Radiology reporting of low-grade glioma growth underestimates tumor expansion

Evaluation of a nnU-Net type automated clinical volumetric tumor segmentation tool for diffuse low-grade glioma follow-up

BACKGROUND AND PURPOSE

Diffuse low-grade gliomas (DLGG) are characterized by a slow and continuous growth and always evolve towards an aggressive grade. Accurate prediction of the malignant transformation is essential as it requires immediate therapeutic intervention. One of its most precise predictors is the velocity of diameter expansion (VDE). Currently, the VDE is estimated either by linear measurements or by manual delineation of the DLGG on T2 FLAIR acquisitions. However, because of the DLGG's infiltrative nature and its blurred contours, manual measures are challenging and variable, even for experts. Therefore we propose an automated segmentation algorithm using a 2D nnU-Net, to 1) gain time and 2) standardize VDE assessment.

MATERIALS AND METHODS

The 2D nnU-Net was trained on 318 acquisitions (T2 FLAIR & 3DT1 longitudinal follow-up of 30 patients, including pre- & post-surgery acquisitions, different scanners, vendors, imaging parameters…). Automated vs. manual segmentation performance was evaluated on 167 acquisitions, and its clinical interest was validated by quantifying the amount of manual correction required after automated segmentation of 98 novel acquisitions.

RESULTS

Automated segmentation showed a good performance with a mean Dice Similarity Coefficient (DSC) of 0.82±0.13 with manual segmentation and a substantial concordance between VDE calculations. Major manual corrections (i.e., DSC<0.7) were necessary only in 3/98 cases and 81% of the cases had a DSC>0.9.

CONCLUSION

The proposed automated segmentation algorithm can successfully segment DLGG on highly variable MRI data. Although manual corrections are sometimes necessary, it provides a reliable, standardized and time-winning support for VDE extraction to asses DLGG growth.

Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas

PURPOSE

The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator's discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG.

MATERIALS AND METHODS

A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment.

RESULTS

RANO product measurements had poor-to-moderate inter-operator reproducibility (r² = 0.28-0.82; coefficient of variance (CV) = 44-110%; mean percent difference (diff) = 0.4-46.8%) and moderate-to-excellent intra-operator reproducibility (r² = 0.71-0.88; CV = 31-58%; diff = 0.3-23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02).

CONCLUSION

RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths.

Preoperative imaging accuracy in size determination of prostate cancer in men undergoing radical prostatectomy for clinically localised disease

OBJECTIVES

To compare the accuracy of pre-surgical prostate size measurements using mpMRI and USWE with imaging-based 3D-printed patient-specific whole-mount moulds facilitated histopathology, and to assess whether size assessment varies between clinically significant and non-significant cancerous lesions including their locations in different zones of the prostate.

METHODS

The study population included 202 men with clinically localised prostate cancer opting for radical surgery derived from two prospective studies. Protocol-based imaging data was used for measurement of size of prostate cancer in clinically localised disease using MRI (N = 106; USWE (N = 96). Forty-eight men overlapped between two studies and formed the validation cohort. The primary outcome of this study was to assess the accuracy of pre-surgical prostate cancerous size measurements using mpMRI and USWE with imaging-based 3D-printed patient-specific whole-mount moulds facilitated histopathology as a reference standard. Independent-samples T-tests were used for the continuous variables and a nonparametric Mann-Whitney U test for independent samples was applied to examine the distribution and median differences between mpMRI and USWE groups.

RESULTS

A significant number of men had underestimation of prostate cancer using both mpMRI (82.1%; 87/106) and USWE (64.6%; 62/96). On average, tumour size was underestimated by a median size of 7 mm in mpMRI, and 1 mm in USWE. There were 327 cancerous lesions (153 with mpMRI and 174 for USWE). mpMRI and USWE underestimated the majority of cancerous lesions (108/153; 70.6%) and (88/174; 50.6%), respectively. Validation cohort data confirmed these findings MRI had a nearly 20% higher underestimation rate than USWE (χ² (1, N = 327) = 13.580, p = 0.001); especially in the mid and apical level of the gland. Clinically non-significant cancers were underestimated in significantly higher numbers in comparison to clinically significant cancers.

