Journey toward computer-aided diagnosis: role of image texture analysis

CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection

OBJECTIVE AND DESIGN

We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients.

MATERIAL OR SUBJECTS

Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled.

METHODS

CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses.

RESULTS

CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1.

CONCLUSION

Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.

Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone

Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.

When Sex Matters: Differences in the Central Nervous System as Imaged by OCT through the Retina

BACKGROUND

Retinal texture has gained momentum as a source of biomarkers of neurodegeneration, as it is sensitive to subtle differences in the central nervous system from texture analysis of the neuroretina. Sex differences in the retina structure, as detected by layer thickness measurements from optical coherence tomography (OCT) data, have been discussed in the literature. However, the effect of sex on retinal interocular differences in healthy adults has been overlooked and remains largely unreported.

METHODS

We computed mean value fundus images for the neuroretina layers as imaged by OCT of healthy individuals. Texture metrics were obtained from these images to assess whether women and men have the same retina texture characteristics in both eyes. Texture features were tested for group mean differences between the right and left eye.

RESULTS

Corrected texture differences exist only in the female group.

CONCLUSIONS

This work illustrates that the differences between the right and left eyes manifest differently in females and males. This further supports the need for tight control and minute analysis in studies where interocular asymmetry may be used as a disease biomarker, and the potential of texture analysis applied to OCT imaging to spot differences in the retina.

CT Texture Analysis in Nonalcoholic Fatty Liver Disease (NAFLD)

Background

Nonalcoholic fatty liver disease (NAFLD) is the most common form of liver disease worldwide. There are limited biomarkers that can detect progression from simple steatosis to nonalcoholic steatohepatitis (NASH). The purpose of our study was to utilize CT texture analysis to distinguish steatosis from NASH.

Methods

16 patients with NAFLD (38% male, median (interquartile range): age 57 (48-64) years, BMI 37.5 (35.0-46.8) kg/m2) underwent liver biopsy and abdominal non-contrast CT. CT texture analysis was performed to quantify gray-level tissue summaries (e.g., entropy, kurtosis, skewness, and attenuation) using commercially available software (TexRad, Cambridge England). Logistic regression analyses were performed to quantify the association between steatosis/NASH status and CT texture. ROC curve analysis was performed to determine sensitivity, specificity, AUC, 95% CIs, and cutoff values of texture parameters to differentiate steatosis from NASH.

Results

By histology, 6/16 (37%) of patients had simple steatosis and 10/16 (63%) had NASH. Patients with NASH had lower entropy (median, interquartile range (IQR): 4.3 (4.1, 4.8) vs. 5.0 (4.9, 5.2), P = 0.013) and lower mean value of positive pixels (MPP) (34.4 (21.8, 52.2) vs. 66.5 (57.0, 70.7), P = 0.009) than those with simple steatosis. Entropy values below 4.73 predict NASH with 100% (95%CI: 67-100%) specificity and 80% (50-100%) sensitivity, AUC: 0.88. MPP values below 54.0 predict NASH with 100% (67-100%) specificity and 100% (50-100%) sensitivity, AUC 0.90.

Conclusion

Our study provides preliminary evidence that CT texture analysis may serve as a novel imaging biomarker for disease activity in NAFLD and the discrimination of steatosis and NASH.

Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma

Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.

Radiomics applications in cardiac imaging: a comprehensive review

Radiomics is a new emerging field that includes extraction of metrics and quantification of so-called radiomic features from medical images. The growing importance of radiomics applied to oncology in improving diagnosis, cancer staging and grading, and improved personalized treatment, has been well established; yet, this new analysis technique has still few applications in cardiovascular imaging. Several studies have shown promising results describing how radiomics principles could improve the diagnostic accuracy of coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) in diagnosis, risk stratification, and follow-up of patients with coronary heart disease (CAD), ischemic heart disease (IHD), hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD), and many other cardiovascular diseases. Such quantitative approach could be useful to overcome the main limitations of CCTA and MRI in the evaluation of cardiovascular diseases, such as readers' subjectiveness and lack of repeatability. Moreover, this new discipline could potentially overcome some technical problems, namely the need of contrast administration or invasive examinations. Despite such advantages, radiomics is still not applied in clinical routine, due to lack of standardized parameters acquisition, inconsistent radiomic methods, lack of external validation, and different knowledge and experience among the readers. The purpose of this manuscript is to provide a recent update on the status of radiomics clinical applications in cardiovascular imaging.

Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy

Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

Quantitative and visual analyses of the effect of activity reduction on image metrics and quality in 18F-FDG PET/MRI in pediatric oncology

BACKGROUND

: The aim of our study was to evaluate the effect of reduced injected tracer activities on the quantitative image metrics and the visual image quality in whole-body 18F-FDG PET/MRI with TOF capability in pediatric oncology.

METHODS

Seventy-seven PET/MRI examinations of 54 patients were analyzed (standard injected activity: 1.9 MBq/kg, standard PET scan duration: 5 min per bed position). Lower activity PET images (1.2 MBq/kg and 0.9 MBq/kg) were retrospectively simulated from the originally acquired list-mode data sets. Quantitative parameters were assessed by measuring the SUV metrics, signal-to-noise ratio (SNR), contrast-to-noise ratios (CNR), and textural features in each PET data set. PET images were also evaluated visually for image quality by using a scoring system.

RESULTS

SNRs were found as significantly different among PET data sets (p < 0.001) and showed increasing image noise with decreasing activities. CNR values did not show significant differences among PET data sets. The mean relative percentage changes in SUV metrics were found to be lower in 1.2 MBq/kg data set compared to 0.9 MBq/kg data set. Lesion SUVmax, SUVmean, SULpeak, and textural features were significantly different in 0.9 MBq/kg data set compared to the original data set (p < 0.05 for all). However, SUV metrics and textural features did not show a significant difference between the original and 1.2 MBq/kg data sets. While, the mean visual scores in 0.9 MBq/kg data set were significantly different compared to the original data set (p < 0.001), there was no significant difference between the original and 1.2 MBq/kg data sets in terms of general image quality and image sharpness.

DISCUSSION

Our analyses showed that the reduction of injected activity to 1.2 MBq/kg may be feasible in pediatric oncological PET/ MRI, with a smaller relative percentage change in quantitative parameters and with similar image quality to the original data set.

A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging

Simple Summary

The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging. We discuss the essential elements of AI for veterinary practitioners with the aim of helping them make informed decisions in applying AI technologies to their practices and that veterinarians will play an integral role in ensuring the appropriate uses and suitable curation of data. The expertise of veterinary professionals will be vital to ensuring suitable data and, subsequently, AI that meets the needs of the profession.

Abstract

Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent with an increasing number of reported applications. The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging.

Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions

Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of various urological conditions. Urolithiasis is a common benign condition with a high incidence and recurrence rate. The purpose of this scoping review is to evaluate the current evidence of the application of radiomics in urolithiasis, especially its utility in diagnostics and therapeutics. An electronic literature search on radiomics in the setting of urolithiasis was conducted on PubMed, EMBASE, and Scopus from inception to 21 March 2022. A total of 7 studies were included. Radiomics has been successfully applied in the field of urolithiasis to differentiate phleboliths from calculi and classify stone types and composition pre-operatively. More importantly, it has also been utilized to predict outcomes and complications after endourological procedures. Although radiomics in urolithiasis is still in its infancy, it has the potential for large-scale implementation. Its greatest potential lies in the correlation with conventional established diagnostic and therapeutic factors.

Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63–0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis.

Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field.

Prognostic value of fluorodeoxyglucose positron emission tomography derived metabolic parameters and textural features in pediatric sarcoma

PURPOSE

The aim of this study was to evaluate the prognostic value of PET-derived metabolic features and textural parameters of primary tumors in pediatric sarcoma patients.

METHODS

The imaging findings of 43 patients (14 girls and 29 boys; age 11.4 ± 4.4 years) who underwent 18-fluorodeoxyglucose positron emission tomography (PET)/computed tomography for primary staging prior to therapy between 2005 and 2020 were retrospectively evaluated. The diagnoses were osteosarcoma in 10, rhabdomyosarcoma in 10, and Ewing sarcoma in 23 patients. PET metabolic data and textural features of primary tumors were obtained. Cox proportional hazards regression models were used to identify predictors for progression-free survival and overall survival. Survival curves were estimated by using the Kaplan-Meier method.

RESULTS

Distant metastases were detected in primary staging in 13 patients (30.2%). The median follow-up duration after diagnosis was 28 months (range: 10-171 months). In multivariate Cox regression analysis, the presence of distant metastasis and neighborhood grey-level difference matrix_Contrast (ngldm_Contrast) were found as independent predictors for both progression-free survival and overall survival. Grey-level zone length matrix_Zone-length nonuniformity (glzlm_ZLNU) was also found as an independent predictor for overall survival. The Kaplan-Meier survival analysis showed that higher ngldm_Contrast and glzlm_ZLNU values of primary tumors were significantly associated with shorter progression-free survival and overall survival.

