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Review

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

by
Jacobo Porto-Álvarez
1,*,
Antonio Mosqueira Martínez
1,*,
Javier Martínez Fernández
1,
Marta Sanmartín López
1,
Miguel Blanco Ulla
1,
Fernando Vázquez Herrero
1,
José Manuel Pumar
1,
Manuel Rodríguez-Yáñez
2,
Anxo Manuel Minguillón Pereiro
2,
Alberto Bolón Villaverde
3,
Ramón Iglesias Rey
4 and
Miguel Souto-Bayarri
1
1
Radiology Department, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain
2
Neurology Department, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain
3
Anaesthesia Department, Complexo Hospitalario Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain
4
Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2023, 13(18), 10061; https://doi.org/10.3390/app131810061
Submission received: 2 August 2023 / Revised: 31 August 2023 / Accepted: 5 September 2023 / Published: 6 September 2023
(This article belongs to the Section Applied Neuroscience and Neural Engineering)
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Abstract

:
Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. The field of radiological imaging has experienced growth in recent years, which could be boosted by the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology and has shown promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years, and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorize how radiomics may change the paradigm for these patients in the coming years.

1. Introduction

Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide [1]. According to the WHO, stroke is the second leading cause of disability and death worldwide after ischemic heart disease, accounting for 11% of all deaths. With improvements in the quality of life and longevity of patients, the incidence of stroke is expected to continue to rise in the coming years [2]. More than 80% of strokes are ischemic in origin [3]. Recent advances in the management of these patients have focused more on their treatment, where endovascular therapies have radically changed the prognosis of these patients [4,5,6]. In recent years, numerous studies have explored the many diagnostic aspects of ischemic stroke. However, since the introduction of cerebral perfusion and diffusion imaging more than a decade ago, technological advances with clinical applicability have been somewhat stagnant [7].
Today, medicine is immersed in a digital world. Specialties such as radiology are growing in demand. In recent years, with the advent of artificial intelligence, applications such as radiomics have emerged to harness this technological revolution and improve diagnostic techniques in the search for personalized clinical management of patients. Since the coining of the term radiomics in 2012 [8], articles on this topic have become increasingly common.
Radiomics is a radiological discipline based on the idea that images are more than just pictures and that there is a lot of data that is invisible to the human eye, but which we can quantify and analyze by looking for data patterns associated with certain pathologies. Initially, radiomics was mainly focused on the field of oncology [9]. All cancer patients have at least one staging CT scan, so the most commonly used imaging technique for feature extraction in these studies was the CT [10]. The aims of these studies have been diverse, ranging from identifying prognostic factors or estimating overall survival to predicting response to treatment. The use of radiomics for gene determination (also called radiogenomics) has also been developed in pathologies such as brain tumours [11], lung cancer [12] and abdominal tumours [13]. In recent years, the use of radiomics has even been extended to other imaging modalities such as MRI, positron emission tomography (PET) based on both CT and MRI, and ultrasound.
In the field of neuroradiology, radiomics features obtained from magnetic resonance imaging (MRI) were used most frequently in the articles. In these articles, radiomics also focused mainly on oncological patients. The objectives ranged from differentiation of high-grade gliomas from brain metastases, prediction of EGFR mutations, or prognostic prediction, to determination of overall survival. In recent years, the use of radiomics has extended to brain pathologies other than tumours. There are articles on the use of radiomic features for prognostic prediction in acute ischemic stroke (AIS) [14], for predicting degenerative diseases [15], or even for predicting aneurysm rupture [16].
The aim of this review is to summarize published articles on the performance of radiomics in AIS patients. In addition, we aim to determine whether radiomics can provide valuable information for the clinical management of AIS patients. We are at a key moment in the evolution of radiology, as we have seen with other technological advances such as diffusion MRI, helical CT or, more recently, dual-energy CT. We need to be at the forefront of this technological evolution so that our patients can benefit.

