# The Role of Artificial Intelligence in Neuro-oncology Imaging

Jennifer Soun, Lu-Aung Yosuke Masudathaya, Arabdha Biswas, Daniel S Chow

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Jennifer Soun, Lu-Aung Yosuke Masudathaya, Arabdha Biswas, Daniel S Chow. The Role of Artificial Intelligence in Neuro-oncology Imaging. Olivier Colliot. Machine Learning for Brain Disorders, Springer US, pp.963-976, 2023, 10.1007/978-1-0716-3195-9_30. hal-04254372

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# The Role of Artificial Intelligence in Neuro-oncology Imaging

Jennifer Soun, Lu-Aung Yosuke Masudathaya, Arabdha Biswas,
and Daniel S. Chow

## Abstract

Diagnostic imaging is widely used to assess, characterize, and monitor brain tumors. However, there remain several challenges in each of these categories due to the heterogeneous nature of these tumors. This may include variations in tumor biology that relate to variable degrees of cellular proliferation, invasion, and necrosis that in turn have different imaging manifestations. These variations have created challenges for tumor assessment, including segmentation, surveillance, and molecular characterizations. Although several rule-based approaches have been implemented that relates to tumor size and appearance, these methods inherently distill the rich amount of tumor imaging data into a limited number of variables. Approaches in artificial intelligence, machine learning, and deep learning have been increasingly leveraged to computer vision tasks, including tumor imaging, given their effectiveness for solving image-based challenges. This objective of this chapter is to summarize some of these advances in the field of tumor imaging.

**Key words** Brain tumors, Radiogenomics, Tumor segmentation, Response Assessment in Neuro-Oncology (RANO), Response Evaluation Criteria in Solid Tumors (RECIST)

## 1 Introduction

With the recent emergence of artificial intelligence in neuroimag-
ing, there is great interest in harnessing the power of new compu-
tational approaches that are inherently quantitative to
non-invasively measure and classify features of brain tumors on
routine and advanced magnetic resonance imaging (MRIs). Artifi-
cial intelligence (AI), including both machine learning (ML) and
deep learning (DL), has the potential to automatically detect pat-
terns in images that remain elusive to the eye of a neuroimager and
to surpass human-level performance in the prediction of glioma
genetics, treatment response, and long-term outcome. Theoreti-
cally, these features of AI may enable clinicians to provide greater
value to the patient by allowing for expedited and more tailored
treatments. This chapter will provide a brief review of primary brain
tumor epidemiology with emphasis on gliomas, evaluate present challenges in brain tumor imaging, and describe potential applications for AI.

## 2 Brain Tumor Epidemiology

Primary central nervous system (CNS) tumors are a rare form of cancer, with an incidence rate in adults estimated to be 23.8 per 100,000 persons [1] (see Box 1) [2]. However, while these tumors are rare, they constitute a significant fraction of cancer morbidity and mortality. Within the United States, approximately 10 per 100,000 are diagnosed with a primary brain tumor each year, and 6 to 7 per 100,000 are diagnosed with a primary malignant brain tumor [3]. Brain cancer incidence is the highest in Europe (age-standardized incidence rate [ASR]: 5.5 per 100,000 persons) and North America (ASR: 5.3 per 100,000 persons), along with Australia and Western Asia [3, 4]. With regard to tumor types, astrocytomas and gliomas are the second most common malignant brain tumor in adults following metastasis, and gliomas represent approximately 30% of brain tumors and 80% of all primary malignant brain tumors [4]. Gliomas vary in histology from potentially surgically curable grade 1 tumors (e.g., pilocytic astrocytoma) to aggressive grade 4 tumors (e.g., glioblastoma, GBM) with a high risk of recurrence and/or progression [5]. Accurately classifying and characterizing tumors is vital to diagnosing tumors and producing precise prognostication.

