Research graph
References from Machine learning in metastatic cancer research: Potentials, possibilities, and prospects. Local targets link to admitted publications; unresolved targets remain external evidence.
American cancer society guideline for diet and physical activity for cancer prevention
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10.3322/caac.21660
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The risk factors of bone metastases in patients with lung cancer
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Clinical characteristics and prognostic factors in bone metastases from lung cancer
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Early identification and intervention matters: a comprehensive review of current evidence and recommendations for the monitoring of bone health in patients with cancer
10.1016/j.ctrv.2017.09.008 · 2017 · External reference
A novel xgboost method to infer the primary lesion of 20 solid tumor types from gene expression data
10.3389/fgene.2021.632761 · 2021 · External reference
Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases
10.1038/s41598-021-93804-6 · 2021 · External reference
Molecular features and gene expression signature of metastatic colorectal cancer
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Machine learning in oncology: a clinical appraisal
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Brain metastasis detection using machine learning: a systematic review and meta-analysis
10.1093/neuonc/noaa232 · 2021 · External reference
Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review
2021 · External reference
10.1016/j.eclinm.2020.100669
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Prediction models applying machine learning to oral cavity cancer outcomes: a systematic review
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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
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Insights into machine learning-based approaches for virtual screening in drug discovery: Existing strategies and streamlining through fp-cadd
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10.1038/s41575-019-0240-9 · External reference
Machine-learning scoring functions for structure-based virtual screening
2021 · External reference
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From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
2020 · External reference
The development of target-specific machine learning models as scoring functions for docking-based target prediction
10.1021/acs.jcim.8b00773 · 2019 · External reference
Stacked ensemble for bioactive molecule prediction
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10.1007/s11030-021-10217-3
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10.1021/acs.jcim.2c00224
10.1021/acs.jcim.2c00224 · External reference
High-throughput virtual laboratory for drug discovery using massive datasets
10.1177/10943420211001565 · 2021 · External reference
Psbp-svm: a machine learning-based computational identifier for predicting polystyrene binding peptides
10.3389/fbioe.2020.00245 · 2020 · External reference
Improving structure-based virtual screening with ensemble docking and machine learning
10.1021/acs.jcim.1c00511 · 2021 · External reference
Efficient prediction of drug–drug interaction using deep learning models
10.1049/iet-syb.2019.0116 · 2020 · External reference
Ensembled machine learning framework for drug sensitivity prediction
10.1049/iet-syb.2018.5094 · 2020 · External reference
10.1007/978-1-4939-8955-3_17
10.1007/978-1-4939-8955-3_17 · 2019 · External reference
Olaparib monotherapy in patients with advanced cancer and a germline brca1/2 mutation
10.1200/jco.2014.56.2728 · 2015 · External reference
10.1007/978-981-16-9221-5_21
10.1007/978-981-16-9221-5_21 · 2022 · External reference
Machine learning predicts outcomes of phase iii clinical trials for prostate cancer
10.3390/a14050147 · 2021 · External reference
10.1038/s41388-021-01807-4
10.1038/s41388-021-01807-4 · External reference
10.5483/bmbrep.2019.52.7.152
10.5483/bmbrep.2019.52.7.152 · External reference
Targeting stat3 in cancer immunotherapy
2020 · External reference
Machine learning-based virtual screening for stat3 anticancer drug target
10.2174/1381612828666220728120523 · 2022 · External reference
10.1016/j.molstruc.2022.133627
10.1016/j.molstruc.2022.133627 · External reference
Unresolved reference
2021 · External reference
Moldiscovery: learning mass spectrometry fragmentation of small molecules
2021 · External reference
A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery
10.1093/bioinformatics/btz293 · 2019 · External reference
Single-cell techniques and deep learning in predicting drug response
2020 · External reference