CONCLUSION

In addition to the presence of distant metastasis at initial diagnosis, textural features of primary tumors may be used as prognostic biomarkers to identify patients with worse prognosis in pediatric sarcoma. Higher tumor heterogeneity is significantly associated with shorter progression-free survival and OS.

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review

Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.

Texture Analysis of Magnetic Resonance Images Enables Phenotyping of Potentially Painful Annular Fissures

STUDY DESIGN

Retrospective analysis of prospectively collected data.

OBJECTIVE

To investigate whether intervertebral disc (IVD) image features, extracted from magnetic resonance (MR) images, can depict the extension and width of annular fissures and associate them to pain.

SUMMARY OF BACKGROUND DATA

Annular fissures are suggested to be associated with low back pain (LBP). Magnetic resonance imaging (MRI) is a sensitive method, yet fissures are sometimes unobservable in T2-weighted MR-images, even though fissure information is present in the image. Image features can mathematically be calculated from MR-images and might reveal fissure characteristics.

METHODS

Forty four LBP patients who underwent MRI, low-pressure discography (<50 psi), and computed tomography (CT) sequentially in 1 day, were reviewed. After semi-automated segmentation of 126 discs, image features were extracted from the T2-weighted images. The number of image features was reduced with principle component analysis (PCA). CT-discograms were graded and dichotomized regarding extension and width of fissures. IVDs were divided into fissures extending to outer annulus versus short/no fissures. Fissure width was dichotomized into narrow (<10%) versus broad fissures (>10%), and into moderately broad (10%-50%) versus very broad fissures (>50%). Logistic regression was performed to investigate if image features could depict fissure extension to outer annulus and fissure width. As a sub-analysis, the association between image features used to depict fissure characteristics and discography-provoked pain-response were investigated.

RESULTS

Fissure extension could be depicted with sensitivity/specificity = 0.97/0.77 and area under curve (AUC) = 0.97. Corresponding results for width depiction were sensitivity/specificity = 0.94/0.39 and 0.85/0.62, and AUC = 0.86 and 0.81 for narrow versus broad and moderately broad versus very broad fissures respectively. Pain prediction with image features used for depicting fissure characteristics showed sensitivity/specificity = 0.90/0.36, 0.88/0.4, 0.93/0.33; AUC = 0.69, 0.75, and 0.73 respectively.

CONCLUSION

Standard MR-images contains fissure information associated to pain that can be depicted with image features, enabling non-invasive phenotyping of potentially painful annular fissures.Level of Evidence: 2.

Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis

Non-tuberculous mycobacterial (NTM) infection is an emerging infectious entity that often presents as lymphadenitis in the pediatric age group. Current practice involves invasive testing and excisional biopsy to diagnose NTM lymphadenitis. In this study, we performed a retrospective analysis of 249 lymph nodes selected from 143 CT scans of pediatric patients presenting with lymphadenopathy at the Montreal Children’s Hospital between 2005 and 2018. A Random Forest classifier was trained on the ten most discriminative features from a set of 1231 radiomic features. The model classifying nodes as pyogenic, NTM, reactive, or proliferative lymphadenopathy achieved an accuracy of 72%, a precision of 68%, and a recall of 70%. Between NTM and all other causes of lymphadenopathy, the model achieved an area under the curve (AUC) of 89%. Between NTM and pyogenic lymphadenitis, the model achieved an AUC of 90%. Between NTM and the reactive and proliferative lymphadenopathy groups, the model achieved an AUC of 93%. These results indicate that radiomics can achieve a high accuracy for classification of NTM lymphadenitis. Such a non-invasive highly accurate diagnostic approach has the potential to reduce the need for invasive procedures in the pediatric population.

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

Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. ¹⁸F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of ¹⁸F-FDG PET/CT in the field of oncology is well established. Clinical application of ¹⁸F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of ¹⁸F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of ¹⁸F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of ¹⁸F-FDG PET/CT in lung disease management.