Cancer mortality is dependent on subtype and staging, and survival time after diagnosis varies greatly by grade [6, 7]. Gliomas are classified and graded based on histological and molecular markers [6, 7]. GBM is a subtype of glioma which arises from normal glial cells and consists of a group of genetically and phenotypically heterogeneous tumors [7, 8]. GBM is the most common primary CNS tumor in adults, with an incidence of 3.2 per 100,000 adults each year in Europe and America [9]. The incidence increases significantly with age, with a mean age of diagnosis at 64 for primary GBM and a peak incidence of 15.2 cases per 100,000 between the ages of 75 and 84 [9]. GBM occurrence has been associated with several genetic diseases, including tuberous sclerosis, neurofibromatosis type I, and Li-Fraumeni syndrome; however, less than 20% of patients with GBM have a strong family history of cancer, and the only well-established environmental risk factor is exposure to ionizing radiation [10]. GBM has the poorest overall survival among gliomas, with 0.05–4.7% patient survival after 5 years of diagnosis in the United States from 1995 to 2010 (95% CI 4.4–5.0) [4, 11]. Overall, mortality and prognosis vary tremendously depending on grade and subtype, and methods to more accurately predict these factors would help improve treatment and outcomes.
Box 1 Main Primary Central Nervous System Tumors

<table><tr><td>Malignant</td><td></td></tr><tr><td>Astrocytomas</td><td>20-25%</td></tr><tr><td>Oligodendrogliomas</td><td>1-2%</td></tr><tr><td>Ependymal tumors</td><td>&lt;2%</td></tr><tr><td>Other</td><td>8%</td></tr><tr><td>Non-malignant</td><td></td></tr><tr><td>Meningiomas</td><td>37%</td></tr><tr><td>Pituitary</td><td>16%</td></tr><tr><td>Nerve sheath</td><td>8%</td></tr><tr><td>Other</td><td>7%</td></tr></table>

GBM remains one of the most lethal malignant solid tumors. The 1-year overall survival of newly diagnosed GBM is 17-30% with a 5-year survival rate of less than 5% [6]. Surgical resection followed by chemotherapy and radiotherapy remains the cornerstone treatment choice for GBM. However, the response to chemotherapy is variable, and nearly all patients suffer from recurrent disease [4]. Additionally, these tumors most frequently arise within the frontal lobe, leading to both cognitive and motor disabilities that result in loss of independence in many patients. Increasingly, molecular markers are being used for glioma classification and characterization. Mutations such as IDH1 can be a strong predictor of favorable prognosis and can assist in distinguishing among glioma subtypes [12]. Characterizing certain genetic features such as IDH1 status can aid in more accurate diagnoses and prognostication.

# 3 Present Challenges with Brain Tumor Imaging

## 3.1 Segmentation

While there have been significant advances in neuro-oncology imaging, there remain several challenges in providing accurate measurements of brain tumors. For example, a present limitation is that commonly used techniques to monitor tumor size use unidimensional and bidimensional manual measurements. While this may work for solid tumors that have a more spherical shape, the postsurgical cavity and tumors themselves of neuro-oncology patients tend to be highly irregular in shape, which increases the difficulty in obtaining accurate measurements. This stems from the fact that GBMs themselves and their recurrence commonly
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Head tilt affecting designation. Patient with glioblastoma after resection. Simulation of tilting the patient's head up results in progression of disease (a) while in routine positioning demonstrates stable disease (b), and tilting downward results in partial response (c)</figcaption></figure>

demonstrate eccentric and nodular growth. For patients, such inconsistencies and potential inaccuracies may result in classifying effective treatments as ineffective or vice versa (Fig. 1). Ultimately, this challenge heightens the importance for the need for reliable and reproducible techniques for tumor size measurements.

**3.2 Surveillance**

In addition to tumor segmentation, radiographic assessment has served as an essential tool to monitor patients with brain tumors and has played an important role in clinical trials. Historically, increases and decreases in tumor size using gadolinium contrast-enhanced sequences have served as imaging markers for progression and treatment response, respectively [13, 14]. However, there are limitations of relying solely on contrast enhancement for assessing disease status. Specifically, treatment-related increases in enhancement were observed to mimic progression with increasing frequency following the introduction of standard of care therapy of radiation and temozolomide (TMZ) [15]. This tumor pseudoprogression (psPD) is observed in 20-60% of patients who have undergone radiotherapy with TMZ and defined as increases in edema and contrast enhancement on MRI with or without clinical deterioration that subsequently stabilizes or resolves (Fig. 2) [15-17]. Additionally, the incidence has been reported to be as high as 90% in patients that have increased sensitivity to TMZ, identified with methylation status of the methyltransferase (MGMT) promoter in glioma cells [18].
<figure><img src="image_3.png" /><figcaption>**Fig. 2** Pseudoprogression. Example of a 45-year-old female with GBM. Axial post-contrast images immediately after resection show minimal enhancing disease (a). Follow-up MRI at 1 month demonstrates new thick enhancement (b) that subsequently reduced on images 12 months out (c).</figcaption></figure>

