Radiomics in the CT diagnosis of ovarian cystic malignancies - a pilot study.

Background and aims: The conventional computed tomography (CT) appearance of ovarian cystic masses is often insufficient to adequately differentiate between benign and malignant entities. This study aims to investigate whether texture analysis of the fluid component can augment the CT diagnosis of ovarian cystic tumors. Methods: Eighty-four patients with adnexal cystic lesions who underwent CT examinations were retrospectively included. All patients had a final diagnosis that was established by histological analysis in forty four cases. The texture features of the lesions content were extracted using dedicated software and further used for comparing benign and malignant lesions, primary tumors and metastases, malignant and borderline lesions, and benign and borderline lesions. Texture features' discriminatory ability was evaluated through univariate and receiver operating characteristics analysis and also by the use of the k-nearest-neighbor classifier. Results: The univariate analysis showed statistically significant results when comparing benign and malignant lesions (the Difference Variance parameter, p=0.0074) and malignant and borderline tumors (the Correlation parameter, p=0.488). The highest accuracy (83.33%) was achieved by the classifier when discriminating primary tumors from ovarian metastases. Conclusion: Texture parameters were able to successfully discriminate between different types of ovarian cystic lesions based on their content, but it is not entirely clear whether these differences are a result of the physical properties of the fluids or their appartenance to a particular histopathological group. If further validated, radiomics can offer a rapid and non-invasive alternative in the diagnosis of ovarian cystic tumors.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

BACKGROUND: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. METHODS: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. RESULTS: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85). CONCLUSIONS: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment.

(1): Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome by using a quantitative approach, such as the new emerging domain of radiomics. (2) Aim: To systematically review published studies on the use of MRI-based radiomics in bladder cancer. (3) Materials and Methods: We performed literature research using the PubMed MEDLINE, Scopus, and Web of Science databases using PRISMA principles. A total of 1092 papers that addressed the use of radiomics for BC staging, grading, and treatment response were retrieved using the keywords "bladder cancer", "magnetic resonance imaging", "radiomics", and "textural analysis". (4) Results: 26 papers met the eligibility criteria and were included in the final review. The principal applications of radiomics were preoperative tumor staging (n = 13), preoperative prediction of tumor grade or molecular correlates (n = 9), and prediction of prognosis/response to neoadjuvant therapy (n = 4). Most of the developed radiomics models included second-order features mainly derived from filtered images. These models were validated in 16 studies. The average radiomics quality score was 11.7, ranging between 8.33% and 52.77%. (5) Conclusions: MRI-based radiomics holds promise as a quantitative imaging biomarker of BCa characterization and prognosis. However, there is still need for improving the standardization of image preprocessing, feature extraction, and external validation before applying radiomics models in the clinical setting.

A Light, 3D UNet-based Architecture for Fully Automatic Segmentation of Prostate Lesions from T2-MRI Images.

INTRODUCTION: Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour and its separation from the healthy parenchyma, which is of primordial importance. METHOD: As a solution to this unmet medical need, we aimed to develop a decision support system based on artificial intelligence, which automatically segments the prostate and any suspect area from the 3D MRI images. We assessed retrospective data from all patients diagnosed with PCa by MRI-US fusion prostate biopsy, who underwent prostate MRI in our department due to a clinical or biochemical suspicion of PCa (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the prostate and all lesions. A total of 145 augmented datasets were generated. The performance of our fully automated end-to-end segmentation model based on a 3D UNet architecture and trained in two learning scenarios (on 14 or 28 patient datasets) was evaluated by two loss functions. RESULTS: Our model had an accuracy of over 90% for automatic segmentation of prostate and PCa nodules, as compared to manual segmentation. We have shown low complexity networks, UNet architecture with less than five layers, as feasible and to show good performance for automatic 3D MRI image segmentation. A larger training dataset could further improve the results. CONCLUSION: Therefore, herein, we propose a less complex network, a slim 3D UNet with superior performance, being faster than the original five-layer UNet architecture.

Fully automated bladder tumor segmentation from T2 MRI images using 3D U-Net algorithm.

