A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

Inter-equipment validation of PET-based radiomics for predicting EGFR mutation statuses in patients with non-small cell lung cancer

AIM

To validate the inter-equipment generality of the radiomics based on PET images to predict the EGFR mutation status of patients with non-small cell lung cancer.

MATERIALS AND METHODS

Patients were retrospectively collected in the departments of nuclear medicine of Heyi branch (Siemens equipment) and East branch (General Electric (GE) equipment) of the first affiliated hospital of Zhengzhou university. 5 predicting logistic regression models were established. The 1st one was trained and tested by the GE dataset; The 2nd one was trained and tested by the Siemens dataset; The 3rd one was trained and tested by the mixed dataset consisting of GE and Siemens. The 4th one was trained by GE and tested by Siemens; The 5th one was trained by Siemens and tested by GE.

RESULTS

For the 1st ∼ 5th models, the mean values of AUCs for training/testing datasets were 0.78/0.73, 0.74/0.72, 0.75/0.70, 0.74/0.65 and 0.68/0.63, respectively.

CONCLUSION

The AUCs of the models trained and tested on the datasets from the same equipment were higher than those for different equipment. The inter-equipment generality of the radiomics was not good enough in clinical practice.

Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study

Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward.

Value of multi-center 18F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma

BACKGROUND

Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F-FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi-center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR-related research involved model visualization and interpretation, and multi-center trial.

PURPOSE

To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi-center 18F-FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD.

METHODS

Baseline 18F-FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross-combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis.

RESULTS

STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort).

CONCLUSIONS

Explainable machine learning models combined with radiomics features extracted from multi-center/scanner 18F-FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

PURPOSE

This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).

METHODS

We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.

RESULTS

From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.

CONCLUSION

Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Disulfidptosis-related lncRNAs signature predicting prognosis and immunotherapy effect in lung adenocarcinoma

PURPOSE

Lung adenocarcinoma (LUAD) is a prevalent malignant tumor worldwide, with high incidence and mortality rates. However, there is still a lack of specific and sensitive biomarkers for its early diagnosis and targeted treatment. Disulfidptosis is a newly identified mode of cell death that is characteristic of disulfide stress. Therefore, exploring the correlation between disulfidptosis-related long non-coding RNAs (DRGs-lncRNAs) and patient prognosis can provide new molecular targets for LUAD patients.

METHODS

The study analysed the transcriptome data and clinical data of LUAD patients in The Cancer Genome Atlas (TCGA) database, gene co-expression, and univariate Cox regression methods were used to screen for DRGs-lncRNAs related to prognosis. The risk score model of lncRNA was established by univariate and multivariate Cox regression models. TIMER, CIBERSORT, CIBERSORT-ABS, and other methods were used to analyze immune infiltration and further evaluate immune function analysis, immune checkpoints, and drug sensitivity. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of DRGs-lncRNAs in LUAD cell lines.

RESULTS

A total of 108 lncRNAs significantly associated with disulfidptosis were identified. A prognostic model was constructed by screening 10 lncRNAs with independent prognostic significance through single-factor Cox regression analysis, LASSO regression analysis, and multiple-factor Cox regression analysis. Survival analysis of patients through the prognostic model showed that there were obvious survival differences between the high- and low-risk groups. The risk score of the prognostic model can be used as an independent prognostic factor independent of other clinical traits, and the risk score increases with stage. Further analysis showed that the prognostic model was also different from tumor immune cell infiltration, immune function, and immune checkpoint genes in the high- and low-risk groups. Chemotherapy drug susceptibility analysis showed that high-risk patients were more sensitive to Paclitaxel, 5-Fluorouracil, Gefitinib, Docetaxel, Cytarabine, and Cisplatin. Additionally, RT-PCR analysis demonstrated differential expression of DRGs-lncRNAs between LUAD cell lines and the human bronchial epithelial cell line.

CONCLUSIONS

The prognostic model of DRGs-lncRNAs constructed in this study has certain accuracy and reliability in predicting the survival prognosis of LUAD patients, and provides clues for the interaction between disulfidptosis and LUAD immunotherapy.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

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

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

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.

