Ki-67 proliferation index but not mitotic thresholds integrates the molecular prognostic stratification of lower grade gliomas

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index

BACKGROUND

The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor.

PURPOSE

To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index.

MATERIAL AND METHODS

The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index.

RESULTS

The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade.

CONCLUSION

Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Ki-67 is necessary during DNA replication for fork protection and genome stability

BACKGROUND

The proliferation antigen Ki-67 has been widely used in clinical settings for cancer staging for many years, but investigations on its biological functions have lagged. Recently, Ki-67 has been shown to regulate both the composition of the chromosome periphery and chromosome behaviour in mitosis as well as to play a role in heterochromatin organisation and gene transcription. However, how the different roles for Ki-67 across the cell cycle are regulated and coordinated remain poorly understood. The progress towards understanding Ki-67 function have been limited by the tools available to deplete the protein, coupled to its abundance and fluctuation during the cell cycle.

RESULTS

Here, we use a doxycycline-inducible E3 ligase together with an auxin-inducible degron tag to achieve a rapid, acute and homogeneous degradation of Ki-67 in HCT116 cells. This system, coupled with APEX2 proteomics and phospho-proteomics approaches, allows us to show that Ki-67 plays a role during DNA replication. In its absence, DNA replication is severely delayed, the replication machinery is unloaded, causing DNA damage that is not sensed by the canonical pathways and dependent on HUWE1 ligase. This leads to defects in replication and sister chromatids cohesion, but it also triggers an interferon response mediated by the cGAS/STING pathway in all the cell lines tested.

CONCLUSIONS

We unveil a new function of Ki-67 in DNA replication and genome maintenance that is independent of its previously known role in mitosis and gene regulation.

Mitotic count is prognostic in IDH mutant astrocytoma without homozygous deletion of CDKN2A/B. Results of consensus panel review of EORTC trial 26053 (CATNON) and EORTC trial 22033-26033

BACKGROUND

Gliomas with IDH1/2 mutations without 1p19q codeletion have been identified as the distinct diagnostic entity of IDH mutant astrocytoma (IDHmut astrocytoma). Homozygous deletion of Cyclin-dependent kinase 4 inhibitor A/B (CDKN2A/B) has recently been incorporated in the grading of these tumors. The question of whether histologic parameters still contribute to prognostic information on top of the molecular classification, remains unanswered. Here we evaluated consensus histologic parameters for providing additional prognostic value in IDHmut astrocytomas.

METHODS

An international panel of seven neuropathologists scored 13 well-defined histologic features in virtual microscopy images of 192 IDHmut astrocytomas from EORTC trial 22033-26033 (low-grade gliomas) and 263 from EORTC 26053 (CATNON) (1p19q non-codeleted anaplastic glioma). For 192 gliomas the CDKN2A/B status was known. Consensus (agreement ≥ 4/7 panelists) histologic features were tested together with homozygous deletion (HD) of CDKN2A/B for independent prognostic power.

RESULTS

Among consensus histologic parameters, the mitotic count (cut-off of 2 mitoses per 10 high power fields standardized to a field diameter of 0.55 mm and an area of 0.24 mm2) significantly influences PFS (P = .0098) and marginally the OS (P = .07). Mitotic count also significantly affects the PFS of tumors with HD CDKN2A/B, but not the OS, possibly due to limited follow-up data.

CONCLUSION

The mitotic index (cut-off 2 per 10 40× HPF) is of prognostic significance in IDHmut astrocytomas without HD CDKN2A/B. Therefore, the mitotic index may direct the therapeutic approach for patients with IDHmut astrocytomas with native CDKN2A/B status.

Updates on the WHO diagnosis of IDH-mutant glioma

PURPOSE

The WHO classification of Tumors of the Central Nervous System represents the international standard classification for brain tumors. In 2021 the 5th edition (WHO CNS5) was published, and this review summarizes the changes regarding IDH-mutant gliomas and discusses unsolved issues and future perspectives.

METHODS

This review is based on the 5th edition of the WHO Blue Book of CNS tumors (WHO CNS5) and relevant related papers.

RESULTS

Major changes include taxonomy and nomenclature of IDH-mutant gliomas. Essential and desirable criteria for classification were established considering technical developments. For the first time molecular features are not only relevant for the classification of IDH-mutant gliomas but may impact grading as well.

