Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI

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.

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.

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.

Diffusion-weighted imaging does not seem to be a predictor of consistency in pituitary adenomas

PURPOSE

To prospectively evaluate the usefulness of T1-weighted imaging (T1WI) and diffusion-weighted imaging (DWI) sequences in predicting the consistency of macroadenomas. In addition, to determine their values ​​as prognostic factors of surgical outcomes.

METHODS

Patients with pituitary macroadenoma and surgical indication were included. All patients underwent pre-surgical magnetic resonance imaging (MRI) that included the sequences T1WI before and after contrast administration and DWI with the apparent diffusion coefficient (ADC) map. Post-surgical MRI was performed at least 3 months after surgery. The consistency of the macroadenomas was evaluated at surgery, and they were grouped into soft and intermediate/hard adenomas. Mean ADC values, signal on T1WI and the ratio of tumor ADC values ​​to pons (ADCR) were compared with tumor consistency and grade of surgical resection.

RESULTS

A total of 80 patients were included. A softened consistency was found at surgery in 53 patients and hardened in 27 patients. The median ADC in the soft consistency group was 0.532 × 10-3 mm2/sec (0.306 - 1.096 × 10-3 mm2/sec), and in the intermediate/hard consistency group was 0.509 × 10-3 mm2/sec (0.308 - 0.818 × 10-3 mm2/sec). There was no significant difference between the median values ​​of ADC, ADCR and signal on T1W between the soft and hard tumor groups, or between patients with and without tumor residue.

CONCLUSION

Our results did not show usefulness of the DWI and T1WI for assessing the consistency of pituitary macroadenomas, nor as a predictor of the degree of surgical resection.

Development and validation of a prediction model for consistency of pituitary adenoma: the PiTCon score

PURPOSE

Pituitary adenomas (PAs) usually have a soft consistency, facilitating gross total resection. However, 5-13% of PAs with fibrous consistency are challenging to remove entirely and are accompanied by greater morbimortality. This study aims to identify the clinical and radiological characteristics that correlate with PA fibrous consistency preoperatively. A simple scoring system has been proposed to predict incidence of fibrous PAs.

MATERIALS AND METHODS

Consecutive interventions (226) were analyzed, all performed through an endoscopic endonasal transsphenoidal approach. Univariable and multivariable logistic regression analysis was performed. Hosmer-Lemeshow test and receiver operating characteristic (ROC) curves were assessed to evaluate the model. A point scoring system (PiTCon) was derived based on the multivariable regression model. Our study aimed to identify the clinical and radiological characteristics that correlate with fibrous tumor consistency preoperatively.

RESULTS

The best diagnostic accuracy for predicting PA consistency consisted of five predictive factors: age, compressive symptoms, panhypopituitarism, craniocaudal extension of the PA in mm, and prior surgery. The multivariable model achieved good discrimination with an area under the curve (AUC) of the ROC curve being 0.82 and the 95% CI 0.76 to 0.88. Internal validation yielded an optimism-adjusted C-statistic of 0.80 (95% CI 0.74 to 0.86). A point scoring system (PiTCon score) was designed using the best predictive model.

CONCLUSIONS

PA consistency can be estimated preoperatively regarding clinical and radiological characteristics. We propose a point-based scoring system (PiTCon score) that can better guide neurosurgeons in clinical decision-making and surgical risk assessment and help establish and describe patient prognosis.

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning

Introduction

In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI).

Methods

A total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model's performance was tested on an independent dataset. The model's performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score.

Results and discussion

The model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders.

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.

Machine Learning Approaches to Differentiate Sellar-Suprasellar Cystic Lesions on Magnetic Resonance Imaging

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.

MRI and Trouillas' grading system of pituitary tumors: the usefulness of T2 signal intensity volumetric values

PURPOSE

To classify pituitary macroadenomas according to the Trouillas' grading system; to compare this grading system with T2 values of volumetric signal intensity to determine T2 values able to predict the final grade.

METHODS

A total of 106 patients with macroadenomas were grouped according to the grading system score combining proliferation and invasiveness criteria of Trouillas' classification. Normalized volumetric signal intensity values were extracted from coronal T2-weighted images (nT2mean, nT2Max, nT2min) and were compared with the final grading score system.