Radiogenomics: a key component of precision cancer medicine

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics

PURPOSE

We investigated the feasibility of preoperative ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients.

MATERIALS AND METHODS

Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters.

RESULTS

The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent predictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015).

CONCLUSION

Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.

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.

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives

Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods

A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results

Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion

AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

Extending artificial intelligence research in the clinical domain: a theoretical perspective

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

Thoracic Imaging in China: Yesterday, Today, and Tomorrow

Thoracic imaging has been revolutionized through advances in technology and research around the world, and so has China. Thoracic imaging in China has progressed from anatomic observation to quantitative and functional evaluation, from using traditional approaches to using artificial intelligence. This article will review the past, present, and future of thoracic imaging in China, in an attempt to establish new accepted strategies moving forward.

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

Automated data preparation for in vivo tumor characterization with machine learning

Background

This study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts.

Methods

A collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts.

Results

Sixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps.

Conclusions

This study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings.

Prediction of TTF-1 expression in non-small-cell lung cancer using machine learning-based radiomics

PURPOSE

To explore the feasibility and performance of machine learning-based radiomics models in predicting thyroid transcription factor-1 (TTF-1) expression in non-small cell lung cancer (NSCLC).

METHODS

A total of 227 NSCLC patients were included in this retrospective study and divided into the training set and test set with a ratio of 8:2 randomly. Lung tumors on CT images were semi-automatic segmented utilizing 3D Slicer. Radiomic features quantifying tumor intensity, shape, texture, and transformed wavelet were extracted using a Python toolkit. Variance threshold (VT), principal component analysis (PCA), and least absolute shrinkage selection operator (LASSO) were used to reduce features; logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to develop classifier, respectively. The performance of the models was evaluated by areas under the curves (AUC) of receiver operating characteristic (ROC) curves. Different models were compared by the Delong test to determine the optimal algorithms.

RESULTS

Total 1968 radiomic features were extracted from the lung tumors images, and then 13, 15, and 13 stable features were selected by VT, PCA, and LASSO, respectively. Each classifier could discriminate against the TTF-1-positive groups with average AUC ranging from 0.601 to 0.784 in the training set. Among the models, three models constructed by the LASSO method showed satisfactory performance in the test set with AUC ranging from 0.715 to 0.787. The Delong test showed no significant difference between the LASSO models (P > 0.05).

CONCLUSION

Machine learning-based radiomics model could predict the expression of TTF-1 in NSCLC patients.

Artificial Intelligence-Powered Prediction of ALK Gene Rearrangement in Patients With Non-Small-Cell Lung Cancer

PURPOSE

Several studies reported the possibility of predicting genetic abnormalities in non-small-cell lung cancer by deep learning (DL). However, there are no data of predicting ALK gene rearrangement (ALKr) using DL. We evaluated the ALKr predictability using the DL platform.

MATERIALS AND METHODS

We selected 66 ALKr-positive cases and 142 ALKr-negative cases, which were diagnosed by ALKr immunohistochemical staining in our institution from January 2009 to March 2019. We generated virtual slide of 300 slides (150 ALKr-positive slides and 150 ALKr-negative slides) using NanoZoomer. HALO-AI was used to analyze the whole-slide imaging data, and the DenseNet network was used to build the learning model. Of the 300 slides, we randomly assigned 172 slides to the training cohort and 128 slides to the test cohort to ensure no duplication of cases. In four resolutions (16.0/4.0/1.0/0.25 μm/pix), ALKr prediction models were built in the training cohort and ALKr prediction performance was evaluated in the test cohort. We evaluated the diagnostic probability of ALKr by receiver operating characteristic analysis in each ALKr probability threshold (50%, 60%, 70%, 80%, 90%, and 95%). We expected the area under the curve to be 0.64-0.85 in the model of a previous study. Furthermore, in the test cohort data, an expert pathologist also evaluated the presence of ALKr by hematoxylin and eosin staining on whole-slide imaging.