CONCLUSION

WHO CNS5 classification moves forward towards a classification which is founded on tumor biology and serves clinical needs. The rapidly increasing knowledge on the molecular landscape of IDH-mutant gliomas is expected to further refine classification and grading in the future.

Events in CNS Tumor Pathology Post-2016 WHO CNS: cIMPACT-NOW Updates and Other Advancements: A Comprehensive Review Plus a Summary of the Salient Features of 2021 WHO CNS 5

Introduction

The 2016 World Health Organization Classification (WHO) of Tumors of the Central Nervous System (CNS) represented a major change. It recommended an "integrated diagnosis" comprising histologic and molecular information facilitating a more precise diagnosis of specific CNS tumors. Its goal was to provide greater diagnostic precision and reproducibility resulting in more clinical relevance and predictive value, ultimately leading to better patient care. Advances in molecular classification, mostly resulting from DNA methylation array profiling of CNS tumors, were occurring at a very rapid pace and required more rapid integration into clinical practice.

Methods

cIMPACT-NOW updates and other recent papers plus salient features of 2021 WHO CNS5 in this comprehensive write-up were reviewed.

Results

CNS tumor classification needs to be updated at a rapid pace and mechanisms put into place to guide diagnosticians and clinicians in the interim period if major changes in the classification of tumor types came to light. Recognizing the need to integrate these into clinical practice more rapidly and without inordinate delay, the International Society of Neuropathology (ISN) 2016 sponsored an initiative called cIMPACT-NOW.

Discussion and/or Conclusion

Goal of cIMPACT-NOW was to provide clarification regarding contentious issues arising in the wake of the 2016 WHO CNS update as well as report new advancements in molecular classification of CNS tumors and new tumor entities emerging as a result of these advancements. cIMPACT-NOW updates: It thus laid the foundation for the 5th edition of the WHO Classification of CNS tumors (2021 WHO CNS 5). We have discussed cIMPACT updates in detail in this review. In addition, molecular diagnostics including DNA methylation-based classification of CNS tumors and the practical use of molecular classification in the prognostication and treatment of CNS tumors is discussed. Finally, the salient features of the new CNS tumor classification are summarized.

Brain Tumor Imaging: Applications of Artificial Intelligence

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.

Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System

The grading of gliomas based on histological features has been a subject of debate for several decades. A consensus has not yet been reached because of technical limitations and inter-observer variations. While the traditional grading system has failed to stratify the risk of IDH-mutant astrocytoma, canonical histological and proliferative markers may be applicable to the risk stratification of IDH-wild-type astrocytoma. Numerous studies have examined molecular markers in order to obtain more clinically relevant information that will improve the risk stratification of gliomas. The CDKN2A/B homozygous deletion for IDH-mutant astrocytoma and the following three criteria for IDH-wild-type astrocytoma: the concurrent gain of whole chromosome 7 and loss of whole chromosome 10, TERT promoter mutations, and EGFR amplification, were identified as independent molecular markers of the worst clinical outcomes. Therefore, the 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System adopted these molecular markers into the revised grading criteria of IDH-mutant and -wild-type astrocytoma, respectively, as a grading system within tumor types. Of note, several recent studies have shown that some low-grade IDH-wild-type astrocytoma lacking both the molecular glioblastoma signature and genetic alterations typical of pediatric-type gliomas may demonstrate a relatively indolent clinical course, suggesting the existence of lower-grade adult IDH-wild-type astrocytoma. In terms of oligodendroglioma, IDH-mutant, and 1p/19q codeleted, consistent makers that predict poor outcomes have not yet been identified, and, thus, the current criteria have remained unchanged. Molecular testing to fulfill the revised WHO criteria is, however, not always available worldwide, and in that case, an integrated diagnosis combining all available complementary information is highly recommended. This review discusses controversial issues surrounding legacy grading systems and newly identified potential genetic markers of adult diffuse gliomas and provides perspectives on future grading systems.

