Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images

Radiomics of pituitary adenoma using computer vision: a review

Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.

Role of artificial intelligence in brain tumour imaging

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.

Diagnosis of choroid plexus papilloma: Current perspectives and future directions

Choroid plexus papilloma (CPP) is a rare, slow-growing, and typically benign brain tumor that predominantly affects children. CPP is characterized by well-defined circular or lobulated masses in the ventricles, leading to symptoms related to increased intracranial pressure and hydrocephalus. CPP diagnosis relies on a combination of clinical presentation, imaging findings, and histological examination. The World Health Organization (WHO) classification categorizes choroid plexus tumors into CPP (Grade І), atypical CPP (aCPP, Grade II), and choroid plexus carcinoma (CPC, Grade III). This article reviewed current diagnostics modalities and explored the emergence of new diagnostic methods for CPP. Research on molecular markers and genetic alterations associated with CPP is ongoing, and some potential markers have been identified. These results offered insights into potential therapeutic targets and personalized treatment approaches for CPP. Advancements in radiomics and liquid biopsy hold promise for improving diagnostic accuracy and monitoring treatment outcomes for choroid plexus tumors. Radiomics can provide quantitative data from imaging studies, whereas liquid biopsy can analyze tumor-derived genetic material and molecular markers from body fluids, such as cerebrospinal fluid (CSF) and blood. The rapidly evolving fields of molecular and genetic research and novel diagnostic methods require continuous updates and advancements before their application in clinical practice. We hope that these advancements will lead to earlier and more precise diagnoses, better treatment options, and improved outcomes in patients with CPP and other brain tumors.

Artificial Intelligence in Rhinology

Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical care and promote personalized medicine. Numerous research studies have been published demonstrating the development and clinical potential of AI models within the field. Most describe in silico evaluation models without direct clinical implementation. The major themes of existing studies include diagnostic or clinical decisions support, clustering patients into specific phenotypes or endotypes, predicting post-treatment outcomes, and surgical planning.

Pituitary MRI Radiomics Improves Diagnostic Performance of Growth Hormone Deficiency in Children Short Stature: A Multicenter Radiomics Study

RATIONALE AND OBJECTIVES

To develop an efficient machine-learning model using pituitary MRI radiomics and clinical data to differentiate growth hormone deficiency (GHD) from idiopathic short stature (ISS), making the diagnostic process more acceptable to patients and their families.

MATERIALS AND METHODS

A retrospective cohort of 297 GHD and 300 ISS children (4-12 years) were enrolled as training and validation cohorts (8:2 ratio). An external cohort from another institution (49 GHD and 51 ISS) was employed as the testing cohort. Radiomics features extracted from the anterior pituitary gland on sagittal T1-weighted image (1.5 T or 3.0 T) were used to develop a radiomics model after feature selection. Hematological biomarkers were selected to create a clinical model and combine with the optimal radiomics features to create a clinical-radiomics model. The area under the receive operating characteristic curve (AUC) and Delong test compared the diagnostic performance of the previously mentioned three models across different validation and testing cohorts.

RESULTS

17 radiomics features were selected for the radiomics model, and total protein, total cholesterol, free triiodothyronine, and triglyceride were utilized for the clinical model. In the training and validation cohorts, the diagnostic performance of the clinical-radiomics model (AUC=0.820 and 0.801) was comparable to the radiomics model (AUC=0.812 and 0.779, both P >0.05), both outperforming the clinical model (AUC=0.575 and 0.593, P <0.001). In the testing cohort, the clinical-radiomics model exhibited the highest AUC of 0.762 than the clinical and radiomics model (AUC=0.604 and 0.741, respectively, P <0.05). In addition, the clinical and radiomics models demonstrated similar diagnostic performance in the testing cohort (P >0.05).

CONCLUSION

Integrating radiomics features from conventional pituitary MRI with clinical indicators offers a minimally invasive approach for identifying GHD and shows robustness in a multicenter setting.

Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review

PURPOSE

Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.

METHODS

PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.

RESULTS

Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.

CONCLUSION

AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.

Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI

RATIONALE AND OBJECTIVES

Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI.