Diagnosis and Treatment of Acromegaly: An Update

Acromegaly is typically caused by a growth hormone-secreting pituitary adenoma, driving excess secretion of insulin-like growth factor 1. Acromegaly may result in a variety of cardiovascular, respiratory, endocrine, metabolic, musculoskeletal, and neoplastic comorbidities. Early diagnosis and adequate treatment are essential to mitigate excess mortality associated with acromegaly. PubMed searches were conducted using the keywords growth hormone, acromegaly, pituitary adenoma, diagnosis, treatment, pituitary surgery, medical therapy, and radiation therapy (between 1981 and 2021). The diagnosis of acromegaly is confirmed on biochemical grounds, including elevated serum insulin-like growth factor 1 and lack of growth hormone suppression after glucose administration. Pituitary magnetic resonance imaging is advised in patients with acromegaly to identify an underlying pituitary adenoma. Transsphenoidal pituitary surgery is generally first-line therapy for patients with acromegaly. However, patients with larger and invasive tumors (macroadenomas) are often not in remission postoperatively. Medical therapies, including somatostatin receptor ligands, cabergoline, and pegvisomant, can be recommended to patients with persistent disease after surgery. Select patients may also be candidates for preoperative medical therapy. In addition, primary medical therapy has a role for patients without mass effect on the optic chiasm who are unlikely to be cured by surgery. Clinical, endocrine, imaging, histologic, and molecular markers may help predict the response to medical therapy; however, confirmation in prospective studies is needed. Radiation therapy is usually a third-line option and is increasingly administered by a variety of stereotactic techniques. An improved understanding of the pathogenesis of acromegaly may ultimately lead to the design of novel, efficacious therapies for this serious condition.

Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis

•

A novel model incorporating multiparametric MRI-based radiomic signature with clinically independent risk factors can greatly improve the non-invasive diagnostic accuracy in differentiating PC from MFCP.

•

The nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models.

•

The mixed model may aid in formulating treatment strategies and help to avoid unnecessary surgical operations for doctors.

Purpose

It is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions.

Methods

A total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts.

Results

ADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group.

Conclusion

The mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.

Texture features of primary tumor on 18F-FDG PET images in non-small cell lung cancer: The relationship between imaging and histopathological parameters

PURPOSE

The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients.

METHODS

PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, MTV, TLG) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded.

RESULTS

Among the two histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis.

CONCLUSIONS

Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC.

Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve

Background

Current intra-tumoral heterogeneous feature extraction in radiology is limited to the use of a single slice or the region of interest within a few context-associated slices, and the decoding of intra-tumoral spatial heterogeneity using whole tumor samples is rare. We aim to propose a mathematical model of space-filling curve-based spatial correspondence mapping to interpret intra-tumoral spatial locality and heterogeneity.

Methods

A Hilbert curve-based approach was employed to decode and visualize intra-tumoral spatial heterogeneity by expanding the tumor volume to a two-dimensional (2D) matrix in voxels while preserving the spatial locality of the neighboring voxels. The proposed method was validated using three-dimensional (3D) volumes constructed from lung nodules from the LIDC-IDRI dataset, regular axial plane images, and 3D blocks.

Results

Dimensionality reduction of the Hilbert volume with a single regular axial plane image showed a sparse and scattered pixel distribution on the corresponding 2D matrix. However, for 3D blocks and lung tumor inside the volume, the dimensionality reduction to the 2D matrix indicated regular and concentrated squares and rectangles. For classification into benign and malignant masses using lung nodules from the LIDC-IDRI dataset, the Inception-V4 indicated that the Hilbert matrix images improved accuracy (85.54% vs. 73.22%, p < 0.001) compared to the original CT images of the test dataset.

Conclusions

Our study indicates that Hilbert curve-based spatial correspondence mapping is promising for decoding intra-tumoral spatial heterogeneity of partial or whole tumor samples on radiological images. This spatial-locality-preserving approach for voxel expansion enables existing radiomics and convolution neural networks to filter structured and spatially correlated high-dimensional intra-tumoral heterogeneity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01100-8.

Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors

PURPOSE

To evaluate and compare the diagnostic performances of whole-lesion apparent diffusion coefficient (ADC) histogram analysis and single-slice ADC measurement in the differentiation of benign and malignant soft tissue tumors.

