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.

A Comparative Analysis of Erector Spinae Plane Block Versus Conscious Sedation in Managing Percutaneous Cholecystostomy Pain.

PURPOSE: This study investigates the efficacy of erector spinae plane block (ESPB) for managing perioperative and postoperative pain in patients undergoing percutaneous cholecystostomy (PC) for acute cholecystitis, particularly in high-risk elderly patients with extensive comorbidities and limited functional status. METHODS: In a retrospective single-center study, 58 high-risk patients scheduled for PC were assessed. ESPB was administered to 23 patients, while 22 received conscious sedation. Pain intensity was measured using the numeric rating scale before any analgesic or ESPB administration, during the procedure and at 1 and 12 h post-procedure and secondary outcomes included adverse effects and additional analgesic requirements. RESULTS: The ESPB group experienced significant pain reduction during and post-procedure compared to the conscious sedation group (p = 0.002). Procedure times were shorter (p = 0.015), and postoperative tramadol was less frequently needed in the ESPB group (p = 0.007). The incidence of nausea was also lower in the ESPB group (p = 0.001). No ESPB-related complications were reported. CONCLUSION: ESPB significantly alleviates perioperative and postoperative pain in PC patients, reducing additional analgesic use and side effects. It holds promise as a key component of pain management for high-risk surgical patients. LEVEL OF EVIDENCE: Level 3, Non-randomized controlled cohort/follow-up study.

Percutaneous CT-Guided Microwave Ablation for the Treatment of Osteoid Osteomas: A Single Center Experience.

RATIONALE AND OBJECTIVES: The aim of the current study was to evaluate the feasibility and effectiveness of CT-guided microwave ablation (MWA) in the treatment of osteoid osteomas (OO). MATERIALS AND METHODS: Data from 59 consecutive patients who underwent percutaneous CT-MWA for OO treatment were examined in the current retrospective study. The period of this study spanned from January 2021 to May 2023 at a single institution. The study involved an evaluation of clinical and radiological characteristics, procedural data, Visual Analog Scale (VAS) pain scores, complication incidences, as well as clinical and technical success rates. Statistical analyses were performed by using the Wilcoxon test with Bonferroni correction, Friedman, Spearman, Mann-Whitney U test. RESULTS: 59 patients with an average age of 17.31 ± 8.53 years underwent CT-guided MWA for the treatment of OO. The procedure demonstrated a high success rate, with 96.6% of cases achieving both technical and clinical success. However, recurrence was observed in two patients (3.4%) at the three-month follow-up. These cases were successfully managed with a second MWA procedure. The median VAS pain scores reported by the patients was significantly improved post-procedure: from 8.64 ± 1.14 before treatment to 0.63 ± 0.98 in the first month, 0.41 ± 1.02 in the third month, and 0.15 ± 0.45 in the sixth month. Only one patient (1.7%) experienced a minor complication; no major complications were recorded in this study. CONCLUSION: CT-guided percutaneous MWA is a minimally invasive and a highly effective and safe approach for the treatment of OO.

Successful treatment of a rheumatoid arthritis patient with severe synovial hypertrophy and impaired shoulder function with microwave ablation technique.

Rheumatoid arthritis (RA) is a chronic, inflammatory systemic disorder of synovial joints and results in polyarthritis, chronical degeneration, and finally deformities and ankylosis in severe cases. Synovitis and pannus formation are results of inflammatory changes and lead into restriction in joint movement. Shoulders are among the later affected and larger joints and formation of synovitis in early active stages and pannus in later stages might be concluded with frozen shoulder and severe impairment in functionality. These late-term changes cannot be controlled with systemic or local anti-inflammatory agents and synovectomy is chosen in some cases. However, the results are not satisfactory and recurrence is common. In this case report, we presented a case of RA with severe shoulder pain, restricted movement due to synovial hypertrophy, and pannus formation which are resistant to local and systemic interventions and not suitable for surgical or chemical synovectomy. Microwave ablation (MWA) was performed successfully without any complication and she well responded in terms of DAS-28, functional, and pain scores. Range of motion and funcitonal restriction were recovered. This case report describes the use and promising results of MWA in RA with severe synovial hypertrophy and pannus formation even in the absence of active arthritis and effusion. MWA is a safe and minimally invasive technique that can be easily performed in coordinance of rheumatologists and interventional radiologists in proper cases.

Effectiveness of polidocanol sclerotherapy in alleviating symptoms in patients with venous malformations.

