A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

Radiomic features based automatic classification of CT lung findings for COVID-19 patients

Introduction. The lung CT images of COVID-19 patients can be typically characterized by three different findings- Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.Methods. Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with (1) K-Nearest-Neighbor (GA-KNN), (2) binary-decision-tree (GA-BDT) and (3) Artificial-Neural-Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.Results. Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).Conclusion. Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.

Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom

Previously, we developed an "activity painting" tool for PET image simulation; however, it could simulate heterogeneous patterns only in the air. We aimed to improve this phantom technique to simulate arbitrary lesions in a radioactive background to perform relevant multi-center radiomic analysis. We conducted measurements moving a 22Na point source in a 20-liter background volume filled with 5 kBq/mL activity with an adequately controlled robotic system to prevent the surge of the water. Three different lesion patterns were "activity-painted" in five PET/CT cameras, resulting in 8 different reconstructions. We calculated 46 radiomic indeces (RI) for each lesion and imaging setting, applying absolute and relative discretization. Reproducibility and reliability were determined by the inter-setting coefficient of variation (CV) and the intraclass correlation coefficient (ICC). Hypothesis tests were used to compare RI between lesions. By simulating precisely the same lesions, we confirmed that the reconstructed voxel size and the spatial resolution of different PET cameras were critical for higher order RI. Considering conventional RIs, the SUVpeak and SUVmean proved the most reliable (CV<10%). CVs above 25% are more common for higher order RIs, but we also found that low CVs do not necessarily imply robust parameters but often rather insensitive RIs. Based on the hypothesis test, most RIs could clearly distinguish between the various lesions using absolute resampling. ICC analysis also revealed that most RIs were more reproducible with absolute discretization. The activity painting method in a real radioactive environment proved suitable for precisely detecting the radiomic differences derived from the different camera settings and texture characteristics. We also found that inter-setting CV is not an appropriate metric for analyzing RI parameters' reliability and robustness. Although multicentric cohorts are increasingly common in radiomics analysis, realistic texture phantoms can provide indispensable information on the sensitivity of an RI and how an individual RI parameter measures the texture.

PET radiomics in lung cancer: advances and translational challenges

Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.

18F-FDG-PET/CT Uptake by Noncancerous Lung as a Predictor of Interstitial Lung Disease Induced by Immune Checkpoint Inhibitors

RATIONALE AND OBJECTIVES

Immune checkpoint inhibitors (ICIs) have improved lung cancer prognosis; however, ICI-related interstitial lung disease (ILD) is fatal and difficult to predict. Herein, we hypothesized that pre-existing lung inflammation on radiological imaging can be a potential risk factor for ILD onset. Therefore, we investigated the association between high uptake in noncancerous lung (NCL) on 18F- FDG-PET/CT and ICI-ILD in lung cancer.

METHODS

Patients with primary lung cancer who underwent FDG-PET/CT within three months prior to ICI therapy were retrospectively included. Artificial intelligence was utilized for extracting the NCL regions (background lung) from the lung contralateral to the primary tumor. FDG uptake by the NCL was assessed via the SUVmax (NCL-SUVmax), SUVmean (NCL-SUVmean), and total glycolytic activity (NCL-TGA)defined as NCL-SUVmean×NCL volume [mL]. NCL-SUVmean and NCL-TGA were calculated using the following four SUV thresholds: 0.5, 1.0, 1.5, and 2.0.

RESULTS

Of the 165 patients, 28 (17.0%) developed ILD. Univariate analysis showed that high values of NCL-SUVmax, NCL-SUVmean2.0 (SUV threshold=2.0), and NCL-TGA1.0 (SUV threshold=1.0) were significantly associated with ILD onset (all p = 0.003). Multivariate analysis adjusted for age, tumor FDG uptake, and pre-existing interstitial lung abnormalities revealed that a high NCL-TGA1.0 (≥149.45) was independently associated with ILD onset (odds ratio, 6.588; p = 0.002). Two-year cumulative incidence of ILD was significantly higher in the high NCL-TGA1.0 group than in the low group (58.4% vs. 14.4%; p < 0.001).

CONCLUSION

High uptake of NCL on FDG-PET/CT is correlated with ICI-ILD development, which could serve as a risk stratification tool before ICI therapy in primary lung cancer.

Application of 18F-FDG PET/CT imaging radiomics in the differential diagnosis of single-nodule pulmonary metastases and second primary lung cancer in patients with colorectal cancer

OBJECTIVE

It is crucially essential to differentially diagnose single-nodule pulmonary metastases (SNPMs) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC), which has important clinical implications for treatment strategies. In this study, we aimed to establish a feasible differential diagnosis model by combining 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET) radiomics, computed tomography (CT) radiomics, and clinical features.