Prognostic role of Ki-67 in glioblastomas excluding contribution from non-neoplastic cells

Survival of glioblastoma patients varies and prognostic markers are important in the clinical setting. With digital pathology and improved immunohistochemical multiplexing becoming a part of daily diagnostics, we investigated the prognostic value of the Ki-67 labelling index (LI) in glioblastomas more precisely than previously by excluding proliferation in non-tumor cells from the analysis. We investigated the Ki-67 LI in a well-annotated population-based glioblastoma patient cohort (178 IDH-wildtype, 3 IDH-mutated). Ki-67 was identified in full tumor sections with automated digital image analysis and the contribution from non-tumor cells was excluded using quantitative double-immunohistochemistry. For comparison of the Ki-67 LI between WHO grades (II-IV), 9 IDH-mutated diffuse astrocytomas and 9 IDH-mutated anaplastic astrocytomas were stained. Median Ki-67 LI increased with increasing WHO grade (median 2.7%, 6.4% and 27.5%). There was no difference in median Ki-67 LI between IDH-mutated and IDH-wildtype glioblastomas (p = 0.9) and Ki-67 LI was not associated with survival in glioblastomas in neither univariate (p = 0.9) nor multivariate analysis including MGMT promoter methylation status and excluding IDH-mutated glioblastomas (p = 0.2). Ki-67 may be of value in the differential diagnostic setting, but it must not be over-interpreted in the clinico-pathological context.

The intrinsically disorderly story of Ki-67

Ki-67 is one of the most famous marker proteins used by histologists to identify proliferating cells. Indeed, over 30 000 articles referring to Ki-67 are listed on PubMed. Here, we review some of the current literature regarding the protein. Despite its clinical importance, our knowledge of the molecular biology and biochemistry of Ki-67 is far from complete, and its exact molecular function(s) remain enigmatic. Furthermore, reports describing Ki-67 function are often contradictory, and it has only recently become clear that this proliferation marker is itself dispensable for cell proliferation. We discuss the unusual organization of the protein and its mRNA and how they relate to various models for its function. In particular, we focus on ways in which the intrinsically disordered structure of Ki-67 might aid in the assembly of the still-mysterious mitotic chromosome periphery compartment by controlling liquid-liquid phase separation of nucleolar proteins and RNAs.

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Molecular mechanisms of lower-grade (II-III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context.

Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion

Background

Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.

Methods

In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.

Results

From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).

Conclusion

Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

[Histomolecular diagnosis of glial and glioneuronal tumours]

While rare compared to extra-cranial neoplasms, glial and glioneuronal tumors are responsible of high morbidity and mortality. In 2016, the World Health Organization introduced histo-molecular ("integrated") diagnostics for central nervous system tumors based on morphology, immunohistochemistry and the presence of key genetic alterations. This combined phenotypic-genotypic classification allows for a more objective diagnostic of brain tumors. The implementation of such a classification in daily practice requires immunohistochemical surrogates to detect common genetic alterations and sometimes expensive and not widely available molecular biology techniques. The first step in brain tumor diagnostics is to inquire about the clinical picture and the imaging findings. When dealing with a glial tumor, the pathologist needs to assess its nature, infiltrative or circumscribed. If the tumor is infiltrative, IDH1/2 genes (prognostic marker) and chromosomes 1p/19q (diagnosis of oligodendroglioma) need to be assessed. If the tumor appears circumscribed, the pathologist should look for a neuronal component associated with the glial component (glioneuronal tumor). A limited immunohistochemistry panel will help distinguish between diffuse glioma (IDH1-R132H, ATRX, p53) and circumscribed glial/glioneuronal tumor (CD34, neuronal markers, BRAF-V600E), and some antibodies may reliably detect genetic alterations (IDH1-R132H, BRAF-V600E and H3-K27M mutations). Chromosomal imbalances (1p/19q codeletion in oligodendroglioma; chromosome 7 gain/chromosome 10 loss and EGFR amplification in glioblastoma) and gene rearrangements (BRAF fusion, FGFR1 fusion) will be identified by molecular biology techniques. The up-coming edition of the WHO classification of the central nervous system tumors will rely more heavily on molecular alterations to accurately diagnose and treat brain tumors.

CDKN2A homozygous deletion is a strong adverse prognosis factor in diffuse malignant IDH-mutant gliomas

BACKGROUND

The 2016 World Health Organization (WHO) classification of central nervous system tumors stratifies isocitrate dehydrogenase (IDH)-mutant gliomas into 2 major groups depending on the presence or absence of 1p/19q codeletion. However, the grading system remains unchanged and it is now controversial whether it can be still applied to this updated molecular classification.