OBJECTIVE: The objective of this study was to retrospectively evaluate the effectiveness of polidocanol in managing pain, swelling, functional limiting and cosmetic disorders in patients with venous malformations (VMs). METHODS: This retrospective study included patients who underwent sclerotherapy with polidocanol for VMs between 2020 and 2022. Patient records, imaging findings, and evaluation questionnaires used in the preprocedure and follow-up phases were reviewed. After sclerotherapy, patients were followed up at 1, 2, 3, and 6 months. During these visits, the previously used 11-point verbal numerical rating scale (from 0 [no pain] to 10 [worst pain thinkable]) was used to evaluate the severity of symptoms such as pain, swelling, cosmetic discomfort, and functional limitation, and patients were asked to report the number of days per week they experienced these symptoms owing to the VM. RESULTS: A total of 194 sclerotherapy procedures (mean, 1.6 ± 0.3 procedures) in 84 patients (55 female and 29 male patients; mean age, 22.45 ± 11.83 years) were conducted. The majority of these malformations (81%, or 68 patients) were located in the extremities. We found a significant decrease in pain, swelling, functional limitation, cosmetic appearance, and number of painful days between all time points, except for the comparison between months 3 and 6 (P < .001) CONCLUSIONS: Polidocanol sclerotherapy is a safe and effective treatment for VMs that significantly decreases patient complaints and has a very low complication rate. Particularly, following patients at short intervals and administering additional sclerotherapy sessions when necessary will significantly increase patient satisfaction.

A surgical method to be reminded for the treatment of symptomatic ipsilateral central venous occlusions in patients with hemodialysis access: Axillo-axillary venous bypass case report and review of the literature.

BACKGROUND: In this case report, we present two chronic hemodialysis patients with upper extremity swelling due to central venous occlusions together with their clinical presentation, surgical management and brief review of the literature. METHODS: The first patient who was a 63-year-old female patient with a history of multiple bilateral arteriovenous fistulas (AVFs) was referred to our clinic. Physical examination demonstrated a functioning right brachio-cephalic AVF, with severe edema of the right arm, dilated venous collaterals, facial edema, and unilateral breast enlargement. In her history, multiple ipsilateral subclavian venous catheterizations were present for sustaining temporary hemodialysis access. The second patient was a 47-year-old male with a history of failed renal transplant, CABG surgery, multiple AV fistula procedures from both extremities, leg amputation caused by peripheral arterial disease, and decreased myocardial functions. He was receiving 3/7 hemodialysis and admitted to our clinic with right arm edema, accompanied by pain, stiffness, and skin hyperpigmentation symptoms ipsilateral to a functioning brachio-basilic AVF. He was not able to flex his arms, elbow, or wrist due to severe edema. RESULTS: Venography revealed right subclavian vein stenosis with patent contralateral central veins in the first patient. She underwent percutaneous transluminal angioplasty (PTA) twice with subsequent re-occlusions. After failed attempts of PTA, the patient was scheduled for axillo-axillary venous bypass in order to preserve the AV access function. In second patient, venography revealed right subclavian vein occlusion caused secondary to the subclavian venous catheters. Previous attempts for percutaneously crossing the chronic subclavian lesion failed multiple times by different centers. Hence, the patient was scheduled for axillo-axillary venous bypass surgery. CONCLUSION: In case of chronic venous occlusions, endovascular procedures may be ineffective. Since preserving the vascular access function is crucial in this particular patient population, venous bypass procedures should be kept in mind as an alternative for central venous reconstruction, before deciding on ligation and relocation of the AVF.

Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.

PURPOSE: Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. MATERIALS AND METHODS: Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal-Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1-3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). RESULTS: Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. CONCLUSIONS: ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.

Efficacy of Middle Meningeal Artery Embolization in Treatment Resistant Spontaneous Intracranial Hypotension Caused Subdural Hematoma : Report of Two Cases and Review of the Literature.

Spontaneous intracranial hypotension (SIH) most commonly manifests as bilateral subdural hematoma (SH). SIH cases mostly resolve spontaneously but further treatment would be needed via blind epidural blood patch (EBP). Cerebrospinal fluid (CSF) leakage in EBP-refractory cases can be treated surgically only if the localization of CSF leakage is detectable but it cannot be possible in most of the cases. Also surgical evacuation of SH secondary to SIH (SH-SIH) is not favorable without blocking the CSF leakage. Thus the management of these patients is a challenge and alternative treatment options are needed. Although middle meningeal artery embolization (MMAE) is an effective treatment option in non-SIH SH, there is no report about its application in the treatment of SH-SIH. We present two cases of SH-SIH which their clinical and radiological findings were completely resolved by bilateral MMAE treatment.

Development of subcapsular hematoma in the pelvic kidney during peripheral angioplasty.

Pelvic kidney is a mostly asymptomatic pathology resulting from failure of the kidneys to superior migration in fetal development. Herein, we report a 47-year-old female patient who presented with intermittent claudication in her right leg at 100 m. Significant stenosis was detected in the right superficial femoral artery and popliteal artery by computed tomography angiography. Peripheral angioplasty was performed for the treatment of consecutive lesions in the right lower extremity. Severe back pain developed during the procedure. Postprocedure computed tomography angiography showed a 35 mm wide subcapsular hematoma surrounding the pelvic kidney. The patient was followed up with conservative treatment on the first day. However, the next day, the patient's hemoglobin values decreased, and the pain persisted, thus angiography was performed. In the pelvic arteriography, an arteriovenous fistula was observed in the artery supply to the upper pole of the pelvic kidney. The fistula was closed with endovascular coil embolization. The patient who had no decrease in hemoglobin and no symptoms was discharged three days later. It is necessary to pay attention to the pelvic kidney during peripheral angiography, and it should be kept in mind that rare complications such as renal subcapsular hematoma may develop.