METHODS

Fifty-three patients (mean age: 48.5 ± 21.4) with soft tissue tumors (27 benign and 26 malignant) were evaluated with diffusion-weighted MRI. Whole-lesion ADC histogram parameters (mean, median, 10^th percentile, 90^th percentile, minimum, maximum, range, mean absolute deviation, interquartile range, kurtosis, skewness, root mean squared, variance and inhomogeneity) of the lesions were measured using the whole solid tumor volume region of interest (ROI). In other sessions, five ROIs were manually drawn on the tumor slices, and mean ADC and minimum ADC of the measurements were calculated. Diagnostic accuracies of the two methods were assessed and compared.

RESULTS

Mean, median, minimum, 10^th percentile, 90^th percentile, range, root mean squared and inhomogeneity of ADC histogram analysis, and mean ADC and minimum ADC of single-slice ADC measurement were significantly different between malignant and benign lesions (p < 0.001 - p = 0.002). Among the ADC histogram parameters, 10^th percentile had the highest diagnostic performance (AUC = 0.825) followed by mean (AUC = 0.792) and median (AUC = 0.789). For the single-slice ADC measurement, the AUC of mean ADC and minimum ADC were 0.842 and 0.786, respectively. Mean ADC of single-slice measurement had a similar diagnostic performance with the 10^th percentile, mean, and median of ADC histogram analysis (p = 0.070-1.000).

CONCLUSIONS

Both whole-lesion ADC histogram analysis and single-slice ADC measurement can differentiate benign and malignant soft tissue tumors with similar diagnostic performances.

Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy

OBJECTIVE

To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy.

METHODS

A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test.

RESULTS

Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation.

CONCLUSIONS

Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.

Evaluation of 68Ga-PSMA-11 PET-MRI in Patients with Advanced Prostate Cancer Receiving 177Lu-PSMA-617 Therapy: A Radiomics Analysis

¹⁷⁷Lutetium PSMA-617 (Lu-PSMA) therapy in patients with metastatic castration resistant prostate cancer (mCRPC) has gained visibility through the ongoing phase III trial. The data on prediction of therapy outcome and survival out of pretherapeutic imaging parameters is still sparse. In this study, the predictive and prognostic value of radiomic features from ⁶⁸Ga-PSMA-11 PET-MRI are analyzed. In total, 21 patients with mCRPC underwent ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy. The PET-positive tumor volume was defined and transferred to whole-body T2-, T1- and contrast-enhanced T1-weighted MRI-sequences. The radiomic features from PET and MRI sequences were extracted by using a freely available software package. For selecting features that allow differentiation of biochemical response (PSA decrease > 50%), a stepwise dimension reduction was performed. Logistic regression models were fitted, and selected features were tested for their prognostic value (overall survival) in all patients. Eight patients achieved biochemical response after Lu-PSMA therapy. Ten independent radiomic features differentiated well between responders and non-responders. The logistic regression model, including the feature interquartile range from T2-weighted images, revealed the highest accuracy (AUC = 0.83) for the prediction of biochemical response after Lu-PSMA therapy. Within the final model, patients with a biochemical response (p = 0.003) and higher T2 interquartile range values in pre-therapeutic imaging (p = 0.038) survived significantly longer. This proof-of-concept study provides first evidence on a potential predictive and prognostic value of radiomic analysis of pretherapeutic ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy.

HCC advances in diagnosis and prognosis: Digital and Imaging

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death worldwide. Understanding of the pathogenesis of HCC has significantly improved in the past few years due to advances in genetics, molecular biology and pathology. Several subtypes have been identified with different backgrounds and outcomes, leading to possible changes in disease management and challenging the role of imaging. Indeed, despite its pivotal role in the diagnostic workup, prognosis, and the decision-making process in patients with HCC, these recent developments are progressively redefining the role of imaging. First and most important, liver imaging is shifting from a purely qualitative to a quantitative paradigm, integrating quantitative imaging and radiomics in a digital era. Second, to improve patient management, imaging has gradually moved beyond tumor-centered assessment to include a broader evaluation of the liver and its function. This review describes and discusses these advances in the imaging for the diagnosis and prognosis of HCC.

CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).

Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.

Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.

Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

Texture features of primary tumor on 18F-FDG PET images in non-small cell lung cancer: The relationship between imaging and histopathological parameters

OBJECTIVES

The aims of this study were to evaluate the relationships between textural features of the primary tumor on FDG PET images and clinical-histopathological parameters which are useful in predicting prognosis in newly diagnosed non-small cell lung cancer (NSCLC) patients.

