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Cystic lesions are common lesions of the sellar region with various pathological types, including pituitary apoplexy, Rathke's cleft cyst, cystic craniopharyngioma, etc. Suggested surgical approaches are not unique when dealing with different cystic lesions. However, cystic lesions with different pathological types were hard to differentiate on MRI with the naked eye by doctors. This study aimed to distinguish different pathological types of cystic lesions in the sellar region using preoperative magnetic resonance imaging (MRI). Radiomics and deep learning approaches were used to extract features from gadolinium-enhanced MRIs of 399 patients enrolled at Peking Union Medical College Hospital over the past 15 years. Paired imaging differentiations were performed on four subtypes, including pituitary apoplexy, cystic pituitary adenoma (cysticA), Rathke's cleft cyst, and cystic craniopharyngioma. Results showed that the model achieved an average AUC value of 0.7685. The model based on a support vector machine could distinguish cystic craniopharyngioma from Rathke's cleft cyst with the highest AUC value of 0.8584. However, distinguishing cystic apoplexy from pituitary apoplexy was difficult and almost unclassifiable with any algorithms on any feature set, with the AUC value being only 0.6641. Finally, the proposed methods achieved an average Accuracy of 0.7532, which outperformed the traditional clinical knowledge-based method by about 8%. Therefore, in this study, we first fill the gap in the existing literature and provide a non-invasive method for accurately differentiating between these lesions, which could improve preoperative diagnosis accuracy and help to make surgery plans in clinical work. [ABSTRACT FROM AUTHOR]

Purpose: Cushing's disease (CD) is a rare disease that contributes to 70–80% hypercortisolemia, which presents similarities and differences between pediatric and adult patients, and even between male and female patients. However, the comparative study of CD between different age groups and different genders is still insufficient. The aim of the study is to make a systematic comparison to reveal the gender differences in children and adult patients of CD, helping clinicians to provide optimal treatment for different groups of patients. Methods: We conducted a retrospective research consisting of 30 pediatric and 392 adult CD patients in a single center in Peking Union Medical College Hospital. All 422 patients showed symptoms related to hypercortisolism and received adenoma excision surgery in the department of neurosurgery between 2014 and 2020. Results: For the accuracy of diagnosis, the sensitivity of BIPSS at baseline in pediatric patients was lower than in adults (75 vs. 91%, P = 0.054) but increased greatly after desmopressin stimulation (94 vs. 95%). However, the accuracy of lateralization for BIPSS was not preferred for prediction. As for clinical manifestations, growth retardation, weight gain, hirsutism, and acne were more prevalent for children, while for adults, hypertension, osteopenia, glucometabolic disorder, easy bruising, hair loss, and weight loss were more frequently seen. As previously reported, we observed a significant difference between the male prevalence of pediatric and adult patients (50 vs. 17%, P < 0.001), which was possibly caused by the more severe and earlier onset of a series of symptoms. Gender-related comparison showed greater morbidity of nephrolithiasis, hypokalemia, hypertension, easy bruising, osteopenia, and striae for male patients, while irregular menses, hirsutism, and hair loss were more common for female patients. Further analysis showed that the secretory activity of the PA axis was higher for males, presenting as the more remarkable alteration of laboratory parameters and contributing to the more severe clinical manifestations. For patients treated with transsphenoidal pituitary surgery (TSS), the immediate prognosis could be predicted by operation history, invasiveness, Ki-67, and information provided by MRI, including tumor size and Knosp grading. However, we still lack methods to predict long-term prognosis. Conclusions: Our study is the first detailed and systematic comparison between pediatric and adult CD patients. Further exploration of the impact of CD on different genders reveals a more severe and probably an earlier-onset pattern of CD for male patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]