Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.

OBJECTIVE: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations. METHODS: Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed. RESULTS: A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively. CONCLUSION: Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. CLINICAL RELEVANCE STATEMENT: Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. KEY POINTS: • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.

Self-reported checklists and quality scoring tools in radiomics: a meta-research.

OBJECTIVE: To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. METHODS: Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers. RESULTS: The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of - 7.2 (standard deviation, 6.8). CONCLUSION: Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable. CLINICAL RELEVANCE STATEMENT: Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward. KEY POINTS: • In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools.

Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.

OBJECTIVE: To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS: We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS: Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS: Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT: Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS: The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.

The role of anticomplement therapy in the management of the kidney allograft.

As the number of patients living with kidney failure grows, the need also grows for kidney transplantation, the gold standard kidney replacement therapy that provides a survival advantage. This may result in an increased rate of transplantation from HLA-mismatched donors that increases the rate of antibody-mediated rejection (AMR), which already is the leading cause of allograft failure. Plasmapheresis, intravenous immunoglobulin therapy, anti-CD20 therapies (i.e., rituximab), bortezomib and splenectomy have been used over the years to treat AMR as well as to prevent AMR in high-risk sensitized kidney transplant recipients. Eculizumab and ravulizumab are monoclonal antibodies targeting the C5 protein of the complement pathway and part of the expanding field of anticomplement therapies, which is not limited to kidney transplant recipients, and also includes complement-mediated microangiopathic hemolytic anemia, paroxysmal nocturnal hemoglobinuria, and ANCA-vasculitis. In this narrative review, we summarize the current knowledge concerning the pathophysiological background and use of anti-C5 strategies (eculizumab and ravulizumab) and C1-esterase inhibitor in AMR, either to prevent AMR in high-risk desensitized patients or to treat AMR as first-line or rescue therapy and also to treat de novo thrombotic microangiopathy in kidney transplant recipients.

Pancreatic steatosis is an independent risk factor for post-transplant diabetes mellitus in kidney transplant patients.

BACKGROUND AND AIM: Post-transplant diabetes mellitus (PTDM) is associated with an increased risk of post-transplant cardiovascular diseases, and several risk factors of PTDM have been shown in the literature. Yet, the relationship between hepatic and pancreatic steatosis with post-transplant diabetes mellitus remains vague. We aimed to evaluate pancreatic steatosis, a novel component of metabolic syndrome, and hepatic steatosis association with post-transplant diabetes mellitus in a single-center retrospective cohort study conducted on kidney transplant recipients. METHOD: We have performed a single-center retrospective cohort study involving all kidney transplant recipients. We have utilized pretransplant Fibrosis-4, nonalcoholic fatty liver disease fibrosis score, and abdominal computed tomography for the assessment of visceral steatosis status. RESULTS: We have included 373 kidney transplant recipients with a mean follow-up period of 32 months in our final analysis. Post-transplant diabetes mellitus risk is associated with older age (p < .001), higher body-mass index (p < .001), nonalcoholic fatty liver disease-fibrosis score (p = .002), hepatic (p < .001) or pancreatic (p < .001) steatosis on imaging and higher pre-transplant serum triglyceride (p = .003) and glucose levels (p = .001) after multivariate analysis. CONCLUSION: Our study illustrates that recipients' pancreatic steatosis is an independent predictive factor for post-transplant diabetes mellitus including in kidney transplant patients.

Spontaneous intracranial hypotension: Exploring the viability of non-contrast FLAIR as a substitute for contrast-enhanced T1WI in assessing pachymeningeal thickening.