Introduction: Bladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet medical need, we aimed to develop an artificial intelligence-based decision support system, which automatically segments the bladder wall and the tumor as well as any suspect area from the 3D MRI images. Materials: We retrospectively assessed all patients diagnosed with bladder cancer, who underwent MRI at our department (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the bladder wall and all lesions. First, the performance of our fully automated end-to-end segmentation model based on a 3D U-Net architecture (by considering various depths of 4, 5 or 6 blocks) trained in two data augmentation scenarios (on 5 and 10 augmentation datasets per original data, respectively) was tested. Second, two learning setups were analyzed by training the segmentation algorithm with 7 and 14 MRI original volumes, respectively. Results: We obtained a Dice-based performance over 0.878 for automatic segmentation of bladder wall and tumors, as compared to manual segmentation. A larger training dataset using 10 augmentations for 7 patients could further improve the results of the U-Net-5 model (0.902 Dice coefficient at image level). This model performed best in terms of automated segmentation of bladder, as compared to U-Net-4 and U-Net-6. However, in this case increased time for learning was needed as compared to U-Net-4. We observed that an extended dataset for training led to significantly improved segmentation of the bladder wall, but not of the tumor. Conclusion: We developed an intelligent system for bladder tumors automated diagnostic, that uses a deep learning model to segment both the bladder wall and the tumor. As a conclusion, low complexity networks, with less than five-layers U-Net architecture are feasible and show good performance for automatic 3D MRI image segmentation in patients with bladder tumors.

Texture Analysis in Uterine Cervix Carcinoma: Primary Tumour and Lymph Node Assessment.

The conventional magnetic resonance imaging (MRI) evaluation and staging of cervical cancer encounters several pitfalls, partially due to subjective evaluations of medical images. Fifty-six patients with histologically proven cervical malignancies (squamous cell carcinomas, n = 42; adenocarcinomas, n = 14) who underwent pre-treatment MRI examinations were retrospectively included. The lymph node status (non-metastatic lymph nodes, n = 39; metastatic lymph nodes, n = 17) was assessed using pathological and imaging findings. The texture analysis of primary tumours and lymph nodes was performed on T2-weighted images. Texture parameters with the highest ability to discriminate between the two histological types of primary tumours and metastatic and non-metastatic lymph nodes were selected based on Fisher coefficients (cut-off value > 3). The parameters' discriminative ability was tested using an k nearest neighbour (KNN) classifier, and by comparing their absolute values through an univariate and receiver operating characteristic analysis. Results: The KNN classified metastatic and non-metastatic lymph nodes with 93.75% accuracy. Ten entropy variations were able to identify metastatic lymph nodes (sensitivity: 79.17-88%; specificity: 93.48-97.83%). No parameters exceeded the cut-off value when differentiating between histopathological entities. In conclusion, texture analysis can offer a superior non-invasive characterization of lymph node status, which can improve the staging accuracy of cervical cancers.

A systematic review of the clinical value and applications of three-dimensional virtual reconstructions in renal tumors.

Three-dimensional (3D) virtual reconstruction (VR) in the medical sciences has emerged as a novel, exciting and effective tool, with promising results for patients, trainees, and even experienced surgeons. The purpose of this review is to summarize the information on the clinical value and applications of 3D VR in renal tumors published in the last ten years. A literary search of PubMed-MEDLINE databases was performed to identify studies reporting the clinical application and usefulness of 3D VR models in renal tumors. Thirty-seven studies were found to meet the selection criteria and were included in the analysis. Most studies have provided a quantitative assessment focused on the accuracy of 3D VR models in replication of anatomy and renal tumor, on measuring 3D tumor volume and on the clinical value and utility of 3D VR in pre-surgical planning and simulation of renal procedures, with significant reductions of intraoperative complications. Fourteen studies provided a qualitative assessment of the usefulness of 3D VR models, with results showing an improved patient understanding of renal anatomy and pathology, improved undergraduate and postgraduate urology education. Moreover, 3D printing technology is a novel technique, and we are currently in the dynamic era, expanding into new surgical nephron-sparing procedures and the development of printed kidneys for transplantation.

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis-A Systematic Review.

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords "prostate cancer", "MRI", "radiomics", "textural analysis", "artificial intelligence", "computer assisted diagnosis", out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach.

The commonly used magnetic resonance (MRI) criteria can be insufficient for discriminating mucinous from non-mucinous pancreatic cystic lesions (PCLs). The histological differences between PCLs' fluid composition may be reflected in MRI images, but cannot be assessed by visual evaluation alone. We investigate whether additional MRI quantitative parameters such as signal intensity measurements (SIMs) and radiomics texture analysis (TA) can aid the differentiation between mucinous and non-mucinous PCLs. Fifty-nine PCLs (mucinous, n = 24; non-mucinous, n = 35) are retrospectively included. The SIMs were performed by two radiologists on T2 and diffusion-weighted images (T2WI and DWI) and apparent diffusion coefficient (ADC) maps. A total of 550 radiomic features were extracted from the T2WI and ADC maps of every lesion. The SIMs and TA features were compared between entities using univariate, receiver-operating, and multivariate analysis. The SIM analysis showed no statistically significant differences between the two groups (p = 0.69, 0.21-0.43, and 0.98 for T2, DWI, and ADC, respectively). Mucinous and non-mucinous PLCs were successfully discriminated by both T2-based (83.2-100% sensitivity and 69.3-96.2% specificity) and ADC-based (40-85% sensitivity and 60-96.67% specificity) radiomic features. SIMs cannot reliably discriminate between PCLs. Radiomics have the potential to augment the common MRI diagnosis of PLCs by providing quantitative and reproducible imaging features, but validation is required by further studies.

