Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth

Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks

This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Volumetric analysis: Rethinking brain metastases response assessment

Background

The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) criteria are the gold standard for assessing brain metastases (BMs) treatment response. However, they are limited by their reliance on 1D, despite the routine use of high-resolution T1-weighted MRI scans for BMs, which allows for 3D measurements. Our study aimed to investigate whether volumetric measurements could improve the response assessment in patients with BMs.

Methods

We retrospectively evaluated a dataset comprising 783 BMs and analyzed the response of 185 of them from 132 patients who underwent stereotactic radiotherapy between 2007 and 2021 at 5 hospitals. We used T1-weighted MRIs to compute the volume of the lesions. For the volumetric criteria, progressive disease was defined as at least a 30% increase in volume, and partial response was characterized by a 20% volume reduction.

Results

Our study showed that the proposed volumetric criteria outperformed the RANO-BM criteria in several aspects: (1) Evaluating every lesion, while RANO-BM failed to evaluate 9.2% of them. (2) Classifying response effectively in 140 lesions, compared to only 72 lesions classified by RANO-BM. (3) Identifying BM recurrences a median of 3.3 months earlier than RANO-BM criteria.

Conclusions

Our study demonstrates the superiority of volumetric criteria in improving the response assessment of BMs compared to the RANO-BM criteria. Our proposed criteria allow for evaluation of every lesion, regardless of its size or shape, better classification, and enable earlier identification of progressive disease. Volumetric criteria provide a standardized, reliable, and objective tool for assessing treatment response.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images

PURPOSE

This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.

METHODS

The publicly available training dataset provided for the 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting of 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well as ground truth manual segmentation were used as input for the model. The data were split into a training set of 1151 cases and testing set of 100 cases, with the testing set remaining constant throughout. Deep convolutional neural network segmentation models were trained using the NiftyNet platform. To test the viability of active learning in training a segmentation model, an initial reference model was trained using all 1151 training cases followed by two additional models using only 575 cases and 100 cases. The resulting predicted segmentations of these two additional models on the remaining training cases were then addended to the training dataset for additional training.

RESULTS

It was demonstrated that an active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas (0.906 reference Dice score vs 0.868 active learning Dice score) while only requiring manual annotation for 28.6% of the data.

CONCLUSION

The active learning approach when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.

CRITICAL RELEVANCE STATEMENT

Active learning concepts were applied to a deep learning-assisted segmentation of brain gliomas from MR images to assess their viability in reducing the required amount of manually annotated ground truth data in model training.

KEY POINTS

• This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model. • The active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas. • Active learning when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

BACKGROUND

Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice.

PURPOSE

To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning.

STUDY TYPE

Retrospective.

POPULATION

A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men.

FIELD STRENGTH/SEQUENCE

The 1.0 T, 1.5 T, and 3.0 T; three-dimensional, T₁ -weighted gradient-echo imaging with contrast enhancement.

ASSESSMENT

The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth.

STATISTICAL TESTS

According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis.

RESULTS

A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1 cm³ were 0.769 and 0.780 with the IVS and EVS, respectively.

DATA CONCLUSION

A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Transient Enlargement in Meningiomas Treated with Stereotactic Radiotherapy

Simple Summary

Accurate assessment of treatment efficacy is a prerequisite for the improvement in therapeutic outcomes in clinical trials. However, it is very challenging to accurately track the size of meningiomas after radiotherapy, because of their complex shapes and often slow growth. Measuring the whole tumor volume as opposed to simple diameter measurements to assess treatment efficacy, therefore, is very promising but little is known on expected volumetric changes of meningiomas following radiotherapy. Therefore, in this study, we meticulously investigated volumetric changes in meningiomas following radiotherapy incorporating volumetric measurements from 468 MRI studies and evaluated newly proposed RANO volumetric criteria in the context of radiotherapy. We found that temporary tumor enlargement after radiotherapy overall was rare but occurred significantly more frequently after high than after low single doses of radiation, represented an important differential diagnosis to tumor progression and would have skewed results in a clinical trial if not accounted for.