2. Radiomics

2.1. Radiomics Workflow

The radiomics workflow consists of five consecutive steps (Figure 1):
  • Obtaining images: Radiomics can be applied to all types of medical imaging. There are published articles on the performance of radiomics in ultrasound, Non-Contrast Enhancement-CT (NCECT), Computed Tomography Angiography (CTA), MRI and PET. In this step, there can be a great deal of heterogeneity when using images acquired in different hospitals or even on different machines within the same hospital. Therefore, the acquired images are subjected to a standardization process in which we try to correct this source of heterogeneity [8].
  • Pre-processing: The quality of the images can be improved by using the pre-processing tools. In this step, some image filters are used to reduce noise. The aim is to increase the predictive power of the classifiers [17].
  • Segmentation: In this step, the region of interest is selected in the radiological image. The segmentation of this region can be performed in three ways: manual, semi-automatic and automatic. Manual segmentation is the gold standard and the most commonly used method in the studies reviewed in this article. The main advantage of this model is the intervention of an expert radiologist in its performance. The main disadvantage of this model is the time required to manually segment the entire region of interest [18]. Automatic segmentation is based on automatic detection of the region of interest without human intervention. Finally, semi-automatic segmentation is performed under the supervision of an expert radiologist who can edit an initial automatic pre-segmentation. The advantage of this method is the speed of the segmentation and the fact that the human component remains [19]. With today’s increasingly sophisticated segmentation software, semi-automatic 3D segmentation of an area of interest can be performed quickly and comfortably for the radiologist.
  • Feature extraction and classification: There is a wealth of numerical data that can be extracted from medical images, known as radiomic features. There are several classes of radiomic features: Histogram features (grey level mean, maximum, minimum, variance and percentiles), texture features (absolute gradient, grey level co-occurrence matrix—GLCM—, grey level run length matrix—GLRLM—, grey level size zone matrix—GLSZM—, grey level distance zone matrix—GLDZM—, Neighborhood Grey Level Difference Matrix—NGTDM—, and Neighborhood Grey Level Dependence Matrix—NGLDM—), model-based features, transform-based features (Fourier, Garbor and Wavelet) and shape-based features (geometric properties of ROIs) [20]. These numerical data are classified by automatic classifiers. These classifiers are capable of recognizing different groups of patterns depending on the objective we set for them. In this way, the classifiers learn the data patterns with a training cohort. Then, with a test cohort, these classifiers are able to classify the data we provide into the pattern that most closely resembles it. In addition to numerical data, automatic classifiers can be used to input clinical data to search for a combined model.
  • Data analysis.

2.2. Radiomics in AIS

Articles analyzing the performance of a radiomic model in neurological pathology have become increasingly frequent. The most frequently published articles are about neurological tumour pathology. However, in recent years there has been an increase in the publication of articles about non-tumour neurological pathology.
Regarding the application of radiomics in acute cerebral ischemic pathology, there has been an exponential increase in the publication of articles since 2018 (Figure 2). The objectives of these articles on radiomics in acute ischemic stroke are the prognostic prediction of AIS patients, the detection of early ischemic stroke, prediction of the outcome of revascularization treatment, prediction of complications, prediction of the etiology of AIS, prediction of the time since stroke and differentiation between hemorrhage or contrast extravasation in patients undergoing mechanical thrombectomy (Figure 3) (Table 1).

2.3. Prognostic Prediction

The use of radiomics for the prognostic prediction of AIS patients is the most commonly published. Avery et al. in their study of 829 patients used radiomic features (RFs) obtained from CTA images of patients with anterior circulation Large Vessel Occlusion (LVO). They used masks of the middle cerebral artery (MCA) territory to extract the RF. They used four different input sets: radiomics (only RFs of MCA territory), radiomics + treatment (RF, mTICI score and intravenous thrombolysis), clinical + treatment (clinical variables and treatment) and combined (RF, treatment and clinical variables). The clinical variables included were sex, age and admission NIHSS. The best results were very similar between the clinical + treatment model and the combined model. The area under the curve (AUC) for long-term outcome prediction was 0.81 and 0.82 for the clinical + treatment and combined models, respectively. The AUC for predicting discharge outcome was 0.82 and 0.81 for the clinical + treatment and combined models, respectively. In the internal independent and external test performances, the best results of the different inputs were very similar. There was no significant difference between the best results of the radiomics only model, the radiomics + treatment model, the clinical + treatment model and the combined model. The authors concluded that RFs could help with patients where the collection of clinical information is limited [21].
Other studies of some prognostic predictions have found similar results [22,23,24,25,26,27,28,29,30,31,32,33,34,35]. In these studies, we can see that the performance of radiomics based on different types of images (MRI, CT and CTA) gives good results in predicting prognostic factors such as the mRS scale after AIS or the presence of disability after AIS. Many of these studies compare prediction models based on clinical features, radiomic features and finally evaluate the performance of a combined model by combining clinical and radiomic features. In general, the combined model is the one that performs best.