Computed Tomography for the Diagnosis of Intraperitoneal Infected Fluid Collections after Surgery for Gastric Cancer. Role of Texture Analysis.

BACKGROUND AND AIMS: Several computed tomographic (CT) imaging features have been proposed to describe the infection of postoperative abdominal fluid collections; however, these features are vague, and there is a significant overlap between infected and non-infected collections. We assessed the role of textural parameters as additional diagnostic tools for distinguishing between infected and non-infected peritoneal collections in patients operated for gastric cancer. METHODS: From 527 patients operated for gastric cancer, we retrospectively selected 82 cases with intraperitoneal collections who underwent CT exams. The fluid component was analyzed through a novel method (texture analysis); different patterns of pixel intensity and distribution were extracted and processed through a dedicated software (MaZda). A univariate analysis comparing the parameters of texture analysis between the two groups was performed. Afterwards, a multivariate analysis was performed for the univariate statistically significant parameters. RESULTS: The study included 82 patients with bacteriologically verified infected (n=40) and noninfected (n=42) intraperitoneal effusions. The univariate analysis evidenced statistically significant differences between all the parameters involved. The multivariate analysis highlighted 10 parameters as being statistically significant, adjusted to Bonferroni correction. CONCLUSIONS: Our evidence supports the fact that textural analysis can be used as a complementary diagnostic tool for the detection of infected fluid collections after gastric cancer surgery. Further studies are required to validate the accuracy of this method.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study.

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student's t-test and Mann-Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002-0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60-0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66-0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone.

Due to their similar imaging features, high-grade gliomas (HGGs) and solitary brain metastases (BMs) can be easily misclassified. The peritumoral zone (PZ) of HGGs develops neoplastic cell infiltration, while in BMs the PZ contains pure vasogenic edema. As the two PZs cannot be differentiated macroscopically, this study investigated whether computed tomography (CT)-based texture analysis (TA) of the PZ can reflect the histological difference between the two entities. Thirty-six patients with solitary brain tumors (HGGs, n = 17; BMs, n = 19) that underwent CT examinations were retrospectively included in this pilot study. TA of the PZ was analyzed using dedicated software (MaZda version 5). Univariate, multivariate, and receiver operating characteristics analyses were used to identify the best-suited parameters for distinguishing between the two groups. Seven texture parameters were able to differentiate between HGGs and BMs with variable sensitivity (56.67-96.67%) and specificity (69.23-100%) rates. Their combined ability successfully identified HGGs with 77.9-99.2% sensitivity and 75.3-100% specificity. In conclusion, the CT-based TA can be a useful tool for differentiating between primary and secondary malignancies. The TA features indicate a more heterogenous content of the HGGs' PZ, possibly due to the local infiltration of neoplastic cells.

Magnetic resonance imaging characteristics of chronic prostatitis in patients under the age of 50: is it more than the eye can see?

BACKGROUND: The magnetic resonance (MRI) diagnosis of chronic prostatitis (CP) is insufficiently evaluated. PURPOSE: To evaluate the MRI appearance of CP in young patients by comparing it to individuals with non-prostatic related pathology. MATERIAL AND METHODS: The study included 47 patients with prostatitis-like symptoms evaluated by urologists and referred to pelvic MRI examination (mean age=40.23±7 years; age range=23-49 years) and 93 age-matched individuals with non-prostatic related pathology (mean age=37.5±7 years; age range=21-49 years). All MRI examinations were performed on a 1.5-T machine using a prostate-specific protocol for the prostatitis group and different protocols that included high-resolution small field of view T2-weighted (T2WI) and diffusion-weighted imaging (DWI), for the control group, depending on the clinical indication. RESULTS: Four different T2WI intensity patterns were observed: hyperintense homogenous; slightly to moderate homogenous hypointense; inhomogeneous; and marked hypointense. We found statistically significant differences between the two analyzed groups regarding mean ADC values (P<0.001), distribution of T2WI intensity patterns (P<0.0001), and the presence of dilated venous plexus (P=0.0007). No differences were found regarding prostate volume (P=0.15). In multivariate analysis, all four analyzed imaging parameters were independent predictors of chronic prostatitis (R2=0.67; P<0.0001). Considered together, an age >28 years, an inhomogeneous or marked hypointense T2WI intensity pattern (types 3 and 4), an ADC value ≤1250, and the presence of dilated venous plexus are able to predict CP with an AUC of 93% (sensitivity=85.1%, specificity=88.4%). CONCLUSION: MR parameters like T2WI intensity patterns, ADC values, and venous plexus appearance are promising non-invasive tools in the challenging environment of CP diagnosis.

