bluuebunny/crossref_metadata_2025_split huggingface dataset:
P09.03.B ASSESSMENT OF RESPONSE TO LOMUSTINE-TEMOZOLOMIDE CHEMOTHERAPY IN ADDITION TO RADIOTHERAPY IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA USING FET PET
Authors: Werner J , Wollring M M , Tscherpel C , Rosen E K , Ceccon G , Stetter I , Lohmann P , Weller J , Stoffels G , Baues C , Celik E , Mottaghy F M , Goldbrunner R , Herrlinger U , Fink G R , Langen K , Galldiks N 
Published: October 2024

Abstract

BACKGROUND
We examined the value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for early response assessment in patients with newly diagnosed glioblastoma and methylated O6-methylguanine-DNA methyltransferase promoter treated with lomustine-temozolomide chemotherapy in addition to radiotherapy.

MATERIAL AND METHODS
Thirty-one patients (29-74 years) were treated according to the CeTeG/NOA-09 phase-3 trial. FET PET and anatomical MRI were performed at baseline and follow-up after the third cycle. We obtained mean and maximum tumor-to-brain ratios (TBR) and metabolic tumor volumes (MTV). Thresholds derived from FET PET were defined using receiver operating characteristic analyses to predict progression-free survival (PFS) of ≥12 months. The predictive value of FET PET parameters was subsequently evaluated using univariate and multivariate survival estimates. MRI response assessment was based on the RANO 1.0 criteria. FET PET data were also used to estimate response according to the PET RANO 1.0 criteria.

RESULTS
Patients received a median number of 6 cycles of lomustine-temozolomide chemotherapy (3-6 cycles). After treatment initiation, the median follow-up was 17.2 months (7.2-59.4 months). As of April 2024, we confirmed tumor progression according to the RANO 1.0 criteria in 24 patients (77%). After three cycles of lomustine-temozolomide chemotherapy, a reduced mean TBR value by ≥10% predicted a significantly longer PFS (31.0 vs. 10.4 months; P=0.039). In contrast, changes in maximum TBR values, MTV, the RANO 1.0 criteria, and the PET RANO 1.0 criteria were not predictive for response (P&gt;0.05). Multivariate survival analysis revealed that changes in mean TBR values predicted a significantly prolonged PFS independent of age, the extent of resection, and the Karnofsky Performance Status (P=0.036; HR, 3.892; 95% CI, 1.242-17.230).

CONCLUSION
Our results suggest that FET PET is clinically valuable for identifying responders to lomustine-temozolomide chemotherapy in addition to radiotherapy early after treatment initiation. The evaluation of FET PET’s value in predicting a significantly longer overall survival is ongoing.
P09.04.A BASELINE MR PERFUSION IMAGING OF BRAIN METASTASES PRIOR TO SRS (STARBEAM-X STUDY)
Authors: Dobeson C B , Lewis J , Bhatnagar P , Birkbeck M , West S 
Published: October 2024

Abstract

BACKGROUND
Following treatment with SRS, radionecrosis and tumour progression often have similar appearances on conventional MR imaging, yet have different management approaches. The STARBEAM-X study (Study of assessment of radionecrosis in brain metastases using MR perfusion imaging) has been designed with the aim of assessing the clinical application of baseline MR perfusion imaging prior to SRS as part of the radiotherapy planning process, with the hypothesis that baseline perfusion imaging will aid post-treatment imaging interpretation by enhancing our understanding of how a metastasis’ perfusion characteristics change following treatment with SRS. In this report, we present the participants’ imaging data as proof of feasibility of baseline MR perfusion imaging.

MATERIAL AND METHODS
Participants underwent baseline MR perfusion (DSC) imaging on Siemens Magnetom Sola 1.5T scanner as part of their radiotherapy planning imaging. Relative cerebral blood volume (rCBV) perfusion maps were calculated in Syngo.Via software and interpretation performed by an expert neuroradiologist.