2.4. Detection of Ischemic Stroke

In the detection of ischemic stroke, articles focus on the early detection of ischemic lesions and the prediction of recurrence. The group of Guan Y et al. showed in their study with 56 patients and 1301 RFs that there is a correlation between RFs and AIS detection (best Acc of 0.7748) [36]. Guo Y et al. also investigated the detection of ischemic stroke (IS) and the prediction of NIHSS and other outcomes using RFs obtained from DSC-PWI in 88 patients. The best AUC they obtained was 0.925 for stroke detection. For the prediction of NIHSS and other prognostic predictions, they obtained an AUC of 0.853 and 0.828, respectively [37]. Zhang et al. also tried to predict early brainstem infarction using an NCECT-based radiomics model. They obtained an AUC of 0.99 and 0.91 in the internal and external validation cohorts, respectively. They concluded that NCECT-based radiomics can detect early brainstem infarction and may be very useful in clinical practice [38].
On the other hand, Zhang R et al. used MRI-based radiomics to predict the ischemic penumbra in their study of 241 patients. They extracted 896 radiomic features from the DWI high signal area using manual segmentation. The AUC obtained was 0.92, and sensitivity, specificity and accuracy were 0.93, 0.75 and 0.82, respectively, in the training cohort. In the validation cohort the AUC, sensitivity, specificity and accuracy were 0.90, 0.88, 0.74 and 0.80, respectively. They concluded that a radiomics model based on MRI images can identify the ischemic penumbra in AIS patients [39].
In ischemic stroke recurrence prediction, Su et al. developed a combined model with some clinical factors (age, sex, number of chronic ischemic lesions, routine laboratory tests, carotid ultrasound, cardiovascular risk factors and medical treatment) and CT-based radiomics to predict future ischemic stroke in patients with silent lacunar infarction. They obtained a C-index of 0.786 and 0.714 in the training and validation cohorts, respectively. They concluded that radiomic features can provide important information for predicting future ischemic stroke in patients with silent lacunar infarction [40]. Tang et al. also developed a radiomics signature based on MRI features of patients with symptomatic intracranial atherosclerotic stenosis (SICAS) to predict stroke recurrence. They obtained an AUC with the combined model of 0.899 and 0.803 in the training and validation cohorts, respectively. They concluded that radiomic features are useful for predicting stroke recurrence in patients with SICAS [41]. Wang et al. also predicted 1-year ischemic stroke recurrence in 1003 AIS patients based on MRI images. They obtained similar results [42].
Many patients without vessel occlusion on CTA images or with inconclusive results on perfusion images require MRI imaging to determine the presence of ischemic lesions. In many cases, MRI images are not available in the first hours of management of these patients. In these situations, radiomics could provide valuable information for the early management of these patients.

2.5. Treatment Predictions

Regarding radiomic models predicting the outcome of reperfusion treatments, most studies focus on predicting the first-pass effect or the TICI scale after thrombectomy. Only one study focuses on predicting recanalization after alteplase treatment.
To predict first-pass recanalization, Hofmeister et al. performed clot segmentation in their study of 156 patients. They used RFs to identify patients with first-pass recanalization by aspiration. They also predicted the total number of passes required to achieve good recanalization with a mechanical thrombectomy device (MTB). They achieved an AUC of 0.88 for predicting patients with first attempt recanalization. They concluded that clot-based radiomics can predict an MTB strategy in patients with AIS [43]. Sarioglu et al. attempted to predict first attempt recanalization in AIS patients using clot-based radiomics features obtained from NCECT. They achieved an accuracy of 0.83 using a combined model with radiomic features and clinical data (ASPECTS scale and gender). They also concluded that clot-based radiomic features can help to estimate successful recanalization in AIS patients [44]. Patel et al. also developed a NCECT/CTA-based radiomic model to predict the first-pass effect in AIS patients with similar results (AUC of 0.832) [45].
In recanalization prediction, Zhang et al. attempted to predict successful recanalization (TICI ≥ 2c) using MRI-based radiomics in 141 AIS patients. They extracted 321 radiomic features from the ischemic lesion and performed a manual segmentation. They obtained an AUC of 0.7442, a sensitivity of 0.7556 and a specificity of 0.7675. They concluded that MRI-based radiomics may be useful in predicting patient response to thrombectomy [46]. Xiong et al. also attempted to predict successful recanalization (TICI ≥ 2b) in AIS patients undergoing stent retrieval. They improved the results of Zhang et al. with an AUC of 0.860 and 0.849 in the training and validation cohorts, respectively, with the combined model. They also conclude that the radiomics model can successfully predict recanalization after stent retrieval [47]. Finally, Van Voorst et al. developed a clot-based radiomics model using the CT images of 699 patients to predict TICI ≥ 2b, first attempt reperfusion, reperfusion within three attempts and mRS scale ≤ 2. They obtained similar results with the clinical, radiomics and morphological features models [48].
In terms of predicting recanalization after treatment with alteplase, Qiu et al. obtained an AUC of 0.85. They concluded that clot-based radiomics was more predictive of recanalization with alteplase than other classic thrombus imaging features (length, volume and permeability) [49].
As we have seen in this section, the performance of radiomic models in predicting the outcome of thrombectomy in patients with AIS is very good. In this situation, radiomics can provide valuable information for the immediate management of these patients, since knowing in advance the risk of achieving inadequate reperfusion with a specific thrombectomy technique may lead the neuroradiologist to change the recanalization strategy in the event of an unsuccessful first pass.