METHODS

In a large cohort of 911 high-grade IDH-mutant gliomas from the French national POLA network (including 428 IDH-mutant gliomas without 1p/19q codeletion and 483 anaplastic oligodendrogliomas, IDH-mutant and 1p/19q codeleted), we investigated the prognostic value of the cyclin-dependent kinase inhibitor 2A (CDKN2A) gene homozygous deletion as well as WHO grading criteria (mitoses, microvascular proliferation, and necrosis). In addition, we searched for other retinoblastoma pathway gene alterations (CDK4 amplification and RB1 homozygous deletion) in a subset of patients. CDKN2A homozygous deletion was also searched in an independent series of 40 grade II IDH-mutant gliomas.

RESULTS

CDKN2A homozygous deletion was associated with dismal outcome among IDH-mutant gliomas lacking 1p/19q codeletion (P < 0.0001 for progression-free survival and P = 0.004 for overall survival) as well as among anaplastic oligodendrogliomas, IDH-mutant + 1p/19q codeleted (P = 0.002 for progression-free survival and P < 0.0001 for overall survival) in univariate and multivariate analysis including age, extent of surgery, adjuvant treatment, microvascular proliferation, and necrosis. In both groups, the presence of microvascular proliferation and/or necrosis remained of prognostic value only in cases lacking CDKN2A homozygous deletion. CDKN2A homozygous deletion was not recorded in grade II gliomas.

CONCLUSIONS

Our study pointed out the utmost relevance of CDKN2A homozygous deletion as an adverse prognostic factor in the 2 broad categories of IDH-mutant gliomas stratified on 1p/19q codeletion and suggests that the grading of these tumors should be refined.

Multiparametric and multiregional diffusion features help predict molecule information, grade and survival in lower-grade gliomas: a feasibility study

OBJECTIVE

This study was to investigate the relationship of diffusion features with molecule information, and then predict grade and survival in lower-grade gliomas.

METHODS

65 patients with primary lower-grade gliomas (WHO Grade II & III) who underwent conventional MRI and diffusion tensor imaging were retrospectively studied. The tumor region was automatically segmented into contrast-enhancing tumor, non-enhancing tumor, edematous and necrotic volumes. Diffusion features, including fractional anisotropy (FA), axial diffusivity, radial diffusivity and apparent diffusion coefficient (ADC), were extracted from each volume using histogram analysis. To estimate molecule biomarkers and predict clinical characteristics of grade and survival, support vector machine, generalized linear model, logistic regression and Cox regression were performed on the related features.

RESULTS

The diffusion features in non-enhancing tumor volume showed differences between isocitrate dehydrogenase mutant and wild-type gliomas. And the mean accuracy of support vector machine classifiers was 0.79. Ki-67 labeling index was correlated with these features, which were combined to significantly estimate Ki-67 expression level (r = 0.657, p < 0.001). These features also showed differences between Grade II and III gliomas. A combination of them for grade classification resulted in an area under the curve of 0.914 (0.857-0.971). Mean FA and fifth percentile of ADC were independently associated with overall survival, with lower FA and higher ADC showing better survival outcome.

CONCLUSION

In lower-grade gliomas, multiparametric and multiregional diffusion features could help predict molecule information, histological grade and survival.

ADVANCES IN KNOWLEDGE

The multi parametric diffusion features in non-enhancing tumor were associated with molecule information, grade and survival in lower-grade gliomas.

Novel, improved grading system(s) for IDH-mutant astrocytic gliomas

According to the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO), IDH-mutant astrocytic gliomas comprised WHO grade II diffuse astrocytoma, IDH-mutant (AII_IDHmut), WHO grade III anaplastic astrocytoma, IDH-mutant (AAIII_IDHmut), and WHO grade IV glioblastoma, IDH-mutant (GBM_IDHmut). Notably, IDH gene status has been made the major criterion for classification while the manner of grading has remained unchanged: it is based on histological criteria that arose from studies which antedated knowledge of the importance of IDH status in diffuse astrocytic tumor prognostic assessment. Several studies have now demonstrated that the anticipated differences in survival between the newly defined AII_IDHmut and AAIII_IDHmut have lost their significance. In contrast, GBM_IDHmut still exhibits a significantly worse outcome than its lower grade IDH-mutant counterparts. To address the problem of establishing prognostically significant grading for IDH-mutant astrocytic gliomas in the IDH era, we undertook a comprehensive study that included assessment of histological and genetic approaches to prognosis in these tumors. A discovery cohort of 211 IDH-mutant astrocytic gliomas with an extended observation was subjected to histological review, image analysis, and DNA methylation studies. Tumor group-specific methylation profiles and copy number variation (CNV) profiles were established for all gliomas. Algorithms for automated CNV analysis were developed. All tumors exhibiting 1p/19q codeletion were excluded from the series. We developed algorithms for grading, based on molecular, morphological and clinical data. Performance of these algorithms was compared with that of WHO grading. Three independent cohorts of 108, 154 and 224 IDH-mutant astrocytic gliomas were used to validate this approach. In the discovery cohort several molecular and clinical parameters were of prognostic relevance. Most relevant for overall survival (OS) was CDKN2A/B homozygous deletion. Other parameters with major influence were necrosis and the total number of CNV. Proliferation as assessed by mitotic count, which is a key parameter in 2016 CNS WHO grading, was of only minor influence. Employing the parameters most relevant for OS in our discovery set, we developed two models for grading these tumors. These models performed significantly better than WHO grading in both the discovery and the validation sets. Our novel algorithms for grading IDH-mutant astrocytic gliomas overcome the challenges caused by introduction of IDH status into the WHO classification of diffuse astrocytic tumors. We propose that these revised approaches be used for grading of these tumors and incorporated into future WHO criteria.

