bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-36. MR PERFUSION IS A BETTER PREDICTOR OF TUMOR RECURRENCE AND TREATMENT RESPONSE THAN STANDARD MRI IN SOME OLIGODENDROGLIOMA PATIENTS
Authors: Connelly Jennifer , Prah Melissa , Schmainda Kathleen 
Published: November 2022

Abstract
Dynamic susceptibility contrast (DSC) perfusion MRI (pMRI) has proven efficacy in the evaluation of glioma grade, progression and response to therapy. While not currently integrated within RANO (Response Assessment in Neuro Oncology) criteria, pMRI is often assessed alongside standard imaging of astrocytic glioma. In contrast, evaluation of pMRI in oligodendroglioma, has been less well-studied despite its potential to evaluate the comparatively greater vascularity of its “chicken-wire” capillary networks. Moreover, standard imaging to evaluate oligodendroglioma course can be confounded by slow-growing enhancement that does not meet RANO threshold for progression, or disease control despite low or absent radiographic response, and radiation-induced leukoencephalopathy resembling hyperintense changes on T2w and FLAIR imaging. Consequently, there is a clinical need to understand the potential role of pMRI in the evaluation of oligodendroglioma. As an initial step, we present here a case study of a patient with oligodendroglioma for which pMRI bested standard imaging in evaluation throughout radiographic disease surveillance. A 32 y/o gentleman with oligodendroglioma WHO grade 2 (IDH mutant, 1p19q codeletion) completed standard treatment, including surgical resection, irradiation, and chemotherapy. During surveillance imaging, a region of hyperperfusion on pMRI developed. However, since standard imaging was stable, RANO criteria was not met and observation was continued without intervention. Subsequent standard imaging demonstrated clear radiographic progression, reflecting the radiographic findings of pMRI but with delayed manifestation. Systemic chemotherapy was resumed with successively stable standard MR imaging. In contrast, the previous region of hyperperfusion improved over serial pMRI, and more consistently mirrored the patient’s clinical course. This case illustrates the timely radiographic importance of pMRI in assessing stability of tumor as well as treatment response. Considerations for the inclusion of pMRI within RANO criteria should be explored further.
NIMG-37. JOINT LEARNING OF IMAGING AND GENOMIC DATA REVEALS DISTINCT GLIOBLASTOMA SUBTYPES
Authors: Guo Jun , Kazerooni Anahita Fathi , Akbari Hamed , Toorens Erik , Sako Chiharu , Mamourian Elizabeth , Koumenis Constantinos , Bagley Stephen , Binder Zev A , Lustig Robert , O'Rourke Donald , Ganguly Tapan , Bakas Spyridon , Nasrallah MacLean , Davatzikos Christos 
Published: November 2022

Abstract

PURPOSE
The significant heterogeneity of glioblastoma is typically displayed on both phenotypical and molecular levels. Non-invasive in vivo approaches to characterize this heterogeneity would potentially facilitate personalized therapies. Here we leverage advanced unsupervised machine learning to integrate radiomic imaging features and genomics to identify distinct subtypes of glioblastoma.

METHODS
A retrospective cohort of 571 IDH-wildtype glioblastoma patients were collected with pre-operative multi-parametric MRI (T1, T1CE, T2, T2-FLAIR, DSC, DTI) scans (available in 462/571 patients) and targeted next-generation sequencing (NGS) data (available in 355/571 patients). Radiomic features (n= 971) were extracted from these MRI scans and a subset of 12 features were selected by L21-norm minimization. A total of 14 key driver genes in the 5 main pathways that are most frequently altered in glioblastoma were chosen. Subtypes were identified by a joint learning approach called Anchor-based Partial Multi-modal Clustering (APMC) on both radiomic and genomic modalities.