RESULTS

The maximum area under the curve was 0.73 (50% threshold: 95% CI, 0.65 to 0.82) in the resolution of 1.0 μm/pix. In this resolution, with an ALKr probability of 50% threshold, the sensitivity and specificity were 73% and 73%, respectively. The expert pathologist's sensitivity and specificity in the same test cohort were 13% and 94%.

CONCLUSION

The ALKr prediction by DL was feasible. Further study should be addressed to improve accuracy of ALKr prediction.

Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis

PURPOSE

More and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication.

METHODS

This retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts.

RESULTS

The AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts.

CONCLUSION

T2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.

IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER

OBJECTIVE

Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features.

METHODS

Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model.

RESULTS

For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%).

CONCLUSIONS

In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.

Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer

Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

Identification and verification of a prognostic ferroptosis-related lncRNAs signature for patients with lung adenocarcinoma

Lung cancer has been identified as one of the deadliest malignant tumors worldwide. Mounting evidence suggests that ferroptosis is a well-known non-apoptotic cell death process that participates in pathological mechanisms and is a new cancer treatment strategy. Aberrantly expressed long non-coding RNAs (lncRNAs) that drive lung cancer progression have attracted increasing attention. Herein, we explored the prognostic significance of ferroptosis-related lncRNAs in lung cancer patients. LUAD gene expression patterns and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) database. Based on LASSO-Cox regression, A 14 ferroptosis-related differentially expressed lncRNAs (FRDELs) signature was constructed. Subsequently, a nomogram model for predicting the prognosis of LUAD patients was constructed based on clinicopathological data and the 14 - FRDELs signature. The signature was shown to be correlated with tumor mutational burden (TMB) and immune cell infiltration within the tumor microenvironment. Furthermore, Gene Set Enrichment Analysis (GSEA) confirmed that the signature was correlated with LUAD-related biological functions such as the P53 signaling pathway, DNA replication, and cell cycle. The roles and mechanisms of PACERR in the signature were explored by si-lncRNA-mediated knockdown and transfection-mediated overexpression via in vitro experiments in A549 and H1299 cells. PACERR was significantly upregulated in A549 and H1299 cells, and higher expression promoted LUAD cell proliferation, migration, and invasion via in vitro experiments, while knockdown of PACERR presented the opposite effects. In conclusion, our study provided information regarding ferroptosis-related lncRNA expression and established a prognostic nomogram based on 14 FRDELs to predict overall survival in LUAD accurately. Additionally, our results in vitro revealed that PACERR played an oncogenic role in LUAD proliferation and metastasis, which provides mechanistic insights into the roles of ferroptosis-related lncRNA in LUAD progression and that it may be a potential biomarker for LUAD treatment.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter

Background

To investigate the value of ¹⁸F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm).

Methods

A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA).

Results

According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM.

Conclusions

The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00895-x.

[Relationship between EGFR, ALK Gene Mutation and Imaging and Pathological Features in Invasive Lung Adenocarcinoma]

BACKGROUND

At present, the research progress of targeted therapy for epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) gene mutations in lung adenocarcinoma is very rapid, which brings new hope for the treatment of advanced lung adenocarcinoma patients. However, the specific imaging and pathological features of EGFR and ALK gene mutations in adenocarcinoma are still controversial. This study will further explore the correlation between EGFR, ALK gene mutations and imaging and pathological features in invasive lung adenocarcinoma.

METHODS

A total of 525 patients with lung adenocarcinoma who underwent surgery in our center from January 2018 to December 2019 were included. According to the results of postoperative gene detection, the patients were divided into EGFR gene mutation group, ALK gene mutation group and wild group, and the EGFR gene mutation group was divided into exon 19 and exon 21 subtypes. The pathological features of the mutation group and wild group, such as histological subtype, lymph node metastasis, visceral pleural invasion (VPI) and imaging features such as tumor diameter, consolidation tumor ratio (CTR), lobulation sign, spiculation sign, pleural retraction sign, air bronchus sign and vacuole sign were analyzed by univariate analysis and multivariate Logistic regression analysis to explore whether the gene mutation group had specific manifestations.