Ultrasonography in the Differentiation of Endometriomas from Hemorrhagic Ovarian Cysts: The Role of Texture Analysis.

The ultrasonographic (US) features of endometriomas and hemorrhagic ovarian cysts (HOCs) are often overlapping. With the emergence of new computer-aided diagnosis techniques, this is the first study to investigate whether texture analysis (TA) could improve the discrimination between the two lesions in comparison with classic US evaluation. Fifty-six ovarian cysts (endometriomas, 30; HOCs, 26) were retrospectively included. Four classic US features of endometriomas (low-level internal echoes, perceptible walls, no solid components, and less than five locules) and 275 texture parameters were assessed for every lesion, and the ability to identify endometriomas was evaluated through univariate, multivariate, and receiver operating characteristics analyses. The sensitivity (Se) and specificity (Sp) were calculated with 95% confidence intervals (CIs). The texture model, consisting of seven independent predictors (five variations of difference of variance, image contrast, and the 10th percentile; 100% Se and 100% Sp), was able to outperform the ultrasound model composed of three independent features (low-level internal echoes, perceptible walls, and less than five locules; 74.19% Se and 84.62% Sp) in the diagnosis of endometriomas. The TA showed statistically significant differences between the groups and high diagnostic value, but it remains unclear if the textures reflect the intrinsic histological characteristics of the two lesions.

Ultrasonography in the Diagnosis of Adnexal Lesions: The Role of Texture Analysis.

The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA's capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. A total of 123 adnexal lesions (benign, 88; malignant, 35) were retrospectively included. The USTA was performed on dedicated software. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The features' ability to identify ovarian malignancies was evaluated through univariate, multivariate, and receiver operating characteristics analyses, and also by the use of the k-nearest neighbor (KNN) classifier. Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). Benign and malignant lesions were differentiated with 90.48% sensitivity and 93.1% specificity by the prediction model (which included the three independent predictors), and with 71.43-80% sensitivity and 87.5-89.77% specificity by the KNN classifier. The USTA shows statistically significant differences between the textures of the two groups, but it is unclear whether the parameters can reflect the true histopathological characteristics of adnexal lesions.

Differentiation of Endometriomas from Hemorrhagic Cysts at Magnetic Resonance: The Role of Quantitative Signal Intensity Measurements.

BACKGROUND: Endometriomas and functional hemorrhagic cysts (FHCs) are a common gynecological encounter. OBJECTIVE: This study aimed to assess the diagnostic efficiency of magnetic resonance imaging (MRI) using signal intensity measurements in differentiating endometriomas from FHCs. METHODS: Forty-six patients who underwent pelvic MRI examinations (endometriomas, n=28; FHCs, n=18) were retrospectively included. The "T2 shading" sign was evaluated subjectively and quantitatively by measuring the T1-T2 signal intensity difference and calculating the percentage of signal decrease between T1 and T2-weighted sequences. The resulted values, along with the measurement of the Apparent Diffusion Coefficient (ADC) and the signal intensity on three diffusion- weighted sequences (DWI) (b50, b400, and b800), were compared between groups by using the Mann-Whitney U test. Also, the receiver operating characteristic analysis was performed for the statistically significant results (P<0.016), and the area under the curve (AUC) was also calculated. RESULTS: The two quantitative assessment methods showed similar efficiency in detecting endometriomas (P<0.001; sensitivity, 100%; specificity, 81.82%; AUC>0.86), outperforming the classic subjective evaluation of the "T2 shading" sign (sensitivity, 92.86%; specificity, 66.67%). ADC (P=0.52) and DWI measurements (P=0.49, P=0.74, and P=0.78) failed to distinguish between the two entities. CONCLUSION: The quantitative analysis and interpretation of the "T2 shading" sign can significantly improve the differential diagnosis between endometriomas and FHCs.

Computed Tomography in the Diagnosis of Ascites: The Role of Fluid Attenuation Values.

