Differentiate cavernous hemangioma from schwannoma with artificial intelligence (AI)

Abstract

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.

Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool

“…33,34 In Bi's research, based T1-weighted contrast-enhanced sequence model, the diagnosis between OCVM and schwannoma reached an accuracy of 91.13%, a sensitivity of 86.84%, a specificity of 93.02%, and an AUC of 0.9535, which carried out similar diagnostic level. 19 In another research, the result of MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation reached an accuracy of 73.02%, a sensitivity of 72.97%, a specificity of 73.08%, and an AUC of 0.73. 18 The results of this study were significantly higher than that diagnostic performance in the lymphoma study.…”

“…Since we only used CT plain scan image data, and it is not reasonable to make accurate qualitative diagnosis based on current medical knowledge in the clinic under such conditions, so we did not conduct a direct comparative analysis between imaging scientists and AI discussed in relative studies 33,34 . In Bi's research, based T1-weighted contrast-enhanced sequence model, the diagnosis between OCVM and schwannoma reached an accuracy of 91.13%, a sensitivity of 86.84%, a specificity of 93.02%, and an AUC of 0.9535, which carried out similar diagnostic level 19 . In another research, the result of MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation reached an accuracy of 73.02%, a sensitivity of 72.97%, a specificity of 73.08%, and an AUC of 0.73 18 .…”

“…A recent AI study focusing on differential diagnosis OCVM and schwannoma achieved 90% of precision in judging lesion location and lesion type, which reach the level of expert radiologist with MRI enhancement images, proving that the neural network models based on large samples can quickly identify, diagnose and predict. 19 In this study, we aimed to use non-enhanced CT images of the orbit to construct an ML model to assist clinicians in distinguishing between OCVM and non-OCVM. The model with high qualitative diagnostic ability may provide more valuable clinical information, facilitating decreasing in number of enhanced CT and MRI for clinical suspected OCVM patients for fewer side effects and lower costs.…”

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

“…A complete list of the excluded articles and the respective reasons for exclusion is provided in Supplementary Table S1 . Thirty three articles were excluded due to predicting only pathological features, e.g., grade (n = 16), or differentiating between tumor entities (n = 8) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Thirty four articles were excluded due to predicting only clinical parameters, e.g., tumor consistency (n = 7), response/treatment outcome (n = 12) or brain/bone invasion (n = 4) [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ...…”

Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

“…Over the past few years, artificial intelligence (AI), especially deep learning (DL) [10], has played a major role in the field of medicine, including image recognition [11], auxiliary diagnosis [12], drug development [13], and health care management [14]. In fact, DL based systems have been developed to detect eyelid melanoma and basal cell carcinoma using dermoscopic images [15,16] or pathological images [17,18].…”