MATERIALS AND METHODS

First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression.

RESULTS

The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression.

CONCLUSION

The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.

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.

Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives

Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.

A Novel Magnetic Resonance Imaging-Based Radiomics and Clinical Predictive Model for the Regrowth of Postoperative Residual Tumor in Non-Functioning Pituitary Neuroendocrine Tumor

Background and Objectives: To develop a novel magnetic resonance imaging (MRI)-based radiomics-clinical risk stratification model to predict the regrowth of postoperative residual tumors in patients with non-functioning pituitary neuroendocrine tumors (NF-PitNETs). Materials and Methods: We retrospectively enrolled 114 patients diagnosed as NF-PitNET with postoperative residual tumors after the first operation, and the diameter of the tumors was greater than 10 mm. Univariate and multivariate analyses were conducted to identify independent clinical risk factors. We identified the optimal sequence to generate an appropriate radiomic score (Rscore) that combined pre- and postoperative radiomic features. Three models were established by logistic regression analysis that combined clinical risk factors and radiomic features (Model 1), single clinical risk factors (Model 2) and single radiomic features (Model 3). The models' predictive performances were evaluated using receiver operator characteristic (ROC) curve analysis and area under curve (AUC) values. A nomogram was developed and evaluated using decision curve analysis. Results: Knosp classification and preoperative tumor volume doubling time (TVDT) were high-risk factors (p < 0.05) with odds ratios (ORs) of 2.255 and 0.173. T1WI&T1CE had a higher AUC value (0.954) and generated an Rscore. Ultimately, the AUC of Model 1 {0.929 [95% Confidence interval (CI), 0.865-0.993]} was superior to Model 2 [0.811 (95% CI, 0.704-0.918)] and Model 3 [0.844 (95% CI, 0.748-0.941)] in the training set, which were 0.882 (95% CI, 0.735-1.000), 0.834 (95% CI, 0.676-0.992) and 0.763 (95% CI, 0.569-0.958) in the test set, respectively. Conclusions: We trained a novel radiomics-clinical predictive model for identifying patients with NF-PitNETs at increased risk of postoperative residual tumor regrowth. This model may help optimize individualized and stratified clinical treatment decisions.

Comparing MRI volume measurement techniques for pituitary macroadenoma: Investigating volume reduction and its relationship with biochemical control

Pituitary adenomas are one of the most common types of primary intracranial tumors. Measuring pituitary adenoma volume is fundamental for effective management. This study aimed to assess the reliability of the ellipsoid method in comparison with the perimeter method for measuring pituitary macroadenoma volume. In addition, we investigated the correlation between adenoma size reduction and biochemical control in functioning adenomas. This was a retrospective cross-sectional cohort study including 113 patients with pituitary macroadenomas. MRI was obtained for volume measurement by ellipsoid and perimeter methods using two types of DICOM viewer software. Both ellipsoid and perimeter methods exhibit positive, strong, and significant correlations in pituitary macroadenomas in pre-treatment and post-treatment volume (Spearman correlation coefficient 0.95, p-value <0.0001). There was no significant difference in the mean post-treatment pituitary adenoma volume measurements utilizing the ellipsoid and the perimeter methods in different treatment modalities. There were significant differences in the pre-treatment volume measurements between the two methods, both in NFPA and prolactinoma. No correlation was found between volume variability measured by ellipsoid and perimeter methods and the degree of hormonal control in functioning pituitary adenomas. Both the ellipsoid and perimetric methods can be utilized for pituitary adenoma volume measurements as they demonstrate a strong and positive correlation. However, it is important to note that the ellipsoid method tends to result in overestimated tumor volume. There was no correlation between the adenoma size reduction and the degree of biochemical response in functioning adenomas.

