The histopathological diagnosis of atypical meningioma: glass slide versus whole slide imaging for grading assessment

Descriptive epidemiology of 30,223 histopathologically confirmed meningiomas in France: 2006-2015

BACKGROUND AND OBJECTIVES

Meningioma is one of the most common neoplasm of the central nervous system. To describe the epidemiology of meningioma operated in France and, to assess grading and histopathological variability among the different neurosurgical centres.

METHODS

We processed the French Brain Tumour Database (FBTDB) to conduct a nationwide population-based study of all histopathologically confirmed meningiomas between 2006 and 2015.

RESULTS

30,223 meningiomas cases were operated on 28,424 patients, in 61 centres. The average number of meningioma operated per year in France was 3,022 (SD ± 122). Meningioma was 3 times more common in women (74.1% vs. 25.9%). The incidence of meningioma increased with age and, mean age at surgery was 58.5 ± 13.9 years. Grade 1, 2, and 3 meningiomas accounted for 83.9%, 13.91% and, 2.19% respectively. There was a significant variability of meningioma grading by institutions, especially for grade 2 which spanned from 5.1% up to 22.4% (p < 0.001). Moreover, the proportion of grade 2 significantly grew over the study period (p < 0.001). There was also a significant variation in grade 1 subtypes diagnosis among the institutions (p < 0.001). 89.05% of the patients had solely one meningioma surgery, 8.52% two and, 2.43% three or more. The number of surgeries was associated to the grade of malignancy (p < 0.001).

CONCLUSION

The incidence of meningioma surgery increased with age and, peaked at 58.5 years. They were predominantly benign with meningothelial subtype being the most common. However, there was a significant variation of grade 1 subtypes diagnosis among the centres involved. The proportion of grade 2 meningioma significantly grew over the study time, on contrary to malignant meningioma proportion, which remained rare and, stable over time around 2%. Likewise, there was a significant variability of grade 2 meningioma rate among the institutions.

Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology

Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future.

Virtual Versus Light Microscopy Usage among Students: A Systematic Review and Meta-Analytic Evidence in Medical Education

BACKGROUND

The usage of whole-slide images has recently been gaining a foothold in medical education, training, and diagnosis.

OBJECTIVES

The first objective of the current study was to compare academic performance on virtual microscopy (VM) and light microscopy (LM) for learning pathology, anatomy, and histology in medical and dental students during the COVID-19 period. The second objective was to gather insight into various applications and usage of such technology for medical education.

MATERIALS AND METHODS

Using the keywords "virtual microscopy" or "light microscopy" or "digital microscopy" and "medical" and "dental" students, databases (PubMed, Embase, Scopus, Cochrane, CINAHL, and Google Scholar) were searched. Hand searching and snowballing were also employed for article searching. After extracting the relevant data based on inclusion and execution criteria, the qualitative data were used for the systematic review and quantitative data were used for meta-analysis. The Newcastle Ottawa Scale (NOS) scale was used to assess the quality of the included studies. Additionally, we registered our systematic review protocol in the prospective register of systematic reviews (PROSPERO) with registration number CRD42020205583.

RESULTS

A total of 39 studies met the criteria to be included in the systematic review. Overall, results indicated a preference for this technology and better academic scores. Qualitative analyses reported improved academic scores, ease of use, and enhanced collaboration amongst students as the top advantages, whereas technical issues were a disadvantage. The performance comparison of virtual versus light microscopy meta-analysis included 19 studies. Most (10/39) studies were from medical universities in the USA. VM was mainly used for teaching pathology courses (25/39) at medical schools (30/39). Dental schools (10/39) have also reported using VM for teaching microscopy. The COVID-19 pandemic was responsible for the transition to VM use in 17/39 studies. The pooled effect size of 19 studies significantly demonstrated higher exam performance (SMD: 1.36 [95% CI: 0.75, 1.96], p < 0.001) among the students who used VM for their learning. Students in the VM group demonstrated significantly higher exam performance than LM in pathology (SMD: 0.85 [95% CI: 0.26, 1.44], p < 0.01) and histopathology (SMD: 1.25 [95% CI: 0.71, 1.78], p < 0.001). For histology (SMD: 1.67 [95% CI: -0.05, 3.40], p = 0.06), the result was insignificant. The overall analysis of 15 studies assessing exam performance showed significantly higher performance for both medical (SMD: 1.42 [95% CI: 0.59, 2.25], p < 0.001) and dental students (SMD: 0.58 [95% CI: 0.58, 0.79], p < 0.001).

