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Preoperative differential diagnosis of pineal region tumor can greatly assist clinical decision-making and avoid economic costs and complications caused by unnecessary radiotherapy or invasive procedures. The present study was performed to pre-operatively distinguish pineal region germinoma and pinealoblastoma using a clinicoradiomic model by incorporating radiomic and clinical features.134 pineal region tumor patients (germinoma, 69; pinealoblastoma, 65) with complete clinic-radiological and histopathological data from Tiantan hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, then the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to select the clinical features, and a clinicoradiomic model incorporating the fusion radiomic model and selected clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated.Seven significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.920 and 0.880 in the training and validation sets, respectively. A clinicoradiomic model that incorporated the radiomic model and four selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.950 in the training set and 0.940 in the validation set. The analysis of the decision curve showed that the radiomic model and clinicoradiomic model were clinically useful for patients with pineal region tumor.Our clinicoradiomic model showed great performance and high sensitivity in the differential diagnosis of germinoma and pinealoblastoma, and could contribute to non-invasive development of individualized diagnosis and treatment of patients with pineal region tumor.

Context The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability. Objective This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency. Methods Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency. Results A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively. Conclusion The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods’ clinical usefulness for supporting neurosurgeons in judging patients’ conditions, predicting prognosis, and other downstream tasks during the preoperative period.

Transsphenoidal surgery (TSS) is a first-line treatment for Cushing disease (CD). However, a subset of patients with CD have no visible adenoma on magnetic resonance imaging (MRI), and whether MRI results affect surgical outcomes is controversial. The aim of this study was to compare the surgical outcomes of CD patients with negative MRI findings to those of patients with positive MRI findings.The clinical features and outcomes of CD patients who underwent TSS between January 2000 and July 2019 at Peking Union Medical College Hospital were collected from medical records. The clinical, endocrinologic, histopathologic, surgical outcomes, and a minimum 12-month follow-up of 125 consecutive CD patients with negative MRI findings were compared with those of 1,031 consecutive CD patients with MRI-visible adenomas.The total remission rate was 73.3% after TSS, and 11.8% of patients experienced recurrence. Of 1,031 patients with MRI-visible adenomas, postoperative remission was achieved in 762 patients (73.9%), and the recurrence of CD was observed in 94 (12.3%) patients. Of the 125 patients with negative MRI findings, postoperative remission was achieved in 85 (68%) patients, and recurrence was observed in 6 (7.1%) patients. The remission rate and recurrence rate were not significantly different between patients with negative MRI findings and those with positive MRI findings (all P.05). The remission rate was not significantly different between patients who did or did not undergo bilateral inferior petrosal sinus sampling (BIPSS) in patients with negative MRI findings (P.05). In the patients with negative MRI findings who underwent BIPSS, the remission rate of patients with positive BIPSS results was not different from that in patients with negative BIPSS results (P.05). The lack of prior TSS, the detection of a tumor during operation, and pathologic confirmation of adenoma were associated with a higher surgical remission rate in patients with negative MRI findings (all P.05). Similar results were observed in the patients with positive MRI findings (all P.05). In addition, the major perioperative complications, including intraoperative cerebrospinal fluid leakage, hypopituitarism, and transient diabetes insipidus, were not related to the MRI results (all P.05).The remission rate and recurrence rate were not different between patients with negative MRI findings and those with positive MRI findings. If CD is clearly diagnosed according to biochemical tests, radiologic examinations, and BIPSS, we recommend TSS as the first-line treatment for patients, even if the MRI results are negative.