2.6. Prediction of Complications after AIS

There are some studies that have used RFs to predict some complications in patients with AIS. Fu B et al. obtained an AUC of 0.96 in predicting malignant cerebral edema (MCE) using a combined model with RFs from NECT of patients with AIS [50]. Jiang et al. also predicted edema after acute ischemic stroke using MRI-based radiomics in 389 patients, with similar results [51]. Wen et al. developed a radiomic model to predict malignant acute middle cerebral artery infarction (mMCAi) based on CT radiomic features (NCECT and CTA). They obtained an AUC of 0.917 and 0.913 in the training and validation cohorts, respectively. They concluded that radiomic features could be an instrumental tool to predict the risk of mMCAi [52].
On the other hand, Meng et al. attempted to predict hemorrhage transformation in AIS patients based on MRI radiomics features. They extracted 5400 radiomic features from 71 AIS patients. They performed manual segmentation. They obtained an AUC of 911 and an accuracy of 0.894 in predicting hemorrhage transformation with a combined model. They concluded that a combined model with MRI-based radiomics can provide important information to physicians in the diagnosis of AIS patients [53]. Xie et al. also developed a CT-based radiomics model to predict hemorrhagic transformation in AIS patients and achieved similar results (AUC of 0.845–0.750) [54].
Finally, Liu et al. developed a radiomics model based on NCECT to predict hemorrhage expansion in patients with thrombolysis/thrombectomy related hemorrhagic transformation. They concluded that CT-based radiomics is valuable in predicting hemorrhage extension [55].

2.7. Etiology Prediction

In the etiology prediction of AIS using radiomics models, Chen Y et al. tried to differentiate between cardioembolic and atherosclerotic etiology in patients with acute ischemic stroke. They included 82 patients, and they obtained RFs from CTA images. They performed a manual segmentation of the embolic region and extracted 116 RFs. In their results, the best AUC was 0.9018 and the best Acc was 0.8929. They concluded that radiomics can effectively predict the subtype of ischemic stroke and help doctors to correctly manage these patients [56]. Jiang J et al. also studied a clot-based model in 403 AIS patients to identify cardioembolic (CE) stroke before MTB. They achieved an AUC of 0.838 for predicting CE stroke. They concluded that clot-based RFs can reliably predict CE strokes [57].
The current determination of the etiology of AIS is based on a number of clinical, analytical and medical imaging criteria. The management of these patients depends on the etiology of AIS. However, in many cases the etiology determination may be delayed or even undetermined. In this sense, radiomics can provide valuable information to support the diagnosis of patients with cardioembolic or atherothrombotic AIS and thus optimize the management of these patients.

2.8. Time since Stroke Prediction

Chen Y et al. attempted to estimate the time since stroke onset (TSS). They manually outlined the clot on CTA images of 221 patients and obtained 944 RFs. They divided the patients into two different groups: <4.5 h and >4.5 h since stroke onset. They analyzed the performance of the radiomics model and the combined model (radiomics + clinical data). The AUC was 0.803 for the combined model and 0.813–0.803 for the combined model (training and test cohorts). The clinical data used were age, diabetes and atrial fibrillation. They concluded that there was no significant difference between the radiomics and combined models. They also concluded that radiomics based on RFs obtained from CTA images can estimate TSS in patients with AIS [58]. Yao et al. also developed a CT-based radiomic model to classify the onset time of basal ganglia infarcts. They classified the patients into two groups (>4.5 h and <4.5 h). In the validation cohort the results were an AUC of 0.974, sensitivity of 0.951 and specificity of 0.961 [59]. Wen et al. also developed a CT-based radiomics model to predict infarct onset time in patients with MCAO. They obtained similar results with the combined model (AUC of 0.808–0.833) [60]. Finally, Zhang et al. also developed an MRI-based radiomics model to predict TSS with similar results [61].
Determining the time since stroke onset may play an important role in strokes that occur during sleep. In this sense, knowing whether or not a stroke has occurred in time to receive recanalization therapy with alteplase is critical to the management of these patients.