Prognostic Models in Growth-Hormone- and Prolactin-Secreting Pituitary Neuroendocrine Tumors: A Systematic Review

Growth-hormone (GH)- and prolactin (PRL)-secreting PitNETs (pituitary neuroendocrine tumors) are divided into multiple histological subtypes, which determine their clinical and biological variable behavior. Proliferation markers alone have a questionable degree of prediction, so we try to identify validated prognostic models as accurately as possible. (1) Background: The data available so far show that the use of staging and clinical-pathological classification of PitNETs, along with imaging, are useful in predicting the evolution of these tumors. So far, there is no consensus for certain markers that could predict tumor evolution. The application of the WHO (World Health Organisation) classification in practice needs to be further evaluated and validated. (2) Methods: We performed the CRD42023401959 protocol in Prospero with a systematic literature search in PubMed and Web of Science databases and included original full-text articles (randomized control trials and clinical trials) from the last 10 years, published in English, and the search used the following keywords: (i) pituitary adenoma AND (prognosis OR outcome OR prediction), (ii) growth hormone pituitary adenoma AND (prognosis OR outcome OR prediction), (iii) prolactin pituitary adenoma AND (prognosis OR outcome OR prediction); (iv) mammosomatotroph adenoma AND (prognosis OR outcome OR prediction). (3) Results: Two researchers extracted the articles of interest and if any disagreements occurred in the selection process, these were settled by a third reviewer. The articles were then assessed using the ROBIS bias assessment and 75 articles were included. (4) Conclusions: the clinical-pathological classification along with factors such as GH, IGF-1, prolactin levels both preoperatively and postoperatively offer valuable information.

Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review

PURPOSE

Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction.

METHOD

The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

RESULTS

Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability.

CONCLUSIONS

The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.

Analysis of the Prognostic and Immunological Role of HSPB1 in Pituitary Adenoma: A Potential Target for Therapy

Background and Objectives: The diagnosis and treatment of pituitary adenomas with cavernous sinus invasion pose significant challenges for clinicians. The objective of this study is to investigate the expression profile and prognostic value of HSPB1 (heat shock protein beta-1) in pituitary adenomas with invasive and non-invasive features. Additionally, we aim to explore the potential relationship between HSPB1 expression and immunological functions in pituitary adenoma. Materials and Methods: A total of 159 pituitary adenoma specimens (73 invasive tumours and 86 non-invasive tumours) underwent whole-transcriptome sequencing. Differentially expressed genes and pathways in invasive and non-invasive tumours were analysed. HSPB1 was subjected to adequate bioinformatics analysis using various databases such as TIMER, Xiantao and TISIDB. We investigated the correlation between HSPB1 expression and immune infiltration in cancers and predicted the target drug of HSPB1 using the TISIDB database. Results: HSPB1 expression was upregulated in invasive pituitary adenomas and affected immune cell infiltration. HSPB1 was significantly highly expressed in most tumours compared to normal tissues. High expression of HSPB1 was significantly associated with poorer overall survival. HSPB1 was involved in the regulation of the immune system in most cancers. The drugs DB11638, DB06094 and DB12695 could act as inhibitors of HSPB1. Conclusions: HSPB1 may serve as an important marker for invasive pituitary adenomas and promote tumour progression by modulating the immune system. Inhibitors of HSPB1 expression are currently available, making it a potential target for therapy in invasive pituitary adenoma.

The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era

The evolution of endoscopic trans-sphenoidal surgery raises the question of the role of transcranial surgery for pituitary tumors, particularly with the effectiveness of adjunct irradiation. This narrative review aims to redefine the current indications for the transcranial approaches for giant pituitary adenomas in the endoscopic era. A critical appraisal of the personal series of the senior author (O.A.-M.) was performed to characterize the patient factors and the tumor's pathological anatomy features that endorse a cranial approach. Traditional indications for transcranial approaches include the absent pneumatization of the sphenoid sinus; kissing/ectatic internal carotid arteries; reduced dimensions of the sella; lateral invasion of the cavernous sinus lateral to the carotid artery; dumbbell-shaped tumors caused by severe diaphragm constriction; fibrous/calcified tumor consistency; wide supra-, para-, and retrosellar extension; arterial encasement; brain invasion; coexisting cerebral aneurysms; and separate coexisting pathologies of the sphenoid sinus, especially infections. Residual/recurrent tumors and postoperative pituitary apoplexy after trans-sphenoidal surgery require individualized considerations. Transcranial approaches still have a critical role in giant and complex pituitary adenomas with wide intracranial extension, brain parenchymal involvement, and the encasement of neurovascular structures.

Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence

Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.

Preoperative prediction and histological stratification of intracranial solitary fibrous tumours by machine-learning models

AIM

To explore the effectiveness and feasibility of machine-learning models based on magnetic resonance imaging (MRI) radiomics features in differentiating intracranial solitary fibrous tumour (ISFT) from angiomatous meningioma (AM) and stratifying ISFT histologically.

MATERIALS AND METHODS

This study retrospectively recruited 268 patients with a histological diagnosis of ISFT (n=120) or AM (n=148), and 116 of the ISFT patients were used for stratified analysis of histological grade. The radiomics features were extracted from axial T1-weighted imaging (WI), T2WI and contrast-enhanced T1WI sequences. All patients were assigned randomly to the training group and test group in a ratio of 7:3. The models were optimised by 10-fold cross-validation in the training group, and the independent test group was used for further testing of the models. The performances of machine-learning models based on radiomics, clinical, and fusion features in predicting and stratifying ISFT were evaluated.

RESULTS

ISFT and AM differed significantly in terms of age, tumour shape, enhancement pattern, and margin. There was no significant difference in the clinical characteristics between World Health Organization (WHO) grade II and WHO grade III ISFT. When used to differentiate ISFT from AM, the area under the curve (AUC) values of the machine-learning models based on radiomics, clinical, and fusion features in the test group were 0.917, 0.923 and 0.950, respectively. When used for histological stratification of ISFT, the model based on the radiomics signature achieved an AUC value of 0.786 in the test group.

CONCLUSIONS

Machine-learning models can contribute in the prediction and histological stratification of ISFT non-invasively, which can help clinical differential diagnosis and treatment decisions.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas

OBJECTIVES

Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model.

METHODS

Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation.

RESULTS

Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set.

CONCLUSION

The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas.

KEY POINTS

• Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.

The Knosp Criteria Revisited: 3-Dimensional Volumetric Analysis as a Predictive Tool for Extent of Resection in Complex Endoscopic Pituitary Surgery

BACKGROUND

The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery.

OBJECTIVE

To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas.

METHODS

Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined.

RESULTS

One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm 3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort ( P > .05, area under the curve 0.46).

CONCLUSION

Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.

Machine learning–based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle

OBJECTIVES

To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles.

METHODS

A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists.

RESULTS

The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts.

CONCLUSIONS

The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance.

KEY POINTS

• The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

Quality reporting of radiomics analysis in pituitary adenomas: promoting clinical translation

OBJECTIVE

To evaluate the quality of radiomics studies on pituitary adenoma according to the radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).

METHODS

PubMed MEDLINE and EMBASE were searched to identify radiomics studies on pituitary adenomas. From 138 articles, 20 relevant original research articles were included. Studies were scored based on RQS and TRIPOD guidelines.

RESULTS

Most included studies did not perform pre-processing; isovoxel resampling, signal intensity normalization, and N4 bias field correction were performed in only five (25%), eight (40%), and four (20%) studies, respectively. Only two (10%) studies performed external validation. The mean RQS and basic adherence rate were 2.8 (7.6%) and 26.6%, respectively. There was a low adherence rate for conducting comparison to "gold-standard" (20%), multiple segmentation (25%), and stating potential clinical utility (25%). No study stated the biological correlation, conducted a test-retest or phantom study, was a prospective study, conducted cost-effectiveness analysis, or provided open-source code and data, which resulted in low-level evidence. The overall adherence rate for TRIPOD was 54.6%, and it was low for reporting the title (5%), abstract (0%), explaining the sample size (10%), and suggesting a full prediction model (5%).

CONCLUSION

The radiomics reporting quality for pituitary adenoma is insufficient. Pre-processing is required for feature reproducibility and external validation is necessary. Feature reproducibility, clinical utility demonstration, higher evidence levels, and open science are required. Titles, abstracts, and full prediction model suggestions should be improved for transparent reporting.

