival outcomes of NSCLC. Furthermore, we reviewed the values of 18F-FDG PET-based deep learning. Finally, several challenges and caveats exist in the implementation of 18F-FDG PET-based radiomics for NSCLC. Implementing 18F-FDG PET-based radiomics in clinical practice is necessary to ensure reproducibility. Moreover, basic studies elucidating the underlying biological significance of 18F-FDG PET-based radiomics are lacking. Current inadequacies hamper immediate clinical adoption; however, radiomic studies are progressively addressing these issues. 18F-FDG PET-based radiomics remains an invaluable and indispensable aspect of precision medicine for NSCLC.

Can computed tomography findings and radiomics analysis of mediastinal lymph nodes differentiate between sarcoidosis and lymphoma?

AIMS

To assess the ability of computed tomography (CT) findings and radiomics analysis to differentiate mediastinal lymphadenopathies as sarcoidosis versus lymphoma.

MATERIALS AND METHODS

94 patients with lymphoma and 97 patients with sarcoidosis, who had > 1cm mediastinal lymph node were included. Size, location of lymph nodes, and distribution of the largest lymph nodes in two groups were compared. A total of 636 lymphadenopathies in four different regions were segmented for radiomics. Lesion segmentation was semiautomatically performed with a dedicated commercial software package on chest CT images. 149 patients were grouped as a training cohort, while 42 patients who underwent CT in the oncology hospital were used for external validation. The least absolute shrinkage and selection operator (LASSO) analysis was used to perform feature selection. Using selected features, the classification performance of various data mining methods in separating groups of sarcoidosis and lymphoma was investigated.

RESULTS

Distribution and size of lymphadenopathies were significantly different in sarcoidosis and lymphoma groups (<0.05). Radiomics and data mining methods showed excellent performance in differentiating lymph nodes of sarcoidosis and lymphoma according to both the largest lymphadenopathy and lymphadenopathies in four different mediastinal regions (AUC >0,95).

CONCLUSIONS

Distribution and size of lymphadenopathies can help differential diagnosis in patients with sarcoidosis and lymphoma. CT radiomics analysis can discriminate the lymph nodes of sarcoidosis and lymphoma with great performance regardless of lymph node size and location and it can be used safely in the diagnosis of these diseases, which can sometimes be challenging to distinguish from each other.

Predicting solitary pulmonary lesions in breast cancer patients using 18fluorodeoxyglucose-positron emission tomography/computed tomography combined with clinicopathological characteristics

BACKGROUND

Solitary pulmonary nodules (SPNs) remain difficult to diagnose for clinical therapeutic purposes in patients with a history of breast cancer. This study try to investigate the value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) combined with clinicopathological predictors for the differential diagnosis of SPNs in breast cancer patients.

METHODS

One hundred and twenty breast cancer patients with newly detected SPNs were enrolled in the study and divided into a primary lung cancer (PLC) group and a breast cancer metastasis (BCM) group. The clinicopathological characteristics as well as metabolic and morphological characteristics on 18F-FDG-PET/CT images of 120 patients were retrospectively reviewed. The differences of clinicopathological and 18F-FDG-PET/CT characteristics between the two groups were analyzed, and multivariate analyses for the diagnosis of SPNs were performed.

RESULTS

Clinicopathological terms of carcinoembryonic antigen (CEA) and CA15-3 levels exhibited significant differences between PLC and BCM groups (P = 0.005 and P = 0.001, respectively). Metabolic characteristics of 18F-FDG-PET/CT images included FDG uptake, SUVmax of SPNs, hilar and/or mediastinal lymph node metastasis, SUVmax of hilar and/or mediastinal lymph node, and extrapulmonary metastasis showed significant differences between PLC and BCM groups (P = 0.004, P < 0.001, P = 0.01, P = 0.032 and P = 0.023, respectively). The lobulation sign, spicule sign, and pleural indentation sign were identified as statistically different morphological features of PLC in CT images (all P < 0.001). Among these, the SUVmax of SPNs, lobulation sign, and pleural indentation sign were valuable predictive factors for accurate diagnosis of SPNs in breast cancer patients.

CONCLUSIONS

18F-FDG-PET/CT combined with serum tumor markers are valuable for the diagnosis of SPNs in breast cancer patients.

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics

BACKGROUND

The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next-generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed.

METHODS

Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort.

RESULTS

Twenty-four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN-positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild-type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780).

CONCLUSION

This study suggests that the integration of genomic profiling and clinical features identifies early-invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma

OBJECTIVES

At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT.

METHODS

A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model.

RESULTS

By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model.

CONCLUSIONS

Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

Current status and prospect of PET-related imaging radiomics in lung cancer

Lung cancer is highly aggressive, which has a high mortality rate. Major types encompass lung adenocarcinoma, lung squamous cell carcinoma, lung adenosquamous carcinoma, small cell carcinoma, and large cell carcinoma. Lung adenocarcinoma and lung squamous cell carcinoma together account for more than 80% of cases. Diverse subtypes demand distinct treatment approaches. The application of precision medicine necessitates prompt and accurate evaluation of treatment effectiveness, contributing to the improvement of treatment strategies and outcomes. Medical imaging is crucial in the diagnosis and management of lung cancer, with techniques such as fluoroscopy, computed radiography (CR), digital radiography (DR), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)/CT, and PET/MRI being essential tools. The surge of radiomics in recent times offers fresh promise for cancer diagnosis and treatment. In particular, PET/CT and PET/MRI radiomics, extensively studied in lung cancer research, have made advancements in diagnosing the disease, evaluating metastasis, predicting molecular subtypes, and forecasting patient prognosis. While conventional imaging methods continue to play a primary role in diagnosis and assessment, PET/CT and PET/MRI radiomics simultaneously provide detailed morphological and functional information. This has significant clinical potential value, offering advantages for lung cancer diagnosis and treatment. Hence, this manuscript provides a review of the latest developments in PET-related radiomics for lung cancer.

Synthesis of bioactive oligosaccharides and their potential health benefits

Oligosaccharides, a low polymerization degree of carbohydrate, possess various physiological activities, such as anti-diabetes, anti-obesity, anti-aging, anti-viral, and gut microbiota regulation, having a widely used in food and medical fields. However, due to the limited natural oligosaccharides, many un-natural oligosaccharides from complex polysaccharides are being studied for amplifying the available pool of oligosaccharides. More recently, various oligosaccharides were developed by using several artificial strategies, such as chemical degradation, enzyme catalysis, and biosynthesis, then they can be applied in various sectors. Moreover, it has gradually become a trend to use biosynthesis to realize the synthesis of oligosaccharides with clear structure. Emerging research has found that un-natural oligosaccharides exert more comprehensive effects against various human diseases through multiple mechanisms. However, these oligosaccharides from various routes have not been critical reviewed and summarized. Therefore, the purpose of this review is to present the various routes of oligosaccharides preparations and healthy effects, with a focus on diabetes, obesity, aging, virus, and gut microbiota. Additionally, the application of multi-omics for these natural and un-natural oligosaccharides has also been discussed. Especially, the multi-omics are needed to apply in various disease models to find out various biomarkers to respond to the dynamic change process of oligosaccharides.

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer

BACKGROUND

Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer.

METHODS

Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network.

RESULTS

The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively.

CONCLUSIONS

The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.