PURPOSE: To avoid contrast administration in spontaneous intracranial hypotension (SIH), some studies suggest accepting diffuse pachymeningeal hyperintensity (DPMH) on non-contrast fluid-attenuated inversion recovery (FLAIR) as an equivalent sign to diffuse pachymeningeal enhancement (DPME) on contrast-enhanced T1WI (T1ce), despite lacking thorough performance metrics. This study aimed to comprehensively explore its feasibility. METHODS: In this single-center retrospective study, between April 2021 and November 2023, brain MRI examinations of 43 patients clinically diagnosed with SIH were assessed using 1.5 and 3.0 Tesla MRI scanners. Two radiologists independently assessed the presence or absence of DPMH on FLAIR and DPME on T1ce, with T1ce serving as a gold-standard for pachymeningeal thickening. The contribution of the subdural fluid collections to DPMH was investigated with quantitative measurements. Using Cohen's kappa statistics, interobserver agreement was assessed. RESULTS: In 39 out of 43 patients (90.7%), pachymeningeal thickening was observed on T1ce. FLAIR sequence produced an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 72.1%, 71.8%, 75.0%, 96.6%, and 21.4% respectively, for determining pachymeningeal thickening. FLAIR identified pachymeningeal thickening in 28 cases; however, among these, 21 cases (75%) revealed that the pachymeningeal hyperintense signal was influenced by subdural fluid collections. False-negative rate for FLAIR was 28.2% (11/39). CONCLUSION: The lack of complete correlation between FLAIR and T1ce in identifying pachymeningeal thickening highlights the need for caution in removing contrast agent administration from the MRI protocol of SIH patients, as it reveals a major criterion (i.e., pachymeningeal enhancement) of Bern score.

Multiple sclerosis versus cerebral small vessel disease in MRI: a practical approach using qualitative and quantitative signal intensity differences in white matter lesions.

BACKGROUND: Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are relatively common radiological entities that occasionally necessitate differential diagnosis. PURPOSE: To investigate the differences in magnetic resonance imaging (MRI) signal intensity (SI) between MS and CSVD related white matter lesions. MATERIAL AND METHODS: On 1.5-T and 3-T MRI scanners, 50 patients with MS (380 lesions) and 50 patients with CSVD (395 lesions) were retrospectively evaluated. Visual inspection was used to conduct qualitative analysis on diffusion-weighted imaging (DWI)_b1000 to determine relative signal intensity. The thalamus served as the reference for quantitative analysis based on SI ratio (SIR). The statistical analysis utilized univariable and multivariable methods. There were analyses of patient and lesion datasets. On a dataset restricted by age (30-50 years), additional evaluations, including unsupervised fuzzy c-means clustering, were performed. RESULTS: Using both quantitative and qualitative features, the optimal model achieved a 100% accuracy, sensitivity, and specificity with an area under the curve (AUC) of 1 in patient-wise analysis. With an AUC of 0.984, the best model achieved a 94% accuracy, sensitivity, and specificity when using only quantitative features. The model's accuracy, sensitivity, and specificity were 91.9%, 84.6%, and 95.8%, respectively, when using the age-restricted dataset. Independent predictors were T2_SIR_max (optimal cutoff=2.1) and DWI_b1000_SIR_mean (optimal cutoff=1.1). Clustering also performed well with an accuracy, sensitivity, and specificity of 86.5%, 70.6%, and 100%, respectively, in the age-restricted dataset. CONCLUSION: SI characteristics derived from DWI_b1000 and T2-weighted-based MRI demonstrate excellent performance in differentiating white matter lesions caused by MS and CSVD.

Utility of diffusion tensor imaging and generalized q-sampling imaging for predicting short-term clinical effect of deep brain stimulation in Parkinson's disease.

