Deep-learning-based automatic segmentation and classification for craniopharyngiomas.

Objective: Neuronavigation and classification of craniopharyngiomas can guide surgical approaches and prognostic information. The QST classification has been developed according to the origin of craniopharyngiomas; however, accurate preoperative automatic segmentation and the QST classification remain challenging. This study aimed to establish a method to automatically segment multiple structures in MRIs, detect craniopharyngiomas, and design a deep learning model and a diagnostic scale for automatic QST preoperative classification. Methods: We trained a deep learning network based on sagittal MRI to automatically segment six tissues, including tumors, pituitary gland, sphenoid sinus, brain, superior saddle cistern, and lateral ventricle. A deep learning model with multiple inputs was designed to perform preoperative QST classification. A scale was constructed by screening the images. Results: The results were calculated based on the fivefold cross-validation method. A total of 133 patients with craniopharyngioma were included, of whom 29 (21.8%) were diagnosed with type Q, 22 (16.5%) with type S and 82 (61.7%) with type T. The automatic segmentation model achieved a tumor segmentation Dice coefficient of 0.951 and a mean tissue segmentation Dice coefficient of 0.8668 for all classes. The automatic classification model and clinical scale achieved accuracies of 0.9098 and 0.8647, respectively, in predicting the QST classification. Conclusions: The automatic segmentation model can perform accurate multi-structure segmentation based on MRI, which is conducive to clearing tumor location and initiating intraoperative neuronavigation. The proposed automatic classification model and clinical scale based on automatic segmentation results achieve high accuracy in the QST classification, which is conducive to developing surgical plans and predicting patient prognosis.

Predicting delayed remission in Cushing's disease using radiomics models: a multi-center study.

Purpose: No multi-center radiomics models have been built to predict delayed remission (DR) after transsphenoidal surgery (TSS) in Cushing's disease (CD). The present study aims to build clinical and radiomics models based on data from three centers to predict DR after TSS in CD. Methods: A total of 122 CD patients from Peking Union Medical College Hospital, Xuanwu Hospital, and Fuzhou General Hospital were enrolled between January 2000 and January 2019. The T1-weighted gadolinium-enhanced MRI images and clinical data were used as inputs to build clinical and radiomics models. The regions of interest (ROI) of MRI images were automatically defined by a deep learning algorithm developed by our team. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. In total, 10 machine learning algorithms were used to construct models. Results: The overall DR rate is 44.3% (54/122). According to multivariate Logistic regression analysis, patients with higher BMI and lower postoperative cortisol levels are more likely to achieve a higher rate of delayed remission. Among the 10 models, XGBoost achieved the best performance among all models in both clinical and radiomics models with AUC values of 0.767 and 0.819 respectively. The results from SHAP value and LIME algorithms revealed that postoperative cortisol level (PoC) and BMI were the most important features associated with DR. Conclusion: Radiomics models can be built as an effective noninvasive method to predict DR and might be useful in assisting neurosurgeons in making therapeutic plans after TSS for CD patients. These results are preliminary and further validation in a larger patient sample is needed.

Coexistence of craniopharyngioma and cranial fibrous dysplasia: a case series of clinicopathological study.

BACKGROUND: Craniopharyngioma (CP) and cranial fibrous dysplasia (CFD) are rare embryonic benign cranial diseases that most commonly present during childhood or adolescence. The coexistence of CP and CFD is extremely rare and has not yet been reported. METHODS: We retrospectively reviewed the data of five patients with concomitant CP and CFD treated at Beijing Tiantan Hospital from January 2003 to January 2021 and summarized their clinicopathological features, treatment modalities, and outcomes. We also performed a comprehensive literature review, tested the patients for characteristic GNAS gene mutations related to CFD, and tested the CP specimens for corresponding Gsα protein to explore the potential connection leading to the coexistence of CP and CFD. RESULTS: The cohort comprised four men and one woman (median age, 39 years). The symptoms mainly included headache, dizziness, fatigue, polyuria/polydipsia, hypogonadism, and blurred vision. CFD most commonly involved the sphenoid bone (n = 4). Four patients underwent surgery to remove the CP (one trans-sphenoidal and three transcranial resections); complete and subtotal resection were achieved in two patients, respectively. The tumor subtype was adamantinomatous in three patients and unknown in one. The common postoperative complications were panhypopituitarism, diabetes insipidus, and hypothyroidism. The mean follow-up duration was 57.2 months. Two patients required postoperative hormone replacement therapy. Three patients underwent genetic study of the tumor specimens; GNAS mutations were not detected, but these patients were positive for Gsα protein. CONCLUSIONS: Although a definite causative relationship has not been proved, the coexistence of CP and CFD means that potential interplay or an atypical fibrous dysplasia course as uncommon manifestations of CP cannot be excluded. It is more challenging to initiate prompt diagnosis and appropriate treatment for concomitant CP and CFD than for solitary CP because of skull base deformations. Current management strategies are aimed at surgical treating the CP and regularly monitoring the CFD.