Cushing's disease (CD) is a rare disease that occurs in 1.2–1.4 persons per million population per year. Recurrence prediction after transsphenoidal surgery (TSS) is important for determining individual treatment and follow-up strategies. Between 2000 and 2017, 354 CD patients with initial postoperative remission and long-term follow-up data were enrolled from Peking union medical college hospital (PUMCH) to predict recurrence, and PUMCH is one of the largest CD treatment centers in the world. We first investigated the effect of a factorization machine (FM)-based neural network to predict recurrence after TSS for CD. This method could automatically reduce a portion of the cross-feature selection work with acceptable parameters. We conducted a performance comparison of various algorithms on the collected dataset. To address the lack of interpretability of neural network models, we also used the local interpretable model-agnostic explanations approach, which provides an explanation in the form of relevant features of the predicted results by approximating the model behavior of the variables in a local manner. Compared with existing methods, the DeepFM model obtained the highest AUC value (0.869) and the lowest log loss value (0.256). According to the importance of each feature, three top features for the DeepFM model were postoperative morning adrenocorticotropic hormone level, age, and postoperative morning serum cortisol nadir. In the post hoc explanation phase, the above-mentioned importance-leading features made a great contribution to the prediction probability. The results showed that deep learning-based models could better aid neurosurgeons in recurrence prediction after TTS for patients with CD, and could contribute to determining individual treatment strategies.

Background. Long noncoding RNA gastric cancer highly expressed transcript 1 (lncRNA GHET1) is often reported to be abnormally expressed in multiple cancers, but the situation is different in different cancers. Therefore, a meta-analysis is necessary to clarify the value of lncRNA GHET1 as a prognostic indicator in cancer. Methods. Relevant research studies on lncRNA GHET1 and cancer were retrieved from three electronic literature databases of Web of Science, PubMed, and OVID. Meanwhile, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to explore the relationship between lncRNA GHET1 expression and survival of cancer patients. The odds ratios (ORs) and 95% CIs were calculated to assess the association of lncRNA GHET1 expression with pathological parameters of cancer patients. Results. The meta-analysis included a total of 11 studies involving 714 cancer patients. The pooled HR suggests that high lncRNA GHET1 expression is associated with poor overall survival. In addition, high expression of lncRNA GHET1 was found to be associated with larger tumor size, poor histological grade, high tumor stage, lymph node metastasis, and distant metastasis. Conclusions. High lncRNA GHET1 expression can predict poor survival and pathological parameters. And lncRNA GHET1 could serve as a new indicator in multiple cancers.

Proliferative activity prediction is important for determining individual treatment strategies for patients with acromegaly, and tumor proliferative activity is usually measured by the expression of Ki-67. This study aimed to assess the value of a magnetic resonance imaging (MRI)-based radiomics approach in predicting the Ki-67 index of acromegaly patients. A total of 138 patients with acromegaly were retrospectively reviewed and randomly assigned to primary and validation cohorts. Radiomics features were extracted from MR images, and then the elastic net and recursive feature elimination algorithms were applied to determine critical radiomics features for constructing a radiomics signature. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomics nomogram incorporating a radiomics signature and selected clinical features was constructed for individual predictions. Twenty-five acromegaly patients were enrolled for multicenter model validation. Seventeen radiomics features were selected to construct a radiomics signature that achieved an area under the curve (AUC) value of 0.96 and 0.89 in the primary cohort and the validation cohort, respectively. A radiomics nomogram that incorporated the radiomics signature and eight selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.94 in the primary cohort and 0.91 in the validation cohort. The radiomics signature in the multicenter validation achieved an accuracy of 88.2%. The analysis of the decision curve showed that the radiomics signature and radiomics nomogram were clinically useful for patients with acromegaly. The radiomics signature developed in this study could aid neurosurgeons in predicting the Ki-67 index of patients with acromegaly and could contribute to non-invasive measurement of proliferative activity, affecting individual treatment strategies.