RESULTS
Three distinct glioblastoma subtypes were discovered by APMC based on 14-dimension NGS data together with 12 selected radiomic features representing characteristics from histograms, shape, and volumetric measures for different tumor sub-regions. The identified subtypes were 1) high-risk; 2) medium-risk; and 3) low-risk, in terms of their overall survival outcome in Kaplan-Meier analysis (p= 5.52e-6, log-rank test; HR= 1.51, 95%CI:1.20-1.74, Cox proportional hazard model). The three subtypes also displayed different molecular characteristics: subtype 1 exhibited increased frequency of mutation in [EGFR, PIK3CA, PTEN, NF1], subtype 3 showed frequently mutated [PDGFRA, ATRX], while subtype 2 did not show significant differences for mutations in any of these genes.

CONCLUSION
Our results revealed the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities could help better understand the molecular basis of phenotypical signatures of glioblastoma and further provide insights into the biologic underpinnings of tumor formation and progression.
NIMG-56. PHENOTYPIC ANALYSIS OF RADIO-PATHOMIC MAPS IN DE-NOVO GLIOBLASTOMA IDENTIFIES DIFFERENCES IN BEVACIZUMAB TREATMENT RESPONSE
Authors: Bobholz Samuel , Lowman Allison , Duenweg Savannah , Winiarz Aleksandra , Kyereme Fitzgerald , Cochran Elizabeth , Connelly Jennifer , Mueller Wade , Agarwal Mohit , Banerjee Anjishnu , LaViolette Peter 
Published: November 2022

Abstract

PURPOSE
Bevacizumab (Bev) treatment for recurrent glioblastoma ceases tumor angiogenesis, which changes tumor appearance on Gd enhanced T1-weighted imaging. However, little is known about how non-angiogenic portions of tumor are affected by Bev. Therefore, this study tested the hypothesis that patients with specific tumor appearance phenotypes outside contrast enhancement, as identified using autopsy tissue-based radio-pathomic maps, would show differences in survival benefit from Bev treatment.

METHODS
Previously published methods were used to create machine learning models that generate whole-brain maps of cellularity (Cell), extracellular fluid (ECF) and cytoplasm (Cyt) using glioma autopsy tissue aligned to the patient’s last MRI as ground truth (Bobholz et al. 2022). These maps were then generated for an independent dataset of glioblastoma patients (n=80) using baseline imaging acquired prior to treatment. Patients were then graded for qualitative characteristics of the non-enhancing margin, with phenotypes including Well-Circumscribed (WC) patients with no abnormal tissue surrounding the core, Hypercellular Front (HF) patients with areas of hypercellularity extending from the core, Necrotic Front (NF) patients with areas of necrosis extending from the core, and Hybrid Front (HYF) patients with both hypercellular and necrotic presence beyond the enhancing region. Kaplan Meier analysis was then used to compare survival outcomes between patients who did and did not receive Bev treatment within each phenotype to assess differences in treatment efficacy.

RESULTS
Only patients with NF or HYF showed significant survival increase with bevacizumab treatment (HR=2.35, p=0.02; and HR=2.45, p=0.03, respectively), with no significant or trending difference in survival observed for WC and HF patients.

CONCLUSION
Radio-pathomic phenotypes identify patients who show the greatest survival benefit from Bev treatment, which could be used to direct clinical decision-making.
NIMG-59. EVALUATION OF THE RESPONSE ASSESSMENT CRITERIA IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA
Authors: Youssef Gilbert , Rahman Rifaquat , Bay Camden , Wang Wei , Lim-Fat Mary Jane , Arnaout Omar , Bi Wenya Linda , Cagney Daniel , Chang Yuh-Shin , Cloughesy Timothy , DeSalvo Matthew , Ellingson Benjamin , Gerstner Elizabeth , Castro Luis Nicolas Gonzalez , Guenette Jeffrey , Kim Albert , Lee Eudocia , McFaline-Figueroa J Ricardo , Potter Christopher , Reardon David A , Huang Raymond , Wen Patrick Y 
Published: November 2022

Abstract

BACKGROUND
We sought to compare the Response Assessment in Neuro-Oncology (RANO), modified RANO (mRANO), and immunotherapy RANO (iRANO) in a large population of patients with newly diagnosed (nGBM) and recurrent (rGBM) glioblastoma.