2.9. Differentiation Hemorrhage from Iodinated Contrast Extravasation after Thrombectomy

Chen X et al. in their very interesting study of 101 patients, tried to differentiate intracranial contrast extravasation from intraparenchymal hemorrhage (IPH) after mechanical thrombectomy (MT). They used RFs from non-enhanced CT (NECT) images after MT. They used manual segmentation and extracted 1316 RFs from 166 intraparenchymal areas of hyperattenuation. They obtained very interesting results, with an AUC between 0.848 and 0.826 (training and validation cohorts). The accuracy (ACC), sensitivity (S), specificity (Spec), negative predictive value (NPV) and positive predictive value (PPV) were 0.776, 0.767, 0.789, 0.682 and 0.852, respectively. They concluded that radiomics can effectively differentiate IPH from intracranial contrast extravasation after MT [62]. Yuan Ma et al. also used a CT-based radiomics model to predict bleeding from iodinated extravasation after thrombectomy in AIS patients. They obtained an AUC of 0.972 and 0.926 in the training and validation cohorts, respectively [63].
Differentiating between hemorrhage and contrast extravasation is easy if dual-energy CT is available. However, this technology may not always be available when we need it. In this sense, radiomics could act as an aid to make this distinction in situations where dual-energy CT is not available.
Table 1. Articles published about radiomics in AIS.
Table 1. Articles published about radiomics in AIS.
AuthorYearTypeNTargetTerritoryRFType of ImagingResultsConclusions
Avery et al. [21]2022R829Prognostic predictionALVO1116CTAAUC between 0.81 and 0.82RFs could help in patients with limited clinical information.
Cui H et al. [22]2018R70Prognostic predictionNA251MRIAUC of 0.821–0983The classification model improves when RFs are included.
Wang et al. [23]2021R598Prognostic predictionInfarct402MRIAUC of 0.80A combined nomogram based on MRI has a good performance in prognosis prediction.
Gerbasi A et al. [24]2022R164Prognostic predictionALVO107MRIAUC of 0.85A combined model can help in the long-term functional outcome prediction.
Guo Y et al. [25]2022R56Prognostic predictionNA65800MRIAUC of 0.908 and 0.884 Acc of 0.821 and 0.864RFs can help to predict functional outcomes in ischemic stroke patients.
Guo Y et al. [26]2022R78Prognostic predictionWB1674MRIAUC of 0.971RFs can predict functional recovery after three months.
Jiang L et al. [27]2022R1716Prognostic predictionNANAMRIAUC of 0.862A combined model can predict outcomes of AIS patients.
Li et al. [28]2023R102Prognostic predictionMCA1389MRIAUC of 0.88 and 0.79A combined model can predict a poor prognosis in AIS patients.
Ramos et al. [29]2022R3279Prognostic predictionWB1260CTAAUC of 0.61The combined model obtained better results than the clinical model.
Li et al. [30]2022R260Prognostic predictionInfarct1936MRIAUC of 0.945 and 0.920MRI-based radiomics have high predictive efficiency for the prognostic prediction of AIS patients after thrombectomy.
Quan et al. [31]2021R190Prognostic predictionHyperintensities753MRIAUC of 0.926–0.864MRI-based radiomics can predict unfavorable outcomes (mRS ≥ 2).
Tang et al. [32]2020R168Prognostic predictionPenumbra456CT/MRIAUC of 0.886Radiomics nomogram adds more value to the current clinical decision-making process.
Tolhuisen et al. [33]2022R206Prognostic predictionInfarct100MRIAUC of 0.88–0.81MRI-based radiomics can provide important information to the functional outcome prediction in AIS patients.
Yu et al. [34]2022R148Prognostic predictionInfarct4744MRIAUC of 0.902
Acc of 0.831
Sens of 0.739
Spec of 0.902		A radiomics model based on MRI imaging can predict clinical outcomes in AIS patients.
Zhou et al. [35]2022R522Prognostic predictionIschemic1310MRIAUC of 0.868–0.890A combined model outperformed individual clinical or radiomics models in predicting AIS outcomes.
Guan Y et al. [36]2020R56Identification of ISNA1301CT/MNRBest Acc of 0.7748There are RFs correlated with acute cerebral infarction.
Guo Y et al. [37]2022R88Identification of ISWB1674MRIAUC of 0.925 (IS), 0.853 (NIHSS), and 0.828 (outcome prediction)RF is a potential clinical tool which could help in the diagnosis and outcome prediction before treatment.
Zhang et al. [38]2023R355Identification of ISBrainstem1781CTAUC of 0.99 and 0.91CT-based radiomics can detect early brainstem infarction.