ADVANCES IN KNOWLEDGE

Despite the rapidly increasing number of radiomics researches on pituitary adenoma, the quality of science in these researches is unknown. Our study indicates that the overall quality needs to be significantly improved in radiomics studies on pituitary adenoma, and since the concept of RQS and IBSI is still unfamiliar to clinicians and radiologist researchers, our study may help to reach higher technical and clinical impact in the future study.

Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning

Background

Infiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration.

Methods

A total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit the predictive model based on infiltration features. Furthermore, the receiver operating characteristic curve (ROC) was generated, and the diagnostic performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, precision, recall, and F1 value.

Results

A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm, and 19 optimal features were finally selected. The SVM models for predicting high infiltration yielded an AUC of 0.86 (sensitivity: 0.81, specificity 0.79) in the training set and 0.73 (sensitivity: 0.87, specificity: 0.80) in the validation set. The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set (accuracy: 0.80, precision: 0.82, recall: 0.81, F1 score: 0.81) and independent verification set (accuracy: 0.85, precision: 0.93, recall: 0.87, F1 score: 0.90).

Conclusions

The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration. This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41016-022-00290-4.

A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans

Objective

Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN.

Methods

A total of 1413 coronal and sagittal magnetic resonance images were collected from 695 patients with PAs. The enrolled images were divided into the invasive group (n = 530) and the non-invasive group (n = 883) according to the surgical observation of SFI. Before model training, 100 images were randomly selected for the external testing set. The remaining 1313 cases were randomly divided into the training and validation sets at a ratio of 80:20 for model training. Finally, the testing set was imported to evaluate the model performance.

Results

A CNN model with a 10-layer structure (6-layer convolution and 4-layer fully connected neural network) was constructed. After 1000 epoch of training, the model achieved high accuracy in identifying SFI (97.0 and 94.6% in the training and testing sets, respectively). The testing set presented excellent performance, with a model prediction accuracy of 96%, a sensitivity of 0.964, a specificity of 0.958, and an area under the receptor operator curve (AUC-ROC) value of 0.98. Four images in the testing set were misdiagnosed. Three images were misread with SFI (one with conchal type sphenoid sinus), and one image with a relatively intact sellar floor was not identified with SFI.

Conclusion

This study highlights the potential of the CNN model for the efficient assessment of PA invasion.

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Simple Summary

Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases.

Abstract

Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

Application of Convolutional Neural Network in the Diagnosis of Cavernous Sinus Invasion in Pituitary Adenoma

Objectives

Convolutional neural network (CNN) is a deep-learning method for image classification and recognition based on a multi-layer NN. In this study, CNN was used to accurately assess cavernous sinus invasion (CSI) in pituitary adenoma (PA).

Methods

A total of 371 patients with PA were enrolled in the retrospective study. The cohort was divided into the invasive (n = 102) and non-invasive groups (n = 269) based on surgically confirmed CSI. Images were selected on the T1-enhanced imaging on MR scans. The cohort underwent a fivefold division of randomized datasets for cross-validation. Then, a tenfold augmented dataset (horizontal flip and rotation) of the training set was enrolled in the pre-trained Resnet50 model for transfer learning. The testing set was imported into the trained model for evaluation. Gradient-weighted class activation mapping (Grad-CAM) was used to obtain the occlusion map. The diagnostic values were compared with different dichotomizations of the Knosp grading system (grades 0-1/2-4, 0-2/3a-4, and 0-3a/3b-4).

Results

Based on Knosp grades, 20 cases of grade 0, 107 cases of grade 1, 82 cases of grade 2, 104 cases of grade 3a, 22 cases of grade 3b, and 36 cases of grade 4 were recorded. The CSI rates were 0%, 3.7%, 18.3%, 37.5%, 54.5%, and 88.9%. The predicted accuracies of the three dichotomies were 60%, 74%, and 81%. The area under the receiver operating characteristic (AUC-ROC) of Knosp grade for CSI prediction was 0.84; the cutoff was 2.5 with a Youden value of 0.62. The accuracies of the CNN model ranged from 0.80 to 0.96, with AUC-ROC values ranging from 0.89 to 0.98. The Grad-CAM saliency maps confirmed that the region of interest of the model was around the sellar region.