METHODS
Bidimensional measurements of enhancing disease and FLAIR sequence evaluation were performed by two independent readers on brain MRIs of consecutive patients with IDH-wildtype nGBM and rGBM treated at a single institution. Discrepancies were evaluated by a third reader. Dates of disease progression (PD) were identified using RANO, mRANO, iRANO, and other response assessment criteria variations. Spearman’s correlations between PFS and OS were calculated using iterative multiple imputations for censored observations.

RESULTS
526 nGBM and 580 rGBM cases were included. Spearman’s correlations were not significantly different between RANO and mRANO in nGBM (0.69 [95% CI 0.62 to 0.75] vs. 0.67 [0.60, 0.73]) and rGBM (0.48 [0.40, 0.55] vs. 0.50 [0.42, 0.57]). Evaluation of FLAIR did not improve the correlation in patients who received antiangiogenic therapy. Acquisition of confirmation scans was associated with increased correlation only when PD was identified within 12 weeks of completion of radiation in nGBM. The use of the post-radiation MRI as a baseline was associated with increased correlation compared to use of the pre-radiation MRI in nGBM (0.67 [0.60, 0.73] vs. 0.53 [0.42, 0.62]). The correlation with iRANO was similar to RANO and mRANO among 98 patients with nGBM and 175 patients with rGBM who received immunotherapy.

CONCLUSIONS
RANO and mRANO demonstrated similar correlations between PFS and OS. The evaluation of FLAIR can be omitted, while confirmation scans were only beneficial in nGBM in the first 12 weeks after completion of radiation. There was a trend in favor of the post-radiation MRI as the baseline scan in nGBM. The use of iRANO criteria did not add a significant benefit in patients who received immunotherapy.
NIMG-66. CANNY EDGE DETECTION ALGORITHM FOR QUANTITATIVE DIFFERENTIATION BETWEEN DIFFUSE AND CIRCUMSCRIPT GLIOMA GROWTH PATTERNS ON MRI
Authors: Thiele Frederic , Weller Jonathan , Katzendobler Sophie , Trumm Christoph , Thon Niklas , Tonn Joerg-Christian 
Published: November 2022

Abstract

BACKGROUND
Lower grade gliomas show heterogenous appearance on T2-weighted MRI. Some tumors grow diffusely along axonal structures whereas others distort adjacent brain tissue through local mass effect. The diagnostic, therapeutic and prognostic implication of differential growth patterns on MRI remain unknown and are difficult to assess quantitatively.

METHODS
A web-based application allowing for image preprocessing and providing a comprehensive edge detection tool by means of quantifying tumor border delineation on T2-weighted images based on the canny edge detection algorithm was developed. A sigma value between 1 and 100 determined the threshold where tumor borders where not detected anymore, with 1 equating to the lowest threshold and thus detection of all edges contained in the image. Two experienced faculty members assigned sigma values to axial T2 images of a random sample of 20 WHO grade 2 astrocytomas, IDH-mutant and 1p/19q-non-codeleted. The sigma values were then compared with a binary, subjective rating by the same faculty staff according to the perceived predominant growth pattern (diffuse versus circumscript) of each glioma.

RESULTS
When subjectively categorizing tumors binarily (diffuse versus circumscript), there was moderate interrater variability between observers (cohen’s kappa=0.6). Raters agreed in 16 of 20 cases, terming 7 gliomas unanimously diffuse and 9 gliomas circumscript. In 4 cases, the raters opinions diverged. The sigma values differed significantly between diffuse and circumscript tumors in both raters (rater 1, p=0.002; rater 2, p=0.018). For rater 1, the mean sigma difference between diffuse and circumscript tumors was 10.7 and 9.3 for rater 2. The mean overall sigma value was 13.3 for rater 1 and 18.7 for rater 2 (p=0.005).

