Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Evaluate the Microvascular Invasion of Hepatocellular Carcinoma (≤5 cm) and Recurrence Free Survival with Gadoxetate Disodium-Enhanced MRI-Based Habitat Imaging

BACKGROUND

Tumors are heterogenous and consist of subregions, also known as tumoral habitats, each exhibiting varied biological characteristics. Each habitat corresponds to a cluster of tissue sharing similar structural, metabolic, or functional characteristics. The habitat imaging technique facilitates both the visualization and quantification of these tumoral habitats.

PURPOSE

To evaluate the microvascular invasion (MVI) in hepatocellular carcinoma (HCC) (≤5 cm) and assess the recurrence-free survival (RFS) using gadoxetate disodium-enhanced MRI-based habitat imaging.

STUDY TYPE

Retrospective.

SUBJECTS

180 patients (52.9 years ± 11.7, 156 men) with HCC.

FIELD STRENGTH/SEQUENCE

1.5T/contrast-enhanced T1-weighted gradient-echo sequence.

ASSESSMENT

The enhancement ratio of signal intensity at the arterial phase (AER) and hepatobiliary phase (HBPER) were calculated. The HCC lesions and their peritumoral tissues of 3, 5, and 7 mm were encoded into four habitats. The volume fraction of each habitat was then quantified. The diagnostic performance was assessed using the receiver operating characteristic analysis with 5-fold cross-validation. The RFS was evaluated with Kaplan-Meier curves.

RESULTS

Habitat 2 (with median to high AER and low HBPER) within the peritumoral tissue of 3 mm (f2 -P3 ) and tumor diameter could serve as independent risk factors for MVI and showed the statistical significance (odds ratio (OR) of f2 -P3  = 1.170, 95% CI = 1.099-1.246; OR of tumor diameter: 6.112, 95% CI = 2.162-17.280). A nomogram was developed by incorporating f2 -P3 and tumor diameter, demonstrating high diagnostic accuracy. The area under the curve from 5-fold cross-validation ranged from 0.880 to 1.000. Additionally, the nomogram model demonstrated high efficacy in risk stratification for RFS.

CONCLUSION

Habitat imaging of HCC and its peritumoral microenvironment has the potential for noninvasive and preoperative identification of MVI and prognostic assessment.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Overcoming challenges in glioblastoma treatment: targeting infiltrating cancer cells and harnessing the tumor microenvironment

Glioblastoma (GB) is a highly malignant primary brain tumor with limited treatment options and poor prognosis. Despite current treatment approaches, including surgical resection, radiation therapy, and chemotherapy with temozolomide (TMZ), GB remains mostly incurable due to its invasive growth pattern, limited drug penetration beyond the blood-brain barrier (BBB), and resistance to conventional therapies. One of the main challenges in GB treatment is effectively eliminating infiltrating cancer cells that remain in the brain parenchyma after primary tumor resection. We've reviewed the most recent challenges and surveyed the potential strategies aimed at enhancing local treatment outcomes.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.