Presently, the exact mechanism is still not fully understood, and the only accepted standard to distinguish true progression of disease (PD) from treatment-related psPD is invasive tissue sampling or short interval imaging or clinical follow-up, which may delay and compromise management changes in an aggressive tumor [16, 17]. In 2010, the Response Assessment in Neuro-Oncology (RANO) working group set criteria to address some of these challenges, including psPD [19]. However, evaluation of psPD remains limited with conventional imaging techniques. Challenges in monitoring GBM patients due to psPD are also observed in other newer treatments, including immunotherapies [20, 21]. The immune-related response criteria working group (iRANO) has made guidelines to address challenges of radiographic worsening in order to avoid classifying effective treatments as ineffective in instances of psPD; however, the group acknowledges that future research and solutions incorporating advanced imaging are necessary to improve assessment in these patients [21, 22].

**3.3 Molecular Classification**

**3.3.1 Impact of Glioma**
*Inter-tumoral Heterogeneity*

Glioma inter-tumoral genetic heterogeneity has been shown to impact both prognosis and response to therapy. For example, isocitrate dehydrogenase (IDH)-mutant GBMs demonstrate significantly improved survivorship compared to IDH-wild GBMs (31 months vs. 15 months) [12, 23]. Recognition of the importance of genetic information has led the World Health Organization (WHO) to place considerable emphasis on the integration of molecular markers for its classification schemes in its 2021 update, including IDH status [24]. Regarding treatment response, it is becoming increasingly evident that GBMs' differing genetic attributes also result in mixed responses [25]. One of the early
mutations discovered was O6-methylguanine-DNA methyltransferase (MGMT) promoter silencing, which reduces tumor cells' ability to repair DNA damage from alkylating agents such as temozolomide (TMZ). Hegi et al. [26] subsequently observed that MGMT promoter methylation silencing was observed in 45% of GBM patients, who demonstrated a survival benefit when treated with a combination of TMZ and radiotherapy versus radiotherapy alone (21.7 months versus 15.3 months). It is critical that future GBM monitoring integrates imaging and genetic data in order to provide accurate prognostic information and guide personalized therapies.

### 3.3.2 Challenges of Personalized Therapy

Discoveries in genetic profiling have spurred the development of new targeted therapies [27] with over 140 clinical trials presently evaluating personalized or targeted therapies for GBMs alone. These therapies are tailored to exploit genetically driven therapeutic targets. However, an apparent roadblock to these individualized approaches is the growing evidence of GBM intra-tumoral heterogeneity. Patel et al. demonstrated that GBMs consist of a mixture of cells with variable gene expression profiles using single-cell RNA sequencing [28]. Likewise, Sottoriva et al. observed genome-wide variability using surgical multisampling approach from 11 GBM patients [29]. Thus, each brain tumor may reflect multiple unique tumor habitats with corresponding differences in response and resistance to therapy, challenging the identification, development, and implementation of individualized care.