RESULTS

Thirty-three patients were in grade 1a (non-invasive, non-proliferative tumors), 17 patients in grade 1b (non-invasive, proliferative tumors), 36 patients in grade 2a (invasive, non-proliferative tumors), and 20 patients in grade 2b (invasive, proliferative tumors). No patient was in grade 3 (metastatic tumors). nT2Max and nT2min were the best quantitative values to discriminate invasive from non-invasive grades; in invasive grades, nT2Max intensity values were higher, and nT2min intensity values were lower than in non-invasive grades. Receiver operating characteristic analysis of nT2 values showed that nT2min values had a better diagnostic performance than nT2Max values because they allowed differentiating with a moderate accuracy invasive tumors (2a or 2b grades) from both non-invasive proliferative tumors (1b) and non-invasive-non proliferative tumors (1a) (2a vs 1b: AUCnT2min = 0.78, 2b vs 1b: AUCnT2min = 0.72, 2a vs 1a: AUCnT2min = 0.72, 2b vs 1a AUCnT2min = 0.69).

CONCLUSION

Volumetric nT2Max and nT2min values of MRI might be practical and non-invasive markers for assessing tumor invasiveness although nT2 min signal intensity values have more effects in discriminating tumor's invasive behavior.

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.

Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency

BACKGROUND

The consistency of pituitary adenomas affects the course of surgical treatment.

PURPOSE

To evaluate the diagnostic capabilities of radiomics based on T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) in conjunction with two machine-learning (ML) techniques (support vector machine [SVM] and random forest classifier [RFC]) for assessing the consistency of pituitary adenomas.

MATERIAL AND METHODS

The institutional database was retrospectively scanned for patients who underwent surgical excision of pituitary adenomas. Surgical notes were accepted as a reference for the adenoma consistency. Radiomics analysis was performed on preoperative coronal 3.0T T1W and T2W images. First- and second-order parameters were calculated. Inter-observer reproducibility was assessed with Spearman's Correlation (ρ) and intra-observer reproducibility was evaluated with the intraclass correlation coefficient (ICC). Least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. SVM and RFC were used as ML methods.

RESULTS

A total of 52 patients who produced 206 regions of interest (ROIs) were included. Twenty adenomas that produced 88 ROIs had firm consistency. There was both inter-observer and intra-observer reproducibility. Ten parameters that were based on T2W images with high discriminative power and without correlation were chosen by LASSO. The diagnostic performance of SVM and RFC was as follows: sensitivity = 95.580% and 92.950%, specificity = 83.670% and 88.420%, area under the curve = 0.956 and 0.904, respectively.

CONCLUSION

Radiomics analysis based on T2W MRI combined with various ML techniques, such as SVM and RFC, can provide preoperative information regarding pituitary adenoma consistency with high diagnostic accuracy.

Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network-based model on sparsely annotated MRI

This study aims to develop a fully automated imaging protocol independent system for pituitary adenoma segmentation from magnetic resonance imaging (MRI) scans that can work without user interaction and evaluate its accuracy and utility for clinical applications. We trained two independent artificial neural networks on MRI scans of 394 patients. The scans were acquired according to various imaging protocols over the course of 11 years on 1.5T and 3T MRI systems. The segmentation model assigned a class label to each input pixel (pituitary adenoma, internal carotid artery, normal pituitary gland, background). The slice segmentation model classified slices as clinically relevant (structures of interest in slice) or irrelevant (anterior or posterior to sella turcica). We used MRI data of another 99 patients to evaluate the performance of the model during training. We validated the model on a prospective cohort of 28 patients, Dice coefficients of 0.910, 0.719, and 0.240 for tumour, internal carotid artery, and normal gland labels, respectively, were achieved. The slice selection model achieved 82.5% accuracy, 88.7% sensitivity, 76.7% specificity, and an AUC of 0.904. A human expert rated 71.4% of the segmentation results as accurate, 21.4% as slightly inaccurate, and 7.1% as coarsely inaccurate. Our model achieved good results comparable with recent works of other authors on the largest dataset to date and generalized well for various imaging protocols. We discussed future clinical applications, and their considerations. Models and frameworks for clinical use have yet to be developed and evaluated.

