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.

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.

Schwannomas of Uncommon Peripheral Locations: Analysis of Imaging Findings of 21 Cases

BACKGROUND

Schwannomas are benign slow-growing tumors most often associated with the cranial nerves. Schwannomas often originate from the eighth cranial nerve. They may also originate from the peripheral nervous system of the neck and extremities. However extracranial peripheral schwannomas are considered a rare entity.

OBJECTIVES

The knowledge of rare localizations and typical imaging findings will lead to a successfulradiological diagnosis. Therefore, in this study, we present the clinical findings and MRI characteristics of schwannomas with a rare localization involving the peripheral, lower and upper extremity and intramuscular regions.

MATERIALS AND METHODS

The hospital database was screened for patients with an extracranial soft tissue mass. Twenty-one cases of schwannomas were found in rare localization. We analyzed the MR images of these patients retrospectively. The MR images were evaluated in terms of tumor location, signal intensity, and enhancement pattern. The histological examination of all the patients confirmed the diagnosis of schwannoma.

RESULTS

In 21 patients, the schwannomas were peripheral, localized to upper (n = 6) and lower extremities (n = 11). The remaining four patients had intramuscular schwannomas. The patients diagnosed with intramuscular schwannomas had schwannomas in sternocleidomastoid, gastrocnemius, triceps muscle and lateral wall of the abdomen. The average long-axis diameter of the tumor was 27.7 mm and the average short-axis diameter was 16.4 mm. The contrast pattern was diffused in eight tumors and peripheral in 13.

CONCLUSION

In this study, we present clinical findings and MRI characteristics of schwannomas with a rare localization involving the peripheral, lower and upper extremity and intramuscular regions.

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.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

OBJECTIVE

To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

MATERIALS AND METHODS

This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.

RESULTS

Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.

CONCLUSIONS

The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.

KEY POINTS

• Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation

OBJECTIVE

To develop externally validated, reproducible, and generalizable models for distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning-based quantitative computed tomography (CT) texture analysis (qCT-TA).

MATERIALS AND METHODS

Sixty-eight RCCs were included in this retrospective study for model development and internal validation. Another 26 RCCs were included from public databases (The Cancer Genome Atlas-TCGA) for independent external validation. Following image preparation steps (reconstruction, resampling, normalization, and discretization), 275 texture features were extracted from unenhanced and corticomedullary phase CT images. Feature selection was firstly done with reproducibility analysis by three radiologists, and; then, with a wrapper-based classifier-specific algorithm. A nested cross-validation was performed for feature selection and model optimization. Base classifiers were the artificial neural network (ANN) and support vector machine (SVM). Base classifiers were also combined with three additional algorithms to improve generalizability performance. Classifications were done with the following groups: (i), non-clear cell RCC (non-cc-RCC) versus clear cell RCC (cc-RCC) and (ii), cc-RCC versus papillary cell RCC (pc-RCC) versus chromophobe cell RCC (chc-RCC). Main performance metric for comparisons was the Matthews correlation coefficient (MCC).

RESULTS

Number of the reproducible features is smaller for the unenhanced images (93 out of 275) compared to the corticomedullary phase images (232 out of 275). Overall performance metrics of the machine learning-based qCT-TA derived from corticomedullary phase images were better than those of unenhanced images. Using corticomedullary phase images, ANN with adaptive boosting algorithm performed best for discrimination of non-cc-RCCs from cc-RCCs (MCC = 0.728) with an external validation accuracy, sensitivity, and specificity of 84.6%, 69.2%, and 100%, respectively. On the other hand, the performance of the machine learning-based qCT-TA is rather poor for distinguishing three major subtypes. The SVM with bagging algorithm performed best for discrimination of pc-RCC from other RCC subtypes (MCC = 0.804) with an external validation accuracy, sensitivity, and specificity of 69.2%, 71.4%, and 100%, respectively.

CONCLUSIONS

Machine learning-based qCT-TA can distinguish non-cc-RCCs from cc-RCCs with a satisfying performance. On the other hand, the performance of the method for distinguishing three major subtypes is rather poor. Corticomedullary phase CT images provide much more valuable texture parameters than unenhanced images.