Cancers

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13 pages, 1791 KiB

Open AccessArticle

Multimodal Intraoperative Image-Driven Surgery for Skull Base Chordomas and Chondrosarcomas

by Walid I. Essayed, Parikshit Juvekar, Joshua D. Bernstock, Marcio S. Rassi, Kaith Almefty, Amir Arsalan Zamani, Alexandra J. Golby and Ossama Al-Mefty

Cancers 2022, 14(4), 966; https://doi.org/10.3390/cancers14040966 - 15 Feb 2022

Cited by 1 | Viewed by 2290

Abstract

Given the difficulty and importance of achieving maximal resection in chordomas and chondrosarcomas, all available tools offered by modern neurosurgery are to be deployed for planning and resection of these complex lesions. As demonstrated by the review of our series of skull base [...] Read more.

Given the difficulty and importance of achieving maximal resection in chordomas and chondrosarcomas, all available tools offered by modern neurosurgery are to be deployed for planning and resection of these complex lesions. As demonstrated by the review of our series of skull base chordoma and chondrosarcoma resections in the Advanced Multimodality Image-Guided Operating (AMIGO) suite, as well as by the recently published literature, we describe the use of advanced multimodality intraoperative imaging and neuronavigation as pivotal to successful radical resection of these skull base lesions while preventing and managing eventual complications. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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13 pages, 1925 KiB

Open AccessArticle

TMS Seeded Diffusion Tensor Imaging Tractography Predicts Permanent Neurological Deficits

by Matthew Muir, Sarah Prinsloo, Hayley Michener, Jeffrey I. Traylor, Rajan Patel, Ron Gadot, Dhiego Chaves de Almeida Bastos, Vinodh A. Kumar, Sherise Ferguson and Sujit S. Prabhu

Cancers 2022, 14(2), 340; https://doi.org/10.3390/cancers14020340 - 11 Jan 2022

Cited by 7 | Viewed by 2291

Abstract

Surgeons must optimize the onco-functional balance by maximizing the extent of resection and minimizing postoperative neurological morbidity. Optimal patient selection and surgical planning requires preoperative identification of nonresectable structures. Transcranial magnetic stimulation is a method of noninvasively mapping the cortical representations of the [...] Read more.

Surgeons must optimize the onco-functional balance by maximizing the extent of resection and minimizing postoperative neurological morbidity. Optimal patient selection and surgical planning requires preoperative identification of nonresectable structures. Transcranial magnetic stimulation is a method of noninvasively mapping the cortical representations of the speech and motor systems. Despite recent promising data, its clinical relevance and appropriate role in a comprehensive mapping approach remains unknown. In this study, we aim to provide direct evidence regarding the clinical utility of transcranial magnetic stimulation by interrogating the eloquence of TMS points. Forty-two glioma patients were included in this retrospective study. We collected motor function outcomes 3 months postoperatively. We overlayed the postoperative MRI onto the preoperative MRI to visualize preoperative TMS points in the context of the surgical cavity. We then generated diffusion tensor imaging tractography to identify meaningful subsets of TMS points. We correlated the resection of preoperative imaging features with clinical outcomes. The resection of TMS-positive points was significantly predictive of permanent deficits (p = 0.05). However, four out of eight patients had TMS-positive points resected without a permanent deficit. DTI tractography at a 75% FA threshold identified which TMS points are essential and which are amenable to surgical resection. TMS combined with DTI tractography shows a significant prediction of postoperative neurological deficits with both a high positive predictive value and negative predictive value. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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19 pages, 1109 KiB

Open AccessArticle

Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours

by Hamza Chegraoui, Cathy Philippe, Volodia Dangouloff-Ros, Antoine Grigis, Raphael Calmon, Nathalie Boddaert, Frédérique Frouin, Jacques Grill and Vincent Frouin

Cancers 2021, 13(23), 6113; https://doi.org/10.3390/cancers13236113 - 4 Dec 2021

Cited by 13 | Viewed by 3457

Abstract

Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, [...] Read more.

Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, a high performing network heavily trained on a given tumour type may perform poorly on a rare tumour type for which no labelled cases allows training or transfer learning. Yet, because some visual similarities exist nevertheless between common and rare tumours, in the lesion and around it, one may split the problem into two steps: object detection and segmentation. For each step, trained networks on common lesions could be used on rare ones following a domain adaptation scheme without extra fine-tuning. This work proposes a resilient tumour lesion delineation strategy, based on the combination of established elementary networks that achieve detection and segmentation. Our strategy allowed us to achieve robust segmentation inference on a rare tumour located in an unseen tumour context region during training. As an example of a rare tumour, Diffuse Intrinsic Pontine Glioma (DIPG), we achieve an average dice score of 0.62 without further training or network architecture adaptation. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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16 pages, 1789 KiB

Open AccessArticle

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

by Risa K. Kawaguchi, Masamichi Takahashi, Mototaka Miyake, Manabu Kinoshita, Satoshi Takahashi, Koichi Ichimura, Ryuji Hamamoto, Yoshitaka Narita and Jun Sese

Cancers 2021, 13(14), 3611; https://doi.org/10.3390/cancers13143611 - 19 Jul 2021

Cited by 14 | Viewed by 3664

Abstract

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, [...] Read more.

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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13 pages, 2863 KiB

Open AccessArticle

¹⁸F-FET PET Uptake Characteristics of Long-Term IDH-Wildtype Diffuse Glioma Survivors

by Lena M. Mittlmeier, Bogdana Suchorska, Viktoria Ruf, Adrien Holzgreve, Matthias Brendel, Jochen Herms, Peter Bartenstein, Joerg C. Tonn, Marcus Unterrainer and Nathalie L. Albert

Cancers 2021, 13(13), 3163; https://doi.org/10.3390/cancers13133163 - 24 Jun 2021

Cited by 6 | Viewed by 2832

Abstract

Background: IDHwt diffuse gliomas represent the tumor entity with one of the worst clinical outcomes. Only rare cases present with a long-term survival of several years. Here we aimed at comparing the uptake characteristics on dynamic ¹⁸F-FET PET, clinical and molecular genetic [...] Read more.

Background: IDHwt diffuse gliomas represent the tumor entity with one of the worst clinical outcomes. Only rare cases present with a long-term survival of several years. Here we aimed at comparing the uptake characteristics on dynamic ¹⁸F-FET PET, clinical and molecular genetic parameters of long-term survivors (LTS) versus short-term survivors (STS): Methods: Patients with de-novo IDHwt glioma (WHO grade III/IV) and ¹⁸F-FET PET prior to any therapy were stratified into LTS (≥36 months survival) and STS (≤15 months survival). Static and dynamic ¹⁸F-FET PET parameters (mean/maximal tumor-to-background ratio (TBR_mean/max), biological tumor volume (BTV), minimal time-to-peak (TTP_min)), diameter and volume of contrast-enhancement on MRI, clinical parameters (age, sex, Karnofksy-performance-score), mode of surgery; initial treatment and molecular genetics were assessed and compared between LTS and STS. Results: Overall, 75 IDHwt glioma patients were included (26 LTS, 49 STS). LTS were significantly younger (p < 0.001), had a higher rate of WHO grade III glioma (p = 0.032), of O(6)-Methylguanine-DNA methyltransferase (MGMT) promoter methylation (p < 0.001) and missing Telomerase reverse transcriptase promoter (TERTp) mutations (p = 0.004) compared to STS. On imaging, LTS showed a smaller median BTV (p = 0.017) and a significantly longer TTP_min (p = 0.008) on ¹⁸F-FET PET than STS, while uptake intensity (TBR_mean/max) did not differ. In contrast to the tumor-volume on PET, MRI-derived parameters such as tumor size as well as all other above-mentioned parameters did not differ between LTS and STS (p > 0.05 each). Conclusion: Besides molecular genetic prognosticators, a long survival time in IDHwt glioma patients is associated with a longer TTP_min as well as a smaller BTV on ¹⁸F-FET PET at initial diagnosis. ¹⁸F-FET uptake intensity as well as the MRI-derived tumor size (volume and maximal diameter) do not differ in patients with long-term survival. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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11 pages, 2484 KiB

