Research graph
References from Transformer-Based Multimodal Fusion Integrating Computed Tomography, Pathology, and Clinical Features for Oral Squamous Cell Carcinoma Recurrence Prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2021 · External reference
Head and neck squamous cell carcinoma
10.1038/s41572-020-00224-3 · 2020 · External reference
Prognostic factors of oral squamous cell carcinoma: the importance of recurrence and pTNM stage
10.1186/s40902-024-00410-3 · 2024 · External reference
Improved recurrence rates and progression-free survival in primarily surgically treated oral squamous cell carcinoma
10.1007/s00784-024-05644-z · 2024 · External reference
Tumor recurrence and follow-up intervals in oral squamous cell carcinoma
10.3390/jcm11237061 · 2022 · External reference
Time patterns of recurrence and second primary tumors in a large cohort of patients treated for oral cavity cancer
10.1002/cam4.2124 · 2019 · External reference
Comprehensive survival analysis of oral squamous cell carcinoma patients undergoing initial radical surgery
10.1186/s12903-024-04690-z · 2024 · External reference
The 8th TNM classification for oral squamous cell carcinoma: what is gained, what is lost, and what is missing
10.1016/j.oraloncology.2020.104937 · 2020 · External reference
Deep learning in oral cancer: a systematic review
10.1186/s12903-024-03993-5 · 2024 · External reference
Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists
10.1038/s41598-023-38343-y · 2023 · External reference
Identification of genomic alteration and prognosis using pathomics-based artificial intelligence in oral leukoplakia and head and neck squamous cell carcinoma: a multicenter experimental study
10.1097/js9.0000000000002077 · 2025 · External reference
Integrative prognostic modeling and mediation analysis of recurrence risk in extremely early-stage oral squamous cell carcinoma
10.1016/j.jormas.2025.102708 · 2026 · External reference
Radiomics applications in head and neck tumor imaging: a narrative review
10.3390/cancers15041174 · 2023 · External reference
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics
10.3389/fmed.2023.1217037 · 2023 · External reference
Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma
10.1038/s41746-025-01712-0 · 2025 · External reference
Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
10.1109/tmi.2020.3021387 · 2022 · External reference
Multimodal data fusion for cancer biomarker discovery with deep learning
10.1038/s42256-023-00633-5 · 2023 · External reference
Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
10.1038/s43018-022-00388-9 · 2022 · External reference
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer
10.1038/s43018-022-00416-8 · 2022 · External reference
TIAToolbox as an end-to-end library for advanced tissue image analytics
10.1038/s43856-022-00186-5 · 2022 · External reference
Transfer learning for medical image classification: a literature review
10.1186/s12880-022-00793-7 · 2022 · External reference
Improving feature extraction from histopathological images through a fine-tuning ImageNet model
10.1016/j.jpi.2022.100115 · 2022 · External reference
Quantification of the immune content in neuroblastoma: deep learning and topological data analysis in digital pathology
10.3390/ijms22168804 · 2021 · External reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · 2017 · External reference
The role of magnetic resonance imaging and computed tomography in oral squamous cell carcinoma patients’ preoperative staging
10.3389/fonc.2023.972042 · 2023 · External reference
Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma
10.1038/s41598-023-48048-x · 2023 · External reference
Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis
10.3389/fonc.2024.1395159 · 2024 · External reference
Perineural invasion is a significant prognostic factor in oral squamous cell carcinoma: a systematic review and meta-analysis
10.3390/diagnostics13213339 · 2023 · External reference
Prognostic value of perineural invasion on survival and recurrence in oral squamous cell carcinoma
10.3390/diagnostics12051062 · 2022 · External reference
Perineural invasion predicts poor survival and cervical lymph node metastasis in oral squamous cell carcinoma
10.4317/medoral.25916 · 2023 · External reference
A guide to cross-validation for artificial intelligence in medical imaging
10.1148/ryai.220232 · 2023 · External reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
Attention is all you need
2017 · External reference
Unresolved reference