Objective: Surgery is first-line treatment for corticotroph adenomas. Although most of corticotroph adenomas are noninvasive microadenomas that show expansive growth to surrounding tissues, a small subset of them is locally invasive and difficult to manage. The aim of this study was to evaluate surgical outcome of invasive corticotroph adenomas from a single-center. Patients and Methods: The clinical features and outcomes of CD patients who underwent transsphenoidal surgery (TSS) between January 2000 and September 2019 at Peking Union Medical College Hospital were collected from medical records. The clinical, endocrinological, radiological, histopathological, surgical outcomes and a minimum 12-month follow-up of 86 consecutive CD patients with invasive corticotroph adenomas were retrospectively reviewed. Results: Eighty-six patients with invasive corticotroph adenomas were included in the study. The average age at TSS was 37.7 years (range, 12 to 67 years), with a female-to-male ratio of 3.1:1 (65/21). The median duration of symptoms was 52.6 months (range, 1.0 to 264 months). The average of maximum diameter of tumor was 17.6 mm (range, 4.5–70 mm). All 86 patients with invasive corticotroph adenomas were performed TSS by microscopic or endoscopic approach. Gross-total resection was achieved in 63 patients (73.3%), subtotal resection in 18 (20.9%), and partial resection in 5 (5.8%). After surgery, the overall postoperative immediate remission rate was 48.8% (42/86), 51.2 % (44/86) of patients maintained persistent hypercortisolism. In 42 patients with initial remission, 16.7 % (7/42) of them experienced a recurrence. In these patients with persistent disease and recurrent CD, data about further treatment was available for 30 patients. The radiotherapy was used for 15 patients, and 4 (26.7%) of them achieved biochemical remission. Repeat TSS was performed in 5 patients, and none achieved remission. Medication was administrated in 4 patients, and one of them obtained disease control. Adrenalectomy was performed in 6 patients, and 5 (83.3 %) achieved biochemical remission. At last follow-up, (33.3%) 10 of 30 patients were in remission, and 20 patients still had persistent disease. The remission rate in patients with invasive corticotroph adenomas who underwent gross-total resection and first TSS were significantly higher than that in patients undergoing subtotal resection, partial resection, and a second TSS (all P＜0.05). However, there was no significant difference in the remission rate between patient with different tumor size, Knosp Grade and surgical approaches (P＞0.05).Conclusion: The management of invasive corticotroph adenomas remain a therapeutic challenge due to incomplete resection of invasive and/or a large adenoma. With application of multiple techniques assistance, approximately half of the patients could achieve gross-total resection and biochemical remission via TSS by experienced neurosurgeons. The extent of tumor resection and number of operations were associated with surgical remission rate in invasive corticotroph adenomas. If the remission was not achieved by surgery, other treatments including radiotherapy, medical therapy, and even bilateral adrenalectomy are required.

Preoperative prediction of transsphenoidal surgical (TSS) response is important for determining individual treatment strategies for acromegaly. There is currently no accurate predictive model for TSS response for acromegaly. The current study sought to develop and validate machine learning (ML)-based models for preoperative prediction of TSS response for acromegaly. Six hundred sixty-eight patients with acromegaly were enrolled and divided into training (n = 534) and text datasets (n = 134) in this retrospective, data mining and ML study. The forward search algorithm was used to select features, and six ML algorithms were applied to construct TSS response prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. Model calibration, discrimination ability, and clinical usefulness were also assessed. Three hundred forty-nine (52.2%) patients achieved postoperative remission criteria and exhibited good TSS response. A univariate analysis was conducted and eight features, including age, hypertension, ophthalmic disorders, GH, IGF-1, nadir GH, maximal tumor diameter, and Knosp grade, were significantly associated with the TSS response in patients with acromegaly. After feature selection, the gradient boosting decision tree (GBDT), which was constructed with the eight significant features showed the best favorable discriminatory ability both the training (AUC = 0.8555) and validation (AUC = 0.8178) cohorts. The GBDT model showed good discrimination ability and calibration, with the highest levels of accuracy and specificity, and provided better estimates of TTS responses of patients with acromegaly compared with using only the Knosp grade. Decision curve analysis confirmed that the model was clinically useful. ML-based models could aid neurosurgeons in the preoperative prediction of TTS response for patients with acromegaly, and could contribute to determining individual treatment strategies.

