Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients
Open accessPublished version of a preprint
Abstract
Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440 · see citing works
The 2021 WHO classification of tumors of the central nervous system: a summary
Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H. K., Pfister, S. M., Reifenberger, G., et al. (2021) · Neuro-Oncology · doi:10.1093/neuonc/noab106 · Cited by 12,273 · see citing works
<i>IDH1</i> and <i>IDH2</i> Mutations in Gliomas
Yan, H., Parsons, D. W., Jin, G., McLendon, R., Rasheed, B. A., Yuan, W., Kos, I., Batinic-Haberle, I., Jones, S., Riggins, G. J., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808710 · Cited by 5,847 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
The Somatic Genomic Landscape of Glioblastoma
Brennan, C. W., Verhaak, R. G. W., McKenna, A., Campos, B., Noushmehr, H., Salama, S. R., Zheng, S., Chakravarty, D., Sanborn, J. Z., Berman, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.034 · Cited by 5,016 · see citing works
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers
Goodman, A. M., Kato, S., Bazhenova, L., Patel, S. P., Frampton, G. M., Miller, V., Stephens, P. J., Daniels, G. A., & Kurzrock, R. (2017) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-17-0386 · Cited by 2,264 · see citing works
Ceccarelli, M., Barthel, F. P., Malta, T. M., Sabedot, T. S., Salama, S. R., Murray, B. A., Morozova, O., Newton, Y., Radenbaugh, A., Pagnotta, S. M., et al. (2016) · Cell · doi:10.1016/j.cell.2015.12.028 · Cited by 2,178 · see citing works
EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood
Weller, M., van den Bent, M., Preusser, M., Le Rhun, E., Tonn, J. C., Minniti, G., Bendszus, M., Balana, C., Chinot, O., Dirven, L., et al. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00447-z · Cited by 2,048 · see citing works
Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0482-1 · Cited by 1,995 · cited 1× · see citing works
“Similar to the DeepSurv network 49 , the loss function was set to be the negative log partial likelihood: where N E =1 isthenumberofpatientswithanobservableeventand λ isthe ℓ 2 regularization parameter.”
ResultMaeser, D., Gruener, R. F., & Huang, R. S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab260 · Cited by 1,867 · see citing works
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A. S., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101563 · Cited by 1,445 · see citing works
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152 · see citing works
Weller, M., van den Bent, M. J., Tonn, J. C., Stupp, R., Preusser, M., Moyal, E. C., Henriksson, R., Rhun, É. L., Balañá, C., Chinot, O. L., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30194-8 · Cited by 1,094 · see citing works
Brain and other central nervous system tumor statistics, 2021
Miller, K. D., Ostrom, Q. T., Kruchko, C. A., Patil, N. T., Tihan, T., Cioffi, G., Fuchs, H. E., Waite, K. A., Jemal, A., Siegel, R. L., et al. (2021) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21693 · Cited by 1,004 · see citing works
The Biological Meaning of Radiomic Features
Tomaszewski, M. R., & Gillies, R. J. (2021) · Radiology · doi:10.1148/radiol.2021202553 · Cited by 568 · see citing works
Extending support for mouse data in the Molecular Signatures Database (MSigDB)
Castanza, A. S., Recla, J. M., Eby, D., Thorvaldsdóttir, H., Bult, C. J., & Mesirov, J. P. (2023) · Nature Methods · doi:10.1038/s41592-023-02014-7 · Cited by 514 · see citing works
Limitations of stereotactic biopsy in the initial management of gliomas
