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1.
Molecules ; 29(4)2024 Feb 08.
Article in English | MEDLINE | ID: mdl-38398530

ABSTRACT

Endometriosis is a common gynecological condition with a complex physio-pathological background. This study aimed to assess the role of Rubus idaeus leaf extract (RiDE) as a potential therapeutic agent in reducing the size of the endometriotic lesions and modulate the plasma expression of MMP-2, MMP-9, and TGF-ß1. The endometriotic lesions were induced in a rat model by the autologous transplant of endometrium. Thirty-six female rats, Wistar breed, with induced endometriosis, were divided into four groups and underwent treatment for 28 days. The CTRL group received 0.5 mL/day of the vehicle; the DG group received 1 mg/kg b.w./day dienogest; the RiDG group received 0.25 mL/kg b.w./day RiDE and the D+RiDG group received 1 mg/kg b.w./day dienogest and 0.25 mL/kg b.w./day RiDE, respectively. Rats' weight, endometriotic lesion diameter and grade, and plasma levels of MMP-2, MMP-9, and TGF-ß1 were assessed before and after treatment. The administration of RiDE in association with dienogest vs. dienogest determined a lower weight gain and a reduction in diameter of the endometriotic lesions. RiDE administration restored MMP2 and MMP9 plasma levels to initial conditions. Rubus idaeus extract may help in reducing dienogest-associated weight gain, lower the size of endometriotic lesions, and have anti-inflammatory effects through MMP2 and MMP9 reduction.


Subject(s)
Endometriosis , Rubus , Humans , Rats , Female , Animals , Endometriosis/metabolism , Matrix Metalloproteinase 9/metabolism , Matrix Metalloproteinase 2/metabolism , Rubus/metabolism , Transforming Growth Factor beta1 , Polyphenols/therapeutic use , Rats, Wistar , Plant Breeding , Weight Gain
2.
J Cardiovasc Dev Dis ; 10(9)2023 Aug 24.
Article in English | MEDLINE | ID: mdl-37754788

ABSTRACT

Textural analysis is pivotal in augmenting the diagnosis and outcomes of endovascular procedures for stroke patients. Due to the detection of changes imperceptible to the human eye, this type of analysis can potentially aid in deciding the optimal type of endovascular treatment. We included 40 patients who suffered from acute ischemic stroke caused by large vessel occlusion, and calculated 130 different textural features based on the non-enhanced CT scan using an open-source software (3D Slicer). Using chi-squared and Mann-Whitney tests and receiver operating characteristics analysis, we identified a total of 21 different textural parameters capable of predicting the outcome of thrombectomy (quantified as the mTICI score), with variable sensitivity (50-97.9%) and specificity (64.6-99.4%) rates. In conclusion, CT-based radiomics features are potential factors that can predict the outcome of thrombectomy in patients suffering from acute ischemic stroke, aiding in the decision between aspiration, mechanical, or combined thrombectomy procedure.

3.
Cancers (Basel) ; 15(5)2023 Feb 22.
Article in English | MEDLINE | ID: mdl-36900173

ABSTRACT

Non-Hodgkin Lymphoma (NHL) represents a severe complication and the main cause of morbidity in patients with primary Sjögren's syndrome (pSS). This study aimed to assess the role of textural analysis (TA) in revealing lymphoma-associated imaging parameters in the parotid gland (PG) parenchyma of patients with pSS. This retrospective study included a total of 36 patients (54.93 ± 13.34 years old; 91.6% females) diagnosed with pSS according to the American College of Rheumatology and the European League Against Rheumatism criteria (24 subjects with pSS and no lymphomatous proliferation; 12 subjects with pSS and NHL development in the PG, confirmed by the histopathological analysis). All subjects underwent MR scanning between January 2018 and October 2022. The coronal STIR PROPELLER sequence was employed to segment PG and perform TA using the MaZda5 software. A total of 65 PGs underwent segmentation and texture feature extraction (48 PGs were included in the pSS control group, and 17 PGs were included in the pSS NHL group). Following parameter reduction techniques, univariate analysis, multivariate regression, and receiver operating characteristics (ROC) analysis, the following TA parameters proved to be independently associated with NHL development in pSS: CH4S6_Sum_Variance and CV4S6_Inverse_Difference_Moment, with an area under ROC of 0.800 and 0.875, respectively. The radiomic model (resulting by combining the two previously independent TA features), presented 94.12% sensitivity and 85.42% specificity in differentiating between the two studied groups, reaching the highest area under ROC of 0.931 for the chosen cutoff value of 1.556. This study suggests the potential role of radiomics in revealing new imaging biomarkers that might serve as useful predictors for lymphoma development in patients with pSS. Further research on multicentric cohorts is warranted to confirm the obtained results and the added benefit of TA in risk stratification for patients with pSS.