RESULTS

EGFR gene mutation group was common in women (OR=2.041, P=0.001), with more pleural traction sign (OR=1.506, P=0.042), and had little correlation with lymph node metastasis and VPI (P>0.05). Among them, exon 21 subtype was more common in older (OR=1.022, P=0.036), women (OR=2.010, P=0.007), and was associated with larger tumor diameter (OR=1.360, P=0.039) and pleural traction sign (OR=1.754, P=0.029). Exon 19 subtype was common in women (OR=2.230, P=0.009), with a high proportion of solid components (OR=1.589, P=0.047) and more lobulation sign (OR=2.762, P=0.026). ALK gene mutations were likely to occur in younger patients (OR=2.950, P=0.045), with somking history (OR=1.070, P=0.002), and there were more micropapillary components (OR=4.184, P=0.019) and VPI (OR=2.986, P=0.034) in pathology.

CONCLUSIONS

The EGFR and ALK genes mutated adenocarcinomas have specific imaging and clinicopathological features, and the mutations in exon 19 or exon 21 subtype have different imaging features, which is of great significance in guiding the clinical diagnosis and treatment of pulmonary nodules.

Parameter tuning in machine learning based on radiomics biomarkers of lung cancer

BACKGROUND

Lung cancer is one of the most common cancers, and early diagnosis and intervention can improve cancer cure rate.

OBJECTIVE

To improve predictive performance of radiomics features for lung cancer by tuning the machine learning model parameters.

METHODS

Using a dataset involving 263 cases (125 benign and 138 malignant) acquired from our hospital, each classifier model is trained and tested using 237 and 26 cases, respectively. We initially extract 867 radiomics features of CT images for model development and then test 10 feature selections and 7 models to determine the best method. We further tune the parameter of the final model to reach the best performance. The adjusted final model is then validated using 224 cases acquired from Lung Image Database Consortium (LIDC) dataset (64 benign and 160 malignant) with the same set of selected radiomics features.

RESULTS

During model development, the feature selection via concave minimization method show the best performance of area under ROC curve (AUC = 0.765), followed by l0-norm regularization (AUC = 0.741) and Fisher discrimination criterion (AUC = 0.734). Support vector machine (SVM) and random forest (RF) are the top two machine learning algorithms showing the best performance (AUC = 0.765 and 0.734, respectively), using by the default parameter. After parameter tuning, SVM with linear kernel achieves the best performance (AUC = 0.837), whereas the best tuned RF with the number of trees is 510 and yields a slightly lower performance (AUC = 0.775) in 26 test samples data. During model validation, the SVM and RF models yield AUC = 0.78 and 0.77, respectively.

CONCLUSION

Appropriate quantitative radiomics features and accurate parameters can improve the model's performance to predict lung cancer.

AI in spotting high-risk characteristics of medical imaging and molecular pathology

Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.

Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules

Objectives

We aimed to develop a prediction model to distinguish atypical adenomatous hyperplasia (AAH) from early lung adenocarcinomas in patients with subcentimeter pulmonary ground-glass nodules (GGNs), which may help avoid aggressive surgical resection for patients with AAH.

Methods

Surgically confirmed cases of AAH and lung adenocarcinomas manifesting as GGNs of less than 1 cm were retrospectively collected. A prediction model based on radiomics and clinical features identified from a training set of cases was built to differentiate AAH from lung adenocarcinomas and tested on a validation set.

Results

Four hundred and eighty-five eligible cases were included and randomly assigned to the training (n = 339) or the validation sets (n = 146). The developed radiomics prediction model showed good discrimination performance to distinguish AAH from adenocarcinomas in both the training and the validation sets, with, respectively, 84.1% and 82.2% of accuracy, and AUCs of 0.899 (95% CI: 0.867–0.931) and 0.881 (95% CI: 0.827–0.936).