RESULTS
39 patients underwent baseline MR perfusion - 25 (64.1%) were female, 14 (35.9%) were male. The composition of primary tumour sites were as follows: non-small cell lung cancer (NSCLC) - 14 (35.9%), breast - 10 (25.6%), renal - 5 (12.8%), melanoma - 6 (15.4%), ovary - 2 (5.1%), oesophagus - 1 (2.6%), colorectal - 1 (2.6%). Median age on the date of the MRI scan was 65.71 years (range 35.7 - 81.5 years, SD=11.57). 76 metastatic lesions were assessed in total. 14 (35.9%) patients were receiving systemic treatment at the time of MR perfusion scan. Of the 76 lesions treated, 51 (67.1%) were deemed assessable on perfusion imaging. Of the 51 assessable lesions - median volume was 0.821cm3 (mean 2.49cm3, range 0.19 - 12.75cm3, SD 3.09). Median rCBV ratio across all lesions was 3.24 (range 0.68 - 23.3, SD = 4.16). Median rCBV stratified to tumour site - NSCLC 2.845, breast 4.96, renal 5.88, ovary 4.95, melanoma 5.445 and colorectal 3.39. 24 lesions were not suitable for calculation of perfusion values - median volume was 0.069cm3 (mean 0.128cm3, range 0.011cm3 - 1.027cm3, SD 0.202). In 22 (91.7%) the documented reason for this was due to the peripheral nature of the lesion and overlap with normal brain vasculature.

CONCLUSION
At least two thirds of metastatic brain lesions are suitable for assessment with baseline perfusion MR imaging - factors that impact on suitability are size, peripheral location and proximity to vessels. Participant numbers in this initial study are too low to assess associations between lesional perfusion characteristics and primary tumour site. In individual participants with multiple brain metastases, there was wide variability in perfusion characteristics across the different lesions.
P09.07.A EMERGING TRENDS IN NEURO-ONCOLOGY: THE RECENT ADVANCES OF AMIDE PROTON TRANSFER WEIGHTED (APTW) AND ARTERIAL SPIN LABELLING (ASL) IN BRAIN TUMOUR IMAGING
Authors: Shah M , Awuah W , Sanker V , Tan J , Aderinto N , Ahluwalia A , Poornaselvan J , Ohenewaa Tenkorang P , Darko K , Abdul-Rahman T , Atallah O , Duvuru S 
Published: October 2024

Abstract

BACKGROUND
Accurate diagnosis of brain tumours is crucial for improving patient outcomes. Current diagnostic methods such as MRI and CT scans have limitations in differentiating tumour types and defining margins. Recent advances in radiological modalities, specifically Amide Proton Transfer Weighted (APTw) and Arterial Spin Labelling (ASL) imaging, offer promising avenues to overcome these challenges.

MATERIAL AND METHODS
A systematic review of papers published between 2000 and 2024 that met the inclusion criteria was undertaken using several databases, including PubMed, EMBASE, Google Scholar, the Cochrane Library, and Scopus. Key terms such as “amide proton transfer weighted,” “arterial spin labelling,” “neuro-oncology,” and “brain tumours” were consistently employed.

RESULTS
APTw and ASL techniques play pivotal roles in brain tumour diagnostics, offering unique insights into molecular composition and vascular perfusion patterns, respectively. APTw facilitates differentiation between tumour subtypes, providing crucial data on aggressiveness and therapy response, while ASL evaluates tumour vascularisation, aiding therapeutic decisions, especially for anti-angiogenic treatments. These non-invasive methods enhance patient safety and comfort compared to invasive procedures like biopsy. ASL’s efficacy in distinguishing radiation necrosis from recurrent cancer, with a sensitivity of 94%, surpasses that of FDG PET and DSC. Furthermore, APTw-CEST imaging shows promise in evaluating treatment effects and delineating tumour borders, particularly in distinguishing gliomas from meningiomas. The combination of APTw and ASL enhances diagnostic accuracy, especially in differentiating tumour types or assessing treatment response, with the “APTw-ASL mismatch” emerging as a novel biomarker for specific tumour types like pilocytic astrocytomas. However, challenges such as technical complexities and limited accessibility exist but can be addressed through standardisation and technological advancements.