External reference
Advances in medical image analysis with vision transformers: a comprehensive review
10.1016/j.media.2023.103000 · 2024 · External reference
A tutorial on calibration measurements and calibration models for clinical prediction models
10.1093/jamia/ocz228 · 2020 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · 1988 · External reference
Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans
10.3389/fonc.2024.1380599 · 2024 · External reference
Enhanced diagnostic precision: assessing tumor differentiation in head and neck squamous cell carcinoma using multi-slice spiral CT texture analysis
10.3390/jcm13144038 · 2024 · External reference
Ultrasound-based deep learning radiomics to predict cervical lymph node metastasis in major salivary gland carcinomas
10.1016/j.identj.2025.103895 · 2025 · External reference
Contrast-enhanced CT-based deep learning and habitat radiomics for analysing the predictive capability for oral squamous cell carcinoma
10.1016/j.identj.2025.100914 · 2025 · External reference
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
10.1038/s41551-023-01045-x · 2023 · External reference
TransSurv: transformer-based survival analysis model integrating histopathological images and genomic data for colorectal cancer
10.1109/tcbb.2022.3199244 · 2023 · External reference
Transformer-based unsupervised contrastive learning for histopathological image classification
10.1016/j.media.2022.102559 · 2022 · External reference
A visual-language foundation model for computational pathology
10.1038/s41591-024-02856-4 · 2024 · External reference
Towards a general-purpose foundation model for computational pathology
10.1038/s41591-024-02857-3 · 2024 · External reference
Data-efficient and weakly supervised computational pathology on whole-slide images
10.1038/s41551-020-00682-w · 2021 · External reference
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
10.1016/j.media.2020.101789 · 2020 · External reference
Multi-institutional prognostic modeling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics
10.1158/2767-9764.crc-22-0152 · 2023 · External reference
Time patterns of recurrence and second primary tumors in a large cohort of patients treated for oral cavity cancer
10.1002/cam4.2124 · ExternalCitation · doi-reference
Improved recurrence rates and progression-free survival in primarily surgically treated oral squamous cell carcinoma
10.1007/s00784-024-05644-z · ExternalCitation · doi-reference
Contrast-enhanced CT-based deep learning and habitat radiomics for analysing the predictive capability for oral squamous cell carcinoma
10.1016/j.identj.2025.100914 · ExternalCitation · doi-reference
Ultrasound-based deep learning radiomics to predict cervical lymph node metastasis in major salivary gland carcinomas
10.1016/j.identj.2025.103895 · ExternalCitation · doi-reference
Integrative prognostic modeling and mediation analysis of recurrence risk in extremely early-stage oral squamous cell carcinoma
10.1016/j.jormas.2025.102708 · ExternalCitation · doi-reference
Improving feature extraction from histopathological images through a fine-tuning ImageNet model
10.1016/j.jpi.2022.100115 · ExternalCitation · doi-reference
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
10.1016/j.media.2020.101789 · ExternalCitation · doi-reference
Transformer-based unsupervised contrastive learning for histopathological image classification
10.1016/j.media.2022.102559 · ExternalCitation · doi-reference
Advances in medical image analysis with vision transformers: a comprehensive review
10.1016/j.media.2023.103000 · ExternalCitation · doi-reference
The 8th TNM classification for oral squamous cell carcinoma: what is gained, what is lost, and what is missing
10.1016/j.oraloncology.2020.104937 · ExternalCitation · doi-reference
Data-efficient and weakly supervised computational pathology on whole-slide images
10.1038/s41551-020-00682-w · ExternalCitation · doi-reference
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
10.1038/s41551-023-01045-x · ExternalCitation · doi-reference
Head and neck squamous cell carcinoma
10.1038/s41572-020-00224-3 · ExternalCitation · doi-reference
A visual-language foundation model for computational pathology
10.1038/s41591-024-02856-4 · ExternalCitation · doi-reference
Towards a general-purpose foundation model for computational pathology
10.1038/s41591-024-02857-3 · ExternalCitation · doi-reference
Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists
10.1038/s41598-023-38343-y · ExternalCitation · doi-reference
Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma
10.1038/s41598-023-48048-x · ExternalCitation · doi-reference
Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma
10.1038/s41746-025-01712-0 · ExternalCitation · doi-reference
Multimodal data fusion for cancer biomarker discovery with deep learning
10.1038/s42256-023-00633-5 · ExternalCitation · doi-reference
Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
10.1038/s43018-022-00388-9 · ExternalCitation · doi-reference
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer
10.1038/s43018-022-00416-8 · ExternalCitation · doi-reference
TIAToolbox as an end-to-end library for advanced tissue image analytics
10.1038/s43856-022-00186-5 · ExternalCitation · doi-reference
A tutorial on calibration measurements and calibration models for clinical prediction models
10.1093/jamia/ocz228 · ExternalCitation · doi-reference
Identification of genomic alteration and prognosis using pathomics-based artificial intelligence in oral leukoplakia and head and neck squamous cell carcinoma: a multicenter experimental study
10.1097/js9.0000000000002077 · ExternalCitation · doi-reference
TransSurv: transformer-based survival analysis model integrating histopathological images and genomic data for colorectal cancer
10.1109/tcbb.2022.3199244 · ExternalCitation · doi-reference
Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
10.1109/tmi.2020.3021387 · ExternalCitation · doi-reference
The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · ExternalCitation · doi-reference
A guide to cross-validation for artificial intelligence in medical imaging
10.1148/ryai.220232 · ExternalCitation · doi-reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · ExternalCitation · doi-reference
Multi-institutional prognostic modeling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics
10.1158/2767-9764.crc-22-0152 · ExternalCitation · doi-reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · ExternalCitation · doi-reference
Transfer learning for medical image classification: a literature review
10.1186/s12880-022-00793-7 · ExternalCitation · doi-reference
Deep learning in oral cancer: a systematic review
10.1186/s12903-024-03993-5 · ExternalCitation · doi-reference
Comprehensive survival analysis of oral squamous cell carcinoma patients undergoing initial radical surgery
10.1186/s12903-024-04690-z · ExternalCitation · doi-reference
Prognostic factors of oral squamous cell carcinoma: the importance of recurrence and pTNM stage
10.1186/s40902-024-00410-3 · ExternalCitation · doi-reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · ExternalCitation · doi-reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · ExternalCitation · doi-reference
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics
10.3389/fmed.2023.1217037 · ExternalCitation · doi-reference
The role of magnetic resonance imaging and computed tomography in oral squamous cell carcinoma patients’ preoperative staging
10.3389/fonc.2023.972042 · ExternalCitation · doi-reference
Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans
10.3389/fonc.2024.1380599 · ExternalCitation · doi-reference
Application of CT and MRI images based on artificial intelligence to predict lymph node metastases in patients with oral squamous cell carcinoma: a subgroup meta-analysis
10.3389/fonc.2024.1395159 · ExternalCitation · doi-reference
Radiomics applications in head and neck tumor imaging: a narrative review
10.3390/cancers15041174 · ExternalCitation · doi-reference
Prognostic value of perineural invasion on survival and recurrence in oral squamous cell carcinoma
10.3390/diagnostics12051062 · ExternalCitation · doi-reference
Perineural invasion is a significant prognostic factor in oral squamous cell carcinoma: a systematic review and meta-analysis
10.3390/diagnostics13213339 · ExternalCitation · doi-reference
Quantification of the immune content in neuroblastoma: deep learning and topological data analysis in digital pathology
10.3390/ijms22168804 · ExternalCitation · doi-reference
Tumor recurrence and follow-up intervals in oral squamous cell carcinoma
10.3390/jcm11237061 · ExternalCitation · doi-reference
Enhanced diagnostic precision: assessing tumor differentiation in head and neck squamous cell carcinoma using multi-slice spiral CT texture analysis
10.3390/jcm13144038 · ExternalCitation · doi-reference
Perineural invasion predicts poor survival and cervical lymph node metastasis in oral squamous cell carcinoma
10.4317/medoral.25916 · ExternalCitation · doi-reference