Zhang R et al. [39]2020R241Identification of ischemic penumbraDWI hypodensities896MRIAUC of 0.92 and 0.90
Sens of 0.93 and 0.88
Spec of 0.75 and 0.74
Acc of 0.82 and 0.80		MRI-based radiomics can identify ischemic penumbra in AIS patients.
Su et al. [40]2020R148Stroke predictionLacunar lesions1209CTC-Index of 0.7864–0.7140CT-based radiomics can provide information for the prediction of future ischemic strokes in patients with silent lacunar infarction.
Tang et al. [41]2022R156Stroke recurrencePlaque402MRIAUC of 0.899–0.803MRI-based radiomics provide important information for the prediction of stroke recurrence in SICAS patients.
Wang et al. [42]2022R1003Stroke recurrenceInfarct513MRIAUC of 0.847MRI-based radiomics could help to predict 1-year AIS recurrence.
Hofmeister et al. [43]2020R156Predict MTB strategyClot9CTAUC of 0.88Clot-based RFs can help with the MTB strategy.
Sarioglu et al. [44]2022R52Predict fist pass effectClot12CTAcc of 0.83Clot-based RFs can estimate recanalization successfully.
Patel et al. [45]2023R293Predict first pass effectClot227CT/CTAAUC of 0.832–0.787
Acc of 0.760–0.787		Clot-based radiomics are potential candidate markers for first pass effect prediction
Zhang et al. [46]2021R141TICI scale predictionInfarct321MRIAUC of 0.7442MRI-based radiomics can provide important information about the patients’ response to thrombectomy.
Xiong et al. [47]2023R256TICI scale predictionClot1130CTAUC of 0.860–0.849CT-based radiomics can predict the successfully recanalization in AIS patients after stent retrieve therapy
Van Voorst et al. [48]2022R699RecanalizationClot-CT-Clot-based radiomics are independently associated with reperfusion, but the results of the clinical and radiomics model was similar.
Qui et al. [49]2019R67Recanalization after alteplaseClot326CT/CTAAUC of 0.85Clot-based radiomics are more predictive of recanalization with alteplase than other classical clot features.
Fu B et al. [50]2020R116Predict malignant cerebral edemaMCA13NECTAUC of 0.96Radiomics could help to predict MCE using NECT.
Jiang et al. [51]2022R389Predict malignant cerebral edemaStroke and CSF1316MRIAUC 0.83–0.86
Acc 0.85–0.81		MRI radiomic features can provide information for predicting cerebral edema in AIS patients
Wen et al. [52]2020R126Predict mMCAiMCA territory396CT/CTAAUC of 0.917–0.913CT-based radiomics can be a tool to predict the risk of mMCAi.
Meng et al. [53]2022R71Predict hemorrhage transformationInfarct5400MRIAUC of 0.911
Acc of 0.894		A combined model based on MRI radiomic features can predict the hemorrhage transformation in AIS patients.
Xie et al. [54]2022R118Predict hemorrhage transformationInfarct851CTAUC of 0.845–0.750CT-based radiomics could help in the prediction of hemorrhage transformation in AIS patients.
Liu et al. [55]2021R104Predict hemorrhage expansionHemorrhage1691CTAUC of 0.91–0.87
Sens of 0.83–0.60
Spec 0.89–0.85		CT-based radiomics can predict hemorrhage expansion.
Chen Y et al. [56]2022R82Etiology predictionNA116CTAAUC of 0.9018
Acc of 0.8929		Radiomics could effectively predict the subtype of ischemic stroke.
Jiang J et al. [57]2023R403Etiology predictionClotNACTAUC of 0.838Clot-based RFs can identify the CE strokes.
Cheng Y et al. [58]2022R221Time since strokeClot944CTAAUC of 0.803
AUC of 0.813–0.803		Radiomics can estimate the TSS in patients with AIS.
Yao et al. [59]2020R316Time since strokeInfarct295CTAUC of 0.982–0.974
Sens of 0.929–0.951
Spec of 0.959–0.961		Radiomics is useful in the determination of TSS in basal ganglia infarction.
Wen et al. [60]2021R123Time since strokeASPECTS396CT/CTAAUC of 0.808–0.833CT-based radiomics can discriminate the TSS in patients with MCAO in the M1 segment.
Zhang et al. [61]2022R84Time since stroke-4312MRIAUC of 0.754
Acc of 0.788		MRI-based radiomics could aid in the decision-making for thrombolysis in patients with unknown stroke onset.
Chen X et al. [62]2022R101Differentiate IPH vs. contrastNA1316NECTAUC of 0.848 and 0.826; Acc of 0.776, S of 0.767, and Spec of 0.789RFs can differentiate IPH from contrast extravasation after MT.
Ma Y et al. [63]2022R100Hemorrhage vs. Iodinated contrast extravasationHyperdense area1316CTAUC of 0.972 and 0.926 in training and validation cohortsCombined nomogram based on CT-radiomic features can differentiate between hemorrhage and iodine contrast extravasation.