Abstract

To investigate the occurrence of pseudoprogression/transient enlargement in meningiomas after stereotactic radiotherapy (RT) and to evaluate recently proposed volumetric RANO meningioma criteria for response assessment in the context of RT. Sixty-nine meningiomas (benign: 90%, atypical: 10%) received stereotactic RT from January 2005–May 2018. A total of 468 MRI studies were segmented longitudinally during a median follow-up of 42.3 months. Best response and local control were evaluated according to recently proposed volumetric RANO criteria. Transient enlargement was defined as volumetric increase ≥20% followed by a subsequent regression ≥20%. The mean best volumetric response was −23% change from baseline (range, −86% to +19%). According to RANO, the best volumetric response was SD in 81% (56/69), MR in 13% (9/69) and PR in 6% (4/69). Transient enlargement occurred in only 6% (4/69) post RT but would have represented 60% (3/5) of cases with progressive disease if not accounted for. Transient enlargement was characterized by a mean maximum volumetric increase of +181% (range, +24% to +389 %) with all cases occurring in the first year post-RT (range, 4.1–10.3 months). Transient enlargement was significantly more frequent with SRS or hypofractionation than with conventional fractionation (25% vs. 2%, p = 0.015). Five-year volumetric control was 97.8% if transient enlargement was recognized but 92.9% if not accounted for. Transient enlargement/pseudoprogression in the first year following SRS and hypofractionated RT represents an important differential diagnosis, especially because of the high volumetric control achieved with stereotactic RT. Meningioma enlargement during subsequent post-RT follow-up and after conventional fractionation should raise suspicion for tumor progression.

Machine Learning-Based Radiomics in Neuro-Oncology

In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods, machine learning and artificial intelligence (AI) as tools for data processing and analysis are becoming more and more important. At the forefront of neuro-oncology and AI-research, the field of radiomics has emerged. Non-invasive assessments of quantitative radiological biomarkers mined from complex imaging characteristics across various applications are used to predict survival, discriminate between primary and secondary tumors, as well as between progression and pseudo-progression. In particular, the application of molecular phenotyping, envisioned in the field of radiogenomics, has gained popularity for both primary and secondary brain tumors. Although promising results have been obtained thus far, the lack of workflow standardization and availability of multicenter data remains challenging. The objective of this review is to provide an overview of novel applications of machine learning- and deep learning-based radiomics in primary and secondary brain tumors and their implications for future research in the field.

Emergence of teleradiology, PACS, and other radiology IT solutions in Acta Radiologica

For this historical review, we searched a database containing all the articles published in Acta Radiologica during its 100-year history to find those on the use of information technology (IT) in radiology. After reading the full texts, we selected the presented articles according to major radiology IT domains such as teleradiology, picture archiving and communication systems, image processing, image analysis, and computer-aided diagnostics in order to describe the development as it appeared in the journal. Publications generally follow IT megatrends, but because the contents of Acta Radiologica are mainly clinically oriented, some technology achievements appear later than they do in journals discussing mainly imaging informatics topics.

Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

Objective

To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas.

Methods

The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in K^trans was assessed by Fleiss’ kappa (κ).

Results

There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for K^trans, k_ep and v_e and ICC 0.70 for v_p. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in K^trans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement.

Discussion

AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Volumetric Regression in Brain Metastases After Stereotactic Radiotherapy: Time Course, Predictors, and Significance

Background

There is insufficient understanding of the natural course of volumetric regression in brain metastases after stereotactic radiotherapy (SRT) and optimal volumetric criteria for the assessment of response and progression in radiotherapy clinical trials for brain metastases are currently unknown.

Methods

Volumetric analysis via whole-tumor segmentation in contrast-enhanced 1 mm³-isotropic T1-Mprage sequences before SRT and during follow-up. A total of 3,145 MRI studies of 419 brain metastases from 189 patients were segmented. Progression was defined using a volumetric extension of the RANO-BM criteria. A subset of 205 metastases without progression/radionecrosis during their entire follow-up of at least 3 months was used to study the natural course of volumetric regression after SRT. Predictors for volumetric regression were investigated. A second subset of 179 metastases was used to investigate the prognostic significance of volumetric response at 3 months (defined as ≥20% and ≥65% volume reduction, respectively) for subsequent local control.

Results

Median relative metastasis volume post-SRT was 66.9% at 6 weeks, 38.6% at 3 months, 17.7% at 6 months, 2.7% at 12 months and 0.0% at 24 months. Radioresistant histology and FSRT vs. SRS were associated with reduced tumor regression for all time points. In multivariate linear regression, radiosensitive histology (p=0.006) was the only significant predictor for metastasis regression at 3 months. Volumetric regression ≥20% at 3 months post-SRT was the only significant prognostic factor for subsequent control in multivariate analysis (HR 0.63, p=0.023), whereas regression ≥65% was no significant predictor.

Conclusions

Volumetric regression post-SRT does not occur at a constant rate but is most pronounced in the first 6 weeks to 3 months. Despite decreasing over time, volumetric regression continues beyond 6 months post-radiotherapy and may lead to complete resolution of controlled lesions by 24 months. Radioresistant histology is associated with slower regression. We found that a cutoff of ≥20% regression for the volumetric definition of response at 3 months post-SRT was predictive for subsequent control whereas the currently proposed definition of ≥65% was not. These results have implications for standardized volumetric criteria in future radiotherapy trials for brain metastases.