### 3.3.3 MRI Biomarkers of Tumor Biology and Genetic Heterogeneity

Both spatial and temporal variations in genetic expression result in alterations in tumor biology, including changes in apoptosis, cellular proliferation, cellular invasion, and angiogenesis [30]. In turn, these biologic changes manifest in the heterogeneous imaging features of brain tumors, resulting in varying degrees of enhancement and edema. For example, imaging changes on contrast-enhanced MRI result from the breakdown of the blood-brain barrier and can demonstrate areas of necrosis as a marker for apoptosis. Additionally, MRI sequences based on physiology such as apparent diffusion coefficient (ADC) and perfusion imaging have been shown to relate to tumoral cellularity and angiogenesis, respectively. Furthermore, promising efforts have shown that tumors with lower cerebral blood volume (CBV) on perfusion are more likely to be IDH mutants and have longer overall survival (OS) [31, 32]. Other reports have used enhancement patterns and ADC to predict IDH status with some success [33, 34]. Currently, efforts to provide molecular classification for brain tumors based on these MRI features have had mixed results. For example, classification of IDH and MGMT mutant status has had some success; however, methods for 1p19q and EGFR have demonstrated less reproducibility [35–37]. Different mutations may have similar MRI
features, and a “single” tumor can have multiple different muta-
tions internally. Several approaches have emerged to provide stan-
dardized visual interpretation of gliomas for tissue classification.
For example, the Visually AcceSAble Rembrandt Images (VASARI)
feature set is a rule-based lexicon to improve the reproducibility of
interpretation [38]. However, these methods rely on human visual
interpretation, which is inherently subjective and prone to inter-
rater variability. Ultimately, steps are needed to provide reliable and
reproducible methods to accurately classify molecular subtypes a
priori.

4 Potential Applications for Machine Learning

4.1 Segmentation

Radiographic assessment serves an important role for clinical
follow-up and research trials in oncology. Currently, the RANO
criteria rely on 2D measurements of the enhancing disease as well as
subjective assessment of the FLAIR non-enhancing tumor, which is
then used to guide treatment strategies. However, the postsurgical
cavity tends to be highly irregular in shape, which may increase the
difficulty in obtaining accurate and reproducible measurements.
Additionally, linear measurements obtained for cystic and necrotic
tumors are often overestimated [39]. Intuitively, 3D segmentation
provides a more accurate method for assessing tumor size com-
pared to linear 2D approaches and techniques [40–42]. For exam-
ple, Dempsey et al. [43] observed that 3D segmentation allows for
better survival prediction compared with traditional diameter-
based analysis.

Deep learning, an emerging branch of artificial intelligence, has
been shown to rapidly outperform other machine learning
approaches' imaging benchmarks for various computer vision
tasks [44, 45], including imaging 3D segmentation tasks. For
example, Zhang et al. [46] observed that a CNN approach per-
formed significantly better than other techniques, including ran-
dom forest, support vector machine (SVM, a traditional linear
machine learning technique), coupled level sets, and majority vot-
ing for brain segmentation.

Since 2012, the Multimodal Brain Tumor Image Segmentation
(BraTS) challenge has demonstrated the efficacy of deep learning
approaches for tumor segmentation [47]. This unique dataset pro-
vides developers access to GBM images, which now includes over
2000 patients from 37 institutions. As result, multiple groups have
developed fully automated brain tumor segmentation tools which
rely on various AI techniques to identify lesion margins and provide
a more accurate estimate for disease burden (Fig. 3) [48–51]. In
2020, Isensee et al. [52] took first place with Sørensen-Dice coeffi-
cient scores of 88.95, 85.06, and 82.03 for whole tumor, tumor
core, and enhancing tumor, respectively. Most recently in 2021,
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Example of automated glioma segmentation using deep learning showing FLAIR edema segmentation (left) as well as segmentation of enhancing tissue (right). (Courtesy Peter Chang, MD)</figcaption></figure>

BraTS has partnered with the Radiological Society of North America (RSNA) and the American Society of Neuroradiology (ASNR) [53].

## 4.2 Surveillance

As described previously, psPD cases are not reliably distinguished from true progression using RANO criteria with a recent meta-analysis suggesting that upward of 36% are underdiagnosed [54]. In fact, the only accepted methods to distinguish true PD from treatment-related psPD are invasive tissue sampling and short interval clinical follow-up with imaging, which may delay and compromise disease management in an aggressive tumor [16, 17].