CONCLUSION
Edge detection algorithms can be efficiently applied on MRI scans and are highly accurate in differentiating diffuse from circumscript gliomas. Objectification demands defining imaging criteria for diffuse and circumscript appearance of lower grade gliomas on MRI.
NIMG-71. REAL-WORLD ASSESSMENT OF IDH1 INHIBITOR IVOSIDENIB IN IDH MUTANT LOWER GRADE GLIOMAS – THE JOHNS HOPKINS EXPERIENCE
Authors: Puri Sushant , Shi Jessica , Blair Lindsay , Blakeley Jaishri , Laterra John , Kamson David 
Published: November 2022

Abstract

BACKGROUND
As clinical trials are ongoing to establish the role of IDH inhibitors in the treatment of lower-grade gliomas, long-term data is not yet available. Hence, here we share our real-world experience with off-label ivosidenib.

METHODS
In this single-institution retrospective analysis, we included chemo/radiotherapy-naïve, gadolinium non-enhancing WHO Grade 2/3 IDH1-mutated astrocytomas (AS) and oligodendrogliomas (OG) that were treated with off-label ivosidenib for ≥ 5 months for active tumor growth. We performed semi-automated volumetric analyses on T2/FLAIR images in 3D Slicer v4.11. Annualized growth-rates before and on ivosidenib were calculated, and time to initial response (reduction in growth-rate), growth arrest (growth-rate ≤ 0cc/yr) and shrinkage in patients with confirmatory data available (≥ 1 confirmatory follow-up scan). Progression-free survival (PFS), at 6 and 12 months were assessed (progression defined as ≥ 40% volume increase per RANO 3D).

RESULTS
We studied 12 patients (2F, median age 46yrs [range 26-60], 8/4 AS/OG; 4 grade 3) with an average of 17.2 months (5-33) follow-up on ivosidenib. Eleven (92%) patients had confirmatory data. Response rate was 82% (9/11) with median time of 4.9 (3.5-6.3) months to initial response and 11.1 (4.1-21.4) months to growth arrest. Growth arrest occurred 3-folds faster in OG than in AS (4.2±0.8 vs.12.5±4.2 months, p=0.014). Mean best response was 11.4±12.8% volume reduction, after a median of 12.1±9.3 months on ivosidenib. Mean PFS was 24.4 months (CI95%18.8-29.9). PFS-6 and PFS-12 were 91%, and 88% (7/8), respectively. No major toxicity was noted. There were 3 discontinuations, two for radiographic progression, one for increased seizure frequency.

CONCLUSIONS
In this small cohort, ivosidenib was well-tolerated with a high response rate; however, responses took up to a year to become evident and seemingly faster in oligodendrogliomas. These data suggest that ivosidenib might require more than 5 months of commitment and volumetrics may help the detection of early response.
NIMG-73. PREDICTING PERITUMORAL GLIOBLASTOMA INFILTRATION USING QUANTITATIVE MAGNETIC RESONANCE FINGERPRINTING RADIOMICS
Authors: Zhao Walter , Deng Shengwen , Chen Yong , Kazerooni Anahita Fathi , Davatzikos Christos , Couce Marta , Wang Xiaofeng , Sloan Andrew , Ma Dan , Badve Chaitra 
Published: November 2022

Abstract

PURPOSE
Standard clinical magnetic resonance (MR) imaging of glioblastoma (GB) cannot identify malignant infiltration into the peritumoral non-enhancing region. This work implements quantitative MR fingerprinting (MRF) based radiomics to predict infiltrative regions of GB.