Jackson, R. J., Fuller, G. N., Abi-Said, D., Lang, F. F., Gokaslan, Z. L., Shi, W. M., Wildrick, D. M., & Sawaya, R. A. (2001) · Neuro-Oncology · doi:10.1093/neuonc/3.3.193 · Cited by 417 · see citing works
Gangoso, E., Southgate, B., Bradley, L., Lehr, S., Gálvez‐Cancino, F., McGivern, N., Güç, E., Kapourani, C.-A., Byron, A., Ferguson, K. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.023 · Cited by 356 · see citing works
Reuss, D. E., Mamatjan, Y., Schrimpf, D., Capper, D., Hovestadt, V., Kratz, A., Sahm, F., Koelsche, C., Korshunov, A., Olar, A., et al. (2015) · Acta Neuropathologica · doi:10.1007/s00401-015-1438-8 · Cited by 348 · see citing works
Huijbers, A., Tollenaar, R. A. �. �., van Pelt, G. W., Zeestraten, E. C. M., Dutton, S., McConkey, C. C., Domingo, E., Smit, V. T. H. B. M., Midgley, R. S., Warren, B. F., et al. (2012) · Annals of Oncology · doi:10.1093/annonc/mds246 · Cited by 339 · see citing works
HeMIS: Hetero-Modal Image Segmentation
Havaei, M., Guizard, N., Chapados, N., & Bengio, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_54 · Cited by 332 · see citing works
The Biological Meaning of Radiomic Features
Tomaszewski, M. R., & Gillies, R. J. (2021) · Radiology · doi:10.1148/radiol.2021219005 · Cited by 327 · see citing works
All the 1p19q codeleted gliomas are mutated on IDH1 or IDH2
Labussière, M., Idbaïh, A., Wang, X.-W., Marie, Y., Boisselier, B., Falet, C., Paris, S., Laffaire, J., Carpentier, C., Crinière, E., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181e1cf3a · Cited by 274 · see citing works
Zhang, Y., He, N., Yang, J., Li, Y., Wei, D., Huang, Y., Zhang, Y., He, Z., & Zheng, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16443-9_11 · Cited by 272 · see citing works
Self-citationChoi, Y. S., Bae, S., Chang, J. H., Kang, S., Kim, S. H., Kim, J., Rim, T. H., Choi, S. H., Jain, R., & Lee, S. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa177 · Cited by 255 · see citing works
An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas
Li, G.-Z., Li, L., Li, Y.-M., Qian, Z.-H., Wu, F., He, Y.-F., Jiang, H.-Y., Li, R.-P., Wang, D., Zhai, Y., et al. (2022) · Brain : a journal of neurology · doi:10.1093/brain/awab340 · Cited by 236 · see citing works
Multi-task deep learning for medical image computing and analysis: A review
Zhao, Y. M., Wang, X., Che, T., Bao, G., & Li, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106496 · Cited by 206 · see citing works
Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning
van der Voort, S. R., Incekara, F., Wijnenga, M. M. J., Kapsas, G., Gahrmann, R., Schouten, J. W., Tewarie, R. N., Nijeholt, G. J. L. À., Hamer, P. C. D. W., Eijgelaar, R. S., et al. (2022) · Neuro-Oncology · doi:10.1093/neuonc/noac166 · Cited by 191 · see citing works
The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset
Calabrese, E., Villanueva-Meyer, J. E., Rudie, J. D., Rauschecker, A. M., Baid, U., Bakas, S., Cha, S., Mongan, J., & Hess, C. P. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220058 · Cited by 188 · see citing works
Hypoxia-induced epithelial to mesenchymal transition in cancer
Hapke, R., & Haake, S. M. (2020) · Cancer Letters · doi:10.1016/j.canlet.2020.05.012 · Cited by 181 · see citing works
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
Dorent, R., Joutard, S., Modat, M., Ourselin, S., & Vercauteren, T. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_9 · Cited by 160 · cited 1× · see citing works
“ToillustratetheadvantageofmmFormer,weconducted comparativestudiesbybenchmarkingitagainstotherpreviouslypub-lishedsegmentationmethods,includingU-HeMIS 16 , U-HVED 17 , and 3D-UNET 18,19 .”