Traditional machine learning models have been previously utilized for psPD characterization from radiologic imaging. Hu et al.'s [55] SVM approach examining multi-parametric MRI data yielded an optimized classifier for psPD with a sensitivity of 89.9% and specificity of 93.7%. Though deep learning methods have been leveraged less frequently, they are showing promise for
characterizing psPD versus true PD [56-58]. Jang et al. [56] assessed a deep learning, a long short-term memory network combined with a CNN (CNN-LSTM), to determine psPD versus tumor PD in GBM. Their dataset consisted of clinical and MRI data from 2 institutions, with 59 patients in the training cohort and 19 patients in the testing cohort. Their CNN-LSTM structure, utilizing both clinical and MRI data, outperformed the two comparison models of CNN-LSTM with MRI data alone and a random forest structure with clinical data alone, yielding an AUC (area under the curve) of 0.83, an AUPRC (area under the precision-recall curve) of 0.87, and an F-1 score of 0.74 [56]. More recently, Lee et al. [58] also utilized a CNN-STM to distinguish PD from psPD with an accuracy range of 0.62-0.75. These examples indicate that utilization of a deep learning approach can outperform a more traditional machine learning approach in analyzing images.

## 4.3 Molecular Classification

Radiogenomics focuses on bridging the associations between medical imaging and gene expression data in order to aid in the understanding of underlying disease mechanisms and improve diagnostics [59]. Certain molecular and genetic alterations in tissue can be observed computationally in terms of radiological appearance, including shape and texture of tissue. Radiogenomics, which leverages the interplay between radiological and genetic features in oncology, is important to improve patient treatment decisions, and artificial intelligence has become a key player that has led to significant advancements in these areas. AI-based radiogenomics has the potential to better characterize diagnosis, prognosis, and survival prediction by detecting key features in images that identify molecular characteristics of disease.

In gliomas, one of the earliest groups that used neural networks to predict tumoral genetic subtypes from imaging features was Levner et al. [60]. In this study, features were extracted from space-frequency texture analysis on the S-transform of brain MRIs to predict MGMT promoter methylation status in newly diagnosed GBM patients. Levner's group achieved an accuracy of 87.7% across 59 patients, among which 31 patients had biopsy-confirmed MGMT promoter methylated tumors. Residual CNN methods have also been used to predict MGMT promoter methylation status [61], as well as IDH mutation status. For example, Chang et al. developed a CNN to simultaneously classify IDH1, 1p19q codeletion, and MGMT promoter methylation status with high accuracy from imaging data derived from 259 patients in the Cancer Imaging Archives dataset [35]. Chang et al. also developed a principal component analysis approach to disentangle the final feature layer and determine the most influential features for each classification (Fig. 4). These features largely overlap with what has been described in the literature by subjective visual assessment. Ryu et al. [62] evaluated glioma heterogeneity via textural analysis and
<figure><img src="image_5.png" /><figcaption>**Fig. 4** MRI separating gliomas by MGMT methylation status. Features include thick enhancement with central necrosis (a) with infiltrative edema patterns (b). In contrast, features predictive of MGMT promoter methylated status include nodular and heterogeneous enhancement (c) with masslike FLAIR edema (d). (Copyright American Journal of Neuroradiology, adapted, with permission, from reference [35])</figcaption></figure>

distinguished low- and high-grade gliomas with 80% accuracy. Additionally, Drabycz et al. [63] were able to classify MGMT promoter methylation status in glioblastoma patients with 71% accuracy using a textural analysis approach.

# 5 Summary

In summary, present challenges in brain tumor imaging in part stem from the heterogeneity of the disease, which results in challenges related to disease characterization. However, the application of novel AI, ML, and DL approaches for brain tumor imaging aims to improve many of these areas due to its ability to accurately and reliably detect imaging patterns beyond human perception. Numerous public competitions (e.g., BraTS) have also spurred the field and have recently begun collaborations with multiple
imaging societies, including the RSNA and ASNR. Ultimately, there is optimism that these tools will continue to yield new opportunities to enhance discovery and care in the future.

## Acknowledgments

The authors wish to acknowledge Jack Grinband, PhD (Columbia University, New York NY); Brent Weinberg, MD PhD (Emory, Atlanta GA); and Peter Chang, MD (University of California, Irvine, Irvine CA), for their expertise and support. The authors also acknowledge the supportive administrative team from the Center for Artificial Intelligence in Diagnostic Medicine at the University of California, Irvine.

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