METHODS
Pre-operative MRF T1 and T2 maps along with multiparametric MR (mpMR) images (T1w, T2w, T1w-Gd, FLAIR, and ADC) from GB patients (n = 10) were analyzed. All subjects had histologically confirmed sites of non-enhancing peritumoral infiltration as identified by intra-operative 5-ALA fluorescence-guided tissue resection. These locations (n = 40) were manually annotated on pre-operative FLAIR by a board-certified neuroradiologist and labeled as “infiltration” (INF). For each patient, another FLAIR region ( &gt; 3 cm from the enhancing tumor margin) was identified and labeled as “edema” (ED). Following image co-registration, 693 handcrafted radiomic voxel-based features were extracted from confirmed infiltration (INF) and edema (ED) voxels using a 3D 5x5x5 voxel sliding kernel. Feature selection was performed using the minimum redundancy maximal relevance (MRMR) algorithm, then INF and ED voxel features were used to train two cross-validated binary support vector machine (SVM) models for voxel-wise infiltration prediction: 1) MRF only model and 2) combined MRF and mpMRI model. Following MRMR selection, the combined model included features from T1, T1w, T2w, T1w-Gd, FLAIR, and ADC images. Model performance was evaluated using one withheld subject with multiple known infiltration sites. Balanced test accuracy and receiver operating characteristic (ROC) curve analysis was used to evaluate classification performance.

RESULTS
The MRF only and combined model achieved balanced test accuracies of 77.87% and 73.21% and AUCs of 0.91 and 0.97, respectively.

CONCLUSIONS
This study demonstrates that MRF-based radiomic features can predict GB tumor infiltration with high accuracy and provide complementary value to standard clinical MRI features. Our results indicate the high potential of employing MRF to guide personalized GB treatment strategies.
NIMG-75. REPEATABILITY OF MANUAL SEGMENTATION OF GLIOBLASTOMA ON MRI - QUALITY ASSURANCE FOR A QUANTITATIVE MRI RADIOMICS REPEATABILITY STUDY
Authors: Chan Edward , Martin Philip , Brighi Caterina , Pillay Sugendran , Holloway Lois , Metcalfe Peter , Koh Eng-Siew 
Published: November 2022

Abstract

BACKGROUND
Accurate and repeatable imaging segmentation of glioblastomas is important for effective radiomics modelling of clinical endpoints such as survival and treatment response. Manual and even automated segmentation are prone to variability. The primary objective of this study is to evaluate the repeatability of manually segmented glioblastomas on MRI which will serve as quality assurance for a quantitative MRI radiomics repeatability study.

METHODS
MRI datasets from n=19 patients with recurrent glioblastoma extracted from The Cancer Imaging Archive (RIDER Neuro MRI dataset) comprised of test-retest scans (time-point 1 and 2) acquired on average 2 days apart. The MRI protocol consisted of dynamic contrast-enhanced 3D FLASH using 0.1mmol/kg Magnevist intravenous injection at 3cc/second (TR 3.8ms, TE 1.8ms, 5mm slices, flip angle 25°), contrast-enhanced 3D FLASH (TR 8.6ms, TE 4.1ms, 1mm slices, flip angle 20°) and contrast-enhanced 3D FLAIR (TR 6000ms, TE 353ms, TI 2200ms, 1mm slices, flip angle 180°) on a 1.5T magnet. Contrast enhancing tumour from n=38 MRI scans were manually segmented by a radiologist slice by slice using contrast-enhanced 3D FLASH T1-weighted images on MIM software (v6.9.5). All cases at time-point 1 were contoured before contouring commenced for time-point 2 in the same order. Repeatability and spatial overlap was assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD) and centroid shift.

RESULTS
A comparison of time-point 1 and 2 demonstrated the median DSC for n=19 cases was 0.84 (interquartile range 0.07), mean HD was only 0.1cm and mean centroid shift was 0.2cm.

CONCLUSION
High repeatability and spatial overlap of manually segmented contrast enhancing regions of recurrent glioblastoma were achieved on serial MRI. This substantiates a high level of repeatability which forms a core component of an MRI radiomics platform under development.
NIMG-77. MAGNETIC RESONANCE IMAGING AND AMINO ACID POSITRON EMISSION TOMOGRAPHY IN DIFFERENTIATING TUMOR RECURRENCE FROM RADIATION NECROSIS IN HIGH GRADE GLIOMAS
Authors: Sahu Arpita , Mathew Ronny , Dasgupta Archya , Ashtekar Renuka , Puranik Ameya , Sridhar Epari , Sahay Ayushi , Chatterjee Abhishek , Shetty Prakash , Moiyadi Aliasgar , Gupta Tejpal 
Published: November 2022

