Preoperative Prediction of Muscle Invasiveness in Bladder Cancer: The Role of 3D Volumetric Radiomics Using Diffusion-Weighted MRI, the VI-RADS Score, or a Combination of Both.

BACKGROUND: Bladder cancer treatment decisions hinge on detecting muscle invasion. The 2018 "Vesical Imaging Reporting and Data System" (VI-RADS) standardizes multiparametric MRI (mp-MRI) use. Radiomics, an analysis framework, provides more insightful information than conventional methods. PURPOSE: To determine how well MIBC (Muscle Invasive Bladder Cancer) and NMIBC (Non-Muscle Invasive Bladder Cancer) can be distinguished using mp-MRI radiomics features. METHODS: We conducted a study with 73 bladder cancer patients diagnosed pathologically, who underwent preoperative mp-MRI from January 2020 to July 2022. Utilizing 3D Slicer (version 4.8.1) and Pyradiomics, we manually extracted radiomic features from apparent diffusion coefficient (ADC) maps created from diffusion-weighted imaging. The LASSO approach identified optimal features, and we addressed sample imbalance using SMOTE. We developed a classification model using textural features alone or combined with VI-RADS, employing a random forest classifier with 10-fold cross-validation. Diagnostic performance was assessed using the area under the ROC curve analysis. RESULTS: Among 73 patients (63 men, 10 women; median age: 63 years), 41 had muscle-invasive and 32 had superficial bladder cancer. Muscle invasion was observed in 25 of 41 patients with VI-RADS 4 and 5 scores and 12 of 32 patients with VI-RADS 1, 2, and 3 scores (accuracy: 77.5%, sensitivity: 67.7%, specificity: 88.8%). The combined VI-RADS score and radiomics model (AUC = 0.92 ± 0.12) outperformed the single radiomics model using ADC MRI (AUC = 0.83 ± 0.22 with 10-fold cross-validation) in this dataset. CONCLUSION: Before undergoing surgery, bladder cancer invasion in muscle might potentially be predicted using a radiomics signature based on mp-MRI.

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images. METHODS: Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions. Imaging-based tumor regression grading was performed according to the MERCURY group guidelines from pre-treatment and post-treatment para-axial T2-weighted MR images (MR-TRG). Tumor signal intensity signatures were extracted by segmenting the tumors volumetrically on the images. Normalized histograms of the signatures were used as input to a deep neural network (DNN) housing long short-term memory (LSTM) units. The output of the network was the tumor regression grading prediction, DNN-TRG. RESULTS: In predicting complete or good response, DNN-TRG demonstrated modest agreement with Dw-TRG (Cohen's kappa= 0.79) and achieved 84.6% sensitivity, 93.9% specificity, and 89.8% accuracy. MR-TRG revealed 46.2% sensitivity, 100% specificity, and 76.3% accuracy. In predicting a complete response, DNN-TRG showed slight agreement with Dw-TRG (Cohen's kappa= 0.75) with 71.4% sensitivity, 97.8% specificity, and 91.5% accuracy. MR-TRG provided 42.9% sensitivity, 100% specificity, and 86.4% accuracy. DNN-TRG benefited from higher sensitivity but lower specificity, leading to higher accuracy than MR-TRG in predicting tumor response. CONCLUSION: The use of deep LSTM neural networks is a promising approach for evaluating the tumor response to nCRT in rectal cancer.

Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas.

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

Evaluating complete response prediction rates in locally advanced rectal cancer with different radiomics segmentation approaches.