Open AccessArticle

Distinct Cerebrovascular Reactivity Patterns for Brain Radiation Necrosis

by Giovanni Muscas, Christiaan Hendrik Bas van Niftrik, Martina Sebök, Alessandro Della Puppa, Katharina Seystahl, Nicolaus Andratschke, Michelle Brown, Michael Weller, Luca Regli, Marco Piccirelli and Jorn Fierstra

Cancers 2021, 13(8), 1840; https://doi.org/10.3390/cancers13081840 - 13 Apr 2021

Cited by 5 | Viewed by 2470

Abstract

Background: Current imaging-based discrimination between radiation necrosis versus recurrent glioblastoma contrast-enhancing lesions remains imprecise but is paramount for prognostic and therapeutic evaluation. We examined whether patients with radiation necrosis exhibit distinct patterns of blood oxygenation-level dependent fMRI cerebrovascular reactivity (BOLD-CVR) as the first [...] Read more.

Background: Current imaging-based discrimination between radiation necrosis versus recurrent glioblastoma contrast-enhancing lesions remains imprecise but is paramount for prognostic and therapeutic evaluation. We examined whether patients with radiation necrosis exhibit distinct patterns of blood oxygenation-level dependent fMRI cerebrovascular reactivity (BOLD-CVR) as the first step to better distinguishing patients with radiation necrosis from recurrent glioblastoma compared with patients with newly diagnosed glioblastoma before surgery and radiotherapy. Methods: Eight consecutive patients with primary and secondary brain tumors and a multidisciplinary clinical and radiological diagnosis of radiation necrosis, and fourteen patients with a first diagnosis of glioblastoma underwent BOLD-CVR mapping. For all these patients, the contrast-enhancing lesion was derived from high-resolution T1-weighted MRI and rendered the volume-of-interest (VOI). From this primary VOI, additional 3 mm concentric expanding VOIs up to 30 mm were created for a detailed perilesional BOLD-CVR tissue analysis between the two groups. Receiver operating characteristic curves assessed the discriminative properties of BOLD-CVR for both groups. Results: Mean intralesional BOLD-CVR values were markedly lower in radiation necrosis than in glioblastoma contrast-enhancing lesions (0.001 ± 0.06 vs. 0.057 ± 0.05; p = 0.04). Perilesionally, a characteristic BOLD-CVR pattern was observed for radiation necrosis and glioblastoma patients, with an improvement of BOLD-CVR values in the radiation necrosis group and persisting lower perilesional BOLD-CVR values in glioblastoma patients. The ROC analysis discriminated against both groups when these two parameters were analyzed together (area under the curve: 0.85, 95% CI: 0.65–1.00). Conclusions: In this preliminary analysis, distinctive intralesional and perilesional BOLD-cerebrovascular reactivity patterns are found for radiation necrosis. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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15 pages, 2557 KiB

Open AccessArticle

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

by Satoshi Takahashi, Masamichi Takahashi, Manabu Kinoshita, Mototaka Miyake, Risa Kawaguchi, Naoki Shinojima, Akitake Mukasa, Kuniaki Saito, Motoo Nagane, Ryohei Otani, Fumi Higuchi, Shota Tanaka, Nobuhiro Hata, Kaoru Tamura, Kensuke Tateishi, Ryo Nishikawa, Hideyuki Arita, Masahiro Nonaka, Takehiro Uda, Junya Fukai, Yoshiko Okita, Naohiro Tsuyuguchi, Yonehiro Kanemura, Kazuma Kobayashi, Jun Sese, Koichi Ichimura, Yoshitaka Narita and Ryuji Hamamoto Show full author list Hide full author list

Cancers 2021, 13(6), 1415; https://doi.org/10.3390/cancers13061415 - 19 Mar 2021

Cited by 32 | Viewed by 4704

Abstract

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the [...] Read more.