4.
Diagnostics (Basel) ; 13(3)2023 Jan 26.
Article in English | MEDLINE | ID: mdl-36766547

ABSTRACT

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.

5.
Int J Mol Sci ; 23(17)2022 Sep 02.
Article in English | MEDLINE | ID: mdl-36077442

ABSTRACT

This study aimed to assess the effectiveness of MRI-based texture features of the lacrimal glands (LG) in augmenting the imaging differentiation between primary Sjögren's Syndrome (pSS) affected LG and healthy LG, as well as to emphasize the possible importance of radiomics in pSS early-imaging diagnosis. The MRI examinations of 23 patients diagnosed with pSS and 23 healthy controls were retrospectively included. Texture features of both LG were extracted from a coronal post-contrast T1-weighted sequence, using a dedicated software. The ability of texture features to discriminate between healthy and pSS lacrimal glands was performed through univariate, multivariate, and receiver operating characteristics analysis. Two quantitative textural analysis features, RunLengthNonUniformityNormalized (RLNonUN) and Maximum2DDiameterColumn (Max2DDC), were independent predictors of pSS-affected glands (p < 0.001). Their combined ability was able to identify pSS LG with 91.67% sensitivity and 83.33% specificity. MRI-based texture features have the potential to function as quantitative additional criteria that could increase the diagnostic accuracy of pSS-affected LG.


Subject(s)
Lacrimal Apparatus , Sjogren's Syndrome , Humans , Lacrimal Apparatus/diagnostic imaging , Magnetic Resonance Imaging , ROC Curve , Retrospective Studies , Sjogren's Syndrome/diagnostic imaging
6.
Cancers (Basel) ; 14(16)2022 Aug 12.
Article in English | MEDLINE | ID: mdl-36010897

ABSTRACT

The purpose of this study was to evaluate the diagnostic performance of radiomic features extracted from standardized hybrid contrast-enhanced ultrasound (CEUS) data for the assessment of hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, tumor grade and Ki-67 in patients with primary breast cancer. METHODS: This prospective study included 72 patients with biopsy-proven breast cancer who underwent CEUS examinations between October 2020 and September 2021. RESULTS: A radiomic analysis found the WavEnHH_s_4 parameter as an independent predictor associated with the HER2+ status with 76.92% sensitivity, and 64.41% specificity and a prediction model that could differentiate between the HER2 entities with 76.92% sensitivity and 84.75% specificity. The RWavEnLH_s-4 parameter was an independent predictor for estrogen receptor (ER) status with 55.93% sensitivity and 84.62% specificity, while a prediction model (RPerc01, RPerc10 and RWavEnLH_s_4) could differentiate between the progesterone receptor (PR) status with 44.74% sensitivity and 88.24% specificity. No texture parameter showed statistically significant results at the univariate analysis when comparing the Nottingham grade and the Ki-67 status. CONCLUSION: Our preliminary data indicate a potential that hybrid CEUS radiomic features allow the discrimination between breast cancers of different receptor and HER2 statuses with high specificity. Hybrid CEUS radiomic features might have the potential to provide a noninvasive, easily accessible and contrast-agent-safe method to assess tumor biology before and during treatment.