Abstract

BACKGROUND AND PURPOSE
Differentiating tumor recurrence (TR) from radiation necrosis (RN) in high grade glioma (HGG) following radiotherapy can be challenging. The ability of conventional magnetic resonance imaging (MRI) to differentiate TR from post-therapeutic effects is often limited. Multiparametric advanced MRI (perfusion) and positron emission tomography (PET) with amino acid tracers, specifically 18F-Fluoroethyl-Tyrosine (FET) can provide relevant additional information on tumor metabolism, which allows for a more accurate diagnosis to differentiate tumor recurrence from radiation necrosis in high grade gliomas.

MATERIALS AND METHODS
Prospective analysis of 62 lesions in 62 patients with HGG who underwent both MRI and FET-PET imaging within three weeks intervals was done independently by a neuroradiologist and nuclear medicine physician. The study was conducted in a tertiary care oncology center between July 2018 and August 2021. Manually segmented regions of interest were placed over the areas of maximum enhancement/suspicion on MRI and FET uptake, which were used to calculate the relative cerebral volume (rCBV) and tumor to background ratios, respectively. Definitive diagnosis (TR versus RN) was made on clinico-radiological follow-up or histopathological report (wherever available).

RESULTS
Out of the 62 lesions that were studied, 46 and 16 had TR and RN, respectively. The sensitivity and specificity for determination of TR in HGG with conventional MRI were 98% and 62.5% respectively, while with FET-PET it was 91% and 87.5% respectively. The PPV , NPV and accuracy for MRI were 88%, 91% , 89% , while for FET-PET it was 95%, 78% and 90% respectively. A combination of MRI and PET parameters (mean target-to-background ratio), demonstrated an increase in diagnostic accuracy to 97%.

CONCLUSION
Cumulative imaging with MRI and FET-PET offers a multiparametric assessment of glioma recurrence that is correlative and complimentary, with higher accuracy and clinical value.
NIMG-79. SPATIALLY MAPPED PREDICTIONS OF EVOLVING TUMOR RESPONSE OF HIGH-GRADE GLIOMA VIA IMAGE-DRIVEN MATHEMATICAL MODELING
Authors: Farhat Maguy , Hormuth David , Langshaw Holly , Bronk Juliana , Curl Brandon , Yadav Divya , Upadhyay Rituraj , Elliot Andrew , Goldman Jodi , Erickson Lily , Talpur Wasif , Lee Maggie , Yankeelov Thomas , Chung Caroline 
Published: November 2022

Abstract

Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) imaging timepoints for achieving diagnostic certainty, which delays therapeutic interventions. Mathematical modeling (MM) of tumor growth and treatment response can provide spatiotemporal information of HGG evolution in response to treatment, thus allowing for prospective early identification of resilient tumor subregions. AIMS: We aim to initialize and calibrate an image-driven MM framework to forecast HGG response, both at the end of chemoradiotherapy (CRT) and at 3-month FU.

METHODS
In a prospective clinical study, weekly mpMRIs (post-contrast T1, T2 FLAIR, and diffusion) for patients with HGG receiving CRT were used to describe tumor extent and cellularity. This data collected from baseline (pre-CRT) till week 3 (mid-CRT) was used to calibrate a model family to forecast HGG response for each individual patient at week 6 (end CRT) and at 3-month FU.

RESULTS
Error between the forecasted and observed responses was assessed globally using percent error in tumor volume, and at the local level by Pearson correlation coefficient (PCC). In an initial cohort of 11 patients, our MM framework predictions had a percent error in tumor volume of less than 8.6% and at week 6 RT and less than 20% at 3 months FU. The PCCs were 0.84 at week 6 RT and 0.72 at 3 months FU.

CONCLUSIONS
Temporal consistency across this early evaluation of the model predictions show promise of image-driven MM for HGG response forecasting to guide timely personalized assessment and adjustment of treatment.