Mitochondrial pyruvate carrier (MPC) proteins MPC1 and MPC2 form a transporter complex to control rate-limiting pyruvate transportation through the inner mitochondrial membrane. MPC1 plays a crucial role in the tumor metabolite and biosynthesis process. However, the role of MPC1 in glioblastoma (GBM) is unknown. The Cancer Genome Atlas (TCGA) data set, which included 631 cases of GBM with genomic and clinical data, was obtained from the UCSC Xena browser. The clinical data set contained demographic, survival rate, and histological and pathological information. The association between MPC1 gene copy number segments and GBM patient overall survival was analyzed by Kaplan–Meier survival analysis, which was performed using the R2 web-based platform to identify the best cut-off. GraphPad Prism 7 was used to compare the differences in MPC1 gene copy number segments between various groups and subtypes. A total of 631 patients with glioblastoma (mean age 57.78 ± 14.36 years, 59% of males) were examined in this study, including 438 cases with MPC1 intact (MPC1 copy number segments > − 0.1, 69.4%) and 157 cases with MPC1 deletion (24.9%) tumors. Among the four GBM subtypes, the proneural group had the highest MPC1 copy number segments and GBM patients diagnosed with proneural subtype showed the best outcome. The expression of MPC1 transcripts was different in the TCGA-GBM dataset compared with the GTEx dataset. MPC1 copy number segments showed a significant correlation with MGMT copy number segments (r = 0.1322, p = 0.0012). MGMT gene expression level in MPC1 intact tumors was significantly lower than that in MPC1 deletion tumors (p = 0.0003). Significant relevancy was observed between better OS and the MPC1 intact group compared with the MPC1 deletion group (p = 0.020). Moreover, patients with MPC1 deletion tumors treated with temozolomide (TMZ) had worse survival than patients with MPC1 intact tumors (p = 0.027). Our results suggest a role of decreased MPC1 copy number segments in reducing overall survival in glioblastoma. MPC1 deletion is associated with poor response to TMZ chemotherapy in GBM.

BackgroundThere are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing’s disease (CD).PurposeOur current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD.MethodsA total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC.ResultsThe overall immediate remission rate was 73.3% (766/1045). First operation (pConclusionWe developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD.

BackgroundPostoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma.ObjectiveTo evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma.MethodsA total of 136 meningioma patients with complete clinical and radiological data were collected for this retrospective study, and they were randomly divided into primary and validation cohorts. Three-dimensional radiomics features were extracted from multisequence MR images, and then screened through Wilcoxon rank sum test, elastic net and recursive feature elimination algorithms. A radiomics signature was established based support vector machine method. By combining clinical with the radiomics signature, a clin-radiomics combined model was constructed for individual CEE prediction.ResultsThree significance radiomics features were selected to construct a radiomics signature, with areas under the curves (AUCs) of 0.86 and 0.800 in the primary and validation cohorts, respectively. Two clinical characteristics (peritumoral edema and tumor size) and radiomics signature were determined to establish the clin-radiomics combined model, with an AUC of 0.91 in the primary cohort and 0.83 in the validation cohort. The clin-radiomics combined model showed good discrimination, calibration, and clinically useful for postoperative CEE prediction.ConclusionsBy integrating clinical characteristics with radiomics signature, the clin-radiomics combined model could assist in postoperative CEE prediction before surgery, and provide a basis for surgical treatment decisions in patients with meningioma.

Background: Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (DR). DR may complicate the interpretation of surgical outcomes in patients with acromegaly and interfere with decision-making regarding postoperative adjuvant therapy. Objective: We aimed to develop and validate machine learning (ML) models for predicting DR in acromegaly patients who have not achieved remission within 6 months of surgery. Methods: We enrolled 306 acromegaly patients and randomly divided them into training and test datasets. We used the recursive feature elimination (RFE) algorithm to select features and applied six ML algorithms to construct DR prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. We used permutation importance, SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. Results: Fifty-five (17.97%) acromegaly patients met the criteria for DR, and five features (post-1w rGH, post-1w nGH, post-6m rGH, post-6m IGF-1, and post-6m nGH) were significantly associated with DR in both the training and the test datasets. After the RFE feature selection, the XGboost model, which comprised the 15 important features, had the greatest discriminatory ability (area under the curve = 0.8349, sensitivity = 0.8889, Youden's index = 0.6842). The XGboost model showed good discrimination ability and provided significantly better estimates of DR of patients with acromegaly compared with using only the Knosp grade. The results obtained from permutation importance, SHAP, and LIME algorithms showed that post-6m IGF-1 is the most important feature in XGboost algorithm prediction and showed the reliability and the clinical practicability of the XGboost model in DR prediction. Conclusions: ML-based models can serve as an effective non-invasive approach to predicting DR and could aid in determining individual treatment and follow-up strategies for acromegaly patients who have not achieved remission within 6 months of surgery.