Tumor-Stroma Ratio Is an Independent Predictor for Survival in Esophageal Squamous Cell Carcinoma
Wang, K., Ma, W., Wang, J., Yu, L., Zhang, X., Wang, Z., Tan, B., Wang, N., Bai, B., Yang, S., et al. (2012) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e318260dfe8 · Cited by 155 · see citing works
Sun, Q., Chen, Y., Liang, C., Zhao, Y., Lv, X., Zou, Y., Yan, K., Zheng, H., Liang, D., & Li, Z. (2021) · Radiology · doi:10.1148/radiol.2021203281 · Cited by 146 · see citing works
Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis
Liu, L., Dou, Q., Chen, H., Qin, J., & Heng, P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2934577 · Cited by 129 · see citing works
Yan, J., Zhang, B., Zhang, S., Cheng, J., Liu, X., Wang, W., Dong, Y., Zhang, L., Mo, X., Chen, Q., et al. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00205-z · Cited by 121 · see citing works
Self-citationBiology-guided deep learning predicts prognosis and cancer immunotherapy response
Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M. U., Ren, Y., Sang, S., Xie, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40890-x · Cited by 115 · see citing works
Self-citationDecuyper, M., Bonte, S., Deblaere, K., & Holen, R. V. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101831 · Cited by 104 · see citing works
Luo, J., Pan, M., Mo, K., Mao, Y., & Zou, D. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.03.006 · Cited by 100 · see citing works
Radiogenomics: a key component of precision cancer medicine
Liu, Z.-Q., Duan, T., Zhang, Y.-Y., Weng, S.-Y., Xu, H.-X., Ren, Y.-Q., Zhang, Z.-Y., & Han, X.-W. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02317-8 · Cited by 98 · see citing works
Self-citationMulti-task multi-modal learning for joint diagnosis and prognosis of human cancers
Shao, W., Wang, T., Sun, L., Dong, T., Han, Z., Huang, Z., Zhang, J., Zhang, D., & Huang, K. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101795 · Cited by 96 · see citing works
Yan, J., Zhao, Y., Chen, Y., Wang, W., Duan, W., Wang, L., Zhang, S., Ding, T., Liu, L., Sun, Q., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103583 · Cited by 88 · see citing works
Self-citationYoo, S.-Y., Park, H. E., Kim, J. H., Wen, X., Jeong, S., Cho, N., Gwon, H. G., Kim, K., Lee, H. S., Jeong, S., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1159 · Cited by 58 · see citing works
van der Voort, S. R., Incekara, F., Wijnenga, M. M. J., Kapsas, G., Gahrmann, R., Schouten, J. W., Dubbink, H. J., Vincent, A. J. P. E., van den Bent, M. J., French, P. J., et al. (2021) · Data in Brief · doi:10.1016/j.dib.2021.107191 · Cited by 51 · see citing works
Choi, S. W., Cho, H.-H., Koo, H., Cho, K. R., Nenning, K., Langs, G., Furtner, J., Baumann, B., Wöehrer, A., Cho, H. J., et al. (2020) · Cancers · doi:10.3390/cancers12071707 · Cited by 36 · see citing works
Fu, S., Lai, H., Li, Q., Liu, Y., Zhang, J., Huang, J., Chen, X., Duan, C., Li, X., Wang, T., et al. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101201 · Cited by 34 · see citing works
Self-citationShojaei, S., Moradi‐Chaleshtori, M., Paryan, M., Koochaki, A., Sharifi, K., & Mohammadi‐Yeganeh, S. (2023) · European journal of medical research · doi:10.1186/s40001-023-01463-2 · Cited by 33 · see citing works
Lee, J. O., Ahn, S. S., Choi, K. S., Lee, J., Jang, J. H., Park, J. H., Hwang, I., Park, C., Park, S., Chung, J. W., et al. (2023) · Neuro-Oncology · doi:10.1093/neuonc/noad202 · Cited by 27 · see citing works
