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References from Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma. Local targets link to admitted publications; unresolved targets remain external evidence.
Global burden of cancer attributable to infections in 2018: A worldwide incidence analysis
10.1016/s2214-109x(19)30488-7 · 2020 · External reference
Nasopharyngeal carcinoma
10.1016/s0140-6736(19)30956-0 · 2019 · External reference
Head and neck cancers, version 2.2020, NCCN clinical practice guidelines in oncology
10.6004/jnccn.2020.0031 · 2020 · External reference
Validation of the 8th Edition of the UICC/AJCC staging system for nasopharyngeal carcinoma from endemic areas in the intensity-modulated radiotherapy era
10.6004/jnccn.2017.0121 · 2017 · External reference
The tumour response to induction chemotherapy has prognostic value for long-term survival outcomes after intensity-modulated radiation therapy in nasopharyngeal carcinoma
10.1038/srep24835 · 2016 · External reference
Refining the 8th edition TNM classification for EBV related nasopharyngeal carcinoma
10.1016/j.ccell.2023.12.020 · 2024 · External reference
Enhancing the prognosis of nasopharyngeal carcinoma using intra- and peritumoral radiomics
10.1016/j.metrad.2026.100206 · 2026 · External reference
A serial MRI-based deep learning model to predict survival in patients with locoregionally advanced nasopharyngeal carcinoma
2025 · External reference
NPC-SurvAI: A fully automated deep learning framework for prognostic prediction and risk stratification in patients with nasopharyngeal carcinoma
10.1016/j.radonc.2025.111223 · 2026 · External reference
Are deep models in radiomics performing better than generic models? A systematic review
10.1186/s41747-023-00325-0 · 2023 · External reference
Artificial intelligence in head and neck cancer diagnosis: A comprehensive review with emphasis on radiomics, histopathological, and molecular applications
10.3390/cancers16213623 · 2024 · External reference
BioCompNet: A deep learning workflow enabling automated body composition analysis toward precision management of cardiometabolic disorders
2025 · External reference
An interpretable machine learning model assists in predicting induction chemotherapy response and survival for locoregionally advanced nasopharyngeal carcinoma using MRI: A multicenter study
10.1007/s00330-025-11396-5 · 2025 · External reference
MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma
10.1007/s00330-019-06211-x · 2020 · External reference
A prognostic predictive system based on deep learning for locoregionally advanced nasopharyngeal carcinoma
10.1093/jnci/djaa149 · 2021 · External reference
MBFusion: Multi-modal balanced fusion and multi-task learning for cancer diagnosis and prognosis
10.1016/j.compbiomed.2024.109042 · 2024 · External reference
Multitask deep learning based on longitudinal CT images facilitates prediction of lymph node metastasis and survival in chemotherapy-treated gastric cancer
10.1158/0008-5472.can-24-4190 · 2025 · External reference
Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: A retrospective study
2022 · External reference
Medical multimodal multitask foundation model for lung cancer screening
10.1038/s41467-025-56822-w · 2025 · External reference
Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients
10.1186/s12885-023-10817-2 · 2023 · External reference
Development and validation of a multimodality model based on whole-slide imaging and biparametric MRI for predicting postoperative biochemical recurrence in prostate cancer
2024 · External reference
Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicentre observational study
2022 · External reference
M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis
10.1016/j.media.2025.103561 · 2025 · External reference
Deep learning radiopathomics based on pretreatment MRI and whole slide images for predicting overall survival in locally advanced nasopharyngeal carcinoma
10.1016/j.radonc.2025.110949 · 2025 · External reference
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
10.1016/j.ejca.2008.10.026 · 2009 · External reference
The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
Data-efficient and weakly supervised computational pathology on whole-slide images
10.1038/s41551-020-00682-w · 2021 · External reference
A multimodal whole-slide foundation model for pathology
10.1038/s41591-025-03982-3 · 2025 · External reference
DARC: Deep adaptive regularized clustering for histopathological image classification
10.1016/j.media.2022.102521 · 2022 · External reference
CA2CL: Cluster-aware adversarial contrastive learning for pathological image analysis
10.1109/jbhi.2025.3552640 · 2025 · External reference
Bias in cross-entropy-based training of deep survival networks
10.1109/tpami.2020.2979450 · 2021 · External reference
Grad-cam: Visual explanations from deep networks via gradient-based localization.
2017 · External reference
Integrating radiomics and deep-learning for prognostic evaluation in nasopharyngeal carcinoma
10.3390/medicina61071310 · 2025 · External reference
Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma
10.1016/j.ebiom.2025.105663 · 2025 · External reference
Multi-task deep learning for medical image computing and analysis: A review
10.1016/j.compbiomed.2022.106496 · 2023 · External reference
A new prognostic histopathologic classification of nasopharyngeal carcinoma
10.1186/s40880-016-0103-5 · 2016 · External reference