MATERIAL AND METHODS

PET/CT images of ninety (90) patients with NSCLC prior to surgery were analyzed retrospectively. All patients had resectable tumors. From the images we acquired data related to metabolism (SUVmax, metabolic tumor volume [MTV] and total lesion glycolysis [TLG]) and texture features of primary tumors. Histopathological tumor types and subgroups, degree of Ki-67 expression and necrosis rates of the primary tumor, mediastinal lymph node (MLN) status and nodal stages were recorded.

RESULTS

Among the 2histologic tumor types (adenocarcinoma and squamous cell carcinoma) significant differences were present regarding metabolic parameters, Ki-67 index with higher values and kurtosis with lower values in the latter group. Textural heterogeneity was found to be higher in poorly differentiated tumors compared to moderately differentiated tumors in patients with adenocarcinoma. While Ki-67 index had significant correlations with metabolic parameters and kurtosis, tumor necrosis rate was only significantly correlated with textural features. By univariate and multivariate analyses of the imaging and histopathological factors examined, only gradient variance was significant predictive factor for the presence of MLN metastasis.

CONCLUSIONS

Textural features had significant associations with histologic tumor types, degree of pathological differentiation, tumor proliferation and necrosis rates. Texture analysis has potential to differentiate tumor types and subtypes and to predict MLN metastasis in patients with NSCLC.

Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions

Background

It is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.

Methods

This retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists’ evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.

Results

The areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists’ evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.

Conclusions

The radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.

Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis

Simple Summary

Radiomics may answer questions where the conventional interpretation of medical imaging has limitations. The aim of our systematic review and meta-analysis was to assess the (current) status of evidence in the application of radiomics in the field of renal masses. We focused on its role in diagnosis, sub-entity discrimination and treatment response assessment in renal cell carcinoma (RCC) and benign renal masses. Our quantitative synthesis showed promising results in discrimination of tumor dignity, nevertheless, the value added to human assessment remains unclear and should be the focus of future research. Furthermore, the benefit regarding treatment response assessment remains unclear as well, since the existing studies are investigating already abandoned systemic therapies (ST), which no longer represent the current “reference” standard. Open science could enable to establish technical and clinical validity of radiomic signatures prior to the incorporation of radiomics into everyday clinical practice.

Abstract

Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25–7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40–3.39, p < 0.001), 3.08 (95%-CI 2.09–4.06, p < 0.001) and 3.57 (95%-CI 2.69–4.45, p < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73–3.62, p < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post-T1pre and T2-FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.

Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

Radiomics in medical imaging-"how-to" guide and critical reflection

Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.

Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors

Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as texture or radiomic analysis. Numerous studies have demonstrated applications for texture and radiomic characterization methods for assessing brain tumors to improve noninvasive predictions of tumor histologic characteristics, molecular profile, distinction of treatment-related changes, and prediction of patient survival. In this review, the current use and future potential of texture or radiomic-based approaches with machine learning for brain tumor image analysis and prediction algorithm construction will be discussed. This technology has the potential to advance the value of diagnostic imaging by extracting currently unused information on medical scans that enables more precise, personalized therapy; however, significant barriers must be overcome if this technology is to be successfully implemented on a wide scale for routine use in the clinical setting. Keywords: Adults and Pediatrics, Brain/Brain Stem, CNS, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Image Postprocessing, Informatics, Neural Networks, Neuro-Oncology, Oncology, Treatment Effects, Tumor Response Supplemental material is available for this article. © RSNA, 2020.

Radiomics and Artificial Intelligence for Renal Mass Characterization

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.

MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands

PURPOSE

Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs).

METHODS

We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes.

RESULTS

Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33-26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression.

CONCLUSION

Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy.

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study

BACKGROUND

Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenectomy. Important in this regard is the presence of a soft pancreatic texture which is a major risk factor. Advances in machine learning and texture analysis of medical images allow identification of features of parenchyma that are invisible to the human eye. The aim of this study was to investigate the potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced computed tomography.

METHODS

We screened a prospectively assessed database including all patients undergoing pancreatoduodenectomy at a tertiary center from 2008 until 2018 for patients based on the occurrence of postoperative pancreatic fistula. In total, 110 patients were included, consisting of 55 patients who developed a postoperative pancreatic fistula and 55 without postoperative pancreatic fistula. For machine learning-based texture analysis preoperative, non-contrast-enhanced computed tomography axial images were used. Machine learning results were tested using 10-fold cross validation. Previously validated clinical fistula risk scores (original and alternative fistula risk scores) served as reference tests.