CONCLUSIONS

Size measurement of prostate cancers on preoperative imaging utilising maximum linear extent technique, underestimated the extent of cancer. Further research is needed to confirm our observations using different sequences, methods and approaches for cancer size measurement.

Low grade gliomas guide-lines elaborated by the tumor section of Spanish Society of Neurosurgery

Adult low-grade gliomas (Low Grade Gliomas, LGG) are tumors that originate from the glial cells of the brain and whose management involves great controversy, starting from the diagnosis, to the treatment and subsequent follow-up. For this reason, the Tumor Group of the Spanish Society of Neurosurgery (GT-SENEC) has held a consensus meeting, in which the most relevant neurosurgical issues have been discussed, reaching recommendations based on the best scientific evidence. In order to obtain the maximum benefit from these treatments, an individualised assessment of each patient should be made by a multidisciplinary team. Experts in each LGG treatment field have briefly described it based in their experience and the reviewed of the literature. Each area has been summarized and focused on the best published evidence. LGG have been surrounded by treatment controversy, although during the last years more accurate data has been published in order to reach treatment consensus. Neurosurgeons must know treatment options, indications and risks to participate actively in the decision making and to offer the best surgical treatment in every case.

The patients' experience of neuroimaging of primary brain tumors: a cross-sectional survey study

PURPOSE

To gain insight into how patients with primary brain tumors experience MRI, follow-up protocols, and gadolinium-based contrast agent (GBCA) use.

METHODS

Primary brain tumor patients answered a survey after their MRI exam. Questions were analyzed to determine trends in patients' experience regarding the scan itself, follow-up frequency, and the use of GBCAs. Subgroup analysis was performed on sex, lesion grade, age, and the number of scans. Subgroup comparison was made using the Pearson chi-square test and the Mann-Whitney U-test for categorical and ordinal questions, respectively.

RESULTS

Of the 100 patients, 93 had a histopathologically confirmed diagnosis, and seven were considered to have a slow-growing low-grade tumor after multidisciplinary assessment and follow-up. 61/100 patients were male, with a mean age ± standard deviation of 44 ± 14 years and 46 ± 13 years for the females. Fifty-nine patients had low-grade tumors. Patients consistently underestimated the number of their previous scans. 92% of primary brain tumor patients did not experience the MRI as bothering and 78% would not change the number of follow-up MRIs. 63% of the patients would prefer GBCA-free MRI scans if diagnostically equally accurate. Women found the MRI and receiving intravenous cannulas significantly more uncomfortable than men (p = 0.003). Age, diagnosis, and the number of previous scans had no relevant impact on the patient experience.

CONCLUSION

Patients with primary brain tumors experienced current neuro-oncological MRI practice as positive. Especially women would, however, prefer GBCA-free imaging if diagnostically equally accurate. Patient knowledge of GBCAs was limited, indicating improvable patient information.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm³ (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method

Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.

The impact of resection in IDH-mutant WHO grade 2 gliomas: a retrospective population-based parallel cohort study

OBJECTIVE

IDH-mutant diffuse low-grade gliomas (dLGGs; WHO grade 2) are often considered to have a more indolent course. In particular, in patients with 1p19q codeleted oligodendrogliomas, survival can be very long. Therefore, extended follow-up in clinical studies of IDH-mutant dLGG is needed. The authors' primary aim was to determine results after a minimum 10-year follow-up in two hospitals advocating different surgical policies. In one center early resection was favored; in the other center an early biopsy and wait-and-scan approach was the dominant management. In addition, the authors present survival and health-related quality of life (HRQOL) in stratified groups of patients with IDH-mutant astrocytoma and oligodendroglioma.