3. Discussion

The year 2022 has seen exponential growth in radiomics, which could have been greater or even earlier had it not been for the appearance of COVID-19. Radiomics can be applied to all types of imaging, as we have seen in this review, and it does not require alternative diagnostic procedures to the usual ones for the management of patients with acute ischemic stroke. Of the studies reviewed, most are based on MRI to obtain radiomic features, but we have also seen that CT-based radiomics gives very good results. This, together with the fact that all patients with AIS undergo imaging, means that we have a virtually inexhaustible source of patients, which is likely to increase in the coming years.
The application of radiomics to the management of patients with AIS could represent a major diagnostic advance. In the results we have observed in this review, we have seen that radiomic models perform excellently in a wide variety of clinical situations in patients with AIS. For example, radiomics could be able to predict clinical status at 90 days, identify ischemic lesions not yet detectable by the neuroradiologist’s eye or by perfusion or diffusion techniques, provide a probability of new AIS, predict the outcome of reperfusion techniques, predict complications of AIS, determine the etiology of AIS, determine the time course of patients with AIS of unknown onset, or differentiate hemorrhagic complications from iodinated contrast extravasation. We find the application of radiomics in predicting the outcome of revascularization, predicting complications after AIS, and predicting etiology of AIS particularly interesting.
Radiomics offers a somewhat astonishing range of possibilities, considering the current paradigms of conventional imaging. In particular, the fact that the radiomic analysis of the thrombus may provide information about its etiological origin makes perfect sense, as different clot compositions may imply specific characteristics. Nonetheless, in the author’s opinion, the results of other studies should be interpreted with more caution.
Currently, the determination of the etiology of stroke is a key point in the management of the AIS patient, as the treatment of such a patient is based on the etiology of the AIS. This lies on several laboratory, clinical and imaging parameters, which frequently imply a several days delay in initiating the specific prevention treatment and sometimes cannot provide a specific diagnosis. Therefore, if radiomics can also detect different data patterns depending on the etiology of the thrombus, we could be looking at a technique that could significantly change the clinical management of the AIS patient.
In this respect, it stands to reason that radiomics can offer prognostic information. This could have two possible explanations. On the one hand, if these results were not true, the retrospective design of the studies may have a big influence but can therefore be proven with further prospective studies to rule out any variables that were not analyzed and could act as a bias (such as the different thrombectomy techniques used, the material used, the skill of the neuroradiologist, etc.). This classic drawback of radiomic studies is due to the lack of prospective studies [64], which could be explained by the recent emergence of these radiological techniques in medical research. On the other hand, if the results are surprisingly true, this could be derived by the intrinsic characteristics of the thrombus, by its frailty and susceptibility to fragmentation or its resistance to thrombectomy devices. These characteristics are the ones that seem to be relevant in the, in many ways, opaque radiomic analysis.
In terms of predicting the outcome of revascularization, radiomics can be useful in predicting the number of thrombectomy attempts required to achieve adequate reperfusion. It can also provide valuable information on the final outcome of thrombectomy by predicting the TICI scale.
With regard to the radiomics models reviewed, the studies that used a combined radiomics and clinical model showed better results than the isolated radiomics or clinical models. For this reason, we think that the first steps of radiomics in the routine clinical management of patients with AIS will be in the form of support for clinical criteria currently used for certain diagnoses.
Segmentation is a fundamental point in radiomics, and manual segmentation is still considered the gold standard; however, the major drawback of this method is the waste of time for the neuroradiologist. Segmentation software is now available that allows semi-automated and 3D segmentation to be performed quickly and easily, which we believe will replace the manual method as the gold standard in the coming years [65]. In terms of feature extraction, we found that this varied from study to study. Heterogeneity in imaging acquisition and feature extraction is a classic problem in radiomic studies and one that initiatives such as the Radiomics Quality Score (RQS) are trying to standardize [66].
Finally, in the studies included in this article, we can see that radiomics provides valuable information that can complement the information provided by conventional imaging studies. The future of radiomics is promising, we may be at a tipping point that will change the paradigm of diagnostic imaging, but we believe that prospective studies are needed to demonstrate that radiomic analysis of an image adds specific diagnostic value.

4. Conclusions

Radiomic models can provide valuable information for the management of patients with AIS. The results obtained with radiomic models are very good and encouraging. The wide variety of clinical situations in which radiomics provides valuable information in patients with AIS suggests that we may be looking at a model that will change the paradigm of clinical management of patients with AIS. Further prospective studies are needed to investigate the utility of radiomics in the mentioned fields of knowledge and others that may appear in the future.