Purpose: Studies examining prediction of complete response (CR) in locally advanced rectum cancer (LARC) from pre/post chemoradiotherapy (CRT) magnetic resonance imaging (MRI) are performed mostly with segmentations of the tumor, whereas only in two studies segmentation included tumor and mesorectum. Additionally, pelvic extramesorectal region, which is included in the clinical target volume (CTV) of radiotherapy, may contain information. Therefore, we aimed to compare predictive rates of radiomics analysis with features extracted from segmentations of tumor, tumor+mesorectum, and CTV. Methods and materials: Ninety-three LARC patients who underwent CRT in our institution between 2012 and 2019 were retrospectively scanned. Patients were divided into CR and non-CR groups. Tumor, tumor+mesorectum and CTV were segmented on T2 preCRT MRI images. Extracted features were compared for best area under the curve (AUC) of CR prediction with 15 machine-learning models. Results: CR was observed in 25 patients (26.8%), of whom 13 had pathological, and 12 had clinical complete response. For tumor, tumor+mesorectum and CTV segmentations, the best AUC were 0.84, 0.81, 0.77 in the training set and 0.85, 0.83 and 0.72 in the test set, respectively; sensitivity and specificity for the test set were 76%, 90%, 76% and 71%, 67% and 62%, respectively. Conclusion: Although the highest AUC result is obtained from the tumor segmentation, the highest accuracy and sensitivity are detected with tumor+mesorectum segmentation and these findings align with previous studies, suggesting that the mesorectum contains valuable insights for CR. The lowest result is obtained with CTV segmentation. More studies with mesorectum and pelvic nodal regions included in segmentation are needed.

Can Only the Shape Feature in Radiomics Help Machine Learning Show That Bladder Cancer Has Invaded Muscles?

OBJECTIVES: The presence of muscle invasion is an important factor in establishing a treatment strategy for bladder cancer (BCa). The aim of this study is to reveal the diagnostic performance of radiomic shape features in predicting muscle-invasive BCa. METHODS: In this study, 60 patients with histologically proven BCa who underwent a preoperative MRI were retrospectively recruited. The whole tumor volume was segmented on apparent diffusion coefficient (ADC) maps and T2W images. Afterward, the shape features of the volume of interest were extracted using PyRadiomics. Machine learning classification was performed using statistically different shape features in MATLAB® (The MathWorks, Inc., Natick, Massachusetts, United States). RESULTS: The findings revealed that 27 bladder cancer patients had muscle invasion, while 33 had superficial bladder cancer (53 men and seven women; mean age: 62±14). Surface area, volume, and relevant features were significantly greater in the invasive group than in the non-invasive group based on the ADC maps (P<0.05). Superficial bladder cancer had a more spherical form compared to invasive bladder cancer (P=0.05) with both imaging modalities. Flatness and elongation did not differ significantly between groups with either modality (P>0.05). Logistic regression had the highest accuracy of 83.3% (sensitivity 82.8%, specificity 84%) in assessing invasion based on the shape features of ADC maps, while K-nearest neighbors had the highest accuracy of 78.2% (sensitivity 79.1%, specificity 69.4%) in assessing invasion based on T2W images. CONCLUSIONS: Shape features can be helpful in predicting muscle invasion in bladder cancer using machine learning methods.

Epidemiology of Endometriosis Awareness in Turkey.

INTRODUCTION: Endometriosis is a chronic painful disease that affects the daily quality of life of individuals. Estimated rates show one in 10 women has endometriosis, although the actual prevalence is unknown. In this study, the impact of endometriosis prevalence and symptoms on women's lives in Turkey was questioned through a web-based questionnaire. METHODS: We utilized a version of the World Endometriosis Research Foundation (WERF) EndoCost tool, which was sent to applicants via social media. Data from women aged 18-50 years were analyzed. RESULTS: The results of 15,673 participants have been analyzed, and 2880 (18.3%) participants had endometriosis. Respondents with endometriosis reported urinary, neurological, and gastrointestinal disorders at statistically higher rates when compared to individuals without endometriosis diagnosis (54.2%, 84.5%, and 89.9% vs. 37.2%, 75.5%, and 81.1%, respectively; p = 0.001). Most respondents with endometriosis (80.1%) reported persistent fatigue and 21.2% of endometriosis participants reported feeling socially isolated related to their condition (p = 0.001). Of the participants with endometriosis, 63.2% mentioned that people did not believe their pain or symptoms and 77.9% experienced financial difficulties due to the cost of therapy. Of the participants with endometriosis, 46.0% reported that they had problems in their personal relationships, 28.3% had difficulties at work/school, and 7.4% were unable to attend class/work due to endometriosis-related symptoms. CONCLUSION: Endometriosis is a chronic, underestimated disease that affects 18% of Turkish women of reproductive age. There is a need for guidelines to inform healthcare providers, population professionals, and patients. Societies and governmental health authorities must work together to resolve this public health issue.