FSRT vs. SRS in Brain Metastases-Differences in Local Control and Radiation Necrosis-A Volumetric Study

Background: While the role of stereotactic radiotherapy for brain metastases is increasing, evidence on the comparative efficacy and safety of fractionated stereotactic radiotherapy (FSRT) and single-session radiosurgery (SRS) is scarce.

Methods: Longitudinal volumetric analysis was performed in a consecutive cohort of 120 patients and 190 brain metastases (>0.065 cm³ in volume / > ~5 mm in diameter) treated exclusively with FSRT (n = 98) and SRS (n = 92), respectively. A total of 972 tumor segmentations was used, averaging 5.1 time points per metastasis. Progression was defined using a volumetric extension of the RANO-BM criteria. Local control and radionecrosis were compared for lesions treated with FSRT and SRS, respectively.

Results: Metastases treated with FSRT were significantly larger at baseline (mean, 4.66 vs. 0.40 cm³, p < 0.001). Biologically effective dose (BED) for metastases (α/β = 12, linear-quadratic-cubic model) was significantly associated with local control, whereas BED for normal brain (α/β = 2, linear-quadratic model) was significantly associated with radionecrosis. Median time to local progression was 22.9 months in the FSRT group compared to 14.5 months in the SRS group (p = 0.022). Overall radionecrosis rate at 12 months was 3.4% for FSRT and 14.8% for SRS (p = 0.010). Radionecrosis °IV requiring resection with histologic proof of radiation necrosis also was significantly reduced in the FSRT group (FSRT 0.0% vs. SRS 3.9%, p = 0.041). In multivariate analysis, FSRT was associated with reduced risk of progression (HR 0.47, p = 0.015) and reduced risk of radionecrosis (HR 0.18, p = 0.045).

Conclusions: This volumetric study provides initial evidence that the improvements in therapeutic ratio expected for FSRT in larger brain metastases, might equally extend into the domain of smaller metastases, traditionally less considered for fractionated treatment. FSRT might constitute an important tool to further increase local control and reduce radionecrosis risk in stereotactic radiotherapy for brain metastases, that should be assessed in randomized intervention trials.

Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolization

Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy and the leading cause of death in patients with cirrhosis. Various treatments for HCC are available, including transarterial chemoembolization (TACE), which is the commonest intervention performed in HCC. Radiologic tumor response following TACE is an important prognostic factor for patients with HCC. We hypothesized that, for large HCC tumors, assessment of treatment response made with automated volumetric response evaluation criteria in solid tumors (RECIST) might correlate with the assessment made with the more time- and labor-intensive unidimensional modified RECIST (mRECIST) and manual volumetric RECIST (M-vRECIST) criteria. Accordingly, we undertook this retrospective study to compare automated volumetric RECIST (A-vRECIST) with M-vRECIST and mRESIST for the assessment of large HCC tumors' responses to TACE. Methods:We selected 42 pairs of contrast-enhanced computed tomography (CT) images of large HCCs. Images were taken before and after TACE, and in each of the images, the HCC was segmented using both a manual contouring tool and a convolutional neural network. Three experienced radiologists assessed tumor response to TACE using mRECIST criteria. The intra-class correlation coefficient was used to assess inter-reader reliability in the mRECIST measurements, while the Pearson correlation coefficient was used to assess correlation between the volumetric and mRECIST measurements. Results:Volumetric tumor assessment using automated and manual segmentation tools showed good correlation with mRECIST measurements. For A-vRECIST and M-vRECIST, respectively, r = 0.597 vs. 0.622 in the baseline studies; 0.648 vs. 0.748 in the follow-up studies; and 0.774 vs. 0.766 in the response assessment (P < 0.001 for all). The A-vRECIST evaluation showed high correlation with the M-vRECIST evaluation (r = 0.967, 0.937, and 0.826 in baseline studies, follow-up studies, and response assessment, respectively, P < 0.001 for all). Conclusion:Volumetric RECIST measurements are likely to provide an early marker for TACE monitoring, and automated measurements made with a convolutional neural network may be good substitutes for manual volumetric measurements.

Paragangliomas of the Head and Neck: Local Control and Functional Outcome Following Fractionated Stereotactic Radiotherapy

OBJECTIVES

To investigate local control and functional outcome following state-of-the-art fractionated stereotactic radiotherapy (FSRT) for paragangliomas of the head and neck.