Comparison of functional patency rates between paclitaxel-eluting versus plain balloon angioplasty in hemodialysis patients with central venous stenosis: An intra-individual comparison study.

Central venous stenosis (CVS) is usually a late-diagnosed clinical entity that is common in hemodialysis patients. It causes various problems ranging from hemodialysis difficulty to loss of the arterio-venous (A-V) fistula. In the present study, we aimed to determine the effect of drug eluting balloon while excluding the influence of other variable factors by evaluating the same individuals with plain and paclitaxel-eluting balloons. This research was a prospective study of 18 symptomatic hemodialysis patients (age 50.9 ± 14.0 years, range 32-72 years; 11 male, 7 female) with CVS who underwent treatment by plain balloon angioplasty (PBA) and paclitaxel-eluting balloon angioplasty (PEBA) in our hospital from January 2016 to June 2017. First, third and sixth month central vein patency rates were compared. The median patency rates of central veins were 109.0 (range: 10-324) days after PBA and 238.5 (range: 157-501) days after PEBA (p < 0.001). There was no statistically significant difference between PBA and PEBA angioplasty in one-month patency (p Ëƒ 0.05). By contrast, a statistically significant difference was found between 3- and 6-month patency rates (p = 0.031 and p Ë‚ 0.001, respectively). Kaplan-Meier analysis revealed that the primary cumulative patency rate of PEBA was significantly longer than that of PBA (p ˂ 0.001). In this prospective study, PEBA patency is superior to PBA patency in the treatment of CVS in dialysis patients.

68Ga-PSMA PET/CT Versus 18F-FDG PET/CT for Imaging of Hepatocellular Carcinoma

Objectives: This study aimed to compare the metabolic parameters obtained from 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) and gallium-68 (68Ga)-prostate-specific membrane antigen (PSMA) PET/CT and investigate the relationship between serum alpha-fetoprotein and PET scan parameters in patients with hepatocellular carcinoma. Methods: Fourteen patients were recruited after dynamic magnetic resonance imaging (MRI) of the upper abdomen, and 18F-FDG and 68Ga-PSMA PET/CT imaging studies were conducted. Regions of interest (ROIs) were drawn from lesion-free liver tissue, abdominal aorta (A), and right medial gluteal muscle (G) for the background activity. Maximum standard uptake value (SUVmax) of these regions were compared with the SUVmax of primary tumor (T). Results: On visual assessment, five patients (36%) experienced low 18F-FDG uptake in the primary lesion, three patients (21%) experienced moderate uptake, and six patients (43%) experienced high uptake. However, only one patient (7%) showed low 68Ga-PSMA uptake, two patients (14%) showed moderate uptake, and 11 patients (79%) showed high uptake. Four patients with a low 18F-FDG uptake showed high 68Ga-PSMA uptake, while one patient exhibited low uptake with both 18F-FDG and 68Ga-PSMA. The number of lesions on 68Ga-PSMA PET/CT and MRI was significantly higher than 18F-FDG PET/CT (p=0.042 and 0.026, respectively). T/A and T/G values were significantly higher in 68Ga-PSMA than 18F-FDG (p=0.002 and 0.002, respectively). Conclusion: 68Ga-PSMA PET/CT is superior to 18F-FDG PET/CT in the staging of hepatocellular carcinoma. High 68Ga-PSMA uptake could be promising for PSMA-targeted radionuclide treatments.

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.

Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion.

PURPOSE: Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach. METHODS: Sixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists. Then, a feature selection algorithm was used to further reduce the high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric. RESULTS: Among 271 texture features, 150 features had excellent reproducibility, which were included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy ranges for predicting LVI were 0.777-0.894 and 76%-81.5%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0.482-0.754 and 54%-68.2%, respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively. CONCLUSION: ML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Overall, the method was more successful in predicting LVI than PNI.

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.

Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment.

PURPOSE: The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period. MATERIALS AND METHODS: CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase. We evaluated and analyzed texture features of six principal categories for differentiating between T stages (T1,2 vs T3,4), N stages (N+ vs N-) and grades (low-intermediate vs. high). Classification was performed based on texture parameters with high model coefficients in linear discriminant analysis (LDA). RESULTS: Dimension-reduction steps yielded five textural features for T stage, three for N stage and two for tumor grade. The discriminatory capacities of T stage, N stage and tumor grade were 90.4%, 81.6% and 64.5%, respectively, when LDA algorithm was employed. CONCLUSION: CT-TA yields potentially useful imaging biomarkers for predicting the T and N stages of patients with GC and can be used for preoperative evaluation before neoadjuvant treatment planning.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.

RATIONALE AND OBJECTIVES: This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis. MATERIALS AND METHODS: Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest. RESULTS: The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively. CONCLUSION: ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.

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.

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.