BACKGROUND: Intraperitoneal fluid accumulations are a common matter in current clinical practice, being encountered by most medical and surgical fields. OBJECTIVE: To assess ascites fluid with attenuation values in the form of Hounsfield units (HU) in order to determine a non-invasive differentiation criterion for the diagnosis of intraperitoneal collections. METHODS: Sixty patients with known intra-peritoneal collections who underwent computed tomography (CT) for reasons such as tumor staging, post-surgical follow-up or other indications, were retrospectively included in this study. All subjects had a final pathological analysis of the fluid collections. Two radiologists measured the attenuation values for each collection. The averaged values were used for comparing benign and malignancy-related ascites (MRA), bland and hemorrhagic ascites and infected and noninfected fluid collections by consuming the Mann-Whitney U test. Also, the receiver operating characteristic analysis was performed for the statistically significant results (P<0.05), and the area under the curve (AUC) was calculated. RESULTS: Attenuation values could differentiate between benign and MRA (P=0.04; AUC=0.656; sensitivity, 65.52%; specificity, 71.43%) but failed to distinguish between bland ascites and ascites with hemorrhagic component (P=0.85), and between infected and noninfected fluid collections (P=0.47). CONCLUSION: Although the results are statistically significant, the substrate of differentiation between benign and MRA ascites cannot be clearly stated. As being the first study to investigate this issue, it opens the way for other researches in the field to determine the dynamics of imaging quantitative measurements according to the fluid's pathological features.

Computed tomography in the diagnosis of intraperitoneal effusions: The role of texture analysis.

The morphological changes advocating for peritoneal carcinomatosis are inconsistent and may be visible only in later stages of the disease. However, malignant ascites represents an early sign, and this fluid exhibits specific histological characteristics. This study aimed to quantify the fluid properties on computed tomography (CT) images of intraperitoneal effusions through texture analysis and evaluate its utility in differentiating benign and malignant collections. Fifty-two patients with histologically proven benign (n=29) and malignant (n=23) intraperitoneal effusions who underwent CT examinations were retrospectively included. Texture analysis of the fluid component was performed on the non-enhanced phase of each examination using dedicated software. Fisher and the probability of classification error and average correlation coefficients were used to select two sets of ten texture features, whose ability to distinguish between the two types of collections were tested using a k-nearest-neighbor classifier. Also, each of the selected feature's diagnostic power was assessed using univariate and receiver operating characteristics analysis with the calculation of the area under the curve. The k-nearest-neighbor classifier was able to distinguish between the two entities with 71.15% accuracy, 73.91% sensitivity, and 68.97% specificity. The highest-ranked texture parameter was Inverse Difference Moment (p=0.0023; area under the curve=0.748), based on which malignant collections could be diagnosed with 95.65% sensitivity and 44.83% specificity. Although successful, the texture assessment of benign and malignant collections most likely does not reflect the cytological differences between the two groups.

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features.

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92-1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

Differentiation of Clear Cell Renal Cell Carcinoma from other Renal Cell Carcinoma Subtypes and Benign Oncocytoma Using Quantitative MDCT Enhancement Parameters.

Background and objectives: The use of non-invasive techniques to predict the histological type of renal masses can avoid a renal mass biopsy, thus being of great clinical interest. The aim of our study was to assess if quantitative multiphasic multidetector computed tomography (MDCT) enhancement patterns of renal masses (malignant and benign) may be useful to enable lesion differentiation by their enhancement characteristics. Materials and Methods: A total of 154 renal tumors were retrospectively analyzed with a four-phase MDCT protocol. We studied attenuation values using the values within the most avidly enhancing portion of the tumor (2D analysis) and within the whole tumor volume (3D analysis). A region of interest (ROI) was also placed in the adjacent uninvolved renal cortex to calculate the relative tumor enhancement ratio. Results: Significant differences were noted in enhancement and de-enhancement (diminution of attenuation measurements between the postcontrast phases) values by histology. The highest areas under the receiver operating characteristic curves (AUCs) of 0.976 (95% CI: 0.924-0.995) and 0.827 (95% CI: 0.752-0.887), respectively, were demonstrated between clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC)/oncocytoma. The 3D analysis allowed the differentiation of ccRCC from chromophobe RCC (chrRCC) with a AUC of 0.643 (95% CI: 0.555-0.724). Wash-out values proved useful only for discrimination between ccRCC and oncocytoma (43.34 vs 64.10, p < 0.001). However, the relative tumor enhancement ratio (corticomedullary (CM) and nephrographic phases) proved useful for discrimination between ccRCC, pRCC, and chrRCC, with the values from the CM phase having higher AUCs of 0.973 (95% CI: 0.929-0.993) and 0.799 (95% CI: 0.721-0.864), respectively. Conclusions: Our observations point out that imaging features may contribute to providing prognostic information helpful in the management strategy of renal masses.