Conclusions

We constructed a CNN model with a high proficiency at CSI diagnosis. A more accurate CSI identification was achieved with the constructed CNN than the Knosp grading system.

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Simple Summary

Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Abstract

The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Thin-Slice Pituitary MRI with Deep Learning-Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study

BACKGROUND AND PURPOSE

Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning-based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI.

MATERIALS AND METHODS

This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modified Knosp classification on 1-mm-slice-thickness MRI + deep learning-based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory findings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery.

RESULTS

The diagnostic performance of 1-mm-slice-thickness MRI + deep learning-based reconstruction (area under the curve, 0.79; 95% CI, 0.69 - 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52-0.70; P < .001). One-millimeter-slice-thickness MRI + deep learning-based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P < .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI + deep learning-based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P < .001; >270°, 38.8% versus 16.4%, P < .001).

CONCLUSIONS

Compared with 3-mm-slice-thickness MRI, 1-mm-slice-thickness MRI + deep learning-based reconstruction showed a higher diagnostic performance in preoperatively predicting cavernous sinus invasion by pituitary adenomas and demonstrated a greater depth and degree of contact in relation to the carotid artery.

Machine intelligence in non-invasive endocrine cancer diagnostics

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.

A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years

Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years.

Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis.

Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718—.860] vs. 0.739, (95% CI: 0.665–0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1.

Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.

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.

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.

Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI.

Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.

Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis.

Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

Machine Learning in Pituitary Surgery

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.

Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas

OBJECTIVE

To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics.

METHODS

A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson's correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC.

RESULTS

Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction.

CONCLUSION

Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies.

KEY POINTS

• Radiomics might improve the preoperative diagnosis of SCAs by MR images. • T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs. • The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.

Current Advances and Challenges in Radiomics of Brain Tumors

Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives

BACKGROUND

Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties.

METHODS

A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool.

RESULTS

41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified.

CONCLUSIONS

In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.

Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma

CONTEXT

Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning.

OBJECTIVE

To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients.

DESIGN

Retrospective study.

SETTING

Severance Hospital, Seoul, Korea.

PATIENTS

A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA nonresponders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set.

RESULTS

The ensemble classifier showed an area under the curve (AUC) of 0.81 [95% confidence interval (CI), 0.74-0.87] in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95% CI, 0.67-0.96), 77.8%, 78.6%, and 77.3%, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set.

CONCLUSIONS

Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.

Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma

Background

Given the similarities in clinical manifestations of cystic-solid pituitary adenomas (CS-PAs) and craniopharyngiomas (CPs), this study aims to establish and validate a nomogram based on preoperative imaging features and blood indices to differentiate between CS-PAs and CPs.

Methods

A departmental database was searched to identify patients who had undergone tumor resection between January 2012 and December 2020, and those diagnosed with CS-PAs or CPs by histopathology were included. Preoperative magnetic resonance imaging (MRI) features as well as blood indices were retrieved and analyzed. Radiological features were extracted from the tumor on contrast-enhanced T1 (CE-T1) weighted and T2 weighted sequences. The two independent samples t-test and principal component analysis (PCA) were used for feature selection, data dimension reduction, and radiomics signature building. Next, the radiomics signature was put in five classification models for exploring the best classifier with superior identification performance. Multivariate logistic regression analysis was then used to establish a radiomic-clinical model containing radiomics and hematological features, and the model was presented as a nomogram. The performance of the radiomics-clinical model was assessed by calibration curve, clinical effectiveness as well as internal validation.

Results

A total of 272 patients were included in this study: 201 with CS-PAs and 71 with CPs. These patients were randomized into training set (n=182) and test set (n=90). The radiomics signature, which consisted of 18 features after dimensionality reduction, showed superior discrimination performance in 5 different classification models. The area under the curve (AUC) values of the training set and the test set obtained by the radiomics signature are 0.92 and 0.88 in the logistic regression model, 0.90 and 0.85 in the Ridge classifier, 0.88 and 0.82 in the stochastic gradient descent (SGD) classifier, 0.78 and 0.85 in the linear support vector classification (Linear SVC), 0.93 and 0.86 in the multilayers perceptron (MLP) classifier, respectively. The predictive factors of the nomogram included radiomic signature, age, WBC count, and FIB. The nomogram showed good discrimination performance (with an AUC of 0.93 in the training set and 0.90 in the test set) and good calibration. Moreover, decision curve analysis (DCA) demonstrated satisfactory clinical effectiveness of the proposed radiomic-clinical nomogram.