7.
Healthcare (Basel) ; 10(7)2022 Jul 10.
Article in English | MEDLINE | ID: mdl-35885807

ABSTRACT

The ability of texture analysis (TA) features to discriminate between different types of infected fluid collections, as seen on computed tomography (CT) images, has never been investigated. The study comprised forty patients who had pathological post-operative fluid collections following gastric cancer surgery and underwent CT scans. Patients were separated into six groups based on advanced microbiological analysis of the fluid: mono bacterial (n = 16)/multiple-bacterial (n = 24)/fungal (n = 14)/non-fungal (n = 26) infection and drug susceptibility tests into: multiple drug-resistance bacteria (n = 23) and non-resistant bacteria (n = 17). Dedicated software was used to extract the collections' TA parameters. The parameters obtained were used to compare fungal and non-fungal infections, mono-bacterial and multiple-bacterial infections, and multiresistant and non-resistant infections. Univariate and receiver operating characteristic analyses and the calculation of sensitivity (Se) and specificity (Sp) were used to identify the best-suited parameters for distinguishing between the selected groups. TA parameters were able to differentiate between fungal and non-fungal collections (ATeta3, p = 0.02; 55% Se, 100% Sp), mono and multiple-bacterial (CN2D6AngScMom, p = 0.03); 80% Se, 64.29% Sp) and between multiresistant and non-multiresistant collections (CN2D6Contrast, p = 0.04; 100% Se, 50% Sp). CT-based TA can statistically differentiate between different types of infected fluid collections. However, it is unclear which of the fluids' micro or macroscopic features are reflected by the texture parameters. In addition, this cohort is used as a training cohort for the imaging algorithm, with further validation cohorts being required to confirm the changes detected by the algorithm.

8.
Healthcare (Basel) ; 10(6)2022 Jun 02.
Article in English | MEDLINE | ID: mdl-35742090

ABSTRACT

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.

9.
Brain Sci ; 12(1)2022 Jan 14.
Article in English | MEDLINE | ID: mdl-35053852

ABSTRACT

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.

10.
Acta Radiol ; 63(6): 839-846, 2022 Jun.
Article in English | MEDLINE | ID: mdl-33940959

ABSTRACT

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.


Subject(s)
Prostatic Neoplasms , Prostatitis , Adult , Diffusion Magnetic Resonance Imaging/methods , Humans , Magnetic Resonance Imaging/methods , Male , Middle Aged , Prostate/diagnostic imaging , Prostate/pathology , Prostatic Neoplasms/pathology , Prostatitis/diagnostic imaging , Prostatitis/pathology , Retrospective Studies , Young Adult
11.
Diagnostics (Basel) ; 11(7)2021 Jul 13.
Article in English | MEDLINE | ID: mdl-34359332

ABSTRACT

BACKGROUND: The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). METHODS: This retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019-December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard. RESULTS: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (p = 0.0943). The k-NN classifier based on the parameter WavEnLL_s_2 had a sensitivity and specificity on training and validating groups of 71.93% and 45.16% vs. 60% and 44.44%, respectively. CONCLUSION: Radiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment.

12.
J Clin Med ; 10(16)2021 Aug 20.
Article in English | MEDLINE | ID: mdl-34442001

ABSTRACT

The purpose of this study was to assess whether total tumor diameter (TTD) and multifocality are predictors for metastatic disease in papillary thyroid microcarcinomas (PTMC). Eighty-two patients with histologically proven PTMC were retrospectively included. Patients were divided according to the presence of metastatic disease in the metastatic (n = 41) and non-metastatic (n = 41) demographic-matched group. The morphological features of PTMCs (primary tumor diameter, multifocality, TTD, number of foci, and tumor site) were compared between groups using univariate, multivariate, and receiver operating characteristic analyses. TTD (p = 0.026), TTD > 10 mm (p = 0.036), and Unilateral Multifocality (UM) (p = 0.019) statistically differed between the groups. The combination of the two independent predictors (TTD and UM) was able to assess metastatic risk with 60.98% sensitivity and 75.61% specificity. TTD and UM can be used to predict metastatic disease in PTMC, which may help to better adapt the RAI therapy decision. We believe that TTD and multifocality are tumor features that should be considered in future guidelines.