MATERIALS AND METHODS

CRC patients with SNPM or SPLC who underwent 18F-FDG PET/CT from January 2013 to July 2022 were enrolled in this retrospective study. The radiomic features were extracted by manually outlining the lesions on PET/CT images, and the radiomic modeling was realized by various screening methods and classifiers. In addition, clinical features were analyzed by univariate analysis and logistic regression (LR) analysis to be included in the combined model. Finally, the diagnostic performances of these models were illustrated by the receiver operating characteristic (ROC) curves and the area under the curve (AUC).

RESULTS

We studied data from 61 patients, including 36 SNPMs and 25 SPLCs, with an average age of 65.56 ± 10.355 years. Spicule sign and ground-glass opacity (GGO) were significant independent predictors of clinical features (P = 0.012 and P < 0.001, respectively) to build the clinical model. We achieved a PET radiomic model (AUC = 0.789), a CT radiomic model (AUC = 0.818), and a PET/CT radiomic model (AUC = 0.900). The PET/CT radiomic models were combined with the clinical model, and a well-performing model was established by LR analysis (AUC = 0.940).

CONCLUSIONS

For CRC patients, the radiomic models we developed had good performance for the differential diagnosis of SNPM and SPLC. The combination of radiomic and clinical features had better diagnostic value than a single model.

Case report: diagnosis and treatment of advanced high-grade serous ovarian carcinoma aided by 68Ga-FAPI PET/MR scan

High-grade serous ovarian cancer (HGSOC) is the most common type of epithelial ovarian cancer with insidious onset, rapid growth, and invasive spread. Here, we reported the diagnosis and treatment of a 53-year-old patient with a history of hysterectomy aided by the 68Ga-FAPI PET/MR scan. The patient was first presented to the local hospital with a lump on the left side of the neck with a biopsy suggesting metastatic cancer. Pelvic ultrasonography revealed two irregular masses. After admission, tumor markers, pathology consultation of the biopsy, and the 68Ga-FAPI PET/MR scan were administered. The biopsy of the lump suggested poorly differentiated adenocarcinoma and CA125 was elevated at 530.6 U/ml. The 68Ga-FAPI PET/MR scan showed several abnormal lymph nodes and two soft tissue masses with borders of dispersed restriction displaying internally uneven signals depicted by slightly elongated T1 and T2 signals within the pelvic cavity suggesting that pelvic mass could be the primary lesion. The patient received cytoreductive surgery including bilateral adnexectomy, omentectomy, and appendectomy. Post-surgical pathology suggested left and right HGSOC with left fallopian tube invasion. The patient completed six courses of first-line chemotherapy and remained progression-free for 14 months up to date. To conclude, 68Ga-FAPI PET/MR aids in primary tumor determination and tumor burden assessment and provides a guide for the management of late-stage HGSOC patients.

Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors

Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan-Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan-Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.

A review on optimization of Wilms tumour management using radiomics

Background

Wilms tumour, a common paediatric cancer, is difficult to treat in low- and middle-income countries due to limited access to imaging. Artificial intelligence (AI) has been introduced for staging, detecting, and classifying tumours, aiding physicians in decision-making. However, challenges include algorithm accuracy, translation into conventional diagnosis, reproducibility, and reliability. As AI technology advances, radiomics, an AI tool, emerges to extract tumour morphology and stage information.

Objectives

This review explores the application of radiomics in Wilms tumour management, including its potential in diagnosis, prognosis, and treatment. Additionally, it discusses the future prospects of AI in this field and potential directions for automation-aided Wilms tumour treatment.

Methods

The review analyses various research studies and articles on the use of radiomics in Wilms tumour management. This includes studies on automated deep learning-based classification, interobserver variability in histopathological analysis, and the application of AI in staging, detecting, and classifying Wilms tumours.

Results

The review finds that radiomics offers several promising applications in Wilms tumour management, including improved diagnosis: it helps in classifying Wilms tumours from other paediatric kidney tumours, prognosis prediction: radiomic features can be used to predict both staging and response to preoperative chemotherapy, Treatment response assessment: Radiomics can be used to monitor the response of Wilms and to predict the feasibility of nephron-sparing surgery.

Conclusions

This review concludes that radiomics has the potential to significantly improve the diagnosis, prognosis, and treatment of Wilms tumours. Despite some challenges, such as the need for further research and validation, AI integration in Wilms tumour management offers promising opportunities for improved patient care.

Advances in knowledge

This review provides a comprehensive overview of the potential applications of radiomics in Wilms tumour management and highlights the significant role AI can play in improving patient outcomes. It contributes to the growing body of knowledge on AI-assisted diagnosis and treatment of paediatric cancers.