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.

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.

Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: An application study of artificial intelligence

OBJECTIVE

To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network.

METHODS

This study retrospectively recruited 621 CKD patients and 625 healthy controls in Jinling hospital and 57 CKD patients in Hainan hospital. These CKD patients were divided into cognitive function impairment (CFI) group and non-CFI group based on diagnostic criteria. All patients underwent brain MRI scan, neuropsychological test and laboratory exam. A deep learning model (Attention MLP) based on structural and functional sub-network (determined by the comparison between the patients and healthy controls) topological properties was developed to generate the MRI signature for the discrimination of CFI. Finally, a clinical-topological logistic regression model was built by combining MRI signature and clinical features. The area under curve (AUC), sensitivity and specificity were calculated to evaluate the model performance. Delong test was used to examine the difference of AUCs between models. The integrated discrimination improvement (IDI) and net reclassification index (NRI) between models were calculated.

RESULTS

Attention MLP model performed well in both internal test set and external test set (AUC = 0.744 and 0.763, respectively). After combining with the clinical features, the model performance was further improved both in the internal (AUC: 0.748) and external test sets (AUC: 0.774), while both IDI and NRI were significant (all p < 0.05) in the external test set. According to the comprehensive comparison, the AUC of the Attention MLP model was significantly or marginal significantly higher than that of traditional machine learning models (logistic regression: AUC = 0.634; support vector machine: AUC = 0.613; decision tree: AUC = 0.539; XGBoost: AUC = 0.639) in internal test set. The results showed that the model built on the combining of structural and functional networks data outperformed those on the single network, as well as the connection matrix.

CONCLUSION

The result indicated that the integration of the clinical information and the MRI signature generated by artificial intelligence model based on structural and functional network topological properties could help to predict the CFI of CKD patients effectively. Our results provided a set of quantifiable imaging biomarkers for CFI which may be beneficial to CKD patients.

Predicting tumor consistency and extent of resection in non-functioning pituitary tumors

PURPOSE

To (1) identify a radiological parameter to predict non-functioning pituitary tumor (NFPT) consistency, (2) examine the relationship between NFPT consistency and extent of resection (EOR), (3) investigate if tumor consistency predictors can anticipate EOR.

METHODS

The ratio (T2SIR) between the T2 min signal intensity (SI) of the tumor and the T2 mean SI of the CSF was the main radiological parameter, being determined through a radiomic-voxel analysis and calculated using the following formula: T2SIR = [(T2 tumor mean SI - SD)/T2 CSF SI]. The tumor consistency was pathologically estimated as collagen percentage (CP). EOR of NFPTs was evaluated by exploiting a volumetric technique and its relationship with the following explanatory variables was explored: CP, Knosp-grade, tumor volume, inter-carotid distance, sphenoidal sinus morphology, Hardy-grade, suprasellar tumor extension.

RESULTS

A statistically significant inverse correlation between T2SIR and CP was demonstrated (p = 0.0001), with high diagnostic power of T2SIR in predicting NFPT consistency (ROC curve analysis' AUC = 0.88; p = 0.0001). The following predictors of EOR were identified in the univariate analysis: CP (p = 0.007), preoperative volume (p = 0.045), Knosp grade (p = 0.0001), tumor suprasellar extension (p = 0.044). The multivariate analysis demonstrated two variables as unique predictors of EOR: CP (p = 0.002) and Knosp grade (p = 0.001). The T2SIR was a significant predictor of EOR both in the univariate (p = 0.01) and multivariate model (p = 0.003).

CONCLUSION

This study offers the potential to improve NFPT preoperative surgical planning and patient counseling by employing the T2SIR as a preoperative predictor of tumor consistency and EOR. Meanwhile, tumor consistency and Knosp grade were found to play an important role in predicting EOR.