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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12 pages, 2184 KiB

Open AccessArticle

[¹⁸F]FET PET Uptake Indicates High Tumor and Low Necrosis Content in Brain Metastasis

by Hanno S. Meyer, Friederike Liesche-Starnecker, Mona Mustafa, Igor Yakushev, Benedikt Wiestler, Bernhard Meyer and Jens Gempt

Cancers 2021, 13(2), 355; https://doi.org/10.3390/cancers13020355 - 19 Jan 2021

Cited by 6 | Viewed by 3270

Abstract

Amino acid positron emission tomography (PET) has been employed in the management of brain metastases. Yet, histopathological correlates of PET findings remain poorly understood. We investigated the relationship of O-(2-[¹⁸F]Fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET, magnetic resonance imaging (MRI), and histology in [...] Read more.

Amino acid positron emission tomography (PET) has been employed in the management of brain metastases. Yet, histopathological correlates of PET findings remain poorly understood. We investigated the relationship of O-(2-[¹⁸F]Fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET, magnetic resonance imaging (MRI), and histology in brain metastases. Fifteen patients undergoing brain metastasis resection were included prospectively. Using intraoperative navigation, 39 targeted biopsies were obtained from parts of the metastases that were either PET-positive or negative and MRI-positive or negative. Tumor and necrosis content, proliferation index, lymphocyte infiltration, and vascularization were determined histopathologically. [¹⁸F]FET PET had higher specificity than MRI (66% vs. 56%) and increased sensitivity for tumor from 73% to 93% when combined with MRI. Tumor content per sample increased with PET uptake (r_s = 0.3, p = 0.045), whereas necrosis content decreased (r_s = −0.4, p = 0.014). PET-positive samples had more tumor (median: 75%; interquartile range: 10–97%; p = 0.016) than PET-negative samples. The other investigated histological properties were not correlated with [¹⁸F]FET PET intensity. Tumors were heterogeneous at the levels of imaging and histology. [¹⁸F]FET PET can be a valuable tool in the management of brain metastases. In biopsies, one should aim for PET hotspots to increase the chance for retrieval of samples with high tumor cell concentrations. Multiple biopsies should be performed to account for intra-tumor heterogeneity. PET could be useful for differentiating treatment-related changes (e.g., radiation necrosis) from tumor recurrence. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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Review

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23 pages, 1432 KiB

Open AccessReview

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part A: Concept, Differential Diagnosis and Tumor Grading

by Lelio Guida, Vittorio Stumpo, Jacopo Bellomo, Christiaan Hendrik Bas van Niftrik, Martina Sebök, Moncef Berhouma, Andrea Bink, Michael Weller, Zsolt Kulcsar, Luca Regli and Jorn Fierstra

Cancers 2022, 14(6), 1432; https://doi.org/10.3390/cancers14061432 - 10 Mar 2022

Cited by 13 | Viewed by 2985

Abstract

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment—glioblastomas, in particular, have a dismal prognosis and are currently incurable—their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of [...] Read more.

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment—glioblastomas, in particular, have a dismal prognosis and are currently incurable—their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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21 pages, 1064 KiB

Open AccessReview

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

by Vittorio Stumpo, Lelio Guida, Jacopo Bellomo, Christiaan Hendrik Bas Van Niftrik, Martina Sebök, Moncef Berhouma, Andrea Bink, Michael Weller, Zsolt Kulcsar, Luca Regli and Jorn Fierstra

Cancers 2022, 14(5), 1342; https://doi.org/10.3390/cancers14051342 - 5 Mar 2022

Cited by 8 | Viewed by 3746

Abstract

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to [...] Read more.