METHODS

The authors conducted a retrospective, population-based, parallel cohort study with extended long-term follow-up. The inclusion criteria were histopathological diagnosis of IDH-mutant supratentorial dLGG from 1998 through 2009 in patients aged 18 years or older. Follow-up ended January 1, 2021; therefore, all patients had primary surgery more than 10 years earlier. In region A, a biopsy and wait-and-scan approach was favored, while early resections were advocated in region B. Regional referral practice ensured population-based data, since referral to respective centers was based strictly on the patient's residential address. Previous data from EQ-5D-3L, European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30, and EORTC BN20 questionnaires were reanalyzed with respect to the current selection of IDH-mutant dLGG and to molecular subgroups. The prespecified primary endpoint was long-term regional comparison of overall survival. Secondarily, between-group differences in long-term HRQOL measures were explored.

RESULTS

Forty-eight patients from region A and 56 patients from region B were included. Early resection was performed in 17 patients (35.4%) from region A compared with 53 patients (94.6%) from region B (p < 0.001). Characteristics at baseline were otherwise similar between cohorts. Overall survival was 7.5 years (95% CI 4.1-10.8) in region A compared with 14.6 years (95% CI 11.5-17.7) in region B (p = 0.04). When stratified according to molecular subgroups, there was only a statistically significant survival benefit in favor of early resection for patients with astrocytomas. The were no apparent differences in the different HRQOL measures between cohorts.

CONCLUSIONS

In an extended follow-up of patients with IDH-mutant dLGGs, early resection was associated with a sustained and clinically relevant survival benefit. The survival benefit was not counteracted by any detectable reduction in HRQOL.

Predictors of tumor progression of low-grade glioma in adult patients within 5 years follow-up after surgery

Background

Glioma originates from glial cells in the brain and is the most common primary intracranial tumor. This study intends to use a retrospective analysis to explore the factors that can predict tumor progression in adult low-grade gliomas, namely WHO II grade patients, within 5 years after surgery.

Methods

Patients with WHO grade II glioma who were surgically treated in our hospital from February 2011 to May 2017 were included. According to the inclusion and exclusion criteria, 252 patients were included in the final analysis. According to the results of the 5-year follow-up (including survival and imaging review results), patients were divided into progression-free group and progression group. Univariate and multivariate analysis were conducted to investigate the related factors of tumor progression during the 5-year follow-up.

Results

The results of the 5-year follow-up showed that 111 (44.0%) cases had no progress (progression free group, PFG), 141 (56.0%) cases had progress (progression group, PG), of which 43 (30.5%) cases were operated again, 37 cases (26.2%) received non-surgical treatments. There were 26 (10.3%) all-cause deaths, and 21 (8.3%) tumor-related deaths. Univariate and multivariate analysis showed that age >45 years old (OR = 1.35, 95% CI, 1.07-3.19, P = 0.027), partial tumor resection (OR = 1.66, 95% CI, 1.15-3.64, P = 0.031), tumor diameter >3 cm (OR = 1.52, 95% CI, 1.14-4.06, P = 0.017) and no radiotherapy (OR = 1.37, 95% CI, 1.12-2.44, P = 0.039) were independent predictors of the progression of tumor during the 5-year follow-up period.

Conclusion

Age >45 years old, partial tumor resection, tumor diameter >3 cm, no radiotherapy are predictors for tumor progression for glioma patients after surgery.

Prediagnosis epilepsy and survival in patients with glioma: a nationwide population-based cohort study from 2009 to 2018

OBJECTIVE

Considering that epilepsy is common, and knowledge is lacking on its role especially for the prognosis of high-grade gliomas, the objective of this study was to investigate the association between epilepsy prior to glioma diagnosis and survival among glioma patients.