Methods of preoperative prediction of pituitary adenoma consistency: a systematic review

This study aims to review the current literature on methods of preoperative prediction of pituitary adenoma consistency. Pituitary adenoma consistency may be a limiting factor for successful surgical removal of tumors. Efforts have been made to investigate the possibility of an accurate assessment of the preoperative consistency to allow for safer and more effective surgery planning. We searched major scientific databases and systematically analyzed the results. A total of 54 relevant articles were identified and selected for inclusion. These studies evaluated methods based on either MRI intensity, enhancement, radiomics, MR elastometry, or CT evaluation. The results of these studies varied widely. Most studies used the average intensity of either T2WI or ADC maps. Firm tumors appeared hyperintense on T2WI, although only 55% of the studies reported statistically significant results. There are mixed reports on ADC values in firm tumors with findings of increased values (28%), decreased values (22%), or no correlation (50%). Multiple contrast enhancement-based methods showed good results in distinguishing between soft and firm tumors. There were mixed reports on the utility of MR elastography. Attempts to develop radiomics and machine learning-based models have achieved high accuracy and AUC values; however, they are prone to overfitting and need further validation. Multiple methods of preoperative consistency assessment have been studied. None demonstrated sufficient accuracy and reliability in clinical use. Further efforts are needed to enable reliable surgical planning.

A current perspective of pituitary adenoma MRI characteristics: a review

INTRODUCTION

MR imaging is an essential and fundamental tool in the diagnosis, management, and follow-up of patients with pituitary adenomas (PAs). Recent advances have continued to enhance the usefulness of this imaging modality.

AREAS COVERED

This article focuses on signal intensity patterns of PAs and associated clinical characteristics, vertical extension patterns, and cavernous sinus invasion with a special focus on the clinical implications that arise. A search using Medline and Google Scholar was conducted using different combinations of relevant keywords, giving preference to recent publications.

EXPERT OPINION

A higher proportion of GH-secreting PAs are hypointense on T2 weighted images compared to other tumor subtypes. Hypointense tumors are generally smaller compared to hyperintense ones, and among the GH-secreting subgroup, a better response to somatostatin analogue treatment was noted together with an association for a densely granulated pattern. Nonfunctional PAs show a predilection to extend upwards while GH-secreting PAs and prolactinomas show a predominantly inferior extension growth pattern. Further studies to better understand the mechanisms responsible for this behavior are anticipated. Further development, refining and validation of predictive scoring systems for tumor behavior might be useful adjuncts in the management of patients with 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.

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.

Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features

Objectives

A subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs.

Methods

From June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs.

Results

Forty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85.

Conclusions

DL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.

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.

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.

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.

Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis

PURPOSE

To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI.

METHODS

PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool.

RESULTS

Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence.

CONCLUSION

This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.

Predicting Subtype of Growth Hormone Pituitary Adenoma based on Magnetic Resonance Imaging Characteristics

Supplemental digital content is available in the text.

This study aimed to investigate the value of magnetic resonance (MR) characteristics in differentiating the subtypes of growth hormone pituitary adenomas.

Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings

BACKGROUND

Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning.

PURPOSE

To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA.

STUDY TYPE

Retrospective.

POPULATION

One hundred and fifty-six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings.

FIELD STRENGTH/SEQUENCE

T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using spin-echo sequences with a 3.0-T scanner.

ASSESSMENT

An automated three-dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top-discriminative features were identified using the minimum-redundancy maximum-relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency.

STATISTICAL TESTS

Mann-Whitney U-test and Chi-square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P-value<0.05 was considered statistically significant.

RESULTS

Eleven mpMRI-based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87-0.92), ACC of 0.87 (CI: 0.84-0.89), SEN of 0.83 (CI: 0.81-0.85), and SPE of 0.87 (CI: 0.85-0.99) in the test cohort.

DATA CONCLUSION

Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

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.

Clinical relevance of tumor consistency in pituitary adenoma

PURPOSE

To review the clinical relevance of pituitary adenoma (PA) consistency and its relationship to clinical presentation, radiologic and histopathological characteristics, and surgical outcomes.

BACKGROUND

PA consistency is a critical factor influencing operative planning, surgical outcomes, and patient counseling. There is no validated classification of PA consistency in the literature, and there are no current preoperative variables capable of predicting it.

REVIEW

We conducted a thorough literature review of the Medline, Embase, Web of Science, and Cochrane Library databases. The inclusion criteria were all articles that described PA consistency and correlated it with preoperative aspects, radiological, pathological, and operative findings, or clinical outcomes.

