Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency

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.

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.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

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.

Recommendations for the diagnosis and radiological follow-up of pituitary neuroendocrine tumours

Pituitary neuroendocrine tumours (PitNETs) constitute a heterogeneous group of tumours with a gradually increasing incidence, partly accounted for by more sensitive imaging techniques and more extensive experience in neuroradiology in this regard. Although most PitNETs are indolent, some exhibit aggressive behaviour, and recurrence may be seen after surgical removal. The changes introduced in the WHO classification in 2017 and terminological debates in relation to neuroendocrine tumours warrant an update of the guidelines for the diagnosis, preoperative and postoperative management, and follow-up of response to treatment of PitNETs. This multidisciplinary document, an initiative of the Neuroendocrinology area of the Sociedad Española de Endocrinología y Nutrición [Spanish Society of Endocrinology and Nutrition] (SEEN), focuses on neuroimaging studies for the diagnosis, prognosis and follow-up of PitNETs. The basic requirements and elements that should be covered by magnetic resonance imaging are described, and a minimum radiology report to aid clinicians in treatment decision-making is proposed. This work supplements the consensus between the Neuroendocrinology area of the SEEN and the Sociedad Española de Anatomía Patológica [Spanish Society of Pathology] (SEAP) for the pathological study of PitNETs.

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.

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.

Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks

The secreting function of pituitary adenomas (PAs) plays a critical role in making the treatment strategies. However, Magnetic Resonance Imaging (MRI) analysis for pituitary adenomas is labor intensive and highly variable among radiologists. In this work, by applying convolutional neural network (CNN), we built a segmentation and classification model to help distinguish functioning pituitary adenomas from non-functioning subtypes with 3D MRI images from 185 patients with PAs (two centers). Specifically, the classification model adopts the concept of transfer learning and uses the pre-trained segmentation model to extract deep features from conventional MRI images. As a result, both segmentation and classification models obtained high performance in two internal validation datasets and an external testing dataset (for segmentation model: Dice score = 0.8188, 0.8091 and 0.8093 respectively; for classification model: AUROC = 0.8063, 0.7881 and 0.8478, respectively). In addition, the classification model considers the attention mechanism for better model interpretation. Taken together, this work provides the first deep learning-based tumor region segmentation and classification models of PAs, which enables early diagnosis and subtyping PAs from MRI images.

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).

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.

Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients

PURPOSE

The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients.Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma.

RESULTS

Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group (P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1-99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9-92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group (P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2-100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3-95.8%).

CONCLUSIONS

Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.

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.

Modern imaging of pituitary adenomas

Decision-making in pituitary disease is critically dependent on high quality imaging of the sella and parasellar region. Magnetic resonance imaging (MRI) is the investigation of choice and, for the majority of patients, combined T1 and T2 weighted sequences provide the information required to allow surgery, radiotherapy (RT) and/or medical therapy to be planned and long-term outcomes to be monitored. However, in some cases standard clinical MR sequences are indeterminate and additional information is needed to help inform the choice of therapy for a pituitary adenoma (PA). This article reviews current recommendations for imaging of PA, examines the potential added value that alternative MR sequences and/or CT can offer, and considers how the use of functional/molecular imaging might allow definitive treatment to be recommended for a subset of patients who would otherwise be deemed unsuitable for (further) surgery and/or RT.