PURPOSE: To assess whether diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) metrics could preoperatively predict the clinical outcome of deep brain stimulation (DBS) in patients with Parkinson's disease (PD). METHODS: In this single-center retrospective study, from September 2021 to March 2023, preoperative DTI and GQI examinations of 44 patients who underwent DBS surgery, were analyzed. To evaluate motor functions, the Unified Parkinson's Disease Rating Scale (UPDRS) during on- and off-medication and Parkinson's Disease Questionnaire-39 (PDQ-39) scales were used before and three months after DBS surgery. The study population was divided into two groups according to the improvement rate of scales: ≥ 50% and < 50%. Five target regions, reported to be affected in PD, were investigated. The parameters having statistically significant difference were subjected to a receiver operating characteristic (ROC) analysis. RESULTS: Quantitative anisotropy (qa) values from globus pallidus externus, globus pallidus internus (qa_Gpi), and substantia nigra exhibited significant distributional difference between groups in terms of the improvement rate of UPDRS-3 scale during on-medication (p = 0.003, p = 0.0003, and p = 0.0008, respectively). In ROC analysis, the best parameter in predicting DBS response included qa_Gpi with a cut-off value of 0.01370 achieved an area under the ROC curve, accuracy, sensitivity, and specificity of 0.810, 73%, 62.5%, and 85%, respectively. Optimal cut-off values of ≥ 0.01864 and ≤ 0.01162 yielded a sensitivity and specificity of 100%, respectively. CONCLUSION: The imaging parameters acquired from GQI, particularly qa_Gpi, may have the ability to non-invasively predict the clinical outcome of DBS surgery.

Trends and statistics of artificial intelligence and radiomics research in Radiology, Nuclear Medicine, and Medical Imaging: bibliometric analysis.

OBJECTIVE: To conduct a comprehensive bibliometric analysis of artificial intelligence (AI) and its subfields as well as radiomics in Radiology, Nuclear Medicine, and Medical Imaging (RNMMI). METHODS: Web of Science was queried for relevant publications in RNMMI and medicine along with their associated data from 2000 to 2021. Bibliometric techniques utilised were co-occurrence, co-authorship, citation burst, and thematic evolution analyses. Growth rate and doubling time were also estimated using log-linear regression analyses. RESULTS: According to the number of publications, RNMMI (11,209; 19.8%) was the most prominent category in medicine (56,734). USA (44.6%) and China (23.1%) were the two most productive and collaborative countries. USA and Germany experienced the strongest citation bursts. Thematic evolution has recently exhibited a significant shift toward deep learning. In all analyses, the annual number of publications and citations demonstrated exponential growth, with deep learning-based publications exhibiting the most prominent growth pattern. Estimated continuous growth rate, annual growth rate, and doubling time of the AI and machine learning publications in RNMMI were 26.1% (95% confidence interval [CI], 12.0-40.2%), 29.8% (95% CI, 12.7-49.5%), and 2.7 years (95% CI, 1.7-5.8), respectively. In the sensitivity analysis using data from the last 5 and 10 years, these estimates ranged from 47.6 to 51.1%, 61.0 to 66.7%, and 1.4 to 1.5 years. CONCLUSION: This study provides an overview of AI and radiomics research conducted mainly in RNMMI. These results may assist researchers, practitioners, policymakers, and organisations in gaining a better understanding of both the evolution of these fields and the importance of supporting (e.g., financial) these research activities. KEY POINTS: • In terms of the number of publications on AI and ML, Radiology, Nuclear Medicine, and Medical Imaging was the most prominent category compared to the other categories related to medicine (e.g., Health Policy & Services, Surgery). • All evaluated analyses (i.e., AI, its subfields, and radiomics), based on the annual number of publications and citations, demonstrated exponential growth, with decreasing doubling time, which indicates increasing interest from researchers, journals, and, in turn, the medical imaging community. • The most prominent growth pattern was observed in deep learning-based publications. However, the further thematic analysis demonstrated that deep learning has been underdeveloped but highly relevant to the medical imaging community.

Seasonal variations of some soil nutrients in a natural and an agricultural olive grove in Adana, Turkey.