The Role of FDG Positron Emission Tomography/Contrast--Enhanced Computed Tomography in Preoperative Staging and Postoperative Follow-up in Rectal Cancer Surgery.

<b> Introduction:</b> F-18 fluorodeoxyglucose (F18-FDG) positron emission tomography-computed tomography (PET/CT) is a valuable functional imaging modality for the clinical diagnosis which provides physiological information based on the altered tissue metabolism. </br></br> <b> Aim:</b> This study aims to investigate the effectiveness of F-18 fluorodeoxyglucose (F18-FDG) positron emission tomography-computed tomography (PET/CT) in preoperative staging and postoperative local recurrence and distant metastases in patients with rectal cancer. </br></br> <b> Material and methods:</b> The imaging of 726 patients with rectal cancer who were operated on at Istanbul University, Istanbul School of Medicine and had F18-FDG PET/CT, CT, and magnetic resonance imaging (MRI) scans between September 2005 and October 2016 were retrospectively analyzed. Of these patients, 170 who had pre- and postoperative PET/CT scans, had their CT scans included in the study. The sensitivity and specificity of PET/CT in preoperative staging and detection of postoperative local recurrence and distant metastases were analyzed. </br></br> <b> Results:</b> Of the patients, 101 were males and 69 were females with a median age of 62.27 (range, 31 to 89) years. The sensitivity and specificity of preoperative PET/CT in detecting liver metastases were 100% (confidence interval [CI]: 66.37-100%) and 94.2% (CI: 89.72-100%), respectively (Cohen's kappa [κ]: 1.00; P < 0.001). The sensitivity and specificity of postoperative PET/ CT in diagnosing liver metastases were 100% (CI: 88.06-100%) and 98% (CI: 9-100%), respectively (Cohen's κ: 1.00; P < 0.001). The sensitivity and specificity of preoperative PET/CT in detecting lung metastases were 100% (CI: 66.37-100%) and 91.8% (CI: 89.72-100%), respectively (Cohen's κ: 1.00; P < 0.001). The sensitivity and specificity of postoperative PET/CT in detecting lung metastases were 100% (CI: 91.4-100%) and 96% (CI: 95.8-100%), respectively (Cohen's κ: 1.00; P < 0.001). The sensitivity and specificity of PET/CT in preoperative staging of pathological pelvic lymph nodes were 100% (CI: 63.06-100%) and 94.29% (CI: 80.84-99.3%), respectively (Cohen's κ: 0.860; P < 0.001). The sensitivity and specificity of postoperative PET/CT in detecting local recurrences were 100% (CI: 78.2-100%) and 76.74% (CI: 61.37-88.24%), respectively (Cohen's κ: 0.219; P < 0.08). </br></br> <b>Results:</b> Given the fact that PET/CT can detect all primary rectal cancer in preoperative staging, it can be effectively used in selected cases, particularly in those suspected of local and advanced disease and with metastases (T3N0, T3N1, and/or T4N1). Due to a relatively low specificity in detecting local recurrences postoperatively, PET/CT can be combined with further modalities.

Complete Response Evaluation of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy Using Textural Features Obtained from T2 Weighted Imaging and ADC Maps.