Author Contributions

Conceptualization, J.P.-Á. and A.M.M.; methodology, J.M.F., M.S.L. and M.S.-B.; software, J.P.-Á. and A.M.M.; validation, M.B.U. and F.V.H.; formal analysis, A.M.M., M.B.U. and F.V.H.; investigation, J.P.-Á., A.M.M., J.M.F. and R.I.R.; resources, M.S.L. and A.M.M.P.; data curation, J.P.-Á. and J.M.F.; writing—original draft preparation, J.P.-Á. and M.S.L.; writing—review and editing, A.M.M., J.M.F., M.B.U., F.V.H., A.M.M.P., M.R.-Y. and A.B.V.; visualization, M.B.U., F.V.H., J.M.P. and R.I.R.; supervision, A.M.M. and J.M.F.; project administration, A.M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Santiago-Lugo (protocol code 2023/299 approved on 21 June 2023).

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AI: Artificial Intelligence; AIS: Acute Ischemic Stroke; AUC: Area Under Curve; ALVO: Anterior Large Vessel Occlusion; C-Index: Concordance Index; CE: Cardioembolic Stroke; CI: confidence interval; CT: Computed Tomography; CSF: Cerebrospinal Fluid; CTA: Computed Tomography Angiography; DWI: Diffusion Weighted Imaging; DSC-PWI: Dynamic susceptibility contrast perfusion-weighted imaging; Acc: Accuracy; EGFR: Epidermal Grow Factor Receptor; GLCM: grey level co-occurrence matrix; GLDZM: grey level distance zone matrix; GLRLM: grey level run length matrix; GLSZM: grey level size zone matrix; IS: Ischemic Stroke; ICA: Internal Carotid Artery; IPH: Intraparenchymal Hemorrhage; LVO: Large Vessel Occlusion; MCA: middle cerebral artery; MCAO: Middle Cerebral Artery Occlusion; MCE: Malignant Cerebral Edema; mMCAi: Malignant Acute Middle Cerebral Artery Infarction; MRI: Magnetic Resonance Imaging; mRS: modified Rankin Scale; MTB or MT: Mechanical Thrombectomy; NCECT: Non Contrast Enhancing Computed Tomography; NGLDM: Neighborhood Grey Level Dependence Matrix; NGTDM: Neighborhood Grey Level Difference Matrix; NIHSS: National Institutes of Health Stroke Scale; NPV: Negative Predictive Value; OR: Odds Ratio; PET: Positron Emission Tomography; PPV: Positive Predictive Value; RF: radiomic features; ROC: Receiver Operating Characteristic; ROI: Region Of Interest; S: Sensitivity; SICAS: Symptomatic Intracranial Atherosclerotic Stenosis; Spec: Specificity; SVM: Support Vector Machine; TICI: Thrombolysis In Cerebral Infarction; TSS: Time Since Stroke; US: Ultrasound; WB: Whole Brain; WHO: World Health Organization.

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Figure 1. Radiomics workflow.
Figure 1. Radiomics workflow.
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Figure 2. (a) Radiomics articles about AIS patients since 2018. (b) Radiomics articles about AIS published per year.
Figure 2. (a) Radiomics articles about AIS patients since 2018. (b) Radiomics articles about AIS published per year.
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Figure 3. Distribution of articles by topic.
Figure 3. Distribution of articles by topic.
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MDPI and ACS Style

Porto-Álvarez, J.; Martínez, A.M.; Martínez Fernández, J.; Sanmartín López, M.; Blanco Ulla, M.; Vázquez Herrero, F.; Pumar, J.M.; Rodríguez-Yáñez, M.; Minguillón Pereiro, A.M.; Bolón Villaverde, A.; et al. How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke? Appl. Sci. 2023, 13, 10061. https://doi.org/10.3390/app131810061

AMA Style

Porto-Álvarez J, Martínez AM, Martínez Fernández J, Sanmartín López M, Blanco Ulla M, Vázquez Herrero F, Pumar JM, Rodríguez-Yáñez M, Minguillón Pereiro AM, Bolón Villaverde A, et al. How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke? Applied Sciences. 2023; 13(18):10061. https://doi.org/10.3390/app131810061

Chicago/Turabian Style

Porto-Álvarez, Jacobo, Antonio Mosqueira Martínez, Javier Martínez Fernández, Marta Sanmartín López, Miguel Blanco Ulla, Fernando Vázquez Herrero, José Manuel Pumar, Manuel Rodríguez-Yáñez, Anxo Manuel Minguillón Pereiro, Alberto Bolón Villaverde, and et al. 2023. "How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?" Applied Sciences 13, no. 18: 10061. https://doi.org/10.3390/app131810061

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