Differentiation of Pituitary Adenoma from Rathke Cleft Cyst: Combining MR Image Features with Texture Features.
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ABSTRACT: Objectives
To differentiate pituitary adenoma from Rathke cleft cyst in magnetic resonance (MR) scan by combing MR image features with texture features.Methods
A total number of 133 patients were included in this study, 83 with pituitary adenoma and 50 with Rathke cleft cyst. Qualitative MR image features and quantitative texture features were evaluated by using the chi-square tests or Mann-Whitney U test. Binary logistic regression analysis was conducted to investigate their ability as independent predictors. ROC analysis was conducted subsequently on the independent predictors to assess their practical value in discrimination and was used to investigate the association between two types of features.Results
Signal intensity on the contrast-enhanced image was found to be the only significantly different MR image feature between the two lesions. Two texture features from the contrast-enhanced images (Histo-Skewness and GLCM-Correlation) were found to be the independent predictors in discrimination, of which AUC values were 0.80 and 0.75, respectively. Besides, the above two texture features (Histo-Skewness and GLCM-Contrast) were suggested to be associated with signal intensity on the contrast-enhanced image.Conclusion
Signal intensity on the contrast-enhanced image was the most significant MR image feature in differentiation between pituitary adenoma and Rathke cleft cyst, and texture features also showed promising and practical ability in discrimination. Moreover, two types of features could be coordinated with each other.
SUBMITTER: Zhang Y
PROVIDER: S-EPMC6854938 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIt is often tricky to differentiate cystic pituitary adenoma from Rathke cleft cyst with visual inspection because of similar MRI presentations between them. We aimed to design an MR-based radiomics model for improving differential diagnosis between them.MethodsConventional diagnostic MRI data (T1-,T2-, and postcontrast T1-weighted MR images) were obtained from 215 pathologically confirmed patients (105 cases with cystic pituitary adenoma and the other 110 cases with Rathke cleft cyst) and were divided into training (n = 172) and test sets (n = 43). MRI radiomics features were extracted from the imaging data, and semantic imaging features (n = 15) were visually estimated by two radiologists. Four classifiers were used to construct radiomics models through 5-fold crossvalidation after feature selection with least absolute shrinkage and selection operator. An integrated model by combining radiomics and semantic features was further constructed. The diagnostic performance was validated in the test set. Receiver operating characteristic curve was used to evaluate and compare the performance of the models at the background of diagnostic performance by radiologist.ResultsIn test set, the combined radiomics and semantic model using ANN classifier obtained the best classification performance with an AUC of 0.848 (95% CI: 0.750-0.946), accuracy of 76.7% (95% CI: 64.1-89.4%), sensitivity of 73.9% (95% CI: 56.0-91.9%), and specificity of 80.0% (95% CI: 62.5-97.5%) and performed better than multiparametric model (AUC = 0.792, 95% CI: 0.674-0.910) or semantic model (AUC = 0.823, 95% CI: 0.705-0.941). The two radiologists had an accuracy of 69.8% and 74.4%, respectively, sensitivity of 69.6% and 73.9%, and specificity of 70.0% and 75.0%.ConclusionsThe MR-based radiomics model had technical feasibility and good diagnostic performance in the differential diagnosis between cystic pituitary adenoma and Rathke cleft cyst.