METHODS

In a nationwide population-based cohort study, we included 3763 adult glioma patients diagnosed between 2009 and 2018 according to the Danish Neuro-Oncology Registry. Information on epilepsy was redeemed through Danish Neuro-Oncology Registry, National Patient Registry, and National Prescription Registry. Cox proportional hazard models with 95% confidence intervals (CIs) were applied to examine hazard ratios (HRs) for the association between epilepsy (< 1 year prior to glioma including epilepsy at onset; 1-10 years prior to glioma; no prior epilepsy) and risk of death, and whether it differed according to tumor grade and size, performance status, and treatment modalities.

RESULTS

A 32% decreased risk of death in patients with epilepsy within 1 year prior to glioma compared to no prior epilepsy was found (HR = 0.68; CI 0.63-0.75). A favorable prognosis was seen for epilepsy in all glioma grades: II (HR = 0.55; CI 0.39-0.77), III (HR = 0.59; CI 0.48-0.73), and IV (HR = 0.85; CI 0.77-0.94).

CONCLUSIONS

Patients with epilepsy within 1 year prior to glioma diagnosis had significant survival benefits compared to patients with no prior epilepsy. This association was significant for both low-grade gliomas (grade II) and high-grade gliomas (grade III and IV). Survival benefits in glioma patients with epilepsy at onset are possibly primarily attributable to tumor-specific histopathology, molecular biomarkers, and early diagnosis.

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review

INTRODUCTION

Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges.

MATERIAL AND METHODS

Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers.

RESULTS

By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score).

CONCLUSION

The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.

A Quantitative Assessment of Pre-Operative MRI Reports in Glioma Patients: Report Metrics and IDH Prediction Ability

Objectives

To measure the metrics of glioma pre-operative MRI reports and build IDH prediction models.

Methods

Pre-operative MRI reports of 144 glioma patients in a single institution were collected retrospectively. Words were transformed to lowercase letters. White spaces, punctuations, and stop words were removed. Stemming was performed. A word cloud method applied to processed text matrix visualized language behavior. Spearman's rank correlation assessed the correlation between the subjective descriptions of the enhancement pattern. The T1-contrast images associated with enhancement descriptions were selected. The keywords associated with IDH status were evaluated by χ2 value ranking. Random forest, k-nearest neighbors and Support Vector Machine algorithms were used to train models based on report features and age. All statistical analysis used two-tailed test with significance at p <.05.

Results

Longer word counts occurred in reports of older patients, higher grade gliomas, and wild type IDH gliomas. We identified 30 glioma enhancement descriptions, eight of which were commonly used: peripheral, heterogeneous, irregular, nodular, thick, rim, large, and ring. Five of eight patterns were correlated. IDH mutant tumors were characterized by words related to normal, symmetric or negative findings. IDH wild type tumors were characterized words by related to pathological MR findings like enhancement, necrosis and FLAIR foci. An integrated KNN model based on report features and age demonstrated high-performance (AUC: 0.89, 95% CI: 0.88-0.90).

Conclusion

Report length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.

EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood

In response to major changes in diagnostic algorithms and the publication of mature results from various large clinical trials, the European Association of Neuro-Oncology (EANO) recognized the need to provide updated guidelines for the diagnosis and management of adult patients with diffuse gliomas. Through these evidence-based guidelines, a task force of EANO provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. The diagnostic component is based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System and the subsequent recommendations of the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy — Not Officially WHO (cIMPACT-NOW). With regard to therapy, we formulated recommendations based on the results from the latest practice-changing clinical trials and also provide guidance for neuropathological and neuroradiological assessment. In these guidelines, we define the role of the major treatment modalities of surgery, radiotherapy and systemic pharmacotherapy, covering current advances and cognizant that unnecessary interventions and expenses should be avoided. This document is intended to be a source of reference for professionals involved in the management of adult patients with diffuse gliomas, for patients and caregivers, and for health-care providers.

Herein, the European Association of Neuro-Oncology (EANO) provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. These evidence-based guidelines incorporate major changes in diagnostic algorithms based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System as well as on evidence from recent large clinical trials.