The bioavailability and cycling of nutrients in soil are two of the most important factors for healthy plant growth and development in natural and agricultural ecosystems. Seasonal variations of some soil macronutrient (phosphorus and potassium) and micronutrient (copper, manganese, and zinc) contents were investigated in a natural olive (Olea europaea L.) grove (NO) and an agricultural olive gene garden (OGG) located in Adana, Turkey. Soils were sampled at 0-10 cm and at 10-20 cm depth in the months of November, February, May, and August between 2013 and 2015. Soil phosphorus, potassium, copper, manganese, and zinc contents were in the range between 0.37 and 8.65 mg kg-1, 181.81 and 1030.67 mg kg-1, 1.41 and 2.74 mg kg-1, 13.88 and 51.06 mg kg-1, and 0.39 and 2.27 mg kg-1, respectively. All soil nutrients significantly decreased as soil depth increased in all sampling times (P < 0.05). In general, significant seasonal effects were observed in all soil nutrients at 0-10 cm depth that was more variable than at 10-20 cm depth. Soil phosphorus negatively and positively correlated with soil potassium in NO and in OGG at 0-10 cm depth, respectively (P < 0.05). Soil zinc was negatively and positively correlated with soil phosphorus in NO and in OGG at 10-20 cm depth, respectively (P < 0.05). In conclusion, soil depth might be a more important factor than seasonality on the vertical distribution of soil nutrients in olive groves. In addition, correlations between soil nutrients in this study should be taken into consideration for the optimum management of agricultural practices in biological olive groves.

Hand-assisted laparoscopic donor nephrectomy: 1864 cases in 15 years of experience.

BACKGROUND: To evaluate hand-assisted laparoscopic donor nephrectomy (HALDN) in terms of intraoperative and postoperative results. METHODS: After institutional review board approval was obtained, a total of 1864 HALDN operations performed between March 2007 and January 2022 were retrospectively analyzed. Age, sex, body mass index (BMI), status of smoking and presence of previous abdominal surgery, laterality, operative time, transfusion requirement, port counts, length of extraction incision, time until mobilization, time until oral intake, donor serum creatinine levels before and one week after the surgery, length of postoperative hospital stay, intraoperative complications, and postoperative recovery and complications were recorded and statistically analyzed. Multiple renal arteries, BMI, right nephrectomy and male sex were also separately evaluated as risk factors for complications and operative time. RESULTS: A total of 825 (44.26%) male and 1039 (55.74%) female patients were enrolled in the study. The mean age of the patients was 45.79 ± 12.88 years. There were a total of 143 complications (7.67% of the total 1864 cases) consisting of 68 (3.65%) intraoperative and 75 (4.02%) postoperative complications. Open conversion was necessary for 10 patients (0.53%) to manage intraoperative complications. Reoperation was needed for 1 patient due to bleeding 6 h after the operation. Multiple renal arteries were a risk factor for intraoperative complications and prolonged operative time. Right nephrectomy and male sex were also related with longer operative times. DISCUSSION: HALDN is a safe procedure associated with low complication rates.

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.

Improving the urine spot protein/creatinine ratio by the estimated creatinine excretion to predict proteinuria in pediatric kidney transplant recipients.

BACKGROUND: Since the daily creatinine excretion rate (CER) is directly affected by muscle mass, which varies with age, gender, and body weight, using the spot protein/creatinine ratio (Spot P/Cr) follow-up of proteinuria may not always be accurate. Estimated creatinine excretion rate (eCER) can be calculated from spot urine samples with formulas derived from anthropometric factors. Multiplying Spot P/Cr by eCER gives the estimated protein excretion rate (ePER). We aimed to determine the most applicable equation for predicting daily CER and examine whether ePER values acquired from different equations can anticipate measured 24 h urine protein (m24 h UP) better than Spot P/Cr in pediatric kidney transplant recipients. METHODS: This study enrolled 23 children with kidney transplantation. To estimate m24 h UP, we calculated eCER and ePER values with three formulas adapted to children (Cockcroft-Gault, Ghazali-Barratt, and Hellerstein). To evaluate the accuracy of the methods, Passing-Bablok and Bland-Altman analysis were used. RESULTS: A statistically significant correlation was found between m24 h UP and Spot P/Cr (p < .001, r = 0.850), and the correlation was enhanced by multiplying the Spot P/Cr by the eCER equations. The average bias of the ePER formulas adjusted by the Cockcroft-Gault, Ghazali-Barratt, and Hellerstein equations were -0.067, 0.031, and 0.064 g/day, respectively, whereas the average bias of Spot P/Cr was -0.270 g/day obtained by the Bland-Altman graphics. CONCLUSION: Using equations to estimate eCER may improve the accuracy and reduce the spot urine samples' bias in pediatric kidney transplantation recipients. Further studies in larger populations are needed for ePER reporting to be ready for clinical practice.

Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.

OBJECTIVE: To evaluate the potential value of the machine learning (ML)-based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG), using various state-of-the-art ML algorithms. MATERIALS AND METHODS: For this retrospective study, 107 patients with LGG were included from a public database. Texture features were extracted from conventional T2-weighted and contrast-enhanced T1-weighted MRI images, using LIFEx software. Training and unseen validation splits were created using stratified 10-fold cross-validation technique along with minority over-sampling. Dimension reduction was done using collinearity analysis and feature selection (ReliefF). Classifications were done using adaptive boosting, k-nearest neighbours, naive Bayes, neural network, random forest, stochastic gradient descent, and support vector machine. Friedman test and pairwise post hoc analyses were used for comparison of classification performances based on the area under the curve (AUC). RESULTS: Overall, the predictive performance of the ML algorithms were statistically significantly different, χ2(6) = 26.7, p < 0.001. There was no statistically significant difference among the performance of the neural network, naive Bayes, support vector machine, random forest, and stochastic gradient descent, adjusted p > 0.05. The mean AUC and accuracy values of these five algorithms ranged from 0.769 to 0.869 and from 80.1 to 84%, respectively. The neural network had the highest mean rank with mean AUC and accuracy values of 0.869 and 83.8%, respectively. CONCLUSIONS: The ML-based MRI texture analysis might be a promising non-invasive technique for predicting the 1p/19q codeletion status of LGGs. Using this technique along with various ML algorithms, more than four-fifths of the LGGs can be correctly classified. KEY POINTS: • More than four-fifths of the lower-grade gliomas can be correctly classified with machine learning-based MRI texture analysis. Satisfying classification outcomes are not limited to a single algorithm. • A few-slice-based volumetric segmentation technique would be a valid approach, providing satisfactory predictive textural information and avoiding excessive segmentation duration in clinical practice. • Feature selection is sensitive to different patient data set samples so that each sampling leads to the selection of different feature subsets, which needs to be considered in future works.

Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency.

OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items. MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative. RESULTS. Thirty studies were included in this systematic review. Overall, the methodologic quality items were mostly favorable for modeling (63%) and performance evaluation (63%). Even so, the studies (57%) more frequently constructed their work on nonrobust features. Furthermore, only a few studies (10%) had a generalizability assessment with independent or external validation. The studies were mostly unsuccessful in terms of clinical utility evaluation (89%) and transparency (97%) items. For clinical utility, the interesting findings were lack of comparisons with both radiologists' evaluation (87%) and traditional models (70%) in most of the studies. For transparency, most studies (97%) did not share their data with the public. CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.

Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies.

OBJECTIVE. The purpose of this study was to systematically review the radiomics literature on renal mass characterization in terms of reproducibility and validation strategies. MATERIALS AND METHODS. With use of PubMed and Google Scholar, a systematic literature search was performed to identify original research papers assessing the value of radiomics in characterization of renal masses. The data items were extracted on the basis of three main categories: baseline study characteristics, radiomic feature reproducibility strategies, and statistical model validation strategies. RESULTS. After screening and application of the eligibility criteria, a total of 41 papers were included in the study. Almost one-half of the papers (19 [46%]) presented at least one reproducibility analysis. Segmentation variability (18 [44%]) was the main theme of the analyses, outnumbering image acquisition or processing (3 [7%]). No single paper considered slice selection bias. The most commonly used statistical tool for analysis was intraclass correlation coefficient (14 of 19 [74%]), with no consensus on the threshold or cutoff values. Approximately one-half of the papers (22 [54%]) used at least one validation method, with a predominance of internal validation techniques (20 [49%]). The most frequently used internal validation technique was k-fold cross-validation (12 [29%]). Independent or external validation was used in only three papers (7%). CONCLUSION. Workflow characteristics described in the radiomics literature about renal mass characterization are heterogeneous. To bring radiomics from a mere research area to clinical use, the field needs many more papers that consider the reproducibility of radiomic features and include independent or external validation in their workflow.