Proliferative Index in Pediatric Pilocytic Astrocytoma by Region of Origin and Prediction of Clinical Behavior

BACKGROUND/AIMS

Pilocytic astrocytomas are common pediatric tumors. Molecular profiles vary with location of origin. Comparisons of proliferation have not been reported. We sought to identify differences in growth by region and whether these predict clinical behavior.

METHODS

A retrospective review of all patients undergoing surgery for a pilocytic astrocytoma at Children's Hospital LA from 2003 to 2015 was completed. Tumor location, determined by imaging, was stratified into infratentorial, supratentorial, or optic pathway. Proliferation was measured by Ki-67 immunostaining. A p value of 0.05 was deemed significant.

RESULTS

77 patients were identified. 51 had posterior fossa tumors, 12 had supratentorial tumors, and 14 had optic pathway tumors. Mean Ki-67 score was 3.67, 4.09, and 3.83%, respectively (p = 0.82). Ki-67 of ≥4% trended towards recurrence (p = 0.11), incomplete resection (p = 0.15), and younger age at presentation (p = 0.04). Ki-67 was weakly correlated with shorter survival after surgery (r = -0.103, p = 0.41). Partial resection strongest predicted recurrence (p < 0.001; OR = 13.0).

CONCLUSION

Proliferative index does not change by location. Higher cell proliferation was seen in younger patients and associated with shorter time to and a higher risk of recurrence. Further study is needed to identify predictors for clinical behavior. Importance of Study: This study provides a detailed analysis of the proliferative indices of tumors arising from characteristic locations within the brain. With recent advances in our understanding of the differences in molecular and genetic profiles despite similar histologic diagnoses, we felt that it was important to review whether there were unique components of tumor behavior that could be identified. In turn, we sought to determine whether tumor behavior could be used to predict the clinical course. This knowledge is important, given that not every tumor may undergo complete surgical resection, and that some lesions may require more aggressive upfront adjuvant therapy or be closely monitored for recurrence.

Radiomic features predict Ki-67 expression level and survival in lower grade gliomas

To investigate the radiomic features associated with Ki-67 expression in lower grade gliomas and assess the prognostic values of these features. Patients with lower grade gliomas (n = 117) were randomly assigned into the training (n = 78) and validation (n = 39) sets. A total of 431 radiological features were extracted from each patient. Differential radiological features between the low and high Ki-67 expression groups were screened by significance analysis of microarrays. Then, generalized linear analysis was performed to select features that could predict the Ki-67 expression level. Predictive efficiencies were further evaluated in the validation set. Cox regression analysis was performed to investigate the prognostic values of Ki-67 expression level and Ki-67-related radiological features. A group of nine radiological features were screened for prediction of Ki-67 expression status; these achieved accuracies of 83.3% and 88.6% (areas under the curves, 0.91 and 0.93) in the training and validation sets, respectively. Of these features, only spherical disproportion (SD) was found to be a prognostic factor. Patients in the high SD group exhibited worse outcomes in the whole cohort (overall survival, p < 0.0001; progression-free survival, p < 0.0001). Ki-67 expression level and SD were independent prognostic factors in the multivariate Cox regression analysis. This study identified a radiomic signature for prediction of Ki-67 expression level as well as a prognostic radiological feature in patients with lower grade gliomas.