Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke

Zhou, Yiran; Wu, Di; Yan, Su; Xie, Yan; Zhang, Shun; Lv, Wenzhi; Qin, Yuanyuan; Liu, Yufei; Liu, Chengxia; Lu, Jun; Li, Jia; Zhu, Hongquan; Liu, Weiyin Vivian; Liu, Huan; Zhang, Guiling; Zhu, Wenzhen

doi:10.3348/kjr.2022.0160

Cited by 24 publications

(18 citation statements)

References 36 publications

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“…Chen et al found that, compared with the clinical or radiomics model, the combined model has the highest sensitivity and AUC in predicting hematoma expansion in sICH patients (31). In addition, in a study about predicting the outcomes of acute ischemic stroke at 6 months after hospital discharge, the AUC of the R-C combined model is 0.868 in the training cohort and 0.890 in the validation cohort, which is significantly higher than that of the clinical or radiomics model (32). Similarly, a machine learning model based on PET/CT radiomics and clinical characteristics predicted the tumor immune microenvironment profiles of nonsmall cell lung cancer, which showed that the R-C combined model has the best performance (33).…”

Section: Discussionmentioning

confidence: 96%

Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram

Wang

Zhou²,

Chen³

et al. 2023

Front. Neurol.

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Hypertensive intracerebral hemorrhage (HICH) is the most common type of spontaneous intracerebral hemorrhage in China which is associated with high mortality and disability. We sought to develop and validate a noncontrast computed tomography (NCCT)-based nomogram model to achieve short-term prognostic prediction for patients with HICH. We retrospectively studied 292 patients with HICH from two medical centers, and they were divided into training (n = 151), validation (n = 66), and testing cohorts (n = 75). Based on radiomics, univariate and multivariate, and logistic regression analyses, four models (black hole sign, clinical, radiomics score, and combined models) were established to predict the prognosis of patients with HICH 30 days after the onset. The results suggested that the combined model had the best predictive performance with the area under the receiver operating characteristic curve (AUC) of 0.821, 0.816, and 0.815 in the training, validation, and testing cohorts, respectively. In addition, a radiomics-clinical (R-C) nomogram was visualized. A calibration curve analysis showed that the R-C nomogram had satisfactory calibration in the three cohorts. A decision curve analysis demonstrated that the R-C nomogram was clinically valuable. Our results suggest that the R-C nomogram can accurately and reliably predict the short-term prognosis of patients with HICH and provide a useful evaluation for making individualized treatment plans.

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Section: Discussionmentioning

confidence: 96%

Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram

Wang

Zhou²,

Chen³

et al. 2023

Front. Neurol.

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“…LAHGLE measures the proportion in the image of the joint distribution of larger size zones with higher gray-level values, while GLNN measures the similarity of gray-level intensity values in the image, with a lower GLN value correlating with greater similarity. 17 Despite the limited clinical relevance of the visual interpretation of the nal transformed features, these features are may be integral to the radiomics and may reveal potential applications. 15 By using the above radiological features, combined with clinical information, we developed ve models to predict MLM in patients with colorectal cancer and compared the performance between different models.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based radiomic models for predicting metachronous liver metastases in colorectal cancer patients: a multimodal study

Wang,

Zhang,

Shu

et al. 2023

Preprint

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Purpose The purpose of this study was to investigate whether a multimodal radiomic model powered by machine learning (ML) can accurately predict the occurrence of metachronous liver metastases (MLM) in patients with colorectal cancer (CRC). Patients and methods: A total of 157 consecutive patients with CRC between 2010 and 2020 were retrospectively included. Out of these patients, 67 experienced liver metastases within 2 years of treatment, while the remaining patients did not. Radiomic features were extracted from annotated MR images of the tumor and portal venous phase CT images of the liver for each patient. Following that, ML-based radiomic models were then developed and integrated with the clinical features for MLM prediction by employing LASSO and RF algorithms. The performance of the model was evaluated using the ROC curve, while the clinical utility was measured using the DCA curve. Results A total of 922 and 1216 radiomic features were extracted from the MRI and CT images of each patient, quantifying the intensity, shape, orientation, and texture of the tumor and liver, respectively. The mean area under the curves(AUCs) for predicting metachronous liver metastases were 0.80, 0.68, and 0.82 for the CT, MRI, and Merged models, respectively. For the Clinical and Clinical-Merged models, the AUCs were 0.64 and 0.72, respectively. There was no significant difference between the CT model and the Merged model (p < 0.05). Conclusion Our preliminary results demonstrate the utility of ML-based radiomic models in predicting MLM in patients with CRC. However, further investigation is required to explore the potential of multimodal fusion models, as they offered only minimal improvement in diagnostic performance.