CONCLUSION
The clinical role of APTw and ASL imaging extends beyond diagnosis, aiding in intraoperative monitoring, complication detection, and seizure prediction. Collaborative efforts are needed to refine these techniques and overcome technical challenges, ensuring their wider clinical application and reliability. Despite challenges, their integration into clinical practice could revolutionise neuro-oncology by providing valuable insights into tumour biology and guiding personalised treatment strategies. Further research and standardisation efforts are essential to maximise the clinical utility of these advanced imaging techniques.
P09.13.A METABOLIC ACTIVITY OF PROTON RADIATION THERAPY RELATED EFFECTS IN GLIOMAS MEASURED WITH [18F]FET PET IMAGING
Authors: Madsen K , Muhic A , Scheie D , Rasmussen R , Carlsen J , Vestergaard A , Henriksen O , Law I 
Published: October 2024

Abstract

BACKGROUND
Proton radiation therapy (PRT) has since 2019 become a treatment option alongside photon therapy in Denmark. PRT reduces doses to normal brain structures and for gliomas PRT has been considered in oligodendroglioma WHO grade 2-3 (O2-3) and astrocytoma IDH mutated WHO grade 2-4 (A2-4). Concerns have been raised regarding the frequency of treatment-related effects. The aim of this study is to investigate the association between pathology results and metabolic activity when progression is suspected on MRI after PRT in glioma.

MATERIAL AND METHODS
Forty-nine patients from Rigshospitalet, Copenhagen, Denmark suffering from glioma were treated with PRT until October 2022 (mean age 41 years, range 23-65), and followed for a minimum 1.5 years until April 2024. Progression was suspected in 20 patients clinically and/or at MRI, of which 13 patients fulfilled the criteria of the study: a [18F]FET PET scan followed by first subsequent pathology assessment after PRT (biopsy n=8 and resection n=5, mean delay after PET imaging 18 days (range 1-50 days), mean interval after PRT 1.6 years (range 0.3-3.3 years). The initial pathology of gliomas were O3 n=5, A2 n=1, A3 n=5, A4 n=2. PET scans were performed 20 min after injection of approximately 200 MBq [18F]FET. The maximal metabolic activity relatively to healthy appearing cortex (TBRmax) was calculated for the suspicious lesion.

RESULTS
Tumor relapse was found in four patients (subsequent pathology A3 n=1, A4 n=3) with corresponding TBRmax of mean 3.1 (range 2.3-4.3). Pathology showed only treatment related effects in eight patients (initial pathology O3 n=5, A3 n=2 A4 n=1), with corresponding TBRmax of mean 1.8 (range 1.6-2.3), though one of these patients died 2.3 years later from tumor relapse. Mixed reactive changes and A2 tumor were found in the resected tissue of one patient with corresponding TBRmax of 1.6. Thus, the metabolic activity was significantly higher in tumor tissue compared to treatment-related effects using Students t-test, p=0.02 when classifying mixed as relapse (p= 0.002 when excluding mixed), and ROC AUC was 0.80 providing sens/spec of 80%/88% at an optimal TBRmax cut-off at 2.2.

CONCLUSION
The prevalence of treatment-related effects verified by histopathology was 16% in the patients treated with PRT during at least 1.5 year follow-up. A significantly lower metabolic activity was found in the treatment-related effects compared to tumor relapse measured with [18F]FET PET imaging. Indicating that [18F]FET PET imaging may be a valuable diagnostic tool to discriminate between relapse and treatment-related effects at an early stage after PRT. However, in our experience, reactive changes often expand over time and the metabolic intensity might accordingly increase later in the progress after PRT. For investigating this, systematic repeated [18F]FET PET imaging is needed.
P09.15.A SATURATION TRANSFER MRI ENABLES EARLY DETECTION OF CANCER LESIONS IN THE CNS
Authors: Lehman N , Chudzik A , Lam W W , Rola R , Stanisz G J 
Published: October 2024

Abstract

BACKGROUND
Chemical exchange saturation transfer (CEST) MRI provides chemical-group-specificity (e.g., amide, guanidinium, and aliphatic) to exchangeable protons in dissolved proteins. Magnetization transfer (MT) MRI is sensitive to water bound to macromolecules, e.g., myelin and collagen. The major objective of the study was to identify the chemical exchange saturation transfer- and magnetization transfer-derived magnetic resonance images that have high contrast between orthotopic U87 tumours and normal-appearing brain.