Background Ectopic adrenocorticotropic hormone–secreting pituitary adenomas (EAPAs) are rare adenomas causing Cushing syndrome, which are located in ectopic locations outside the sella turcica. No more than 100 cases of this entity have been reported. Because of its rarity and nonspecific clinical and biochemical features, EAPA is often misdiagnosed, leading to surgical failure and delayed treatment. In the article, the cases of 3 patients with suprasellar EAPAs treated at our institution are reported and a review of the literature is presented. Case Description All 3 patients with Cushing syndrome had ectopic pituitary adenomas located in the suprasellar region and identifiable on preoperative sellar magnetic resonance imaging. Preoperative laboratory evaluation in all patients showed identical results to those observed in Cushing disease. In 2 patients, craniotomies were performed directly to achieve total tumor resection and clinical remission. One patient, who underwent previous negative transsphenoidal exploration, achieved full remission through a second craniotomy despite pulmonary infection as a postoperative complication. Conclusions Although EAPA is an extremely rare entity, it should be considered as a differential diagnosis of Cushing disease. Because of its similar clinical and biochemical behavior to intrasellar pituitary adrenocorticotropic hormone adenoma in Cushing disease, careful examination of the potential occurrence of EAPA on preoperative radiologic imaging is of considerable significance to avoid unnecessary surgery and achieve improved outcomes. Surgical resection EAPA remains the first choice of treatment, and the optimal surgical approach ought to be determined according to the adenoma features, the general condition of the patient, and the surgeon's experience.

Objective: Transsphenoidal surgery (TSS) is the first-line treatment of patients with Cushing’s disease (CD). However, biochemical remission rates after TSS for CD vary from 59 to 95%, and the predictors of surgical outcomes remain unclear. The aim of this study was to identify the predictors of early outcomes in patients with CD treated with TSS. Methods: The clinical features and outcomes of CD patients who underwent TSS between February 2000 and September 2019 at the Peking Union Medical College Hospital were collected from medical records and analyzed. Uni- and multivariate odds ratio (OR) analyses were performed to identify the predictors of early outcomes in patients with CD. Results: A total of 1,045 patients were included. The median age at TSS was 34.0 years (IQR 26.0–45.0), with a female:male ratio of 4.2:1 (844/201). The median duration of symptoms was 46.0 months (IQR 24.0–72.0). After surgery, the overall postoperative immediate remission rate was 73.3%, and 26.7% of patients had persistent hypercortisolism. Univariate analysis demonstrated that the number of operations was correlated with a lower immediate remission rate (OR 0.393, 95% CI 0.266–0.580, p = 0.000), as was tumor size (OR 0.462, 95% CI 0.334–0.639, p = 0.000), the duration of disease (OR 0.996, 95% CI 0.993–0.999, p = 0.003), and preoperative ACTH concentration (0.998, 95% CI 0.996–0.999, p = 0.003). Cavernous sinus invasion has also been identified as an important factor associated with a lower immediate remission rate (OR 0.275, 95% CI 0.166–0.456, p = 0.000). No correlations were detected between the immediate outcomes and age, gender, BMI, the combination of a low- and high-dose dexamethasone suppression test, preoperative morning serum cortisol level, or 24-h urinary free cortisol level (all p > 0.05). The results of multivariate analysis were similar to those of univariate analysis. Preoperative ACTH ≤67.35 ng/L predicted remission with 60.9% sensitivity and 49.5% specificity (AUC 0.553; p = 0.008). A cutoff of ≤64.5 months for disease duration predicted immediate remission with 40.5% sensitivity and 71.0% specificity (AUC 0.552; p = 0.01). Conclusion: Early outcomes of TSS in CD patients can be predicted by factors including the number of operations, duration of disease, tumor invasion, tumor size, and preoperative ACTH concentration. These predictors can be used to improve the perioperative management of CD patients.