Conclusions

A personalized nomogram containing radiomics signature and blood indices was proposed in this study. This nomogram is simple yet effective in differentiating between CS-PAs and CPs and thus can be used in routine clinical practice.

Invasive Corridor of Clivus Extension in Pituitary Adenoma: Bony Anatomic Consideration, Surgical Outcome and Technical Nuances

Background

It is well known that the clivus is composed of abundant cancellous bone and is often invaded by pituitary adenoma (PA), but the range of these cancellous bone corridors is unknown. In addition, we found that PA with clivus invasion is sometimes accompanied by petrous apex invasion, so we speculated that the petrous apex tumor originated from the clivus cancellous bone corridor. The aim of this study was to test this hypothesis by investigating the bony anatomy associated with PA with clival invasion and its clinical significance.

Methods

Twenty-two cadaveric heads were used in the anatomical study to research the bony architecture of the clivus and petrous apex, including six injected specimens for microsurgical dissection and sixteen cadavers for epoxy sheet plastination. The surgical videos and outcomes of PA with clival invasion in our single center were also retrospectively reviewed.

Results

The hypoglossal canal and internal acoustic meatus are composed of bone canals surrounded by cortical bone. The cancellous corridor within clivus starts from the sellar or sphenoid sinus floor and extends downward, bypassing the hypoglossal canal and finally reaching the occipital condyle and the medial edge of the jugular foramen. Interestingly, we found that the cancellous bone of the clivus was connected with that of the petrous apex through petroclival fissure extending to the medial margin of the internal acoustic meatus instead of a separating cortical bone between them as it should be. It is satisfactory that the anatomical outcomes of the cancellous corridor and the path of PA with clival invasion observed intraoperatively are completely consistent. In the retrospective cohort of 49 PA patients, the clival component was completely resected in 44 (89.8%), and only five (10.2%) patients in the early-stage had partial residual cases in the inferior clivus.

Conclusion

The petrous apex invasion of PA is caused by the tumor invading the clivus and crossing the petroclival fissure along the cancellous bone corridor. PA invade the clivus along the cancellous bone corridor and can also cross the hypoglossal canal to the occipital condyle. This clival invasion pattern presented here deepens our understanding of the invasive characteristics of PA.

Assessment of magnetic resonance imaging criteria for the diagnosis of cavernous sinus invasion by pituitary tumors

Cavernous sinus invasion (CSI) by pituitary tumors is associated with subtotal resection and persistent endocrinopathy. The Knosp classification is a magnetic resonance imaging (MRI) tool used to define CSI in the 2017 World Health Organization Classification. However, alternative criteria may have superior diagnostic performance. This study aimed to assess the diagnostic performance of four MRI criteria, using a combination of endoscopy and day 1 MRI as the reference standard for CSI. A cross-sectional study was conducted including patients treated with endoscopic endonasal transsphenoidal surgery for pituitary macroadenomas, recruited from a tertiary pituitary multidisciplinary center in Sydney, Australia between September 2013, and February 2021. The diagnostic performances of four MRI criteria were assessed: the Knosp criteria, percentage encasement of the internal carotid (PEICA), venous compartment obliteration (VCO), and the Fernandez-Miranda classification. Reference CSI was defined using a combination of intraoperative endoscopy and day 1 MRI. A total of 210 cavernous sinuses (105 patients), were analyzed, (51.7 ± 16.3yrs, 43% female), of which 18% had CSI. CSI was best diagnosed by Knosp ≥ 2 (63% sensitivity and 89% specificity), PEICA ≥ 28% (84% sensitivity and 77% specificity) and VCO of ≥ 3 compartments (65% sensitivity and 89% specificity). CSI was unlikely if any of the following signs were present: Knosp < 1, PEICA < 28%, preservation of the medial or superior compartments or sparing of the superior Fernandez-Miranda compartment (negative predictive value 95%, 95%, 94%, 91% and 92% respectively). In conclusion, alternatives to the Knops criteria including PEICA and VCO can aid CSI diagnosis.