Conclusion

The prediction model based on radiomics and clinical features can help differentiate AAH from adenocarcinomas manifesting as subcentimeter GGNs and may prevent aggressive resection for AAH patients, while reserving this treatment for adenocarcinomas.

Identification and Validation of a Ferroptosis-Related Long Non-coding RNA Signature for Predicting the Outcome of Lung Adenocarcinoma

BACKGROUND

Ferroptosis is a recently recognized type of programmed cell death that is involved in the biological processes of various cancers. However, the mechanism of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the role of ferroptosis-associated long non-coding RNAs (lncRNAs) in LUAD and to establish a prognostic model.

METHODS

We downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. We randomly divided the data into training and validation sets. Ferroptosis-associated lncRNA signatures with the lowest Akaike information criteria were determined using COX regression analysis and the least absolute shrinkage and selection operator. The risk scores of ferroptosis-related lncRNAs were calculated, and patients with LUAD were assigned to high- and low-risk groups based on the median risk score. The prognostic value of the risk scores was evaluated using Kaplan-Meier curves, Cox regression analyses, and nomograms. We then explored relationships between ferroptosis-related lncRNAs and the immune response using gene set enrichment analysis (GSEA).

RESULTS

Ten ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan-Meier and Cox regression analyses confirmed that the risk scores were independent predictors of LUAD outcome in the training and validation sets (all P < 0.05). The area under the curve confirmed that the signatures could determine the prognosis of LUAD. The predictive accuracy of the established nomogram model was verified using the concordance index and calibration curve. The GSEA showed that the 10 ferroptosis-related lncRNAs might be associated with tumor immune response.

CONCLUSION

We established a novel signature involving 10 ferroptosis-related lncRNAs (LINC01843, MIR193BHG, AC091185.1, AC027031.2, AL021707.2, AL031667.3, AL606834.1, AC026355.1, AC124045.1, and AC025048.4) that can accurately predict the outcome of LUAD and are associated with the immune response. This will provide new insights into the development of new therapies for LUAD.

Prediction model of emergency mortality risk in patients with acute upper gastrointestinal bleeding: a retrospective study

Background

Upper gastrointestinal bleeding is a common presentation in emergency departments and carries significant morbidity worldwide. It is paramount that treating physicians have access to tools that can effectively evaluate the patient risk, allowing quick and effective treatments to ultimately improve their prognosis. This study aims to establish a mortality risk assessment model for patients with acute upper gastrointestinal bleeding at an emergency department.

Methods

A total of 991 patients presenting with acute upper gastrointestinal bleeding between July 2016 and June 2019 were enrolled in this retrospective single-center cohort study. Patient demographics, parameters assessed at admission, laboratory test, and clinical interventions were extracted. We used the least absolute shrinkage and selection operator regression to identify predictors for establishing a nomogram for death in the emergency department or within 24 h after leaving the emergency department and a corresponding nomogram. The area under the curve of the model was calculated. A bootstrap resampling method was used to internal validation, and decision curve analysis was applied for evaluate the clinical utility of the model. We also compared our predictive model with other prognostic models, such as AIMS65, Glasgow-Blatchford bleeding score, modified Glasgow-Blatchford bleeding score, and Pre-Endoscopic Rockall Score.

Results

Among 991 patients, 41 (4.14%) died in the emergency department or within 24 h after leaving the emergency department. Five non-zero coefficient variables (transfusion of plasma, D-dimer, albumin, potassium, age) were filtered by the least absolute shrinkage and selection operator regression analysis and used to establish a predictive model. The area under the curve for the model was 0.847 (95% confidence interval [0.794–0.900]), which is higher than that of previous models for mortality of patients with acute upper gastrointestinal bleeding. The decision curve analysis indicated the clinical usefulness of the model.

Conclusions

The nomogram based on transfusion of plasma, D-dimer, albumin, potassium, and age effectively assessed the prognosis of patients with acute upper gastrointestinal bleeding presenting at the emergency department.

The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images

Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features-outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large-medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.