Transsphenoidal Surgery of Corticotroph Adenomas With Cavernous Sinus Invasion: Results in a Series of 86 Consecutive Patients.

OBJECTIVE: Transsphenoidal surgery (TSS) is the first-line treatment for corticotroph adenomas. Although most corticotroph adenomas are noninvasive microadenomas, a small subset of them invading cavernous sinus (CS) is notoriously difficult to manage. The aim of this study was to evaluate the surgical outcome of corticotroph adenomas with CSI from a single center. PATIENTS AND METHODS: The clinical features and outcomes of CD patients who underwent TSS between January 2000 and September 2019 at Peking Union Medical College Hospital were collected from medical records. The clinical, endocrinological, radiological, histopathological, and surgical outcomes, and a minimum 12-month follow-up of patients with corticotroph adenomas invading CS were retrospectively reviewed. RESULTS: Eighty-six patients with corticotroph adenomas invading CS 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 included 86 patients underwent TSS using a microscopic or an endoscopic approach. Gross total resection was achieved in 63 patients (73.3%), subtotal resection was attained in 18 (20.9%), and partial resection was achieved 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) experienced a recurrence. In these patients with persistent disease and recurrent CD, data about further treatment were available for 30 patients. 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 administered in 4 patients, and one of them obtained disease control. Adrenalectomy was performed in 6 patients, and 5 (83.3%) achieved biochemical remission. At the last follow-up, 10 of 30 patients (33.3%) were in remission, and 20 patients still had persistent disease.The remission rate in corticotroph adenomas with cavernous sinus invasion (CSI) that underwent gross total resection and first TSS was 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 patients with different tumor sizes, Knosp grades, and surgical approaches (p > 0.05). CONCLUSION: The management of corticotroph adenomas with CSI remain a therapeutic challenge due to incomplete resection of invasive and/or a large adenoma. With the application of multiple techniques, approximately half of the patients could achieve gross total resection and biochemical remission via TSS by experienced neurosurgeons. The extent of tumor resection and the number of operations were associated with surgical remission rate in corticotroph adenomas with CSI. If the remission was not achieved by surgery, other treatments including radiotherapy, medical therapy, and even bilateral adrenalectomy are required.

Non-invasive preoperative imaging differential diagnosis of pineal region tumor: A novel developed and validated multiparametric MRI-based clinicoradiomic model.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing's Disease: Application of Word Embedding.

BACKGROUND: No existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing's disease (CD) after transsphenoidal surgery. PURPOSE: The aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery. METHODS: A total of 419 patients with CD from Peking Union Medical College Hospital were enrolled between January 2014 and August 2020. The EMR of the patients were embedded and transformed into low-dimensional dense vectors that can be included in four ML-based models together with structured features. The area under the curve (AUC) of receiver operating characteristic curves was used to evaluate the performance of the models. RESULTS: The overall remission rate of the 419 patients was 75.7%. From the results of logistic multivariate analysis, operation (p < 0.001), invasion of cavernous sinus from MRI (p = 0.046), and ACTH (p = 0.024) were strongly correlated with IR. The AUC values for the four ML-based models ranged from 0.686 to 0.793. The highest AUC value (0.793) was for logistic regression when 11 structured features and "individual conclusions of the case by doctor" were included. CONCLUSION: An ML-based model was developed using both structured and unstructured features (after being processed using a word embedding method) as input to preoperatively predict postoperative IR.

Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task.

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.

Tumor size measured by multidetector CT in resectable colon cancer: correlation with regional lymph node metastasis and N stage.

BACKGROUND: Lymph node metastasis (LNM) is a risk factor for poor long-term outcomes and a prognostic factor for disease-free survival in colon cancer. Preoperative lymph node status evaluation remains a challenge. The purpose of this study is to determine whether tumor size measured by multidetector computed tomography (MDCT) could be used to predict LNM and N stage in colon cancer. MATERIAL AND METHODS: One hundred six patients with colon cancer who underwent radical surgery within 1 week of MDCT scan were enrolled. Tumor size including tumor length (Tlen), tumor maximum diameter (Tdia), tumor maximum cross-sectional area (Tare), and tumor volume (Tvol) were measured to be correlated with pathologic LNM and N stage using univariate logistic regression analysis, multivariate logistic analysis, and receiver operating characteristic (ROC) curve analysis. RESULTS: The inter- and intraobserver reproducibility of Tlen (intraclass correlation coefficient [ICC] = 0.94, 0.95, respectively), Tdia (ICC = 0.81, 0.93, respectively), Tare (ICC = 0.97, 0.91, respectively), and Tvol (ICC = 0.99, 0.99, respectively) parameters measurement are excellent. Univariate logistic regression analysis showed that there were significant differences in Tlen, Tdia, Tare, and Tvol between positive and negative LNM (p < 0.001, 0.001, < 0.001, < 0.001, respectively). Multivariate logistic regression analysis revealed that Tvol was independent risk factor for predicting LNM (odds ratio, 1.082; 95% confidence interval for odds ratio, 1.039, 1.127, p<0.001). Tlen, Tdia, Tare, and Tvol could distinguish N0 from N1 stage (p < 0.001, 0.041, < 0.001, < 0.001, respectively), N0 from N2 (all p < 0.001), N0 from N1-2 (p < 0.001, 0.001, < 0.001, < 0.001, respectively), and N0-1 from N2 (p < 0.001, 0.001, < 0.001, < 0.001, respectively). The area under the ROC curve (AUC) was higher for Tvol than that of Tlen, Tdia, and Tare in identifying LNM (AUC = 0.83, 0.82, 0.69, 0.79), and distinguishing N0 from N1 stage (AUC = 0.79, 0.78, 0.63, 0.74), N0 from N2 stage (AUC = 0.92, 0.89, 0.80, 0.89, respectively), and N0-1 from N2 stage (AUC = 0.84, 0.79, 0.76, 0.83, respectively). CONCLUSION: Tumor size was correlated with regional LNM in resectable colon cancer. In particularly, Tvol showed the most potential for noninvasive preoperative prediction of regional LNM and N stage.

Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma.

BACKGROUND: Postoperative 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. OBJECTIVE: To evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma. METHODS: A 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. RESULTS: Three 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. CONCLUSIONS: By 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.

Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease.

Background: There 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). Purpose: Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. Methods: A 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. Results: The overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. Conclusion: We developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD.

Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model.

BACKGROUND: Accurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively. METHODS: A total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, 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 construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS: Six significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful. CONCLUSIONS: Our clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.

Predictors of Immediate Remission after Surgery in Cushing's Disease Patients: A Large Retrospective Study from a Single Center.

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.

Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease.

CONTEXT: Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term follow-up. OBJECTIVE: We aimed to develop and validate machine learning (ML) models for predicting DR in non-IR patients with CD. METHODS: We enrolled 201 CD patients, and randomly divided them into training and test datasets. We then used the recursive feature elimination (RFE) algorithm to select features and applied 5 ML algorithms to construct DR prediction models. We used permutation importance and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. RESULTS: Eighty-eight (43.8%) of the 201 CD patients met the criteria for DR. Overall, patients who were younger, had a low body mass index, a Knosp grade of III-IV, and a tumor not found by pathological examination tended to achieve a lower rate of DR. After RFE feature selection, the Adaboost model, which comprised 18 features, had the greatest discriminatory ability, and its predictive ability was significantly better than using Knosp grading and postoperative immediate morning serum cortisol (PoC). The results obtained from permutation importance and LIME algorithms showed that preoperative 24-hour urine free cortisol, PoC, and age were the most important features, and showed the reliability and clinical practicability of the Adaboost model in DC prediction. CONCLUSIONS: Machine learning-based models could serve as an effective noninvasive approach to predicting DR, and could aid in determining individual treatment and follow-up strategies for CD patients.