| S-EPMC8373489 | biostudies-literature
Project description:ObjectiveTo investigate the clinicopathologic features of pituitary adenoma with neuronal differentiation.MethodsFour patients with mixed gangliocytoma-pituitary adenomas between January 2011 and January 2021 and 111 new-onset patients with adenomas between January 2019 and June 2021 who attended the First Affiliated Hospital of Fujian Medical University were included in the study. The histological and immunohistochemical findings were analyzed. Neuronal differentiation marker staining was performed on new-onset adenomas, and the related literature was reviewed.ResultsAltogether, more than 100 mixed gangliocytoma-pituitary adenoma cases have been reported in the literature until now, of which pituitary-specific POU-class homeodomain transcription 1 (PIT1) positive adenomas are more frequently observed. In the present study, all 4 patients we described were female, aged 29 to 53 years (mean 39 years). Clinically, 3/4 patients presented with acromegaly, and 1/2 patients presented with headache. Histologically, the tumor was composed of two distinct mixed components. The one was a population of neoplastic ganglionic cells with large nuclei, prominent nucleoli, and abundant basophilic cytoplasm embedded in a fibrillary background. Stains of chromograninA (CgA), synaptophysin (Syn), Calretinin (CR) were positive. Axotomy-like expression was observed in neurofilament (NF) staining. PIT1 was expressed in partial ganglionic cells in all cases. The other component was a population of small uniform cells with round nuclei and acidophilic cytoplasm. Prolactin (PRL) and growth hormone (GH) were positive in all 4 cases. PIT1 was positive in the nuclei of adenomas. Although adenomas and ganglionic regions varied in histology, there was a population of cells with neuronal differentiation expressing PIT1. Additionally, axotomy-like expression of NF staining could be seen in a distant area of adenoma regions. A total of 111 cases of adenomas without ganglionic cells were included in this study, including 7 cases with neuronal differentiation. Among them, 4 cases were prolactinomas, 2 cases were somatotroph adenomas, and 1 case was corticotroph adenoma. 6/7 cases were PIT1-positive adenomas. And the remaining one case is T-PIT-positive adenoma.ConclusionsMixed gangliocytoma-pituitary adenomas are rare tumors with neuronal differentiation. The majority of MGAs are associated with endocrinopathies, mainly acromegaly. Our results suggest that PIT1-positive pituitary adenomas may have neural differentiation potential, which may not be unusual. This indication supports the possibility that the neuronal transdifferentiation of adenomatous cells is a possible mechanism, and the underlying mechanism requires further elucidation.
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Project description:Germline mutations in genes encoding succinate dehydrogenase (SDH) are frequently involved in pheochromocytoma/paraganglioma (PPGL) development and were implicated in patients with the '3PAs' syndrome (associating pituitary adenoma (PA) and PPGL) or isolated PA. However, the causality link between SDHx mutation and PA remains difficult to establish, and in vivo tools for detecting hallmarks of SDH deficiency are scarce. Proton magnetic resonance spectroscopy (1H-MRS) can detect succinate in vivo as a biomarker of SDHx mutations in PGL. The objective of this study was to demonstrate the causality link between PA and SDH deficiency in vivo using 1H-MRS as a novel noninvasive tool for succinate detection in PA. Three SDHx-mutated patients suffering from a PPGL and a macroprolactinoma and one patient with an apparently sporadic non-functioning pituitary macroadenoma underwent MRI examination at 3 T. An optimized 1H-MRS semi-LASER sequence (TR = 2500 ms, TE = 144 ms) was employed for the detection of succinate in vivo. Succinate and choline-containing compounds were identified in the MR spectra as single resonances at 2.44 and 3.2 ppm, respectively. Choline compounds were detected in all the tumors (three PGL and four PAs), while a succinate peak was only observed in the three macroprolactinomas and the three PGL of SDHx-mutated patients, demonstrating SDH deficiency in these tumors. In conclusion, the detection of succinate by 1H-MRS as a hallmark of SDH deficiency in vivo is feasible in PA, laying the groundwork for a better understanding of the biological link between SDHx mutations and the development of these tumors.
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Project description:PURPOSE:To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). METHODS:This retrospective study included 41 women (ALM?=?22, LMS?=?19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM?=?14, LMS?=?10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. RESULTS:Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p???0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ?3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). CONCLUSIONS:Combination of ?3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. KEY POINTS:• Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ?3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.