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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31 pages, 4547 KiB

Open AccessReview

Diagnosis of Glioblastoma by Immuno-Positron Emission Tomography

by Eduardo Ruiz-López, Juan Calatayud-Pérez, Irene Castells-Yus, María José Gimeno-Peribáñez, Noelia Mendoza-Calvo, Miguel Ángel Morcillo and Alberto J. Schuhmacher

Cancers 2022, 14(1), 74; https://doi.org/10.3390/cancers14010074 - 24 Dec 2021

Cited by 16 | Viewed by 6130

Abstract

Neuroimaging has transformed neuro-oncology and the way that glioblastoma is diagnosed and treated. Magnetic Resonance Imaging (MRI) is the most widely used non-invasive technique in the primary diagnosis of glioblastoma. Although MRI provides very powerful anatomical information, it has proven to be of [...] Read more.

Neuroimaging has transformed neuro-oncology and the way that glioblastoma is diagnosed and treated. Magnetic Resonance Imaging (MRI) is the most widely used non-invasive technique in the primary diagnosis of glioblastoma. Although MRI provides very powerful anatomical information, it has proven to be of limited value for diagnosing glioblastomas in some situations. The final diagnosis requires a brain biopsy that may not depict the high intratumoral heterogeneity present in this tumor type. The revolution in “cancer-omics” is transforming the molecular classification of gliomas. However, many of the clinically relevant alterations revealed by these studies have not yet been integrated into the clinical management of patients, in part due to the lack of non-invasive biomarker-based imaging tools. An innovative option for biomarker identification in vivo is termed “immunotargeted imaging”. By merging the high target specificity of antibodies with the high spatial resolution, sensitivity, and quantitative capabilities of positron emission tomography (PET), “Immuno-PET” allows us to conduct the non-invasive diagnosis and monitoring of patients over time using antibody-based probes as an in vivo, integrated, quantifiable, 3D, full-body “immunohistochemistry” in patients. This review provides the state of the art of immuno-PET applications and future perspectives on this imaging approach for glioblastoma. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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22 pages, 4889 KiB

Open AccessReview

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

by Austin-John Fordham, Caitlin-Craft Hacherl, Neal Patel, Keri Jones, Brandon Myers, Mickey Abraham and Julian Gendreau

Cancers 2021, 13(12), 2960; https://doi.org/10.3390/cancers13122960 - 13 Jun 2021

Cited by 20 | Viewed by 11321

Abstract

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has [...] Read more.

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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12 pages, 2811 KiB

Open AccessReview

Use of PET Imaging in Neuro-Oncological Surgery

by Adrien Holzgreve, Nathalie L. Albert, Norbert Galldiks and Bogdana Suchorska

Cancers 2021, 13(9), 2093; https://doi.org/10.3390/cancers13092093 - 26 Apr 2021

Cited by 25 | Viewed by 7444

Abstract

This review provides an overview of current applications and perspectives of PET imaging in neuro-oncological surgery. The past and future of PET imaging in the management of patients with glioma and brain metastases are elucidated with an emphasis on amino acid tracers, such [...] Read more.

This review provides an overview of current applications and perspectives of PET imaging in neuro-oncological surgery. The past and future of PET imaging in the management of patients with glioma and brain metastases are elucidated with an emphasis on amino acid tracers, such as O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (¹⁸F-FET). The thematic scope includes surgical resection planning, prognostication, non-invasive prediction of molecular tumor characteristics, depiction of intratumoral heterogeneity, response assessment, differentiation between tumor progression and treatment-related changes, and emerging new tracers. Furthermore, the role of PET using specific somatostatin receptor ligands for the management of patients with meningioma is discussed. Further advances in neuro-oncological imaging can be expected from promising new techniques, such as hybrid PET/MR scanners and the implementation of artificial intelligence methods, such as radiomics. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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26 pages, 2646 KiB

Open AccessSystematic Review

Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

by Evi J. van Kempen, Max Post, Manoj Mannil, Benno Kusters, Mark ter Laan, Frederick J. A. Meijer and Dylan J. H. A. Henssen

Cancers 2021, 13(11), 2606; https://doi.org/10.3390/cancers13112606 - 26 May 2021

Cited by 17 | Viewed by 5041

Abstract

Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made [...] Read more.

Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future. Full article

(This article belongs to the Special Issue Advanced Neuroimaging Approaches for Malignant Brain Tumors)

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