MATERIAL AND METHODS
U-87 MG glioblastoma cell line-derived xenografts were implanted in RNU nude rats (n=9). Animals were divided into two groups based on the scanning schedule. The rats were imaged using 7 tesla scanner (PharmaScan; Bruker BioSpin) at three time points for all animals: 3-5, 7-9, and 11-13 days after implantation. Single-slice saturation-transfer-weighted CEST (saturation amplitude: 0.5 and 2 µT) and MT (3 and 5 µT) contrast Z-spectra and T1 and T2 maps were acquired. The MT Z-spectra and T1 map were fitted to a two-pool quantitative MT model to estimate the T2 of the free and macromolecular-bound water molecules, the relative macromolecular pool size (M0, MT), and the magnetization exchange rate from the macromolecular pool to the free pool.

RESULTS
Statistically significant differences, by pairwise Wilcoxon signed-rank tests with Holm-Bonferroni adjustment, were found. The differences between enhancing lesion and contralateral cortex for the magnetization transfer ratio with 2 µT saturation at 3.6 ppm frequency offset (corresponding to the amide chemical group) and M0, MT are both strongly significant (p &lt; 0.001) at all time points.

CONCLUSION
MT and CEST are a promising method of identifying the earliest time points of glioblastoma development. Such an early image biomarker of neoplasia may allow for improved treatment outcomes leading to reduced recurrence events.
P10.02.A A NOVEL APPROACH CONCERNING MEDICAL EDUCATION ON AI AND ML THROUGH THE ASNR MICCAI BRATS 2023 BRAIN METASTASES CHALLENGE
Authors: Janas A , Gkampenis A , Yordanov N , Fehringer P , Krantchev K , Moawad A W , Baid U , Link K E , Velichko Y S , LaBella D , Kofler F , Calabrese E , Shafique U , Conte G , Saluja R , Tahon N H , Maleki N , Abayazeed A , Aneja S , Nada A , Li H , Foltyn M , Bakas S , Rudie J ,   BraTS-METS Challenge 2023 Group, Aboian M 
Published: October 2024

Abstract

BACKGROUND
Artificial intelligence (AI) and machine learning (ML) are in the process of being integrated into modern healthcare, especially in imaging data-dependent fields like Radiology. Unfortunately, data shows that the acquaintance of physicians and medical students with AI is far from sufficient. Therefore, we implemented an educational chapter within the ASNR MICCAI BraTS 2023 Brain Metastases Challenge. Its purpose is to educate medical students on the basics of AI and ML application in the diagnostic imaging and apply deliberate learning approach to teach biology and physics of MRI imaging of brain metastases.

MATERIAL AND METHODS
Pilot group of 15 medical students from EU was expanded to 150, to include radiology residents and research associates. Fundamentals of MR imaging and process of lesion segmentation was taught through lectures and written guides. Trainees were involved in refining preprocessed cases to create ground truth segmentations for AI algorithm training within BraTS-METS Challenge. The annotators received individual support through question and answer sessions and expert reviews. Lectures specifically targeted to AI, ML, ethics, and Open Science introduced trainees to field experts. Specific skills related to building and publishing databases suitable for ML algorithm training, including all necessary preprocessing steps such as quality control, skull stripping, and normalization, were developed throughout the training process. Alternative methods for education, including #BraTSBrainMetsTeachingPearls Twitter series were also implemented which offered in-depth guidance on interpreting the imaging features of brain metastases and the ability to interact with imaging content.

RESULTS
Participants reported an enhanced understanding of neuroradiology, particularly in identifying imaging features of brain metastases. Their gained proficiency in ML/AI empowers them to efficiently assess existing models or replicate the workflow of model training. The early clinical exposure and extensive ML/AI lectures were highly valued, providing deeper insights into the applications and limitations of these technologies. The educational strategy combining constructivist principles with practical experience proved highly beneficial, equipping them with skills relevant to both research and clinical practice.

CONCLUSION
Insufficient integration of AI and ML in medical education necessitates effective and early training. Adopting a constructivist approach has equipped students with vital skills in Open Science, database development, and model training, preparing them to fully utilize these technologies while understanding their limitations.
P10.04.A A STRATEGY FOR MULTIMODAL INTEGRATION OF TRANSCRIPTOMICS, PROTEOMICS, AND RADIOMICS DATA FOR THE PREDICTION OF RECURRENCE IN PATIENTS WITH IDH-MUTANT GLIOMAS
Authors: Chouleur T , Etchegaray C , Villain L , Saut O , Bikfalvi A ,   GliomaPRD Consortium
Published: October 2024

Abstract

BACKGROUND
Isocitrate dehydrogenase-mutant gliomas are lethal brain cancers that impair quality of life in young adults. Although less aggressive than glioblastomas, IDH-mutant gliomas invariably progress to incurable disease with unpredictable recurrence. A better classification of patient risk of recurrence is needed.