DISCUSSION

Although most authors differentiate easily aspirated (soft) tumors from those that are not (fibrous, might require prior fragmentation), there is no universally accepted PA consistency classification. Fibrous PA tends to be hypointense on T2WI and has lower apparent diffusion coefficient (ADC) values. Fibrous tumors seemed to present higher invasion into neighboring structures, including the cavernous sinus. Several articles suggest that dopamine agonists could increase PA consistency and that prior surgery and radiotherapy also make PA more fibrous. The anatomopathological studies identify collagen as being mainly responsible for fibrous consistency of adenomas.

CONCLUSIONS

Preoperative knowledge of PA consistency affords the neurosurgeon substantial benefit, which clearly appears to be relevant to surgical planning, risks, and surgery outcomes. It could also encourage the centralization of these high complexity tumors in reference centers. Further studies may be enhanced by applying standard consistency classification of the PA and analyzing a more extensive and prospective series of fibrous PA.

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

CONTEXT

The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.

OBJECTIVE

This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency.

METHODS

Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency.

RESULTS

A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively.

CONCLUSION

The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods' clinical usefulness for supporting neurosurgeons in judging patients' conditions, predicting prognosis, and other downstream tasks during the preoperative period.

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.

Development and clinical validation of a grading system for pituitary adenoma consistency

OBJECTIVE

Pituitary adenoma (PA) consistency, or texture, is an important intraoperative characteristic that may dictate operative dissection techniques and/or instruments used for tumor removal during endoscopic endonasal approaches (EEAs). The impact of PA consistency on surgical outcomes has yet to be elucidated.

METHODS

The authors developed an objective 5-point grading scale for PA consistency based on intraoperative characteristics, including ease of tumor debulking, manipulation, and instrument selection, ranging from cystic/hemorrhagic tumors (grade 1) to calcified tumors (grade 5). The proposed grading system was prospectively assessed in 306 consecutive patients who underwent an EEA for PAs, and who were subsequently analyzed for associations with surgical outcomes, including extent of resection (EOR) and complication profiles.

RESULTS

Institutional database review identified 306 patients who underwent intraoperative assessment of PA consistency, of which 96% were macroadenomas, 70% had suprasellar extension, and 44% had cavernous sinus invasion (CSI). There were 214 (69.9%) nonfunctional PAs and 92 functional PAs (31.1%). Distribution of scores included 15 grade 1 tumors (4.9%), 112 grade 2 tumors (36.6%), 125 grade 3 tumors (40.8%), 52 grade 4 tumors (17%), and 2 grade 5 tumors (0.7%). Compared to grade 1/2 and grade 3 PAs, grade 4/5 PAs were significantly larger (22.5 vs 26.6 vs 27.4 mm, p < 0.01), more likely to exhibit CSI (39% vs 42% vs 59%, p < 0.05), and trended toward nonfunctionality (67% vs 68% vs 82%, p = 0.086). Although there was no association between PA consistency and preoperative headaches or visual dysfunction, grade 4/5 PAs trended toward preoperative (p = 0.058) and postoperative panhypopituitarism (p = 0.066). Patients with preoperative visual dysfunction experienced greater improvement if they had a grade 1/2 PA (p < 0.05). Intraoperative CSF leaks were noted in 32% of cases and were more common with higher-consistency-grade tumors (p = 0.048), although this difference did not translate to postoperative CSF leaks. Gross-total resection (%) was more likely with lower PA consistency score as follows: grade 1/2 (60%), grade 3 (50%), grade 4/5 (44%; p = 0.045). Extracapsular techniques were almost exclusively performed in grade 4/5 PAs. Assignment of scores showed low variance and high reproducibility, with an intraclass correlation coefficient of 0.905 (95% CI 0.815-0.958), indicating excellent interrater reliability.

CONCLUSIONS

These findings demonstrate clinical validity of the proposed intraoperative grading scale with respect to PA subtype, neuroimaging features, EOR, and endocrine complications. Future studies will assess the relation of PA consistency to preoperative MRI findings to accurately predict consistency, thereby allowing the surgeon to tailor the exposure and prepare for varying resection strategies.

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

Objectives

A subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs.

Methods

Only patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features.

Results

Twenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001).

Conclusions

Our preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.

Advances in the Imaging of Pituitary Tumors

In most patients with pituitary adenomas magnetic resonance imaging (MRI) is essential to guide effective decision-making. T1- and T2-weighted sequences allow the majority of adenomas to be readily identified. Supplementary MR sequences (e.g. FLAIR; MR angiography) may also help inform surgery. However, in some patients MRI findings are 'negative' or equivocal (e.g. with failure to reliably identify a microadenoma or to distinguish postoperative change from residual/recurrent disease). Molecular imaging [e.g. ¹¹C-methionine PET/CT coregistered with volumetric MRI (Met-PET/MR^CR)] may allow accurate localisation of the site of de novo or persistent disease to guide definitive treatment (e.g. surgery or radiosurgery).

Artificial intelligence and pituitary adenomas: A review

The aim of this review was to provide an overview of the main concepts in machine learning (ML) and to analyze the ML applications in the imaging of pituitary adenomas. After describing the clinical, pathological and imaging features of pituitary tumors, we defined the difference between ML and classical rule-based algorithms, we illustrated the fundamental ML techniques: supervised, unsupervised and reinforcement learning and explained the characteristic of deep learning, a ML approach employing networks inspired by brain’s structure. Pre-treatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging. Regarding pre-treatment assessment, ML methods were used to have information about tumor consistency, predict cavernous sinus invasion and high proliferative index, discriminate null cell adenomas, which respond to neo-adjuvant radiotherapy from other subtypes, predict somatostatin analogues response and visual pathway injury. Regarding neurosurgical outcome prediction, the following applications were discussed: Gross total resection prediction, evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery. Although clinical applicability requires more replicability, generalizability and validation, results are promising, and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.

Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI

Purpose

Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery.

Methods

Data of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images. Feature stability analysis and a multistep feature selection were performed. After oversampling to balance the classes, 80% of the data was used for hyperparameter tuning via stratified 5-fold cross-validation, while a 20% hold-out set was employed for its final testing, using an Extra Trees ensemble meta-algorithm. The reference standard was based on surgical findings.

Results

A total of 1118 texture features were extracted, of which 741 were stable. After removal of low variance (n = 4) and highly intercorrelated (n = 625) parameters, recursive feature elimination identified a subset of 14 features. After hyperparameter tuning, the Extra Trees classifier obtained an accuracy of 93%, sensitivity of 100%, and specificity of 87%. The area under the receiver operating characteristic and precision-recall curves was 0.99.

Conclusion

Preoperative T2-weighted MRI texture analysis and machine learning could predict pituitary macroadenoma consistency.

Electronic supplementary material

The online version of this article (10.1007/s00234-020-02502-z) contains supplementary material, which is available to authorized users.

Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study

OBJECTIVE

Pituitary macroadenomas (PAs) are usually defined as benign intracranial tumors. However, they may present local aggressive course. High Ki67 labelling index (LI) values have been related to an aggressive tumor behavior. A recent clinicopathological classification of PA based on local invasiveness and proliferation indexes, divided them in groups with different prognosis. We evaluated the utility of conventional MRI (cMRI) and diffusion-weighted imaging (DWI), in predicting the Ki67- LI according the clinicopathological classification.

METHODS

17 patients (12 M and 5 F) who underwent surgical removal of a PA were studied. cMRI features, quantification of T₁W and T₂W signal intensity, degree of contrast uptake (enhancement ratio, ER) and apparent diffusion coefficient (ADC) values were evaluated by using a 3 T scan. Statistics included Mann-Whitney test, Spearman's test, and receiver operating characteristic analysis. A value of p ≤ 0.05 was considered significant for all the tests.

RESULTS

Negative correlations were observed between Ki-67 LI, ADCm (ρ = - 0.67, p value = 0.005) and ER values (ρ = -0.62; p = 0.008). ER values were significantly lower in the proliferative PA group (p = 0.028; p = 0.017). ADCm showed sensitivity and specificity of 90 and 85% respectively into predict Ki67-LI value. A value of ADCm ≤0, 711 x 10-6 mm² emerged as a cut-off of a value of Ki67-LI ≥ 3%.

CONCLUSION

Adding quantitative measures of ADC values to cMRI could be used routinely as a non-invasive marker of specific predictive biomarker of the proliferative activity of PA.

ADVANCES IN KNOWLEDGE

Routinely use of DWI on diagnostic work-up of pituitary adenomas may help in establish the likely biological aggressive lesions.

Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

PURPOSE

To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.

METHOD

We presented an overview of the fundamental concepts in ML, its various stages used in healthcare, and highlighted the key components typically present in an imaging-based tumor analysis pipeline. A search was conducted across four databases (PubMed, Ovid, Embase, and Google Scholar) to gather research articles from the past 10 years (2009-2019) involving imaging related to pituitary tumor and ML. We grouped the studies by imaging modalities and analyzed the ML tasks in terms of the data inputs, reference standards, methodologies, and limitations.

RESULTS

Of the 16 studies included in our analysis, 10 appeared in 2018-2019. Most of the studies utilized retrospective data and followed a semi-automatic ML pipeline. The studies included use of magnetic resonance imaging (MRI), facial photographs, surgical microscopic video, spectrometry, and spectroscopy imaging. The objectives of the studies covered 14 distinct applications and majority of the studies addressed a binary classification problem. Only five of the 11 MRI-based studies had an external validation or a holdout set to test the performance of a final trained model.

CONCLUSION

Through our concise evaluation and comparison of the studies using the concepts presented, we highlight future directions so that potential ML applications using different imaging modalities can be developed to benefit the clinical care of pituitary tumor patients.

Radiomics with artificial intelligence: a practical guide for beginners

Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high number of quantitative features from medical images. Artificial intelligence (AI) is broadly a set of advanced computational algorithms that basically learn the patterns in the data provided to make predictions on unseen data sets. Radiomics can be coupled with AI because of its better capability of handling a massive amount of data compared with the traditional statistical methods. Together, the primary purpose of these fields is to extract and analyze as much and meaningful hidden quantitative data as possible to be used in decision support. Nowadays, both radiomics and AI have been getting attention for their remarkable success in various radiological tasks, which has been met with anxiety by most of the radiologists due to the fear of replacement by intelligent machines. Considering ever-developing advances in computational power and availability of large data sets, the marriage of humans and machines in future clinical practice seems inevitable. Therefore, regardless of their feelings, the radiologists should be familiar with these concepts. Our goal in this paper was three-fold: first, to familiarize radiologists with the radiomics and AI; second, to encourage the radiologists to get involved in these ever-developing fields; and, third, to provide a set of recommendations for good practice in design and assessment of future works.

A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging

PURPOSE

The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with immunohistochemical staining after surgery. In this study, a model to precisely immunohistochemically classify the PA subtypes by radiomic features based on preoperative MR images was developed.

METHODS

Two hundred thirty-five pathologically diagnosed PAs, including t-box pituitary transcription factor (Tpit) family tumors (n = 55), pituitary transcription factor 1 (Pit-1) family tumors (n = 110), and steroidogenic factor 1 (SF-1) family tumors (n = 70), were retrospectively studied. T1-weighted, T2-weighted and contrast-enhanced T1-weighted images were obtained from all patients. Through imaging acquisition, feature extraction and radiomic data processing, 18 radiomic features were used to train support vector machine (SVM), k-nearest neighbors (KNN) and Naïve Bayes (NBs) models. Ten-fold cross-validation was applied to evaluate the performance of these models.

RESULTS

The SVM model showed high performance (balanced accuracy 0.89, AUC 0.9549) whereas the KNN (balanced accuracy 0.83, AUC 0.9266) and NBs (balanced accuracy 0.80, AUC 0.9324) models displayed low performance based on the T2-weighted images. The performance of the T2-weighted images was better than that of the other two MR sequences. Additionally, significant sensitivity (P = 0.031) and specificity (P = 0.012) differences were observed when classifying the PA subtypes by T2-weighted images.

CONCLUSIONS

The SVM model was superior to the KNN and NBs models and can potentially precisely immunohistochemically classify PA subtypes with an MR-based radiomic analysis. The developed model exhibited good performance using T2-weighted images and might offer potential guidance to neurosurgeons in clinical decision-making before surgery.

Magnetic resonance imaging in the management of prolactinomas; a review of the evidence

PURPOSE

This review aimed to evaluate data on the use of magnetic resonance imaging in the management of prolactinomas.

METHODS

Recent literature about prolactinoma behavior and magnetic resonance imaging in the management of prolactinomas is reviewed.

RESULTS

A review of evidence regarding prolactinoma pituitary MRI follow-up; techniques and sequences, recent data on possible gadolinium retention, the role and a review of T2-weighted images in the identification of prolactinomas and frequently encountered clinical scenarios, as well as MRI correlation with prolactin secretion, tumor growth and prediction of response to medical therapy are presented.

CONCLUSION

The underlying decision to perform serial imaging in prolactinoma patients should be individualized on a case-by-case basis. Future studies should focus on alternative imaging methods and/or contract agents.

Differentiation of Pituitary Adenoma from Rathke Cleft Cyst: Combining MR Image Features with Texture Features

Objectives

To differentiate pituitary adenoma from Rathke cleft cyst in magnetic resonance (MR) scan by combing MR image features with texture features.

Methods

A total number of 133 patients were included in this study, 83 with pituitary adenoma and 50 with Rathke cleft cyst. Qualitative MR image features and quantitative texture features were evaluated by using the chi-square tests or Mann–Whitney U test. Binary logistic regression analysis was conducted to investigate their ability as independent predictors. ROC analysis was conducted subsequently on the independent predictors to assess their practical value in discrimination and was used to investigate the association between two types of features.

Results

Signal intensity on the contrast-enhanced image was found to be the only significantly different MR image feature between the two lesions. Two texture features from the contrast-enhanced images (Histo-Skewness and GLCM-Correlation) were found to be the independent predictors in discrimination, of which AUC values were 0.80 and 0.75, respectively. Besides, the above two texture features (Histo-Skewness and GLCM-Contrast) were suggested to be associated with signal intensity on the contrast-enhanced image.

Conclusion

Signal intensity on the contrast-enhanced image was the most significant MR image feature in differentiation between pituitary adenoma and Rathke cleft cyst, and texture features also showed promising and practical ability in discrimination. Moreover, two types of features could be coordinated with each other.

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning

PURPOSE

Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class.

METHODS

A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach.

RESULTS

Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson's test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients.

CONCLUSIONS

Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.

Identifying patients with neuronal intranuclear inclusion disease in Singapore using characteristic diffusion-weighted MR images

PURPOSE

Adult-onset neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder described mainly in the Japanese population, with characteristic DWI abnormalities at the junction between gray and white matter. We identify possible cases of NIID in the picture archive and communication system (PACS) of a tertiary neurological referral hospital in Singapore and describe their radiological features.

METHODS

The neuroradiology imaging database was reviewed using keyword search of radiological reports to identify patients who had "subcortical U fibre" abnormalities on DWI. MRI were retrospectively reviewed, and those fulfilling inclusion criteria were invited for skin biopsy to detect nuclear inclusions by light and electron microscopy.

RESULTS

Twelve Chinese patients (nine female; median age 70.5 years) were enrolled. Seven patients were being assessed for dementia and five for other neurological indications. In all patients, DWI showed distinctive subcortical high signal with increased average apparent diffusion coefficient (ADC), involving frontal, parietal, and temporal more than occipital lobes; the corpus callosum and external capsule were affected in some patients. On T2-weighted images, cerebral and cerebellar atrophy and white matter hyperintensity of Fazekas grade 2 and above were seen in all patients. Three patients underwent skin biopsy; all were positive for intranuclear hyaline inclusion bodies on either p62 staining or electron microscopy, which are pathognomonic for NIID.

CONCLUSION

Previously undiagnosed patients with NIID can be identified by searching for abnormalities at the junction between gray and white matter on DWI in PACS and subsequently confirmed by skin biopsy. Radiologists should recognize the distinctive neuroimaging pattern of this dementing disease.