PET/CT in assessment of colorectal liver metastases: a comprehensive review with emphasis on 18F-FDG

Approximately 25% of those who are diagnosed with colorectal cancer will develop colorectal liver metastases (CRLM) as their illness advances. Despite major improvements in both diagnostic and treatment methods, the prognosis for patients with CRLM is still poor, with low survival rates. Accurate employment of imaging methods is critical in identifying the most effective treatment approach for CRLM. Different imaging modalities are used to evaluate CRLM, including positron emission tomography (PET)/computed tomography (CT). Among the PET radiotracers, fluoro-18-deoxyglucose (18F-FDG), a glucose analog, is commonly used as the primary radiotracer in assessment of CRLM. As the importance of 18F-FDG-PET/CT continues to grow in assessment of CRLM, developing a comprehensive understanding of this subject becomes imperative for healthcare professionals from diverse disciplines. The primary aim of this article is to offer a simplified and comprehensive explanation of PET/CT in the evaluation of CRLM, with a deliberate effort to minimize the use of technical nuclear medicine terminology. This approach intends to provide various healthcare professionals and researchers with a thorough understanding of the subject matter.

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology"

Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.

Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation

PURPOSE

To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods.

METHODS

One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test.

RESULTS

A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test.

CONCLUSION

It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.

Optimized 18F-FDG PET-CT Method to Improve Accuracy of Diagnosis of Metastatic Cancer

The diagnosis of cancer by FDG PET-CT is often inaccurate owing to subjectivity of interpretation. We compared the accuracy of a novel normalized (standardized) method of interpretation with conventional non-normalized SUV. Patients (n = 393) with various malignancies were studied with FDG PET/CT to determine the presence or absence of cancer. Target lesions were assessed by two methods: (1) conventional SUV_max (conSUV_max) and (2) a novel method that combined multiple factors to optimize SUV (optSUVmax), including the patient's normal liver SUV_max, a liver constant (k) derived from a review of the literature, and use of site-specific thresholds for malignancy. The two methods were compared to pathology findings in 154 patients being evaluated for mediastinal and/or hilar lymph node (MHLNs) metastases, 143 evaluated for extra-thoracic lymph node (ETLNs) metastases, and 96 evaluated for liver metastases. OptSUV_max was superior to conSUV_max for all patient groups. For MHLNs, sensitivity was 83.8% vs. 80.7% and specificity 88.7% vs. 9.6%, respectively; for ETLNs, sensitivity was 92.1% vs. 77.8% and specificity 80.1% vs. 27.6%, respectively; and for lesions in the liver parenchyma, sensitivity was 96.1% vs. 82.3% and specificity 88.8% vs. 23.0%, respectively. Optimized SUV_max increased diagnostic accuracy of FDG PET-CT for cancer when compared with conventional SUV_max interpretation.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Classification of solid pulmonary nodules using a machine-learning nomogram based on 18F-FDG PET/CT radiomics integrated clinicobiological features

Background

To develop and validate an ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and clinico-biological features-based nomogram for distinguishing solid benign pulmonary nodules (BPNs) from malignant pulmonary nodules (MPNs).

Methods

A total of 280 patients with BPN (n=128) or MPN (n=152) were collected retrospectively and randomized into the training set (n=196) and validation set (n=84). Pretherapeutic clinicobiological markers, PET/CT metabolic features and radiomic features were analyzed and selected to develop prediction models by the machine-learning method [Least Absolute Shrinkage and Selection Operator (LASSO) regression]. These prediction models were validated using the area under the curve (AUC) of the receiver-operator characteristic (ROC) analysis and decision curve analysis (DCA). Then, the factors of the model with the optimal predictive efficiency were used to constructed a nomogram to provide a visually quantitative tool for distinguishing BPN from MPN patients.

Results

We developed 3 independent models (Clinical Model, Radiomics Model and Combined Model) to distinguish patients with BPN from those with MPN in the training set. The Combined Model was validated to hold the optimal efficiency and clinical utility with the lowest false positive rate (FPR) in classifying the solid pulmonary nodules in two sets (AUCs of 0.91 and 0.94, FPRs of 18.68% and 5.41%, respectively; P<0.05). Thus, the quantitative nomogram was developed based on the Combined Model, and a good consistency between the predictions and the actual observations was validated by the calibration curves.

Conclusions

This study presents a machine-learning nomogram integrated clinico-biologico-radiological features that can improve the efficiency and reduce the FPR in the noninvasive differentiation of BPN from MPN.

The role of PET imaging in inflammatory bowel diseases: state-of-the-art review

Inflammatory bowel diseases (IBD), i.e. Crohn disease and ulcerative colitis, are autoimmune processes of undetermined origin characterized by the chronic inflammation of the digestive tract. There is no single gold-standard to diagnose IBD which is therefore carried out through the combination of endoscopy, biopsy, radiological and biological investigations; and the development of non-invasive technique allowing the assessment and monitoring of these diseases is necessary. In this state-of-the-art review of the literature, we present the results of PET imaging studies for the diagnosis and staging of IBD (suspected or known), response evaluation to treatment and evaluation of one the main complication, i.e. strictures; explain the reasons why this examination is currently not considered in the IBD guidelines, e.g. radiation exposure, lack of standardization and not validated performances; and finally discuss the perspectives that could possibly allow it to find a place in the future, e.g. digital PET-CT, dynamic PET images acquisition, new radiopharmaceuticals, use of radiomics and use of artificial intelligence for automatically characterize and quantify digestive [¹⁸F]FDG uptake.