Zhang, Z.-Y., Zhan, Y., Zhang, F., Yu, B., Ji, Y., Zhou, J., Bai, Y., Wang, Y., Wang, L., Jing, Y., et al. (2019) · Aging · doi:10.18632/aging.102186 · Cited by 26 · see citing works
Self-citationMulti-Level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas From MRI
Zhang, J., Cao, J., Tang, F., Xie, T., Feng, Q., & Huang, M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3279433 · Cited by 25 · see citing works
Jawad, S. F., Altalbawy, F. M. A., Hussein, R. M., Fadhil, A. A., Jawad, M. A., Zabibah, R. S., Taraki, T. Y., Mohan, C. D., & Rangappa, K. S. (2023) · Cancer and Metastasis Reviews · doi:10.1007/s10555-023-10129-8 · Cited by 22 · see citing works
Pei, D., Guan, F., Hong, X., Liu, Z., Wang, W., Qiu, Y., Duan, W., Wang, M., Sun, C., Wang, W., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09459-6 · Cited by 18 · see citing works
Self-citationGuo, Y., Ma, Z., Pei, D., Duan, W., Guo, Y. F., Liu, Z., Guan, F., Wang, Z., Xing, A., Guo, Z., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28630 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Radiogenomics: bridging the gap between imaging and genomics for precision oncology
He, W., Huang, W., Zhang, L., Wu, X.-W., Zhang, S.-X., & Zhang, B. (2024) · MedComm · doi:10.1002/mco2.722 · Cited by 65 · see citing works
Self-citationAdvances in Precision Oncology: From Molecular Profiling to Regulatory-Approved Targeted Therapies
Brlek, P., Škaro, V., Hrvatin, N., Bulić, L., Petrović, A., Projić, P., Smolić, M., Shah, P., & Primorac, D. (2025) · Cancers · doi:10.3390/cancers17213500 · Cited by 22 · see citing works
Farahani, S., Hejazi, M., Tabassum, M., Ieva, A. D., Mahdavifar, N., & Liu, S. (2025) · European Radiology · doi:10.1007/s00330-025-11898-2 · Cited by 12 · see citing works
Ahmadzadeh, A. M., Lomer, N. B., Ashoobi, M. A., Elyassirad, D., Gheiji, B., Vatanparast, M., Rostami, A., Mehrizi, M. A. A., Tabari, A., Bathla, G., et al. (2025) · Neuroradiology · doi:10.1007/s00234-025-03631-z · Cited by 10 · see citing works
Chen, Y., Liao, Y., Li, P., Wei, J., Fang, J., Huang, J., Feng, Y., Xie, C., Li, R., Jin, Q., et al. (2025) · Displays · doi:10.1016/j.displa.2025.103072 · Cited by 7 · see citing works
Song, P., Shen, C., Fan, Z., Yin, F., Wu, Y., Liu, F., Zhao, C., Zeng, W., Xie, X., Wu, G., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01112-3 · Cited by 5 · see citing works
Predicting IDH Mutation in Glioma Patients Using Deep Learning Algorithms with Conformal Prediction
Elyassirad, D., Gheiji, B., Vatanparast, M., Ahmadzadeh, A. M., Masoudi, M., Gholami, M., Moassefi, M., & Faghani, S. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01580-w · Cited by 5 · see citing works
Hajikarimloo, B., Tos, S. M., Kooshki, A., Alvani, M. S., Eftekhar, M. S., Hasanzade, A., Tavanaei, R., Akhlaghpasand, M., Hashemi, R., Ghaffarzadeh-Esfahani, M., et al. (2025) · Neuroradiology · doi:10.1007/s00234-025-03597-y · Cited by 4 · see citing works
Lee, J., Jeon, Y. S., Jang, J., Eum, H., Kim, M., Park, S. H., Park, C.-K., Choi, S. H., Ahn, S. S., & Choi, K. S. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250675 · Cited by 3 · see citing works
Zhang, H., Xiang, X., Ma, X., Zhang, B., Xu, S., He, X., & Xu, J. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1527909 · Cited by 2 · see citing works
Self-citationYan, R., Zhang, X., Cao, Q., Xu, J., Chen, Y., Qin, S., Zhang, S., Zhao, W., Xing, X., Yang, W., et al. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70012 · Cited by 2 · see citing works