Factors that affect response to chemotherapy and survival of patients with advanced head and neck cancer
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10.15171/apb.2017.041
10.15171/apb.2017.041 · External reference
Mechanisms and insights into drug resistance in cancer
10.3389/fphar.2013.00028 · 2013 · External reference
Resistance to cancer chemotherapy: failure in drug response from adme to p-gp
10.1186/s12935-015-0221-1 · 2015 · External reference
Ferroptosis in cancer therapy: a novel approach to reversing drug resistance
10.1186/s12943-022-01530-y · 2022 · External reference
Particle swarm optimized gaussian process classifier for treatment discontinuation prediction in multicohort metastatic castration-resistant prostate cancer patients
2021 · External reference
Missing data and multiple imputation in clinical epidemiological research
10.2147/clep.s129785 · 2017 · External reference
Folfox treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms
10.1002/cam4.2786 · 2020 · External reference
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
10.1186/1471-2105-9-319 · 2008 · External reference
Biological determinants of chemo-radiotherapy response in hpv-negative head and neck cancer: a multicentric external validation
10.3389/fonc.2019.01470 · 2020 · External reference
Biological and molecular factors predicting response to adoptive cell therapies in cancer
2022 · External reference
Development of a machine learning model for survival risk stratification of patients with advanced oral cancer
10.1001/jamanetworkopen.2020.11768 · 2020 · External reference
Effects of adjuvant endocrine therapy adherence and radiation on recurrence and survival among older women with early-stage breast cancer
10.1245/s10434-021-10064-4 · 2021 · External reference
Machine learning to predict tamoxifen nonadherence among us commercially insured patients with metastatic breast cancer
10.1200/cci.20.00102 · 2021 · External reference
Deep learning predicts lung cancer treatment response from serial medical imaging
10.1158/1078-0432.ccr-18-2495 · 2019 · External reference
Early recurrence after hepatectomy for colorectal liver metastases: what optimal definition and what predictive factors?
10.1634/theoncologist.2015-0468 · 2016 · External reference
Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases
10.1002/mp.14563 · 2021 · External reference
Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases
10.1002/ijc.33427 · 2021 · External reference
10.1038/s41568-020-00300-6
10.1038/s41568-020-00300-6 · External reference
Circulating mirnas with prognostic value in metastatic breast cancer and for early detection of metastasis
10.1093/carcin/bgw008 · 2016 · External reference
10.2147/cmar.s176763
10.2147/cmar.s176763 · External reference
10.1016/j.clbc.2017.11.020
10.1016/j.clbc.2017.11.020 · External reference
microrna-124 inhibits bone metastasis of breast cancer by repressing interleukin-11
2018 · External reference
Cancer survival statistics for patients and healthcare professionals–a tutorial of real-world data analysis
10.1111/joim.13139 · 2021 · External reference
Development and validation of a deep learning model for non-small cell lung cancer survival
10.1001/jamanetworkopen.2020.5842 · 2020 · External reference
10.1016/j.csbj.2021.08.006
10.1016/j.csbj.2021.08.006 · External reference
Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer
10.1001/jamanetworkopen.2020.11625 · 2020 · External reference
A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer
10.1002/cam4.3490 · 2020 · External reference
A national cancer database-based nomogram to predict lymph node metastasis in penile cancer
10.1111/bju.14652 · 2019 · External reference
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study
10.1016/j.annonc.2020.04.003 · 2020 · External reference
Patterns of metastasis in colon and rectal cancer
10.1038/srep29765 · 2016 · External reference
Predicting overall survival in patients with metastatic rectal cancer: a machine learning approach
10.1007/s11605-019-04373-z · 2020 · External reference
Machine learning and mechanistic modeling for prediction of metastatic relapse in early-stage breast cancer
10.1200/cci.19.00133 · 2020 · External reference
Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning
10.1016/j.compbiomed.2021.104715 · 2021 · External reference
Machine learning and treatment outcome prediction for oral cancer
10.1111/jop.13089 · 2020 · External reference
A deep-learning approach for diagnosis of metastatic breast cancer in bones from whole-body scans
10.3390/app10030997 · 2020 · External reference
Machine learning algorithms for early detection of bone metastases in an experimental rat model
2020 · External reference
Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study
10.1002/1878-0261.12732 · 2020 · External reference
