Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians

A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients

Purpose

It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups.

Methods

Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results

The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79-0.86), 0.77(95 %CI 0.72-0.83) and 0.841(95 %CI 0.78-0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility.

Conclusions

The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

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).

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning.

METHODS

The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier.

RESULTS

104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87.

CONCLUSIONS

The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model

INTRODUCTION

Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN).

METHODOLOGY

Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes' theorem.

RESULTS

The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80.

CONCLUSIONS

Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.

Predicting amyloid positivity from FDG-PET images using radiomics: A parsimonious model

BACKGROUND AND OBJECTIVE

Amyloid plaques are one of the physical hallmarks of Alzheimer's disease. The objective of this study is to predict amyloid positivity non-invasively from FDG-PET images using a radiomics approach.

METHODS

We obtained FDG-PET images of 301 individuals from various groups, including control normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD), from the ADNI database. Following the utilization of the CSF Aβ1-42 (192) and Standardized Uptake Value Ratio (SUVR) (1.11) thresholds derived from Florbetapir scans, the subjects were categorized into two categories: those with a positive amyloid status (n = 185) and those with a negative amyloid status (n = 116). The process of segmenting the entire brain into 95 classes using the DKT-atlas was utilized. Following that, we obtained 120 characteristics for each of the 95 regions of interest (ROIs). We employed eight feature selection methods to analyze the features. Additionally, we utilized eight different classifiers on the 20 most significant features extracted from each feature selection method. Finally, in order to improve interpretability, we selected the most important features and ROIs.

RESULT

We found that the GNB classifier and the LASSO feature selection method had the best performance with an average accuracy of (AUC=0.924) while using 18 features on 15 ROIs. We were then able to reduce the model to three regions (Hippocampus, inferior parietal, and isthmus cingulate) and three gray-level based features (AUC=0.853).

CONCLUSION

The FDG-PET images which serve to study metabolic activity can be used to predict amyloid positivity without the use of invasive methods or another PET tracer and study. The proposed method has superior prediction accuracy with respect to similar studies reported in the literature using other imaging modalities. Only three brain regions had a high impact on amyloid positivity results.

CT evaluation of interstitial lung disease related to systemic sclerosis: visual versus automated assessment. A systematic review

AIM

To identify similarities and differences between visual (VA) and automated assessment (AA) of systemic sclerosis-related interstitial lung disease (SSc-ILD) at chest computed tomography (CT) in terms of clinical applicability.

MATERIALS AND METHODS

Medline, Embase, and Web of Science were searched to identify all studies investigating VA and AA for SSc-ILD assessment, from inception to 31 July 2022. Exclusion criteria were manuscripts not in English, absence of full-text, reviews, diseases other than ILD in SSc, CT not analysed with both VA and AA, VA and AA not adopted for the same purpose or not compared, overlap syndromes, SSc-ILD data not extractable, and studies with <10 patients.

RESULTS

Ten full-text studies (804 patients) were included. The most adopted VAs were the Warrick or Goh score (four studies each), while densitometry (eight studies) or lung texture analysis (LTA, two studies) were utilised as AAs. The main field of investigation was the correlation with baseline pulmonary function tests (PFT, six studies). Warrick VA showed lower correlations compared to densitometry, while Goh VA demonstrated more heterogeneous results. Compared to LTA, Goh VA obtained lower correlations with lung volumes but similar or stronger coefficients with alveolar diffusibility.

CONCLUSIONS

VA and AA may show heterogeneous results comparing their correlations with PFT, probably depending on the specific analysis adopted for each method. More data are needed on VA versus LTA. Comparisons between VA and AA regarding correlation with PFT follow-up and as prognostic elements, or for disease monitoring, are lacking. AAs in progressive fibrosis diagnosis remain to be tested.

Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung

Radiomic features were used in efforts to characterize radiation-induced normal tissue injury as well as identify if human embryonic stem cell (hESC) derived Extracellular Vesicle (EV) treatment could resolve certain adverse complications. A cohort of mice (n=12/group) were given whole lung irradiation (3×8Gy), local irradiation to the right lung apex (3×12Gy), or no irradiation. The hESC-derived EVs were systemically administered three times via retro-orbital injection immediately after each irradiation. Cone-Beam Computed Tomography (CBCT) images were acquired at baseline and 2 weeks after the final radiation/EV treatment. Whole lung image segmentation was performed and radiomic features were extracted with wavelet filtering applied. A total of 851 features were extracted per image and recursive feature elimination was used to refine, train and validate a series of random forest classification models. Classification models trained to identify irradiated from unirradiated animals or EV treated from vehicle-injected animals achieved high prediction accuracies (94% and 85%). In addition, radiomic features from the locally irradiated dataset showed significant radiation impact and EV sparing effects that were absent in the unirradiated left lung. Our data demonstrates that radiomics has the potential to characterize radiation-induced lung injury and identify therapeutic efficacy at early timepoints.

Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients' prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC

PURPOSE

To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC).

METHODS

Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs.

RESULTS

RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts.

CONCLUSIONS

Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.

An Artificial Intelligence Platform for the Radiologic Diagnosis of Pulmonary Sarcoidosis: An Initial Pilot Study of Chest Computed Tomography Analysis to Distinguish Pulmonary Sarcoidosis from a Negative Lung Cancer Screening Scan

PURPOSE

To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imaging Reporting and Data System score 1, Lung-RADS score 1).

METHODS

Chest CT scans of pulmonary sarcoidosis were evaluated by a clinician experienced with sarcoidosis and a chest radiologist for clinical and radiologic evidence of sarcoidosis and exclusion of alternative or concomitant pulmonary diseases. The AI/DL based method used an ensemble network architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The method was applied to 126 pulmonary sarcoidosis and 96 Lung-RADS score 1 CT scans. The analytic approach of training and validation of the AI/DL method used a fivefold cross-validation technique, where 4/5th of the available data set was used to train a diagnostic model and tested on the remaining 1/5th of the data set, and repeated 4 more times with non-overlapping validation/test data. The probability values were used to generate Receiver Operating Characteristic (ROC) curves to assess the model's discriminatory power.

RESULTS

The sensitivity, specificity, positive and negative predictive value of the AI/DL method for the 5 folds of the training/validation sets and the entire set of CT scans were all over 94% to distinguish pulmonary sarcoidosis from LUNG-RADS score 1 chest CT scans. The area under the curve for the corresponding ROC curves were all over 97%.

CONCLUSION

This AL/DL model shows promise to distinguish sarcoidosis from alternative pulmonary conditions using minimal radiologic data.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics

Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.

Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization

PURPOSE

The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements.

MATERIALS AND METHODS

Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons.

RESULTS

At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC).

CONCLUSIONS

The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

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.

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

High resolution propagation-based lung imaging at clinically relevant X-ray dose levels

Absorption-based clinical computed tomography (CT) is the current imaging method of choice in the diagnosis of lung diseases. Many pulmonary diseases are affecting microscopic structures of the lung, such as terminal bronchi, alveolar spaces, sublobular blood vessels or the pulmonary interstitial tissue. As spatial resolution in CT is limited by the clinically acceptable applied X-ray dose, a comprehensive diagnosis of conditions such as interstitial lung disease, idiopathic pulmonary fibrosis or the characterization of small pulmonary nodules is limited and may require additional validation by invasive lung biopsies. Propagation-based imaging (PBI) is a phase sensitive X-ray imaging technique capable of reaching high spatial resolutions at relatively low applied radiation dose levels. In this publication, we present technical refinements of PBI for the characterization of different artificial lung pathologies, mimicking clinically relevant patterns in ventilated fresh porcine lungs in a human-scale chest phantom. The combination of a very large propagation distance of 10.7 m and a photon counting detector with [Formula: see text] pixel size enabled high resolution PBI CT with significantly improved dose efficiency, measured by thermoluminescence detectors. Image quality was directly compared with state-of-the-art clinical CT. PBI with increased propagation distance was found to provide improved image quality at the same or even lower X-ray dose levels than clinical CT. By combining PBI with iodine k-edge subtraction imaging we further demonstrate that, the high quality of the calculated iodine concentration maps might be a potential tool for the analysis of lung perfusion in great detail. Our results indicate PBI to be of great value for accurate diagnosis of lung disease in patients as it allows to depict pathological lesions non-invasively at high resolution in 3D. This will especially benefit patients at high risk of complications from invasive lung biopsies such as in the setting of suspected idiopathic pulmonary fibrosis (IPF).

Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass

BACKGROUND

This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors.

METHODS

Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models.

RESULTS

Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score.

CONCLUSIONS

The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.

Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications

Rectal cancer (RC) is one of the deadliest malignancies worldwide. Surgery is the most common treatment for RC, performed in 63.2% of patients. The type of surgical approach chosen aims to achieve maximum residual function with the lowest risk of recurrence. The selection is made by a multidisciplinary team that assesses the characteristics of the patient and the tumor. Total mesorectal excision (TME), including both low anterior resection (LAR) and abdominoperineal resection (APR), is still the standard of care for RC. Radical surgery is burdened by a 31% rate of major complications (Clavien-Dindo grade 3-4), such as anastomotic leaks and a risk of a permanent stoma. In recent years, less-invasive techniques, such as local excision, have been tested. These additional procedures could mitigate the morbidity of rectal resection, while providing acceptable oncologic results. The "watch and wait" approach is not a globally accepted model of care but encouraging results on selected groups of patients make it a promising strategy. In this plethora of treatments, the radiologist is called upon to distinguish a physiological from a pathological postoperative finding. The aim of this narrative review is to identify the main post-surgical complications and the most effective imaging techniques.

Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.

Immunotherapy Assessment: A New Paradigm for Radiologists

Immunotherapy denotes an exemplar change in an oncological setting. Despite the effective application of these treatments across a broad range of tumors, only a minority of patients have beneficial effects. The efficacy of immunotherapy is affected by several factors, including human immunity, which is strongly correlated to genetic features, such as intra-tumor heterogeneity. Classic imaging assessment, based on computed tomography (CT) or magnetic resonance imaging (MRI), which is useful for conventional treatments, has a limited role in immunotherapy. The reason is due to different patterns of response and/or progression during this kind of treatment which differs from those seen during other treatments, such as the possibility to assess the wide spectrum of immunotherapy-correlated toxic effects (ir-AEs) as soon as possible. In addition, considering the unusual response patterns, the limits of conventional response criteria and the necessity of using related immune-response criteria are clear. Radiomics analysis is a recent field of great interest in a radiological setting and recently it has grown the idea that we could identify patients who will be fit for this treatment or who will develop ir-AEs.

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.

Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity

The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.

Advances in diagnostic tools for respiratory tract infections. From tuberculosis to COVID19: changing paradigms?

Respiratory tract infections (RTI) are one of the commonest reasons for seeking healthcare, but are amongst the most challenging diseases in terms of clinical decision making. Proper and timely diagnosis is critical in order to optimize management and prevent further emergence of antimicrobial resistance by misuse, or overuse of antibiotics. Diagnostic tools for RTI include those involving syndromic and etiological diagnosis: from clinical and radiological features to laboratory methods targeting both pathogen detection and host biomarkers, as well as their combinations in terms of clinical algorithms. They also include tools for predicting severity and monitoring treatment response. Unprecedented milestones have been achieved in the context of the COVID-19 pandemic, involving the most recent applications of diagnostic technologies both at genotypic and phenotypic level, which have changed paradigms in infectious respiratory diseases in terms of why, how and where diagnostics are performed. The aim of this review is to discuss advances in diagnostic tools that impact clinical decision making, surveillance and follow-up of RTI and tuberculosis. If properly harnessed, recent advances in diagnostic technologies, including omics and digital transformation emerge as an unprecedented opportunity to tackle ongoing and future epidemics while handling antimicrobial resistance from a One Health perspective.

Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier

Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1-score: 2% and AUC: 1%).

Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study

Background

Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity.

Methods

CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed.

Results

All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81–0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, “kurtosis” had a high predictive value of early death (AUC at first year: 0.70–0.75 in two independent cohorts), negative association with histopathological grade (Spearman’s r: − 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915–11.561) than histopathological staging and grading.

Conclusions

We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.

Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.

Combined obstructive airflow limitation associated with interstitial lung diseases (O-ILD): the bad phenotype ?