It is important to clarify the significance of long noncoding RNA MIR31 host gene (lncRNA

Background Whether conservative treatment or surgical management is the most appropriate treatment for pituitary apoplexy (PA) is controversial. In general, if severe symptoms of compression occur, such as oculomotor nerve palsy, neurosurgery is performed to relieve the compression of anatomical structures near the PA. Case description We describe the case of a 79-year-old man who was found to have an intrasellar pituitary incidentaloma. The tumor was discovered accidentally, during an investigation into the cause of his dizziness. Two months later, he suddenly developed headaches, left ophthalmoplegia, decreased vision, severe blepharoptosis and diplopia. He was diagnosed with PA and hypocortisolemia based on magnetic resonance imaging (MRI) and blood tests, respectively. His symptoms of oculomotor palsy and optic nerve compression were serious, but his ophthalmological deficits were nonprogressive and his hormone levels improved through conservative treatment (hydrocortisone supplementation). Due to this older patient's poor physical condition and serious coronary heart disease, after multidisciplinary consultation and according to his family's wishes, we continued the conservative treatment and watched closely for any changes in the patient's condition. After 6 months of conservative treatment, the patient's symptoms of oculomotor nerve paralysis, pupil and vision defects completely disappeared, and no new complications occurred. Repeated MRI tests showed that the PA lesion gradually improved. The patient's hypocortisolemia was completely relieved through oral supplementation with low-dose hydrocortisone. Conclusion In older PA patients who have surgical contraindications, even with symptoms of compression, such as oculomotor nerve palsy, according to the international guidelines, if conservative treatment is effective and the condition is not progressing, it is possible to monitor patients' condition closely and continue conservative treatment, which may yield good results.

It has been reported that HOXA transcript at the distal tip (HOTTIP) is dysregulated in various cancers. We performed this meta-analysis to clarify its promising functions as a prognosis marker in malignant tumors.The electronic databases, including PubMed, Medline, OVID, Cochrane Library, and Web of Science were searched from inception to September 23, 2016. The hazard ratio (HR) and 95% confidence interval (CI) were calculated to explore the relationship between HOTTIP expression and overall survival (OS), which were extracted from the eligible studies. The odds ratio (OR) was calculated to assess the association between HOTTIP expression and pathological parameters by using RevMan5.3 software.Seven studies were included in the study, with a total of 652 patients. The pooled HR suggested that high HOTTIP expression was significantly correlated with poor OS (HR=2.16, 95% CI: 1.69-2.76, P0.00001) in cancer patients without obvious heterogeneity. The results showed there was a significant difference in the incidence of lymph node metastasis (LNM) between high HOTTIP expression group and low HOTTIP expression group (OR=2.30, 95% CI: 1.58-3.35, P0.0001). A similar result was observed in the association between HOTTIP expression and distant metastasis (DM), the odds ratio was 3.30 (95% CI: 1.78-6.12, P=0.0001) without obvious heterogeneity. In addition, high HOTTIP expression was significantly associated with high tumor stage (OR=3.30, 95% CI: 0.25-0.64) without heterogeneity.This meta-analysis demonstrated that high HOTTIP expression significantly predicts poor OS, lymph node metastasis, distant metastasis and tumor stage, suggesting that high HOTTIP expression may serve as a novel biomarker for poor prognosis in cancers.