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“…Radiomics is a novel developed data analysis technique that can transform medical images into high-throughput quantitative features, assess the heterogeneity of diseased tissue, and reflect the physiological and pathological status and has been applied to the prediction of clinical outcomes. At present, radiomics has a promising application prospect in stroke, including the diagnosis of stroke (Peter et al, 2017),early prediction of clinical outcome (Wen et al, 2020) and evaluation of medium and long term prognosis (Tang et al, 2020;Quan et al, 2021;Wang et al, 2021;Zhou et al, 2022). Wen et al (2020) developed a model based on radiological features extracted from computed tomography non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) to predict the development of malignant acute middle cerebral Artery Infarction (mMCAi) in patients with cerebral infarction.…”

Section: Introductionmentioning

confidence: 99%

“…Wen et al (2020) developed a model based on radiological features extracted from computed tomography non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) to predict the development of malignant acute middle cerebral Artery Infarction (mMCAi) in patients with cerebral infarction. Several recent studies have shown that the clinical-radiomics model extracted from diffusion-weighted imaging (DWI), fluid attenuated inversion recovery (FLAIR) or apparent diffusion coefficient (ADC) achieved satisfactory performance in predicting AIS outcomes (Tang et al, 2020;Quan et al, 2021;Wang et al, 2021;Zhou et al, 2022). Most patients with ischaemic stroke receive only routine sequences, including DWI, ADC and FLAIR.…”

Section: Introductionmentioning

confidence: 99%

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke

Zhu²,

Zhang³

et al. 2023

Front. Neurosci.

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ObjectiveWe explored whether radiomics features extracted from diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images can predict the clinical outcome of patients with acute ischaemic stroke. This study was conducted to investigate and validate a radiomics nomogram for predicting acute ischaemic stroke prognosis.MethodsA total of 257 patients with acute ischaemic stroke from three clinical centres were retrospectively assessed from February 2019 to July 2022. According to the modified Rankin scale (mRS) at 3 months, the patients were divided into a favourable outcome group (mRS of 0–2) and an unfavourable outcome group (mRS of 3−6). The high-throughput features from the regions of interest (ROIs) within the radiologist-drawn contour by AK software were extracted. We used two feature selection methods, minimum redundancy and maximum (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO), to select the features. Three radiomics models (DWI, FLAIR, and DWI-FLAIR) were established. A radiomics nomogram with patient characteristics and radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the test and validation sets. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram.ResultsThe FLAIR, DWI, and DWI-FLAIR radiomics model exhibited good prediction performance, with area under the curve (AUCs) of 0.922 (95% CI: 0.876−0.968), 0.875 (95% CI: 0.815−0.935), and 0.895 (95% CI: 0.840−0.950). The radiomics nomogram with clinical characteristics including the overall cerebral small vessel disease (CSVD) burden score, hemorrhagic transformation (HT) and admission National Institutes of Health Stroke Scale score (NIHSS) score and the FLAIR Radscore presented good discriminatory potential in the training set (AUC = 0.94; 95% CI: 0.90−0.98) and test set (AUC = 0.94; 95% CI: 0.87−1), which was validated in the validation set 1 (AUC = 0.95; 95% CI: 0.88−1) and validation set 2 (AUC = 0.90; 95% CI: 0.768−1). In addition, it demonstrated good calibration, and decision curve analysis confirmed the clinical value of this nomogram.ConclusionThis non-invasive clinical-FLIAR radiomics nomogram shows good performance in predicting ischaemic stroke prognosis after thrombolysis.

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Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke

Cited by 24 publications

References 36 publications

Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram

Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram

Machine learning-based radiomic models for predicting metachronous liver metastases in colorectal cancer patients: a multimodal study

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke

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