CONCLUSIONS

The results of qualitative and quantitative analyses show that VM technology and digitization of glass slides enhance the teaching and learning of microscopic aspects of disease. Additionally, the COVID-19 global health crisis has produced many challenges to overcome from a macroscopic to microscopic scale, for which modern virtual technology is the solution. Therefore, medical educators worldwide should incorporate newer teaching technologies in the curriculum for the success of the coming generation of health-care professionals.

Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies

Objective

Digital pathology with whole-slide imaging (WSI) has many potential clinical and non-clinical applications. In the past two decades, despite significant advances in WSI technology adoption remains slow for primary diagnosis. The aim of this study was to identify common pitfalls of WSI reported in validation studies and offer measures to overcome these challenges.

Methods

A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all validation studies designed to evaluate the feasibility of WSI for diagnostic clinical use in pathology. Technical and diagnostic problems encountered with WSI in these studies were recorded.

Results

A total of 45 studies were identified in which technical issues were reported in 15 (33%), diagnostic issues in 8 (18%), and 22 (49%) reported both. Key technical problems encompassed slide scan failure, prolonged time for pathologists to review cases, and a need for higher image resolution. Diagnostic challenges encountered were concerned with grading dysplasia, reliable assessment of mitoses, identification of microorganisms, and clearly defining the invasive front of tumors.

Conclusion

Despite technical advances with WSI technology, some critical concerns remain that need to be addressed to ensure trustworthy clinical diagnostic use. More focus on the quality of the pre-scanning phase and training of pathologists could help reduce the negative impact of WSI technical difficulties. WSI also seems to exacerbate specific diagnostic tasks that are already challenging among pathologists even when examining glass slides with conventional light microscopy.

Clinical Implications of the Mitotic Index as a Predictive Factor for Malignant Transformation of Atypical Meningiomas

Objective

Intracranial atypical meningiomas have a poor prognosis and high rates of recurrence. Moreover, up to one-third of the recurrences undergo high-grade transformation into malignant meningiomas. We aimed to investigate the clinical factors that can predict the propensity of malignant transformation from atypical to anaplastic meningiomas.

Methods

Between 2001 and 2018, all patients with atypical meningioma, in whom the tumors had undergone malignant transformation to anaplastic meningioma, were included. The patients' medical records documenting the diagnosis of atypical meningioma prior to malignant transformation were reviewed to identify the predictors of transformation. The control group comprised 56 patients with atypical meningiomas who were first diagnosed between January 2017 and December 2018 and had no malignant transformation.

Results

Nine patients in whom the atypical meningiomas underwent malignant transformation were included. The median time interval from diagnosis of atypical meningioma to malignant transformation was 19 months (range, 7-78). The study group showed a significant difference in heterogeneous enhancement (77.8% vs. 33.9%), bone invasion (55.6% vs. 12.5%), mitotic index (MI; 14.8±4.9 vs. 3.5±3.9), and Ki-67 index (20.7±13.9 vs. 9.5±7.1) compared with the control group. In multivariate analysis, increased MI (odds ratio, 1.436; 95% confidence interval, 1.127-1.900; p=0.004) was the only significant factor for predicting malignant transformation.

Conclusion

An increased MI within atypical meningiomas might be used as a predictor of malignant transformation. Tumors at high risk for malignant transformation might require more attentive surveillance and management than other atypical meningiomas.