The clinical significance of the T2-FLAIR mismatch sign in grade II and III gliomas: a population-based study

Background

The T2-FLAIR mismatch sign is an imaging finding highly suggestive of isocitrate dehydrogenase mutated (IDH-mut) 1p19q non-codeleted (non-codel) gliomas (astrocytomas). In previous studies, it has shown excellent specificity but limited sensitivity for IDH-mut astrocytomas. Whether the mismatch sign is a marker of a clinically relevant subtype of IDH-mut astrocytomas is unknown.

Methods

We included histopathologically verified supratentorial lower-grade gliomas (LGG) WHO grade II-III retrospectively during the period 2010–2016. In the period 2017–2018, patients with suspected LGG radiologically were prospectively included, and in this cohort other diagnoses than glioma could occur. Clinical, radiological and molecular data were collected. For clinical evaluation we included all patients with IDH-mut astrocytomas. In the 2010–2016 cohort DNA methylation analysis with Infinium MethylationEPIC BeadChip (Illumina) was performed for patients with an IDH-mut astrocytoma with available tissue. We aimed to examine the association of the T2-FLAIR mismatch sign with clinical factors and outcomes. Additionally, we evaluated the diagnostic reliability of the mismatch sign and its relation to methylation profiles.

Results

Out of 215 patients with LGG, 135 had known IDH-mutation and 1p19q codeletion status. Fifty patients had an IDH-mut astrocytoma and 12 of these (24.0%) showed a mismatch sign. The sensitivity and specificity of the mismatch sign for IDH-mut detection were 26.4 and 97.6%, respectively. There were no differences between patients with an IDH-mut astrocytoma with or without mismatch sign when grouped according to T2-FLAIR mismatch sign with respect to baseline characteristics, clinical outcomes and methylation profiles. The overall interrater agreement between neuroradiologist and clinical neurosurgeons for the T2-FLAIR mismatch sign was significant when all 215 MRI examination assessed (κ = 0.77, p < 0.001, N = 215).

Conclusion

The T2-FLAIR mismatch sign in patients with an IDH-mut astrocytoma is not associated with clinical presentation or outcome. It seems unlikely that the IDH-mut astrocytomas with mismatch sign represent a specific subentity. Finally, we have validated that the T2-FLAIR mismatch sign is a reliable and specific marker of IDH-mut astrocytomas.

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon.Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons.Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.

Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports

BACKGROUND

Detection of progression is clinically important for the management of glioblastoma. We sought to assess the accuracy of clinical radiological reporting and measured bidimensional products to identify radiological glioblastoma progression. The two were compared to volumetric segmentation.

METHODS

In this retrospective study, we included 106 patients with histopathologically verified glioblastomas and two separate MRI scans obtained before surgery. Bidimensional products based on measurements on the axial slice with the largest tumor area were calculated, and growth estimations from the clinical radiological reports were retrieved. The two growth estimations were compared to manual volumetric segmentations. Inter-observer agreement using the bidimensional product was assessed using Kappa-statistics and by calculating the difference between two neuroradiologist in percentage change of the bidimensional product for each tumor.

RESULTS

Clinical radiological reports and bidimensional products showed fairly equal accuracy when compared to volumetric segmentation with an accuracy of 67% and 66-68%, respectively. There was a difference in median volume increase of 6.9 mL (2.4 vs 9.3 mL, p < 0.001) between tumors evaluated as stable and progressed based on the clinical radiological reports. This difference was 8.1 mL (2.0 vs 10.1 ml, p < 0.001) when using the bidimensional products. The bidimensional product reached a moderate inter-observer agreement with a Kappa value of 0.689. For 32% of the tumors, the two neuroradiologists calculated a difference of more than 25% using bidimensional products.

CONCLUSIONS

Clinical radiological reporting and the bidimensional product exhibit similar accuracy. The bidimensional product has moderate inter-observer agreement and is prone to underestimating tumor progression, as an average glioblastoma had to grow 10 mL in order to be classified as progressed. These findings underline the assumption that one should try to move towards volumetric assessment of glioblastoma growth in the future.