BACKGROUND: The prediction of pathological responses for locally advanced rectal cancer using magnetic resonance imaging (MRI) after neoadjuvant chemoradiotherapy (CRT) is a challenging task for radiologists, as residual tumor cells can be mistaken for fibrosis. Texture analysis of MR images has been proposed to understand the underlying pathology. OBJECTIVE: This study aimed to assess the responses of lesions to CRT in patients with locally advanced rectal cancer using the first-order textural features of MRI T2-weighted imaging (T2-WI) and apparent diffusion coefficient (ADC) maps. METHODS: Forty-four patients with locally advanced rectal cancer (median age: 57 years) who underwent MRI before and after CRT were enrolled in this retrospective study. The first-order textural parameters of tumors on T2-WI and ADC maps were extracted. The textural features of lesions in pathologic complete responders were compared to partial responders using Student's t- or Mann-Whitney U tests. A comparison of textural features before and after CRT for each group was performed using the Wilcoxon rank sum test. Receiver operating characteristic curves were calculated to detect the diagnostic performance of the ADC. RESULTS: Of the 44 patients evaluated, 22 (50%) were placed in a partial response group and 50% were placed in a complete response group. The ADC changes of the complete responders were statistically more significant than those of the partial responders (P = 0.002). Pathologic total response was predicted with an ADC cut-off of 1310 x 10-6 mm2/s, with a sensitivity of 72%, a specificity of 77%, and an accuracy of 78.1% after neoadjuvant CRT. The skewness of the T2-WI before and after neoadjuvant CRT showed a significant difference in the complete response group compared to the partial response group (P = 0.001 for complete responders vs. P = 0.482 for partial responders). Also, relative T2-WI signal intensity in the complete response group was statistically lower than that of the partial response group after neoadjuvant CRT (P = 0.006). CONCLUSION: As a result of the conversion of tumor cells to fibrosis, the skewness of the T2-WI before and after neoadjuvant CRT was statistically different in the complete response group compared to the partial response group, and the complete response group showed statistically lower relative T2-WI signal intensity than the partial response group after neoadjuvant CRT. Additionally, the ADC cut-off value of 1310 × 10-6 mm2/s could be used as a marker for a complete response along with absolute ADC value changes within this dataset.

Detection of visual and frontoparietal network perfusion deficits in Parkinson's disease dementia.

Mild cognitive impairment of Parkinson's disease (PD) may be an early manifestation that may progressively worsen to dementia. Cognitive decline has been associated with changes in the brain perfusion pattern. This study aimed to evaluate cerebral blood flow (CBF) deficits specific to different stages of cognitive decline. Seventeen patients with cognitively normal PD (PD-CN), 18 patients with PD with mild cognitive impairment (PD-MCI), and 16 patients with PD with dementia (PDD) were included in this study. The participants were scanned using a 3 T Philips MRI scanner. Arterial spin labelling magnetic resonance (ASL-MR) images were acquired, followed by calculation of the CBF maps, and registration onto the MNI152 brain atlas. A whole-brain voxel-based CBF comparison was performed among the patient groups using age as a covariate. The mean age of patients with PDD was significantly higher than that of patients with PD-MCI (P = 0.015) and PD-CN (P = 0.001). The CBF values of the three groups were significantly different in the left cuneus of the visual network (VN), left inferior frontal gyrus of the frontoparietal network (FPN), and left dorsomedial nucleus of the thalamus. PDD had lower perfusion values than PD-MCI group in the same regions detected in the main group analysis. Additionally, comparison of PDD with PD-CN and non-demented groups revealed that the perfusion reduction extended into the bilateral cuneus of the VN, bilateral thalami, and left inferior frontal gyrus of the FPN. PDD could be separated from PD-MCI and PD-CN stages with CBF deficits in non-dopaminergically mediated posterior and dopaminergically mediated frontal networks.

Effects of static stretching duration on muscle stiffness and blood flow in the rectus femoris in adolescents.

AIMS: To compare the effects of 2 and 5 min of passive static stretching (SS) on stiffness and blood flow in the rectus femoris in adolescent athletes using shear wave elastography (SWE) and superb microvascular imaging (SMI).Material and methods: This prospective study included 20 male athletes with median age of 14.5 (12.5-16.5) years. The subjects were divided into two groups based on the SS duration as follows: 2 min (n=10) and 5 min (n=10). At rest and after 2 and 5 min of SS, stiffness and blood flow values were compared in the rectus femoris for each group. Inter-operator reliability was also analysed. RESULTS: There was no significant difference between resting and 2 min of SS in terms of stiffness. The stiffness values decreased significantly from resting to 5 min of SS. The blood flow increased significantly from resting to 2 and 5 min of SS. Inter-operator reliability was moderate to perfect for SWE and SMI measurements (ICC: 0.52-0.83). CONCLUSIONS:  SWE and SMI can be used to acquire reliable quantitative data about muscle stiffness and blood flow in adolescents. While stiffness parameters significantly decreased from resting after only 5 min, blood flow significantly increased both after 2 and 5 min. For physical rehabilitation protocols, 5 min of SS may be chosen to reduce stiffness. For competitions, 2 min of SS may be sufficient for warm-up exercise because it increases the blood flow optimally. Five min of SS may be preferred for the cool-down exercise to enhance recovery.