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Project description:ObjectiveTo explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA).MethodsForty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI1), excluding the cystic/necrotic portion, and ROI2, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy.ResultsAmong 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI1. Based on the ROC analyses, T1WI signatures from ROI1 achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI2 was lower than those in the corresponding signature from ROI1.ConclusionTexture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.
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Project description:The secreting function of pituitary adenomas (PAs) plays a critical role in making the treatment strategies. However, Magnetic Resonance Imaging (MRI) analysis for pituitary adenomas is labor intensive and highly variable among radiologists. In this work, by applying convolutional neural network (CNN), we built a segmentation and classification model to help distinguish functioning pituitary adenomas from non-functioning subtypes with 3D MRI images from 185 patients with PAs (two centers). Specifically, the classification model adopts the concept of transfer learning and uses the pre-trained segmentation model to extract deep features from conventional MRI images. As a result, both segmentation and classification models obtained high performance in two internal validation datasets and an external testing dataset (for segmentation model: Dice score = 0.8188, 0.8091 and 0.8093 respectively; for classification model: AUROC = 0.8063, 0.7881 and 0.8478, respectively). In addition, the classification model considers the attention mechanism for better model interpretation. Taken together, this work provides the first deep learning-based tumor region segmentation and classification models of PAs, which enables early diagnosis and subtyping PAs from MRI images.
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Project description:Purpose To assess the day-to-day repeatability of global and local-regional magnetic resonance (MR) imaging texture features derived from primary rectal cancer. Materials and Methods After ethical approval and patient informed consent were obtained, two pretreatment T2-weighted axial MR imaging studies performed prospectively with the same imaging unit on 2 consecutive days in 14 patients with rectal cancer (11 men [mean age, 61.7 years], three women [mean age, 70.0 years]) were analyzed to extract (a) global first-order statistical histogram and model-based fractal features reflecting the whole-tumor voxel intensity histogram distribution and repeating patterns, respectively, without spatial information and (b) local-regional second-order and high-order statistical texture features reflecting the intensity and spatial interrelationships between adjacent in-plane or multiplanar voxels or regions, respectively. Repeatability was assessed for 46 texture features, and mean difference, 95% limits of agreement, within-subject coefficient of variation (wCV), and repeatability coefficient (r) were recorded. Results Repeatability was better for global parameters than for most local-regional parameters. In particular, histogram mean, median, and entropy, fractal dimension mean and standard deviation, and second-order entropy, homogeneity, difference entropy, and inverse difference moment demonstrated good repeatability, with narrow limits of agreement and wCVs of 10% or lower. Repeatability was poorest for the following high-order gray-level run-length (GLRL) gray-level zone size matrix (GLZSM) and neighborhood gray-tone difference matrix (NGTDM) parameters: GLRL intensity variability, GLZSM short-zone emphasis, GLZSM intensity nonuniformity, GLZSM intensity variability, GLZSM size zone variability, and NGTDM complexity, demonstrating wider agreement limits and wCVs of 50% or greater. Conclusion MR imaging repeatability is better for global texture parameters than for local-regional texture parameters, indicating that global texture parameters should be sufficiently robust for clinical practice. Online supplemental material is available for this article.
| S-EPMC6150741 | biostudies-literature
Project description:Pituitary adenomas are classified as typical or atypical, invasive or noninvasive, and aggressive or nonaggressive based on pathological features, radiological findings, and clinical behavior. Only pituitary tumors with cerebrospinal and/or systemic metastasis are considered malignant carcinomas. However, some pituitary adenomas with high Ki-67 indexes exhibit aggressive behaviors, such as rapid growth, early and frequent recurrence, and resistance to conventional treatment, even in the absence of metastasis. Novel terminology is needed to define these tumors. Here, we propose the use of the term "refractory pituitary adenoma" to define malignant pituitary tumors exhibiting 1) a high Ki-67 index and rapid growth, 2) early and high frequency of recurrence, 3) resistance to conventional treatments and/or salvage treatment with temozolomide (TMZ), 4) poor prognosis, 5) and a lack of cerebrospinal or systemic metastases. To illustrate the utility of this refractory pituitary adenoma classification and the difficulty in managing disease in these patients, we examined twelve clinical cases. Correctly identifying refractory pituitary adenomas is crucial for improving patient prognoses. Early identification might encourage the early use of aggressive therapeutic strategies to prevent or delay recurrence.