Magnetic resonance fingerprinting for preoperative differentiation between gonadotroph and non-gonadotroph pituitary macroadenomas

OBJECTIVES

To use magnetic resonance fingerprinting (MRF)-derived T1 and T2 values to differentiate gonadotroph from non-gonadotroph pituitary macroadenomas based on the 2017 World Health Organization classification of pituitary adenomas.

METHODS

A total of 57 patients with suspected pituitary macroadenomas were enrolled for analyses in this study between May 2018 and January 2020. Conventional magnetic resonance imaging (MRI) and MRF were performed in all patients before surgery using a 3-T MRI scanner. MRF-derived T1 and T2 values were compared between the gonadotroph and non-gonadotroph pituitary macroadenomas using a Mann-Whitney U test. The Knosp classification was used to evaluate cavernous sinus invasion by the adenomas. Receiver operating characteristic analyses were used to determine the diagnostic performance of T1 and T2 values.

RESULTS

Quantitative T1 and T2 values yielded from MRF of gonadotroph pituitary macroadenomas were significantly higher than those of the non-gonadotroph pituitary macroadenomas (p < 0.001 and = 0.002, respectively). The AUC for the T2 value (0.888) was significantly greater than that for the T1 value (0.742) (p = 0.034). The AUC for combined T1 and T2 values was 0.885. Non-gonadotroph pituitary macroadenomas were more likely to invade the cavernous sinus than gonadotroph pituitary macroadenomas (55% vs 26%, p = 0.026).

CONCLUSIONS

MRF may help to preoperatively differentiate between gonadotroph and non-gonadotroph pituitary macroadenomas and may be useful in guiding the treatment of these adenomas.

KEY POINTS

• Somatostatin receptor type 3 is the most abundant receptor subtype in gonadotroph pituitary adenomas. • Magnetic resonance fingerprinting may help to preoperatively differentiate between gonadotroph and non-gonadotroph pituitary macroadenomas. • Magnetic resonance fingerprinting shows potential for guiding the treatment of pituitary macroadenomas.

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.

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.

A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years

Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years. Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis. Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718-.860] vs. 0.739, (95% CI: 0.665-0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1. Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.

Comparative Analysis of the MRI Characteristics of Meningiomas According to the 2016 WHO Pathological Classification

OBJECTS

To evaluate the performance of preoperative magnetic resonance imaging (MRI) in evaluating diagnoses, operation methods and recurrence of meningiomas according to the World health organization (WHO) pathological classification.

METHODS

MRI characteristics of 127 meningioma patients were retrospectively analysed according to pathological results (WHO grade) and their association with Simpson's grades (resection) and recurrence.

RESULTS

The T1-weighted imaging (T1WI) signal intensity of WHO grade I meningiomas was slightly hypointense or isointense gray, while the T2-weighted imaging (T2WI) signal intensity was isointense or slightly hyperintense. The T1WI and T2WI signal intensity in WHO grade II and III meningiomas was isointense gray. The enhancement degree and patterns, lobulation, flowing voids, dural tail, maximum diameter, peritumoural oedema, ADC values and margin were significantly different between any 2 grades (P < 0.05). The ADC values were higher for WHO grade I tumors than for WHO grade II and III tumors (P < 0.001). Among all the analyzed characteriscs, ADC values, peritumoural oedema, and margin effectively predicted the diagnosis according to the WHO classification. The operation method and surgical resection were different between WHO grade Ⅰ and WHO grade Ⅱ/Ⅲ meningiomas (P < 0.05). The recurrence rate increased with tumor grade, but there was no statistical difference among the 3 types(P> 0.05).

CONCLUSIONS

WHO grades and pathological subtypes of meningiomas can generally be determined based on their MRI characteristics. In addition, MRI provides significant guidance for the grading of surgical success and prognosis.