Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

Wu, Jiangfen; Xu, Qian; Shen, Yiqing; Chen, Weidao; Xu, Kai; Qi, Xian

doi:10.3390/jcm11154625

Cited by 8 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After preprocessing, deep learning models were proposed to segment lesions, which greatly improved future work efficiency. This work selected a deep learning model method based on the transformer architecture (Swin transformer) because of its superiority in multiple domains [ 15 , 16 ]. The Swin transformer adopts a hierarchical design containing a total of four stages: each stage decreases the resolution of the input feature map and expands the perceptual field layer by layer, similar to a convolutional neural network.…”

Section: Methodsmentioning

confidence: 99%

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Wang

Song

et al. 2023

BMC Med

View full text Add to dashboard Cite

Background Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. Methods A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. Results The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I–II or WHO III–IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. Conclusions This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

show abstract

Section: Methodsmentioning

confidence: 99%

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Wang

Song

et al. 2023

BMC Med

View full text Add to dashboard Cite

show abstract

“…Only T2 images were utilized for this model as they are routinely acquired and are best for IDH genotyping [90] . The authors established a robust T2‐weighted image‐based model that could predict IDH mutation status, [38] suggesting a promising future for the Swin Transformer with bounding box input images in clinical practice with individualized treatment options.…”

Section: The Roles Of Transformers In the Prediction Of Molecular Exp...mentioning

confidence: 99%

“…The identification of GBM genotypes based on these radiographic features is difficult for the radiologist. Wu et al built a Transformer-based model for predicting the mutation status of IDH without using refined tumor segmentation [38] by initially establishing Transformer-and CNN-based models and then defining seven different image inputs with different rectangle sizes and peritumor tissue quantities. Subsequently, relevant clinical information for predictions was incorporated to optimize the model's performance.…”

Section: The Roles Of Transformers In the Prediction Of Molecular Exp...mentioning

confidence: 99%

Potential roles of transformers in brain tumor diagnosis and treatment

Lan

Zou

Qin

et al. 2023

Brain-X

View full text Add to dashboard Cite

Brain tumor (BT) is one of many malignancies that have substantially enhanced global human morbidity and mortality rates. Early detection and characterization of glioma are essential for effective preventive strategies. Currently, the use of Transformers, a deep learning model for BT diagnosis and treatment, is attracting significant attention. The transformer self‐attention mechanism automatically learns the associations between input data for efficient processing and analysis. Research indicates that Transformers could play an essential role in the BT segmentation of magnetic resonance imaging (MRI) images, the MRI and histopathology‐based grading of brain cancer, BT molecular expression prediction, the classification of primary brain metastasis sites, voxel‐level dose and BT radiotherapy outcome prediction, synergistic prediction, and the pathway deconvolution of drug combinations. In this review, the feasibility, accuracy, and applicability of various algorithms are systematically analyzed and their prospects are discussed. Overall, this review aimed to discuss and provide an overview of the increasing applications of Transformers in real‐time BT detection and therapy, indicating their broad prospects and potential. In the future, Transformers are expected to be increasingly used for the diagnosis and subsequent treatment of BT because of the continuous development and improvement of Transformer‐based deep learning technology. However, more work is required to investigate their properties for anomaly detection, medical image classification, network design development, and application to other medical data.

show abstract

“…Traditional detection methods based on immunohistochemistry (IHC) and next-generation sequencing (NGS) are time-consuming and are mostly used for postoperative diagnosis, which cannot meet the needs of early determination of IDH mutation status. The application of deep machine learning using radiomics images has shown potential to be used for the prediction of IDH mutation status 5 – 7 . Moreover, newer detection systems are continually being developed, some of which can rapidly provide molecular information during surgery 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%

Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma

Yu,

Zhong,

Xiao

et al. 2024

npj Precis. Onc.

View full text Add to dashboard Cite

Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.

show abstract

Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

Cited by 8 publications

References 45 publications

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study

Potential roles of transformers in brain tumor diagnosis and treatment

Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma

Contact Info

Product

Resources

About