Self-citationZheng, D., Qin, C., Wang, Z., Yu, L., Liu, Y., Wang, Y., Kan, H., & Hu, J. (2025) · Phytomedicine · doi:10.1016/j.phymed.2025.157491 · Cited by 2 · see citing works
Jing, B., & Wang, J. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/adee73 · Cited by 2 · see citing works
FoundBioNet: A Foundation-Based Model for IDH Genotyping of Glioma from Multi-parametric MRI
Farahani, S., Hejazi, M., Ieva, A. D., & Liu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_25 · Cited by 2 · see citing works
Abbasi, A. A., Kuang, H., Li, X., & Wang, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113228 · Cited by 1 · see citing works
Systematic review of emerging artificial intelligence research in glioma diagnosis and therapy
Karavolias, I., & Mammis, A. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00640-y · Cited by 1 · see citing works
Hwang, M., Lee, J., Kim, S., Kim, M., Choi, S. H., Ahn, S. S., Lee, C., & Choi, K. S. (2026) · Scientific Reports · doi:10.1038/s41598-025-34134-9 · Cited by 1 · see citing works
Li, X., Li, Z., Duan, X., Liang, Q., Wu, J., & Zhang, H. (2026) · BMC Cancer · doi:10.1186/s12885-026-15645-8 · Cited by 1 · see citing works
Du, J., Yang, L., Zhang, D., Wang, J., Wu, S., Liu, D., 衣慧灵, Liang, X., Wang, X., Shi, Q., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351757 · Cited by 1 · see citing works
Madar, I. H., Pawar, J., Muruganantham, P., Ahmed, S. S., Vohra, V., & Velayudam, J. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1854783 · Cited by 1 · see citing works
Chen, P., Wu, X., & Zhang, S. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-012007 · Cited by 1 · see citing works
Self-citationZhong, Y., Zheng, X., Shen, X., Wang, J., Zhao, L., Song, Z., & Zhang, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_59
Self-citationExplainable AI-Driven Insights for Accurate and Transparent Glioma Grading
N, C. S., Ambore, A. K., & Ranjitha, U. N. (2025) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds65426.2025.11167546
Luo, L., An, X., Zhang, J., Zhou, W., Zhao, X. B., Zhao, H., Tian, Y., Chen, T., & Zhao, F. Q. (2025) · La radiologia medica · doi:10.1007/s11547-025-02105-9
Xiong, W., Cao, J., An, T., Liu, S., Luo, Z., Wang, W., Xie, T., Feng, Q., & Huang, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3629805
Self-citationHe, W., Chen, L., Leng, X., Fan, X., & Qin, C. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112569
Xie, Q., Sun, Y., Liang, Y., Shang, Y., Wang, H., Wang, F., Wei, R., Chen, B., Zhang, M., & Niu, C. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1665690
Self-citationMTL-SAM3D: A Multi-Task Learning 3D SAM Framework for 3D MRI Glioma Segmentation and IDH Genotyping
Yang, S., Feng, L., Feng, R., Luo, H., Hu, Y., & Cheng, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357005
Self-citationWavelet Multi-Dimensional and Mamba-Guided Semantic Graph Feature Fusion Network for Glioma Grading
Abbasi, A. A., Kuang, H., Li, X., & Wang, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357037
Zhang, S., Liu, Z., Xie, X., & Chen, D. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70247
Self-citationLigand–Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model
Gao, L., Zhang, R., Zhu, X., Xu, H. X., Chen, Q., Peng, M., & Liu, J. (2026) · Biomedicines · doi:10.3390/biomedicines14051110
Zhang, H., Zhou, B., Jing, S., Zhao, X., Zhang, H., Huang, B., Yang, Z., Wang, J., & Liu, Y. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.04.035
