Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851–0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Sarcoidosis and lymphoma often share common features on ¹⁸F-FDG PET/CT, such as intense hypermetabolic lesions in lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). Methods: We retrospectively collected 420 patients (169 sarcoidosis, 140 HL, and 111 DLBCL) who underwent pretreatment ¹⁸F-FDG PET/CT at the University Hospital of Liege. The studies were randomly distributed to 4 physicians, who gave their diagnostic suggestion among the 3 diseases. The individual and pooled performance of the physicians was then calculated. Interobserver variability was evaluated using a sample of 34 studies interpreted by all physicians. Volumes of interest were delineated over the lesions and the liver using MIM software, and 215 radiomics features were extracted using the RadiomiX Toolbox. Models were developed combining clinical data (age, sex, and weight) and radiomics (original and tumor-to-liver TLR radiomics), with 7 different feature selection approaches and 4 different machine-learning (ML) classifiers, to differentiate sarcoidosis and lymphomas on both lesion-based and patient-based approaches. Results: For identifying lymphoma versus sarcoidosis, physicians’ pooled sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and accuracy were 0.99 (95% CI, 0.97–1.00), 0.75 (95% CI, 0.68–0.81), 0.87 (95% CI, 0.84–0.90), and 89.3%, respectively, whereas for identifying HL in the tumor population, it was 0.58 (95% CI, 0.49–0.66), 0.82 (95% CI, 0.74–0.89), 0.70 (95% CI, 0.64–0.75) and 68.5%, respectively. Moderate agreement was found among observers for the diagnosis of lymphoma versus sarcoidosis and HL versus DLBCL, with Fleiss κ-values of 0.66 (95% CI, 0.45–0.87) and 0.69 (95% CI, 0.45–0.93), respectively. The best ML models for identifying lymphoma versus sarcoidosis showed an AUC of 0.94 (95% CI, 0.93–0.95) and 0.85 (95% CI, 0.82–0.88) in lesion- and patient-based approaches, respectively, using TLR radiomics (plus age for the second). To differentiate HL from DLBCL, we obtained an AUC of 0.95 (95% CI, 0.93–0.96) in the lesion-based approach using TLR radiomics and 0.86 (95% CI, 0.80–0.91) in the patient-based approach using original radiomics and age. Conclusion: Characterization of sarcoidosis and lymphoma lesions is feasible using ML and radiomics, with very good to excellent performance, equivalent to or better than that of physicians, who showed significant interobserver variability in their assessment. Visual Abstract

Background: Prostate-specific membrane antigen (PSMA) ligand PET/CT has already provided promising results in prostate cancer (PC) imaging, yet simple and reproductible reporting criteria are still lacking. This study aimed at retrospectively evaluating interobserver agreement of [⁶⁸Ga]Ga-PSMA-11 PET/CT images interpretation according to PC molecular imaging standardized evaluation (PROMISE) criteria and reproducibility of PSMA reporting and data systems (RADS). Methods: Forty-three patients with newly diagnosed, histologically proven intermediate- or high-risk PC, eligible for radical prostatectomy and who underwent [⁶⁸Ga]Ga-PSMA-11 PET/CT before surgery were retrospectively included. Three nuclear medicine physicians (2 experienced and 1 resident) independently reviewed PET/CT images. Interpretation of [⁶⁸Ga]Ga-PSMA-11 PET/CT images was based on PROMISE criteria including miTNM staging and lesions miPSMA expression score visual estimation and PSMA-RADS version 1.0 for a given scan. Readers’ agreement was measured using Krippendorff’s coefficients Results: Agreement between observers was almost perfect (coefficient ≥ 0.81) for miM; it was substantial (coefficient ≥ 0.61) for the following criteria: miT, miN, PSMA-RADS, and miPSMA expression score of primary PC lesion and metastases. However, agreement was moderate (coefficient = 0.41–0.60) for miPSMA score of positive lymph nodes and for detection of PC primary lesion. Conclusion: Visual interpretation of [⁶⁸Ga]Ga-PSMA-11 PET/CT images in patients with newly diagnosed PC in a clinical setting leads to at least substantial agreement for PROMISE criteria and PSMA-RADS classification except for PC primary lesion detection and for miPSMA expression scoring of positive lymph nodes that might have been hampered by the interindividual variability of reference organs PSMA expression.