[Retracted] MicroRNA-375 inhibits glioma cell proliferation and migration by downregulating RWDD3 in vitro.

The authors of the above article drew to our attention that, in the above paper, they had identified three instances of data overlapping between data panels, suggesting that data purportedly showing results obtained under different experimental conditions had been derived from the same original source. Comparing among the data panels, two pairs of panels in Fig. 4B were shown to be overlapping, and a further pair of panels showed overlapping data in Fig. 6B. The authors were presented with an opportunity to correct their figures in a Corrigendum, although it has subsequently come to light that the replacement figures themselves featured problems with overlapping data. Given the errors that have been identified in the compilation of the figures in this article, the Editor of Oncology Reports has decided that this article should be retracted from the publication owing to a lack of overall confidence in the presented data. The authors all agree to the retraction of this article, and the Editor and the authors apologize for any inconvenience that might result from this retraction. [the original article was published in Oncology Reports 39: 1825-1834, 2018; DOI: 10.3892/or.2018.6261].

[Corrigendum] MicroRNA-375 inhibits glioma cell proliferation and migration by downregulating RWDD3 in vitro.

The authors of the above article drew to our attention that they had identified three instances of data overlapping between data panels, suggesting that data purportedly showing results obtained under different experimental conditions had been derived from the same original source. Comparing between the two figures, two pairs of panels in Fig. 4B (the Mimics control and blank experiments for the U87 and U251 cell lines) were shown to be overlapping, and a further pair of panels showed overlapping data in Fig. 6B (the data panels for the miR­375 mi + .pCDNA/RWDD3 and miR­375 mi + .pCDNA experiments for the U87 cell line). The authors were able to re­examine the original data files and retrieve the correct data panels. The errors in these figures arose through inadvertently assembling Figs. 4 and 6 incorrectly. The revised versions of Figs. 4 and 6, featuring the corrected data panels for the Mimics control and blank experiments for the U87 and U251 cell lines in Fig. 4B, and the correct data for the U87 cell line in Fig. 6B, are shown opposite and on the next page, respectively. Note that the corrections to the data shown in these Figures do not affect the overall conclusions reported in the paper. The authors are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this Corrigendum, and apologize to the readership for any inconvenience caused. [the original article was published in Oncology Reports 39: 1825-1834, 2018; DOI: 10.3892/or.2018.6261].

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.

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.

Clinicopathological and Prognostic Value of Gastric Carcinoma Highly Expressed Transcript 1 in Cancer: A Meta-Analysis.

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.

Cushing Syndrome Caused by Ectopic Adrenocorticotropic Hormone-Secreting Pituitary Adenomas: Case Report and Literature Review.

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.

Significance of lncRNA MIR31HG in predicting the prognosis for Chinese patients with cancer: a meta-analysis.

It is important to clarify the significance of long noncoding RNA MIR31 host gene (lncRNA MIR31HG) in predicting the prognosis for malignant tumors through meta-analysis. Electronic databases were systemically searched, from inception until 2 January 2019, to identify related articles. Meanwhile, the hazard ratios (odds ratios) and 95% CIs were computed for exploring the association between the expression of lncRNA MIR31HG and the survival (pathological variables). Eleven studies with 1041 cases were enrolled into the current meta-analysis. Low expression of lncRNA MIR31HG showed correlation with the dismal overall survival, disease-free survival, high tumor stage and lymph node metastasis among patients with digestive system cancers. Low expression of lncRNA MIR31HG may serve as a potential novel factor to indicate the dismal prognosis and metastasis in patients with digestive system cancers.

Outcomes of Transsphenoidal Surgery in Cushing Disease Patients with Negative Pituitary Magnetic Resonance Imaging Findings: A Single-Center Experience.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.