Case report: A kidney transplant patient with mild COVID-19.

Coronavirus Disease 2019 (COVID-19) is currently a pandemic with a mortality rate of 1%-6% in the general population. However, the mortality rate seems to be significantly higher in elderly patients, especially those hospitalized with comorbidities, such as hypertension, diabetes, or coronary artery diseases. Because viral diseases may have atypical presentations in immunosuppressed patients, the course of the disease in the transplant patient population is unknown. Hence, the management of these patients with COVID-19 is an area of interest, and a unique approach is warranted. Here, we report the clinical features and our treatment approach for a kidney transplant patient with a diagnosis of COVID-19. We believe that screening protocols for SARS-Cov-2 should be re-evaluated in patients with solid-organ transplants.

Risk factors for febrile urinary tract infections in the first year after pediatric renal transplantation.

Urinary tract infection is the most common infectious complication following kidney transplant. Anatomic abnormalities, bladder dysfunction, a positive history of febrile urinary tract infection, and recipient age are reported risk factors. The aim of this study was to determine the risk factors for fUTI, which necessitated hospitalization in the first year after renal transplantation in our pediatric transplant population. A retrospective review of 195 pediatric patients who underwent kidney transplant between 2008 and 2017 from a single institution was performed. All patients admitted to the hospital with fUTI were marked for further analyses. The risk factors including age, gender, dialysis type, history of urologic disorders, and preoperative proteinuria for fUTI in the first year after kidney transplantation and graft survivals were investigated. Independent-sample t test and chi-square tests were used for univariate analysis. Exhaustive CHAID algorithm was used for multivariate analysis. The data of 115 male and 80 female patients were retracted. The mean ages of our cohort for males and females were 9.5 ± 5.1 and 10 ± 4.8 years, respectively. The age of the patients at transplant and their gender were found to be a statistically significant risk factors for developing fUTIs. Multivariate analysis showed that fUTI was common in female patients and a subgroup of male patients who had preoperative proteinuria, but no neurogenic bladder had higher risk compared with male patients without proteinuria. Patient surveillance and antibiotic prophylaxis algorithms can be developed to prevent febrile urinary tract infections seen after pediatric kidney transplantation in risky population.

Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.

BACKGROUND: BRCA1-associated protein 1 (BAP1) mutation is an unfavorable factor for overall survival in patients with clear cell renal cell carcinoma (ccRCC). Radiomics literature about BAP1 mutation lacks papers that consider the reliability of texture features in their workflow. PURPOSE: Using texture features with a high inter-observer agreement, we aimed to develop and internally validate a machine learning-based radiomic model for predicting the BAP1 mutation status of ccRCCs. MATERIAL AND METHODS: For this retrospective study, 65 ccRCCs were included from a public database. Texture features were extracted from unenhanced computed tomography (CT) images, using two-dimensional manual segmentation. Dimension reduction was done in three steps: (i) inter-observer agreement analysis; (ii) collinearity analysis; and (iii) feature selection. The machine learning classifier was random forest. The model was validated using 10-fold nested cross-validation. The reference standard was the BAP1 mutation status. RESULTS: Out of 744 features, 468 had an excellent inter-observer agreement. After the collinearity analysis, the number of features decreased to 17. Finally, the wrapper-based algorithm selected six features. Using selected features, the random forest correctly classified 84.6% of the labelled slices regarding BAP1 mutation status with an area under the receiver operating characteristic curve of 0.897. For predicting ccRCCs with BAP1 mutation, the sensitivity, specificity, and precision were 90.4%, 78.8%, and 81%, respectively. For predicting ccRCCs without BAP1 mutation, the sensitivity, specificity, and precision were 78.8%, 90.4%, and 89.1%, respectively. CONCLUSION: Machine learning-based unenhanced CT texture analysis might be a potential method for predicting the BAP1 mutation status of ccRCCs.