METHODS

In total, 40 consecutive patients with paragangliomas of the head and neck received conventionally FSRT from 2003 to 2016 at the Department of Radiation Oncology of the University Hospital Erlangen. Local control, toxicities, and functional outcome were examined during follow-up. In total, 148 magnetic resonance imaging studies were subjected to longitudinal volumetric analysis using whole tumor segmentation in a subset of 22 patients.

RESULTS

A total of 80.0% (32/40) of patients received radiotherapy as part of their primary treatment. In 20.0% (8/40) of patients, radiation was used as salvage treatment after tumor recurrence in patients initially treated with surgery alone. The median dose applied was 54.0 Gy (interdecile range, 50.4 to 56.0 Gy) in single doses of 1.8 or 2 Gy. Local control was 100% after a median imaging follow-up of 52.2 months (range, 0.8 to 152.9 mo). The volumetric analysis confirmed sustained tumor control in a subset of 22 patients and showed transient enlargement (range, 129.6% to 151.2%) in 13.6% of cases (3/22). After a median volumetric follow-up of 24.6 months mean tumor volume had diminished to 86.1% compared with initial volume. In total, 52.5% (21/40) of patients reported improved symptoms after radiotherapy, 40% (16/40) observed no subjective change with only 7.5% (3/40) reporting significant worsening.

CONCLUSIONS

State-of-the-art FSRT provides excellent control and favorable functional outcome in patients with paragangliomas of the head and neck. The volumetric analysis provides improved evidence for sustained tumor control.

Volumetric assessment of unaffected parenchyma and Wilms' tumours: analysis of response to chemotherapy and surgery using a semi-automated segmentation algorithm in children with renal neoplasms

OBJECTIVE

To present our proof of concept with semi-automatic image recognition/segmentation technology for calculation of tumour/parenchyma volume.

METHODS

We reviewed Wilms' tumours (WTs) between 2000 and 2018, capturing computed tomography images at baseline, after neoadjuvant chemotherapy (NaC) and postoperatively. Images were uploaded into MATLAB-3-D volumetric image processing software. The program was trained by two clinicians who supervised the demarcation of tumour and parenchyma, followed by automatic recognition and delineation of tumour margins on serial imaging, and differentiation from uninvolved renal parenchyma. Volume was automatically calculated for both.

RESULTS

During the study period, 98 patients were identified. Of these, based on image quality and availability, 32 (38 affected moieties) were selected. Most patients (65%) were girls, diagnosed at age 50 ± 37 months of age. NaC was employed in 64% of patients. Surgical management included 27 radical and 11 partial nephrectomies. Automated volume assessment demonstrated objective response to NaC for unilateral and bilateral tumours (68 ± 20% and 53 ± 39%, respectively), as well as preservation on uninvolved parenchyma with partial nephrectomy (70 ± 46 cm³ at presentation to 57 ± 41 cm³ post-surgery).

CONCLUSION

Volumetric analysis is feasible and allows objective assessment of tumour and parenchyma volume in response to chemotherapy and surgery. Our data show changes after therapy that may be otherwise difficult to quantify. Use of such technology may improve surgical planning and quantification of response to treatment, as well as serving as a tool to predict renal reserve and long-term changes in renal function.

Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy

BACKGROUND

The aim of this study was to investigate changes in structural magnetic resonance imaging (MRI) according to the RANO criteria and perfusion- and permeability related metrics derived from dynamic contrast-enhanced MRI (DCE) and dynamic susceptibility contrast MRI (DSC) during radiochemotherapy for prediction of progression and survival in glioblastoma.

METHODS

Twenty-three glioblastoma patients underwent biweekly structural and perfusion MRI before, during, and two weeks after a six weeks course of radiochemotherapy. Temporal trends of tumor volume and the perfusion-derived parameters cerebral blood volume (CBV) and blood flow (CBF) from DSC and DCE, in addition to contrast agent capillary transfer constant (K^trans) from DCE, were assessed. The patients were separated in two groups by median survival and differences between the two groups explored. Clinical- and MRI metrics were investigated using univariate and multivariate survival analysis and a predictive survival index was generated.

RESULTS

Median survival was 19.2 months. A significant decrease in contrast-enhancing tumor size and CBV and CBF in both DCE- and DSC-derived parameters was seen during and two weeks past radiochemotherapy (p < 0.05). A 10%/30% increase in K^trans/CBF two weeks after finishing radiochemotherapy resulted in significant shorter survival (13.9/16.8 vs. 31.5/33.1 months; p < 0.05). Multivariate analysis revealed an index using change in K^trans and relative CBV from DSC significantly corresponding with survival time in months (r² = 0.843; p < 0.001).