| S-EPMC5347795 | biostudies-literature
Project description:BackgroundThe presence of bone invasion in aggressive pituitary adenoma (PA) was found in our previous study, suggesting that PA cells may be involved in the process of osteoclastogenesis. miR-19a (as a key member of the miR-17-92 cluster) has been reported to activate the nuclear factor-кB (NF-кB) pathway and promote inflammation, which could be involved in the process of the bone invasion of pituitary adenoma.MethodsIn this work, FISH was applied to detect miR-19a distribution in tissues from patients with PA. A model of bone invasion in PA was established, GH3 cells were transfected with miR-19a mimic, and the grade of osteoclastosis was detected by HE staining. qPCR was performed to determine the expression of miR-19a throughout the course of RANKL-induced osteoclastogenesis. After transfected with a miR-19a mimic, BMMs were treated with RANKL for the indicated time, and the osteoclast marker genes were detected by qPCR and Western Blot. Pit formation and F-actin ring assay were used to evaluate the function of osteoclast. The TargetScan database and GSEA were used to find the potential downstream of miR-19a, which was verified by Co-IP, Western Blot, and EMSA.ResultsHere, we found that miR-19a expression levels were significantly correlated with the bone invasion of PA, both in clinical samples and animal models. The osteoclast formation prior to bone resorption was dramatically enhanced by miR-19, which was mediated by decreased cylindromatosis (CYLD) expression, increasing the K63 ubiquitination of tumor necrosis factor receptor-associated factor 6 (TRAF6). Consequently, miR-19a promotes osteoclastogenesis by the activation of the downstream NF-кB and mitogen-activated protein kinase (MAPK) pathways.ConclusionsTo summarize, the results of this study indicate that PA-derived miR-19a promotes osteoclastogenesis by inhibiting CYLD expression and enhancing the activation of the NF-кB and MAPK pathways.
| S-EPMC10813535 | biostudies-literature
Project description:Background and purposeTo improve the resection rate of unexpected residual pituitary tumor under image guidance, iCT provides a less time-consuming and more convenient approach of promising the safety of the trans-sphenoidal surgery. However, iCT was thought to have worse image quality than MR imaging. This study was designed to determine the predictive concordance of iCT with standard postoperative high-strength MR imaging for the detection of residual tumors.Materials and methodsFrom February to December 2009, 33 patients with pituitary macroadenomas were enrolled in this prospective study. All patients received endoscopic trans-sphenoidal surgery for tumor removal and underwent iCT before the surgery finished. If an accessible tumor remnant was suspected and resectable, the surgery was continued. To assess the accuracy of intraoperative evaluation of tumor resection, the intraoperative findings were compared with MR imaging findings obtained 2 to 3 months after surgery by individually calculating the residual tumor volume.ResultsThere were no statistically significant differences in the comparison between iCT and postoperative MR imaging findings (P > .05), and the predictive rates were also high (R(2) value >0.9). The GTR rate in the case of the noninvasive and fresh cases was 89% (17/19). The overall GTR rate was 58% (19/33), the second-look rate was 21% (7/33), and only one-fourth of the recurrent cases reached GTR.ConclusionsThe extent of resection in trans-sphenoidal surgery can be reliably assessed by iCT. Compared with postoperative MR imaging findings, the findings in this study provided quantitative evidence that iCT not only holds significant promise for maximizing the extent of tumor resection but also eliminates the unnecessary blind surgical manipulation, thus increasing the safety of the procedure.
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