13.
J Pers Med ; 11(7)2021 Jun 28.
Article in English | MEDLINE | ID: mdl-34203314

ABSTRACT

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.

14.
Diagnostics (Basel) ; 11(5)2021 Apr 29.
Article in English | MEDLINE | ID: mdl-33947150

ABSTRACT

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.

15.
Bosn J Basic Med Sci ; 21(4): 488-494, 2021 Aug 01.
Article in English | MEDLINE | ID: mdl-33357213

ABSTRACT

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.


Subject(s)
Ascites/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Tomography, X-Ray Computed/methods , Aged , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Sensitivity and Specificity
16.
Curr Med Imaging ; 17(4): 524-531, 2021.
Article in English | MEDLINE | ID: mdl-33115394

ABSTRACT

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.


Subject(s)
Cysts , Endometriosis , Ovarian Neoplasms , Endometriosis/diagnostic imaging , Female , Humans , Magnetic Resonance Spectroscopy , Retrospective Studies
17.
Healthcare (Basel) ; 8(4)2020 Oct 14.
Article in English | MEDLINE | ID: mdl-33066370

ABSTRACT

Pathological analysis of ovarian cysts shows specific fluid characteristics that cannot be standardly evaluated on computer tomography (CT) examinations. This study aimed to assess the ovarian cysts' fluid attenuation values on the native (Np), arterial (Ap), and venous (Vp) contrast phases of seventy patients with ovarian cysts who underwent CT examinations and were retrospectively included in this study. Patients were divided according to their final diagnosis into the benign group (n = 32) and malignant group (n = 38; of which 27 were primary and 11 were secondary lesions). Two radiologists measured the fluid attenuation values on each contrast phase, and the average values were used to discriminate between benign and malignant groups and primary tumors and metastases via univariate, multivariate, multiple regression, and receiver operating characteristics analyses. The Ap densities (p = 0.0002) were independently associated with malignant cysts. Based on the densities measured on all three phases, neoplastic lesions could be diagnosed with 89.47% sensitivity and 62.5% specificity. The Np densities (p = 0.0005) were able to identify metastases with 90.91% sensitivity and 70.37% specificity, while the combined densities of all three phases diagnosed secondary lesions with 72.73% sensitivity and 92.59% specificity. The ovarian cysts' fluid densities could function as an adjuvant criterion to the classic CT evaluation of ovarian cysts.

18.
Cancers (Basel) ; 12(11)2020 Oct 25.
Article in English | MEDLINE | ID: mdl-33113852

ABSTRACT

The involvement of micro-ribonucleic acid (microRNAs) in metabolic pathways such as regulation, signal transduction, cell maintenance, and differentiation make them possible biomarkers and therapeutic targets. The purpose of this review is to summarize the information published in the last two and a half years about the involvement of microRNAs in papillary thyroid carcinoma (PTC). Another goal is to understand the perspective offered by the new findings. Main microRNA features such as origin, regulation, targeted genes, and metabolic pathways will be presented in this paper. We interrogated the PubMed database using several keywords: "microRNA" + "thyroid" + "papillary" + "carcinoma". After applying search filters and inclusion criteria, a selection of 137 articles published between January 2018-June 2020 was made. Data regarding microRNA, metabolic pathways, gene/protein, and study utility were selected and included in the table and later discussed regarding the matter at hand. We found that most microRNAs regularly expressed in the normal thyroid gland are downregulated in PTC, indicating an important tumor-suppressor action by those microRNAs. Moreover, we showed that one gene can be targeted by several microRNAs and have nominally described these interactions. We have revealed which microRNAs can target several genes at once.

19.
J Pers Med ; 10(3)2020 Sep 14.
Article in English | MEDLINE | ID: mdl-32937851

ABSTRACT

The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.

20.
Brain Sci ; 10(9)2020 Sep 16.
Article in English | MEDLINE | ID: mdl-32947822

ABSTRACT

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75-87.5% sensitivity, 53.85-88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

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