Background

Patients suffering from combined obstructive and interstitial lung disease (O-ILD) represent a pathological entity which still has to be well clinically described. The aim of this descriptive and explorative study was to describe the phenotype and functional characteristics of a cohort of patients suffering from functional obstruction in a population of ILD patients in order to raise the need of dedicated prospective observational studies and the evaluation of the impact of anti-fibrotic therapies.

Methods

The current authors conducted a retrospective study including 557 ILD patients, with either obstructive (O-ILD, n = 82) or non-obstructive (non O-ILD, n = 475) pattern. Patients included were mainly males (54%) with a mean age of 62 years.

Results

Patients with O-ILD exhibited a characteristic functional profile with reduced percent predicted forced expired volume in 1 s (FEV1) [65% (53–77) vs 83% (71–96), p < 0.00001], small airway involvement assessed by maximum expiratory flow (MEF) 25/75 [29% (20–41) vs 81% (64–108), p < 0.00001], reduced sGaw [60% (42–75) vs 87% (59–119), p < 0.01] and sub-normal functional residual capacity (FRC) [113% (93–134) vs 92% (75–109), p < 0.00001] with no impaired of carbon monoxide diffusing capacity of the lung (DLCO) compared to those without obstruction. Total lung capacity (TLC) was increased in O-ILD patients [93% (82–107) vs 79% (69–91), p < 0.00001]. Of interest, DLCO sharply dropped in O-ILD patients over a 5-year follow-up. We did not identify a significant increase in mortality in patients with O-ILD. Interestingly, the global mortality was increased in the specific sub-group of patients with O-ILD and no progressive fibrosing ILD phenotype and in those with connective tissue disease associated ILD especially in case of rheumatoid arthritis.

Conclusions

The authors individualized a specific functional-based pattern of ILD patients with obstructive lung disease, who are at risk of increased mortality and rapid DLCO decline over time. As classically those patients are excluded from clinical trials, a dedicated prospective study would be of interest in order to define more precisely treatment response of those patients.

Artificial Intelligence in Thyroid Field-A Comprehensive Review

Simple Summary

The incidence of thyroid pathologies has been increasing worldwide. Historically, the detection of thyroid neoplasms relies on medical imaging analysis, depending mainly on the experience of clinicians. The advent of artificial intelligence (AI) techniques led to a remarkable progress in image-recognition tasks. AI represents a powerful tool that may facilitate understanding of thyroid pathologies, but actually, the diagnostic accuracy is uncertain. This article aims to provide an overview of the basic aspects, limitations and open issues of the AI methods applied to thyroid images. Medical experts should be familiar with the workflow of AI techniques in order to avoid misleading outcomes.

Abstract

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Standard spirometry to assess emphysema in patients with chronic obstructive pulmonary disease: the Emphysema Severity Index (ESI)

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a generic term identifying a condition characterized by variable changes in peripheral airways and lung parenchyma. Standard spirometry cannot discriminate the relative role of conductive airways inflammatory changes from destructive parenchymal emphysema changes. The aim of this study was to quantify the emphysema component in COPD by a simple parameter (the Emphysema Severity Index - ESI), previously proved to reflect CT-assessed emphysema.

METHODS

ESI was obtained by fitting the descending limb of MEFV curves by a fully automated procedure providing a 0 to 10 score of emphysema severity. ESI was computed in COPD patients enrolled in the CLIMA Study.

RESULTS

The vast majority of ESI values ranged from 0 to 4, compatible with no-to-mild/moderate emphysema component. A limited proportion of patients showed ESI values >4, compatible with severe-to-very severe emphysema. ESI values were greatly dispersed within each GOLD class indicating that GOLD classification cannot discriminate emphysema and conductive airways changes in patients with similar airflow limitation. ESI and diffusing capacity (DLCO) were significantly correlated (p<0.001). However, the great dispersion in their correlation suggests that ESI and DLCO reflect partially different anatomo-functional determinants in COPD.

CONCLUSIONS

Airflow limitation has heterogenous determinants in COPD. Inflammatory and destructive changes may combine in CT densitometric alterations that cannot be detected by standard spirometry. ESI computation from spirometric data helps to define the prevailing pathogenetic mechanism underlying the measured airflow limitation. ESI could be a reliable advancement to select large samples of patients in clinical or epidemiological trials, and to compare different pharmacological treatments.