Radiomic analysis of the optic nerve at the first episode of acute optic neuritis: an indicator of optic nerve pathology and a predictor of visual recovery?

Cellina, Michaela; Pirovano, Marta; Ciocca, Matteo; Gibelli, Daniele; Floridi, Chiara; Oliva, Giancarlo

doi:10.1007/s11547-020-01318-4

Cited by 42 publications

(35 citation statements)

References 38 publications

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“…Although radiomics is not a panacea for clinical management, it is an up-and-coming and highly relevant method in cancers and in diseases other than malignancy such as neurological and vascular pathologies [ 156 , 186 , 187 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

Granata

Grassi

Fusco³

et al. 2021

Infect Agents Cancer

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This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

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Section: Discussionmentioning

confidence: 99%

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

Granata

Grassi

Fusco³

et al. 2021

Infect Agents Cancer

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“…Although most previous studies focused on various types of tumors, the potential value of radiomics in optic neuropathies has also been explored. One study on optic neuritis patients demonstrated that MRI-based radiomic features of the optic nerve were associated with visual function and visual outcome, implying that radiomic parameters may correlate with axonal integrity [13]. Another study also found that texture features of the optic nerve could assess the involvement of the optic nerve in optic neuritis patients [14].…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have demonstrated that the microscopic radiomic parameters could reflect the underlying pathophysiological process of various diseases, especially many types of tumors [11,12]. As for optic neuropathies, previous studies have suggested that radiomic analysis of the optic nerve from magnetic resonance imaging (MRI) could potentially assess the visual outcome of patients with optic neuritis, while the utility of radiomics in compressive optic neuropathy has never been explored [13,14]. Therefore, we hypothesized that radiomic features of the optic chiasm compressed by pituitary adenomas may be associated with the underlying axonal injury and have the potential to predict vision recovery.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Radiomics of the Optic Chiasm Predict Visual Outcome Following Pituitary Adenoma Surgery

Zhang

Chen

Huang

et al. 2021

JPM

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Preoperative prediction of visual recovery after pituitary adenoma surgery remains a challenge. We aimed to investigate the value of MRI-based radiomics of the optic chiasm in predicting postoperative visual field outcome using machine learning technology. A total of 131 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (N = 79) and the non-recovery group (N = 52) according to visual field outcome following surgical chiasmal decompression. Radiomic features were extracted from the optic chiasm on preoperative coronal T2-weighted imaging. Least absolute shrinkage and selection operator regression were first used to select optimal features. Then, three machine learning algorithms were employed to develop radiomic models to predict visual recovery, including support vector machine (SVM), random forest and linear discriminant analysis. The prognostic performances of models were evaluated via five-fold cross-validation. The results showed that radiomic models using different machine learning algorithms all achieved area under the curve (AUC) over 0.750. The SVM-based model represented the best predictive performance for visual field recovery, with the highest AUC of 0.824. In conclusion, machine learning-based radiomics of the optic chiasm on routine MR imaging could potentially serve as a novel approach to preoperatively predict visual recovery and allow personalized counseling for individual pituitary adenoma patients.

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“…Radiomics features contain characteristics of both imaging and numeric features. Radiomics features generally refer to “agnostic” quantitative measurements that are mathematically extracted and differ from “semantic” features such as those covered by radiological lexicons [ 59 , 60 ]. Four main radiomics phenotypes have been used to capture tissue heterogeneity: 1) volume and shape; 2) first-order.…”

Section: Current Applicationsmentioning

confidence: 99%

Radiomics in hepatic metastasis by colorectal cancer

Granata

Fusco

Barretta

et al. 2021

Infect Agents Cancer

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Background Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. Methods The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. Results We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. Conclusions In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.

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Radiomic analysis of the optic nerve at the first episode of acute optic neuritis: an indicator of optic nerve pathology and a predictor of visual recovery?

Cited by 42 publications

References 38 publications

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

Machine Learning-Based Radiomics of the Optic Chiasm Predict Visual Outcome Following Pituitary Adenoma Surgery

Radiomics in hepatic metastasis by colorectal cancer

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