MATERIAL AND METHODS
We describe a multimodal analytical pipeline integrating imaging, transcriptomic, and proteomic profiles using machine learning.

RESULTS
1) description of the biological characteristics of IDH-mutant glioma subtypes categorized by positron emission tomography (PET) and histology, 2) integration of positron emission tomography (PET) metrics in the classification of IDH-mutant gliomas, and 3) improvement of patient stratification with novel signatures of patient risk of recurrence based on gene expression, protein level, and imaging.

CONCLUSION
We provide herein a useful road-map for the stratification of IDH-mutant glioma patients and their risk of recurrence, which will lead to better monitoring of the clinical evolution of the disease.
P10.08.A AUTOMATED LONGITUDINAL TREATMENT RESPONSE ASSESSMENT OF BRAIN TUMORS: A SYSTEMATIC REVIEW
Authors: Shi T , Linares C , Vercauteren T , Booth T 
Published: October 2024

Abstract

BACKGROUND
Longitudinal assessment of tumor burden using imaging helps to determine whether there has been a response to treatment both in trial and real-world settings. From a clinical trial perspective, the time to develop disease progression (progression free survival) is an important endpoint. However, longitudinal response assessment is time-consuming and subject to interobserver variability. Automated response assessment techniques based on machine learning (ML) promise to enhance accuracy and reduce reliance on expert manual measurement. This systematic review evaluates the quality and performance accuracy of recently published studies.

MATERIAL AND METHODS
Following PRISMA guidelines and the CLAIM checklist, we searched PUBMED, EMBASE, and Web of Science for articles from January 2010 to November 2023. Our PROSPERO-registered study (CRD42024496126) focused on adult brain tumor treatment response assessment studies using ML methodologies. We determined the extent of development and validation of the automated tools and employed QUADAS-2 for study appraisal.

RESULTS
Eighteen studies, including fifteen retrospective and three prospective studies, were included. Data extracted from each study included information on the dataset, automated steps within the pipeline, automated models (referred to as index tests), and reference standards. Only limited conclusions are appropriate given the high bias risk and applicability concerns (particularly in relation to reference standards and patient selection), and the low level of evidence.

CONCLUSION
The study highlights ML’s potential in improving brain tumor longitudinal treatment response assessment. Interpretation is limited due to study bias and limited evidence of generalizability. Prospective studies with external datasets validating the latest neuro-oncology criteria are now required.
P10.12.B DEEP LEARNING-BASED NORMALIZATION AND UNMIXING OF HYPERSPECTRAL IMAGES FOR BRAIN TUMOR SURGERY
Authors: Black D , Gill J , Xie A , Liquet B , Di Ieva A , Stummer W , Suero Molina E 
Published: October 2024

Abstract

BACKGROUND
Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables better visualization of differences between tissues. This can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified models that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. To overcome these challenges, measured spectra are normalized to account for artifacts and spectrally unmixed to isolate the protoporphyrin IX (PpIX) signal, which indicates malignant tissue. Existing methods are simplistic and based on phantoms and are unable to account for nonlinear effects such as multiple scattering or the inhomogeneous optical properties of the tissue. We propose a deep learning (DL)-based pipeline encompassing normalization and unmixing, which can fully account for the nonlinear effects and produce more accurate distributions of fluorophore abundances.

MATERIAL AND METHODS
This study proposes two deep learning models for correction and unmixing, which can capture these effects. Both models use autoencoder-like architectures. While one is trained with protoporphyrin IX (PpIX) concentration labels, the other is semi-supervised, learning hyperspectral unmixing self-supervised and then learning to correct the spectra for optical and geometric effects using a white-light reflectance spectrum in a few-shot manner. The models were evaluated on phantom and pig-brain data with known PpIX concentration.

RESULTS
The supervised and semi-supervised models achieved Pearson correlation coefficients (phantom, pig-brain) between known and computed PpIX concentrations of (0.997, 0.990) and (0.98, 0.91), respectively, whereas the classical approach achieved (0.93, 0.82). On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous.

CONCLUSION
These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery.