Magnetic Resonance Standard for Cavernous Sinus Hemangiomas: Proposal for a Diagnostic Test

He, Kangmin; Chen, Liang; Zhu, Wei; Mao, Ying

doi:10.1159/000358872

Cited by 15 publications

(6 citation statements)

References 30 publications

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“…Since the 1990s, the characteristic appearance of CaSHs on MR images has been described in many studies, which greatly [8]. Some CaSHs are characterized by their progressive enhancing process from heterogeneous to uniform or 'edge-tocenter' enhancement on dynamic MRI, which is similar to hepatic hemangiomas.…”

Section: Discussionmentioning

confidence: 99%

“…Since 1999 when Iwai et al demonstrated a favorable case of GKS for a postsurgical CaSH [10], many studies have reported similar treatment outcomes of GKS for residual CaSHs after surgery [2,9,13,16,21,24,28,31,35]. They also noted the radiological characteristics of these tumors, especially their extremely high signal on MR T2-weighted and FLAIR sequence images, which have greatly improved the diagnosis of these tumors before operation [2,8,12,16,17,19,36]. At present, radiosurgery is being used and becoming a promising option for primary or postoperative management of patients with CaSHs.…”

Section: Introductionmentioning

confidence: 93%

See 1 more Smart Citation

A new classification and clinical results of Gamma Knife radiosurgery for cavernous sinus hemangiomas: a report of 53 cases

Tang

Wang

et al. 2015

Acta Neurochir

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Our study showed that GKS is a useful and safe therapeutic method for CaSHs as both a primary and adjuvant treatment. The new classification of CaSHs may help predict their clinical course during tumor development and treatment response after GKS. Further studies with long-term follow-up and larger numbers of cases are necessary to optimize the treatment conditions and verify the benefit of this treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

A new classification and clinical results of Gamma Knife radiosurgery for cavernous sinus hemangiomas: a report of 53 cases

Tang

Wang

et al. 2015

Acta Neurochir

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“…Figure 4). Therefore, these sequences are considered the most significant reference among all types of slices in the differential diagnosis of the two studied tumor types (23,24). The tumor classification model trained by the transverse T1-weighted contrast-enhanced sequence images and tested on the external validation sets achieved high accuracy, sensitivity, and specificity in automated cavernous hemangioma and schwannoma differential diagnosis in a real-world setting that is completely consistent with the clinical environment.…”

Section: Resultsmentioning

confidence: 92%

Differentiate cavernous hemangioma from schwannoma with artificial intelligence (AI)

Bi¹,

Chen²,

Zhang³

et al. 2020

Ann Transl Med

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Background: Cavernous hemangioma and schwannoma are tumors that both occur in the orbit. Because the treatment strategies of these two tumors are different, it is necessary to distinguish them at treatment initiation. Magnetic resonance imaging (MRI) is typically used to differentiate these two tumor types; however, they present similar features in MRI images which increases the difficulty of differential diagnosis. This study aims to devise and develop an artificial intelligence framework to improve the accuracy of clinicians' diagnoses and enable more effective treatment decisions by automatically distinguishing cavernous hemangioma from schwannoma. Methods: Material: As the study materials, we chose MRI images as the study materials that represented patients from diverse areas in China who had been referred to our center from more than 45 different hospitals. All images were initially acquired on films, which we scanned into digital versions and recut. Finally, 11,489 images of cavernous hemangioma (from 33 different hospitals) and 3,478 images of schwannoma (from 16 different hospitals) were collected. Labeling: All images were labeled using standard anatomical knowledge and pathological diagnosis. Training: Three types of models were trained in sequence (a total of 96 models), with each model including a specific improvement. The first two model groups were eye-and tumor-positioning models designed to reduce the identification scope, while the third model group consisted of classification models trained to make the final diagnosis. Results: First, internal four-fold cross-validation processes were conducted for all the models. During the validation of the first group, the 32 eye-positioning models were able to localize the position of the eyes with an average precision of 100%. In the second group, the 28 tumor-positioning models were able to reach an average precision above 90%. Subsequently, using the third group, the accuracy of all 32 tumor classification models reached nearly 90%. Next, external validation processes of 32 tumor classification models were conducted. The results showed that the accuracy of the transverse T1-weighted contrast-enhanced sequence reached 91.13%; the accuracy of the remaining models was significantly lower compared with the ground truth. Conclusions: The findings of this retrospective study show that an artificial intelligence framework can achieve high accuracy, sensitivity, and specificity in automated differential diagnosis between cavernous hemangioma and schwannoma in a real-world setting, which can help doctors determine appropriate treatments.

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“…( 1234567890 www.nature.com/scientificreports/ Radiomics can provide additional metabolic and biological information in addition to the traditional MRI metrics. Gray contrast, uniformity, depth, and texture roughness have been used to study tumor grading, prediction of genomic information, and differentiation of lesion and non-lesion images [39][40][41] . The present study found that higher-order features could better reflect the degree of tumor heterogeneity and texture information.…”

Section: Discussionmentioning

confidence: 99%

A radiomics-based study for differentiating parasellar cavernous hemangiomas from meningiomas

Wang

You

Zhang

et al. 2022

Sci Rep

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To investigate the value of the radiomic models for differentiating parasellar cavernous hemangiomas from meningiomas and to compare the classification performance with different MR sequences and classifiers. A total of 96 patients with parasellar tumors (40 cavernous hemangiomas and 56 meningiomas) were enrolled in this retrospective multiple-center study. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI scans. Radiomics features were extracted from five MRI sequences using radiomics software. Three feature selection methods and six classifiers were evaluated in the training cohort to construct favorable radiomic machine-learning classifiers. The performance of different classifiers was evaluated using the AUC and compared to neuroradiologists. The detection rates of T1WI, T2WI, and CE-T1WI for parasellar cavernous hemangiomas and meningiomas were approximately 100%. In contrast, the ADC maps had the detection rate of 18/22 and 19/25, respectively, (AUC, 0.881) with 2.25 cm as the critical value diameter. Radiomics models with the SVM and KNN classifiers based on T2WI and ADC maps had favorable predictive performances (AUC > 0.90 and F-score value > 0.80). These models outperformed MRI model (AUC 0.805) and neuroradiologists (AUC, 0.756 and 0.545, respectively). Radiomic models based on T2WI and ADC and combined with SVM and KNN classifiers have the potential to be a viable method for differentiating parasellar hemangiomas from meningiomas. T2WI is more universally applicable than ADC values due to its higher detection rate for parasellar tumors.

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Magnetic Resonance Standard for Cavernous Sinus Hemangiomas: Proposal for a Diagnostic Test

Cited by 15 publications

References 30 publications

A new classification and clinical results of Gamma Knife radiosurgery for cavernous sinus hemangiomas: a report of 53 cases

A new classification and clinical results of Gamma Knife radiosurgery for cavernous sinus hemangiomas: a report of 53 cases

Differentiate cavernous hemangioma from schwannoma with artificial intelligence (AI)

A radiomics-based study for differentiating parasellar cavernous hemangiomas from meningiomas

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