CONCLUSIONS

Significant temporal changes are evident during radiochemotherapy in tumor size (after two weeks) and perfusion-weighted MRI-derived parameters (after four weeks) in glioblastoma patients. While DCE-based metrics showed most promise for early survival prediction, a multiparametric combination of both DCE- and DSC-derived metrics gave additional information.

Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases

Purpose

To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs).

Materials and Methods

This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material-enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen κ, Bland-Altman analyses, and analysis of variance.

Results

In the test cohort, for lesion size smaller than 10 mm (n = 30), 10-20 mm (n = 35), and larger than 20 mm (n = 40), the detection sensitivity of the automated method was 10%, 71%, and 85%; positive predictive value was 25%, 83%, and 94%; Dice similarity coefficient was 0.14, 0.53, and 0.68; maximum symmetric surface distance was 5.2, 6.0, and 10.4 mm; and average symmetric surface distance was 2.7, 1.7, and 2.8 mm, respectively. For manual and user-corrected segmentation, κ values were 0.42 (95% confidence interval: 0.24, 0.63) and 0.52 (95% confidence interval: 0.36, 0.72); normalized interreader agreement for lesion volume was -0.10 ± 0.07 (95% confidence interval) and -0.10 ± 0.08; and mean interaction time was 7.7 minutes ± 2.4 (standard deviation) and 4.8 minutes ± 2.1 (P < .001), respectively.

Conclusion

Automated detection and segmentation of CLM by using deep learning with convolutional neural networks, when manually corrected, improved efficiency but did not substantially change agreement on volumetric measurements.© RSNA, 2019Supplemental material is available for this article.

Frontal Keyhole Craniotomy for Resection of Low- and High-Grade Gliomas

BACKGROUND

Minimally invasive techniques are increasingly being used to access intra-axial brain lesions.

OBJECTIVE

To describe a method of resecting frontal gliomas through a keyhole craniotomy and share the results with these techniques.

METHODS

We performed a retrospective review of data obtained on all patients undergoing resection of frontal gliomas by the senior author between 2012 and 2015. We describe our technique for resecting dominant and nondominant gliomas utilizing both awake and asleep keyhole craniotomy techniques.

RESULTS

After excluding 1 patient who received a biopsy only, 48 patients were included in the study. Twenty-nine patients (60%) had not received prior surgery. Twenty-six patients (54%) were diagnosed with WHO grade II/III tumors, and 22 patients (46%) were diagnosed with glioblastoma. Twenty-five cases (52%) were performed awake. At least 90% of the tumor was resected in 35 cases (73%). Three of 43 patients with clinical follow-up experienced permanent deficits.

CONCLUSION

We provide our experience in using keyhole craniotomies for resecting frontal gliomas. Our data demonstrate the feasibility of using minimally invasive techniques to safely and aggressively treat these tumors.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

Purpose

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI).

Methods

The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour.

Results

The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively.

Conclusions

This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

BACKGROUND AND PURPOSE

Multimodality magnetic resonance imaging (MRI) can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multiparametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semiautomated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method.

METHODS

Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7-T animal scanner preradiation, and at day 1, day 4, and day 8 postradiation. Two experts performed manual and semiautomated methods to extract tumor ROIs on APTw images. Multimodality MRI signals of the tumors, including structural (T₂ and T₁ ), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively.

RESULTS

The semiautomated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the postradiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy.

CONCLUSIONS

The semiautomated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.

Virtual Raters for Reproducible and Objective Assessments in Radiology

Volumetric measurements in radiologic images are important for monitoring tumor growth and treatment response. To make these more reproducible and objective we introduce the concept of virtual raters (VRs). A virtual rater is obtained by combining knowledge of machine-learning algorithms trained with past annotations of multiple human raters with the instantaneous rating of one human expert. Thus, he is virtually guided by several experts. To evaluate the approach we perform experiments with multi-channel magnetic resonance imaging (MRI) data sets. Next to gross tumor volume (GTV) we also investigate subcategories like edema, contrast-enhancing and non-enhancing tumor. The first data set consists of N = 71 longitudinal follow-up scans of 15 patients suffering from glioblastoma (GB). The second data set comprises N = 30 scans of low- and high-grade gliomas. For comparison we computed Pearson Correlation, Intra-class Correlation Coefficient (ICC) and Dice score. Virtual raters always lead to an improvement w.r.t. inter- and intra-rater agreement. Comparing the 2D Response Assessment in Neuro-Oncology (RANO) measurements to the volumetric measurements of the virtual raters results in one-third of the cases in a deviating rating. Hence, we believe that our approach will have an impact on the evaluation of clinical studies as well as on routine imaging diagnostics.