RESULTS

Both the original and the alternative fistula risk scores showed good discrimination between patients without and with postoperative pancreatic fistula (area under the curve 0.76 and 0.72, respectively). Machine learning-based texture analysis showed potential to detect histologic fibrosis (area under the curve 0.84, sensitivity 75%; specificity 92%), histologic lipomatosis (area under the curve 0.82, sensitivity 78%; specificity 89%), and intraoperative pancreatic hardness (area under the curve 0.70, sensitivity 78%; specificity 74%). The features of the machine learning-based texture analysis were most accurate in predicting the occurrence of postoperative pancreatic fistula (area under the curve 0.95, sensitivity of 96%; specificity 98%) after pancreatoduodenectomy.

CONCLUSION

This proof-of-principle study suggests the ability of machine learning in recognizing important features of pancreatic texture associated with an increased risk of postoperative pancreatic fistula based on preoperative computed tomography.

Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules

Aim

The aim of this study is to evaluate the diagnostic value of machine learning- (ML-) based quantitative texture analysis in the differentiation of benign and malignant thyroid nodules.

Materials and methods

A sum of 306 quantitative textural features of 235 thyroid nodules (102 malignant, 43.4%; 133 benign, 56.4%) of a total of 198 patients were investigated using the random forest ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy, sensitivity, specificity, and area under curve (AUC) of the proposed method were compared with the histopathological or cytopathological findings as reference methods.

Results

Of the 306 initial texture features, 284 (92.2%) showed good reproducibility (intraclass correlation ≥0.80). The random forest classifier accurately identified 87 out of 102 malignant thyroid nodules and 117 out of 133 benign thyroid nodules, which is a diagnostic sensitivity of 85.2%, specificity of 87.9%, and accuracy of 86.8%. The AUC of the model was 0.92.

Conclusions

Quantitative textural analysis of thyroid nodules using ML classification can accurately discriminate benign and malignant thyroid nodules. Our findings should be validated by multicenter prospective studies using completely independent external data.

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk

Background

Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK.

Methods

The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk.

Results

The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3–25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0–13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006).

Conclusions

Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1165-5) contains supplementary material, which is available to authorized users.

Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology

Unlabelled Image.

Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis

PURPOSE

To predict the rate of liver regeneration after living donor liver transplantation (LDLT) using pre-operative computed tomography (CT) texture analysis.

MATERIALS AND METHODS

112 living donors who performed right hepatectomy for LDLT were included retrospectively. We measured the volume of future remnant liver (FLR) on pre-operative CT and the volume of remnant liver (LR) on follow-up CT, taken at a median of 123 days after transplantation. The regeneration index (RI) was calculated using the following equation: [Formula: see text]. Computerized texture analysis of the semi-automatically segmented FLR was performed. We used a stepwise, multivariable linear regression to assess associations of clinical features and texture parameters in relation to RI and to make the best-fit predictive model.

RESULTS

The mean RI was 110.7 ± 37.8%, highly variable ranging from 22.4% to 247.0%. Among texture parameters, volume of FLR, standard deviation, variance, and gray level co-occurrence matrices (GLCM) contrast were found to have significant correlations between RI. In multivariable analysis, smaller volume of FLR (ß - 0.17, 95% CI - 0.22 to - 0.13) and lower GLCM contrast (ß - 1.87, 95% CI - 3.64 to - 0.10) were associated with higher RI. The regression equation predicting RI was following: RI = 203.82 + 10.42 × pre-operative serum total bilirubin (mg/dL) - 0.17 × V_FLR (cm³) - 1.87 × GLCM contrast (× 100).

CONCLUSION

Volume of FLR and GLCM contrast were independent predictors of RI, showing significant negative correlations. Pre-operative CT with texture analysis can be useful for predicting the rate of liver regeneration in living donor of liver transplantation.

Computed tomography texture features can discriminate benign from malignant lymphadenopathy in pediatric patients: a preliminary study

BACKGROUND

Differentiation of benign from malignant lymphadenopathy remains challenging in pediatric radiology. Textural analysis (TA) quantitates heterogeneity of tissue signal intensities and has been applied to analysis of CT images.

OBJECTIVE

The purpose of this study was to establish whether CT textural analysis of enlarged lymph nodes visualized on pediatric CT can distinguish benign from malignant lymphadenopathy.

MATERIALS AND METHODS

We retrospectively identified enlarged lymph nodes measuring 10-20 mm on contrast-enhanced CTs of patients age 18 years and younger that had been categorized as benign or malignant based on the known diagnoses. We placed regions of interest (ROIs) over lymph nodes of interest and performed textural analysis with and without feature size filtration. We then calculated test performance characteristics for TA features, along with multivariate logistic regression modeling using Akaike Information Criterion (AIC) minimization, to determine the optimal thresholds for distinguishing benign from malignant lymphadenopathy.

RESULTS

We identified 34 enlarged malignant nodes and 29 benign nodes from 63 patients within the 10- to 20-mm size range. Filtered image TA exhibited 82.4% sensitivity, 86.2% specificity and 84.1% accuracy for detecting malignant lymph nodes using mean and entropy parameters, whereas unfiltered TA exhibited 88.2% sensitivity, 72.4% specificity and 81.0% accuracy using mean and mean value of positive pixels parameters.

CONCLUSION

This preliminary study demonstrates that the use of TA features improves the utility of pediatric CT to distinguish benign from malignant lymphadenopathy. The addition of TA to pediatric CT protocols has great potential to aid the characterization of indeterminate lymph nodes. If definitive differentiation between benign and malignant lymphadenopathy is possible by TA, it has the potential to reduce the need for follow-up imaging and tissue sampling, with reduced associated radiation exposure. However future studies are needed to confirm the clinical applicability of TA in distinguishing benign from malignant lymphadenopathy.

MRI texture analysis as a predictor of tumor recurrence or progression in patients with clinically non-functioning pituitary adenomas

BACKGROUND

There are limited predictors of prognosis in patients with clinically non-functioning pituitary adenomas (NFPAs). We hypothesized that MRI texture analysis may predict tumor recurrence or progression in patients with NFPAs undergoing transsphenoidal pituitary surgery (TSS).

OBJECTIVE

To characterize texture parameters on preoperative MRI examinations in patients with NFPAs in relation to prognosis.

METHODS

Retrospective study of patients with NFPAs who underwent TSS at our institution between 2009 and 2010. Clinical, radiological and histopathological data were extracted from electronic medical records. MRI texture analysis was performed on coronal T1-weighted non-enhanced MR images using ImageJ (NIH). MRI texture parameters were used to predict tumor recurrence or progression. Both logistic regression and Cox proportional hazard analyses were conducted to adjust for potential confounders.

RESULTS

Data on 78 patients were analyzed. On both crude and multivariable-adjusted analyses, mean, median, mode, minimum and maximum pixel intensity were associated with the risk of pituitary tumor recurrence or progression after TSS. Patients whose tumor mean pixel intensity was above the median for the population had a hazard ratio of 0.44 (95% CI: 0.21-0.94, P = 0.034) for recurrence or progression in comparison with tumors below the median.

CONCLUSIONS

Our data suggest that MRI texture analysis can predict the risk of tumor recurrence or progression in patients with NFPAs.

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results

PURPOSE

To test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches.

METHODS

This retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images.

RESULTS

Comparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity.

CONCLUSIONS

TA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations.

Automated and effective content-based image retrieval for digital mammography

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

Investigation of the radiotherapy-related changes in the eye lens using computed tomography entropy analysis

OBJECTIVE

The aim of this study was to apply texture analysis to investigate whether there was a change in the lens following radiotherapy.

PATIENTS AND METHOD

Patients who received radiotherapy (RT) for head and neck cancer or brain tumor were enrolled. Computed tomography (CT) images taken in the last month before RT and the most recent images after RT were compared. Entropy values were calculated using lens attenuation values. The lens doses were obtained from the dose-volume histogram data.

RESULTS

A total of 55 lenses were evaluated. The mean Hounsfield Unit value of the lenses was 66.14±12.16 before RT and 72.02±9.12 after RT (p = 0.007). The mean entropy value was 1.87±0.31 before RT and this reduced to 1.31±0.34 after RT (p < 0.001), respectively. As time increased, the difference in entropy also increased (p = 0.007). A correlation close to statistical significance was determined between the entropy difference and minimum, maximum and mean lens radiation dose (p = 0.052, p = 0.052, p = 0.063, respectively). The entropy difference was significantly reduced in the >4 Gy group (p = 0.046).

CONCLUSION

Study results indicated that the entropy values in the lens were signifcantly changed after radiotherapy and the degree of the change associated with dose and time.