Abdallah, A., Altuwayjiri, B., Albloshi, A. M. K., Althbiti, A., Mahbub, A. A., Alawad, W. M., & Alazzam, M. B. (2026) · Neuroradiology · doi:10.1007/s00234-026-04053-1
Kiermeyer, N., Lenfers, T., Dada, A., Friedrich, J., Khattab, S., Knop, E., Egger, J., Pauly, M., Jung, A., Montavon, G., et al. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001546
Jia, H., Pu, N., & Tian, C. (2026) · Genes · doi:10.3390/genes17070833
Zhuang, Q., Xu, T., Xing, X., Hu, E., Zhou, Y., Zheng, X., Bai, L., Huang, Y., Liu, X., & Chen, Y. (2026) · JCO Precision Oncology · doi:10.1200/po-25-00745
Self-citationAttention-Based Radiomics to Predict Histological Grade of Gliomas
Amato, D., Bavisotto, C. C., Calderaro, S., Bosco, G. L., Palazzotto, F. M., Rizzo, R., Veiceschi, P., & Vella, F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31927-2_2
Saibaba, G., Giwa, O., Booth, T., Kisková-Šimková, T., Uka, A., Skuka, F., Javed, B., Ferreira, D., Ferreira, S., Sengupta, J., et al. (2026) · European Journal of Cancer · doi:10.1016/j.ejca.2026.116991
Multiscale Multiparametric MRI Deep Learning for Short‐Term Survival Assessment in Glioblastoma
Zhang, H., Zhou, B., Zhao, X., Jing, S., Zhang, H., Huang, B., Yang, Z., Wang, J., & Liu, Y. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70497
Christodoulou, R. C., Vamvouras, G., Pitsillos, R., Roka, K., Solomou, E. E., Kalogeropoulou, C., Zampakis, P., & Georgiou, M. F. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1790776
Liu, F., Cheng, J., Tian, X., Wang, J., Hu, Y., Liu, J., & Li, H.-D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38098-2_22
Self-citationGlioVision: A multimodal MRI framework for non-invasive prediction of glioma molecular biomarkers
Nazir, A., Cheema, M. N., Hsu, Y.-C., Jiang, X., Zhu, J.-J., Harmancı, A. S., & Harmanci, A. (2026) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2026.110829
Quality-Aware Expert Fusion with Uncertainty-Guided Review for Glioma Segmentation
Xie, M., Li, X., Han, H., Xie, X., Zhang, W., Xu, J., & Cheng, J. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104910
Self-citation
- Authors
- Wu, X., Zhang, S., Zhang, Z., He, Z., Xu, Z., Wang, W., Jin, Z., You, J., Guo, Y., Zhang, L., Huang, W., Wang, F., Liu, X., Yan, D., Cheng, J., Yan, J., Zhang, S., & Zhang, B.
- Affiliations
- First Affiliated Hospital of Jinan University (CN)
- Beijing Institute of Technology (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
- Henan Provincial People's Hospital (CN)
- Corresponding author
- Zhang, Shuaitong
- Yan, Jing
- ORCID
- Venue
- npj Precision Oncology
- Published
- 2024-08-16
- Volume
- 8
- Issue
- 1
- Pages
- 181-181
- ISSN
- 2397-768X (ISSN-L)
- PMC
- PMC11329669
- OpenAlex
- W4401638634
- Cited by
- 43
- References
- 56
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 1
- FWCI
- 12.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Precision Oncology (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (82227802, 82302306, 202201020022, 2023A03J1038, 82102140, 2023A03J1036)
- Jinan University
- National Key Research and Development Program of China (2023YFF1204600, -2022)
- Topics
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- glioma, multi-task deep learning, outcome prediction, histological grade prediction, molecular alterations, IDH mutation, 1p/19q codeletion, magnetic resonance imaging, overall survival, tumor immune infiltration, DNA methylation, tumor mutational burden, tumor-stroma ratio, oncogenic signaling, biological interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