Background: Prediction of radiotherapeutic response before radiotherapy could help determine individual treatment strategies for patients with acromegaly. Objective: To develop and validate a machine-learning-based multiparametric MRI radiomics model to non-invasively predict radiotherapeutic response in patients with acromegaly. Methods: This retrospective study included 57 acromegaly patients who underwent postoperative radiotherapy between January 2008 and January 2016. Manual lesion segmentation and radiomics analysis were performed on each pituitary adenoma, and 1561 radiomics features were extracted from each sequence. A radiomics signature was built with a support vector machine using leave-one-out cross-validation for feature selection. Multivariable logistic regression analysis was used to select appropriate clinicopathological features to construct a clinical model, which was then combined with the radiomics signature to construct a radiomics model. The performance of this radiomic model was assessed using receiver operating characteristics (ROC) analysis and its calibration, discriminating ability, clinical usefulness. Results: At 3-years after radiotherapy, 25 patients had achieved remission and 32 patients had not. The clinical model incorporating seven clinical features had an area under the ROC (AUC) of 0.86 for predicting radiotherapeutic response, and performed better than any single clinical feature. The radiomics signature constructed with six radiomics features had a significantly higher AUC of 0.92. The radiomics model showed good discrimination abilities and calibration, with an AUC of 0.96. Decision curve analysis confirmed the clinical utility of the radiomics model. Conclusion: Using pre-radiotherapy clinical and MRI data, we developed a radiomics model with favorable performance for individualized non-invasive prediction of radiotherapeutic response, which may help in identifying acromegaly patients who are likely to benefit from radiotherapy.

Central nervous system (CNS) diseases are associated with complexity and diversity; as a result, it is urgent to search for a simple approach for effectively improving the clinical decision-making ability and precise treatment currently. Radiomics can collect plenty of quantitative features based on the massive medical image data; meanwhile, related diagnosis and prediction can be performed through quantitative analysis. The main steps of radiomics analysis include image collection as well as reconstruction, segmentation of the region of interest (ROI), feature extraction as well as quantification, and establishment of the predictive as well as prognostic models. Compared with traditional imaging features, radiomics allows to transform the visual image data to the in-depth features, so as to carry out quantitative research. Our findings suggest that radiomics has broad application prospects in the early screening, accurate diagnosis, grading and staging, treatment and prognosis, and molecular characteristics of CNS diseases, which can improve the capacities to diagnose and predict CNS diseases prognosis through complementing and combining with traditional imaging.

There is controversy regarding the surgical route selection for tuberculum sellae meningiomas (TSMs): the transsphenoidal (TS) or transcranial (TC) approach? We conducted a systematic review and meta-analysis to compare clinical outcomes and postoperative complications between two surgical approaches. Literature search was performed. Relevant articles were selected and evaluated. Data were extracted and analyzed. Eight articles comprising 550 patients met the inclusion criteria. Traditionally, the rates of gross total resection, tumor recurrence, visual improvement, and cerebrospinal fluid leakage were the most common outcomes of interest. We demonstrated that the TS approach was significantly associated with better visual outcomes but more frequent cerebrospinal fluid leakage, while the rates of tumor resection and recurrence showed no significant difference between groups. In addition to surgical results that were consistent with previous studies, we further evaluated the impact of approach selection on common postoperative complications, which were closely related to the recovery course and quality of life. We revealed that the risk of dysosmia was significantly higher in the TS group. There was no significant difference between groups regarding infection, intracranial hemorrhage, and endocrine disorders. Because of the relatively low evidence levels of included retrospective studies, it was difficult to reach a categorical conclusion about the optimal surgical approach for TSMs. Finally, we recommended that the TS approach was an alternative option in patients with smaller TSMs (

