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.

Short histological kaleidoscope - recent findings in histology. Part III.

The paper provides an overview of the current understanding of different cells' biology (e.g., keratinocytes, Paneth cells, myoepithelial cells, myofibroblasts, chondroclasts, monocytes, atrial cardiomyocytes), including their origin, structure, function, and role in disease pathogenesis, and of the latest findings in the medical literature concerning the brown adipose tissue and the juxtaoral organ of Chievitz.

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.

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.

The utility of texture-based classification of different types of ascites on magnetic resonance.

PURPOSE: To quantify specific characteristics of different types of ascitic fluid on magnetic resonance (MR) images and to determine their utility for computer-assisted lesion classification. METHODS: The MR images of 48 patients with intra-abdominal fluid were retrospectively analyzed. Patients were grouped according to the underlying disease and pathological outcomes. The fluid texture was analyzed on Breath Hold Axial T2 FatSat FIESTA sequence, using MaZda software. Most discriminative texture features for the classification of different types of ascites were selected based on Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC). Computer-assisted classification based on k-nearest-neighbor (k-NN) and artificial neural network (ANN) was performed and then accuracy, sensitivity and specificity were calculated. RESULTS: Adequate discriminative power for differentiating benign ascites from malignant ascites was achieved for two textural features, namely the Run Length Nonuniformity computed from both vertical and horizontal directions with 91.84% accuracy (sensitivity 100%; specificity 42.86%), and ten features for differentiating bland from hemorrhagic fluid with 90.00% accuracy (sensitivity 92.31%; specificity 85.71%), both for the ANN classifier. CONCLUSION: Texture analysis revealed several differences in signal characteristics of benign and malignant ascites. Computer-assisted pattern recognition algorithms may aid in the differential diagnosis of ascites types, especially in the early stages when there are few peritoneal modifications or when the cause is difficult to find.