BACKGROUND The expression of RWDD3 is closely related to the prognosis of acromegaly. Therefore, this study aimed to investigate a radiomics method based on MRI to noninvasively evaluate RWDD3 expression in acromegaly. MATERIAL AND METHODS 132 patients with acromegaly were enrolled and divided into primary (n=88) and validation cohorts (n=44) according. The expression of RWDD3 was determined by immunohistochemistry. Radiomic features were extracted from the MR images and determined using the ‘Elastic Net’ feature selection algorithm. A radiomic signature was built using a support vector machine. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomic model, incorporating the radiomic signature and selected clinical features, was constructed and used as the final predictive model. The performance of this radiomic model was validated using receiver operating characteristics analysis, and its calibration, discriminating ability, and clinical usefulness were assessed. RESULTS The radiomic signature, which was constructed with radiomic features selected using the primary cohort, showed a favorable discriminatory ability in the validation cohort. The radiomic model incorporating the radiomic signature and three selected clinical features showed good discrimination abilities and calibration, with an area under the curve (AUC) of 0.89 for the primary cohort and 0.84 for the validation cohort. The radiomic model better estimated the treatment responses of patients with acromegaly than did the clinical features. Decision curve analysis showed the radiomic model was clinically useful. CONCLUSION This radiomic model could aid neurosurgeons in the prediction of RWDD3 expression in patients with acromegaly, and could contribute to predicting of patient prognoses.

Recently, several randomized controlled trials (RCTs) explored the effects of α-blockers with or without phosphodiesterase type 5 inhibitors (PDE5-Is) for lower urinary tract symptoms secondary to benign prostatic hyperplasia (LUTS/BPH). However, the results were inconsistent. We performed this meta-analysis to evaluate the role of combination therapy (α-blockers and PDE5-Is) in patients with LUTS/BPH. Databases including PubMed, Cochrane library, Web of Science, and Embase were searched for qualified RCTs. Pooled mean differences (MDs) and odds ratios (ORs) were calculated to measure the effects and adverse events in combination therapy. Moreover, subgroup analyses of ethnicity, dosage of PDE5-Is, treatment duration, and severity of LUTS/BPH were performed. In addition, trial sequential analyses (TSAs) were used to assess whether the evidence for the results was sufficient. Overall, this study identified 11 eligible RCTs, including 855 LUTS/BPH patients. Patients receiving combination therapy had better improvement in international prostate symptom score (IPSS: MD: 1.66, 95% CI − 3.03 to − 0.29), maximum urinary flow rate (Qmax: MD: 0.94, 95% CI 0.24–1.64), and international index of erectile function (IIEF: MD: 4.73, 95% CI 2.95–6.51), comparing those without PDE5-Is. Besides, subgroup analyses indicated that the effects of combination treatment were associated with ethnicity, treatment duration, and severity of LUTS/BPH. By TSA, the findings in the current study were based on sufficient evidence. Our results indicated that combination therapy can significantly improve IPSS, Qmax, and IIEF in patients with LUTS/BPH. Combination therapy might be more suitable for these patients.

BACKGROUND The preoperative prediction of transsphenoidal surgical (TSS) response is important for determining individual treatment strategies for acromegaly. Therefore, this study aimed to predict TSS response in a non-invasive way based on radiomic analysis. MATERIAL AND METHODS 273 patients with acromegaly were enrolled and divided into primary (n=180) and validation cohorts (n=93) according to time point. Radiomic features were extracted from the MR images and determined using the ‘Elastic Net’ feature selection algorithm. A radiomic signature was built using a support vector machine. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomic model, incorporating the radiomic signature and selected clinical features, was constructed and used as the final predictive model. The performance of this radiomic model was validated using receiver operating characteristics analysis, and its calibration, discriminating ability, and clinical usefulness were assessed. RESULTS The radiomic signature, which was constructed with six radiomic features selected using the primary cohort, showed a favorable discriminatory ability in the validation cohort. The radiomic model incorporating the radiomic signature and three selected clinical features showed good discrimination abilities and calibration, with an area under the curve (AUC) of 0.93 for the primary cohort and 0.89 for the validation cohort. The radiomic model better estimated the treatment responses of patients with acromegaly than did the clinical features. Decision curve analysis showed the radiomic model was clinically useful. CONCLUSION This radiomic model could aid neurosurgeons in the preoperative prediction of TSS response in patients with acromegaly, and could contribute to determining individual treatment strategies.