Occult metastasis
10.1016/s0753-3322(01)00052-x · 2001 · External reference
Occult nodal metastasis in oral cavity cancers
2020 · External reference
Prognostic value of occult lymph node metastases in patients with completely resected esophageal squamous cell carcinoma
2020 · External reference
Value of lingual lymph node metastasis in patients with squamous cell carcinoma of the tongue
10.1002/lary.27927 · 2019 · External reference
Prognostic significance of extranodal extension in head and neck squamous cell carcinoma cn0 patients with occult metastatic neck nodes
10.1016/j.otoeng.2017.07.002 · 2018 · External reference
Predictive value of occult metastasis and survival significance of metabolic tumor volume determined by pet-ct in ct1-2n0 squamous cell carcinoma of the tongue
10.3389/fonc.2020.542530 · 2020 · External reference
Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning
10.1001/jamanetworkopen.2020.32269 · 2021 · External reference
Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma
10.1016/j.oraloncology.2019.03.011 · 2019 · External reference
Genetic secrets of long-term glioblastoma survivors
2019 · External reference
10.3346/jkms.2016.31.8.1208
10.3346/jkms.2016.31.8.1208 · External reference
Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation
2020 · External reference
Earn: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer
10.1186/s12920-021-00974-3 · 2021 · External reference
Pan-cancer metastasis prediction based on graph deep learning method
2021 · External reference
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Prediction algorithm of regional lymph node metastasis of rectal cancer based on improved deep neural network
2021 · External reference
10.1038/nrclinonc.2017.166
10.1038/nrclinonc.2017.166 · External reference
10.1038/bjc.2012.581
10.1038/bjc.2012.581 · External reference
Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches
10.1093/bib/bbaa188 · 2021 · External reference
10.1001/jamanetworkopen.2020.3652
10.1001/jamanetworkopen.2020.3652 · External reference
Machine learning analysis of individual tumor lesions in four metastatic colorectal cancer clinical studies: linking tumor heterogeneity to overall survival
10.1208/s12248-020-0434-7 · 2020 · External reference
Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer
10.1038/s41598-020-75708-z · 2020 · External reference
Cancer heterogeneity and metastasis: Life at the edge
2021 · External reference
10.1038/s41568-019-0180-2
10.1038/s41568-019-0180-2 · External reference
Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced mri
10.1016/j.mri.2012.10.006 · 2013 · External reference
Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on dce-mri
10.1016/j.mri.2019.02.013 · 2019 · External reference
Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer
10.1111/bcp.14937 · 2022 · External reference
Dual energy ct image prediction on primary tumor of lung cancer for nodal metastasis using deep learning
10.1016/j.compmedimag.2021.101935 · 2021 · External reference
Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on mri
10.1007/s00330-021-08146-8 · 2022 · External reference
Quantitative tumor heterogeneity mri profiling improves machine learning–based prognostication in patients with metastatic colon cancer
10.1007/s00330-020-07673-0 · 2021 · External reference
A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative mri and machine learning
10.1038/s41598-021-01024-9 · 2021 · External reference
Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic mri
10.1038/s41598-022-05077-2 · 2022 · External reference
Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology
10.1093/noajnl/vdaa110 · 2020 · External reference
10.1002/jso.27118
10.1002/jso.27118 · External reference
Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18f-fdg pet/ct and machine learning
10.1007/s12149-021-01605-8 · 2021 · External reference
Bmi cut points to identify at-risk asian americans for type 2 diabetes screening
10.2337/dc14-2391 · 2015 · External reference
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Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities
10.1186/s40246-014-0023-x · 2015 · External reference
Ashg denounces attempts to link genetics and racial supremacy
10.1016/j.ajhg.2018.10.011 · 2018 · External reference
10.1016/s0140-6736(20)32076-6
10.1016/s0140-6736(20)32076-6 · External reference
Cancer statistics for african american/black people 2022
2022 · External reference
Association of mental health diagnosis with race and all-cause mortality after a cancer diagnosis: Large-scale analysis of electronic health record data
2021 · External reference
10.1200/jop.2017.025288
10.1200/jop.2017.025288 · External reference
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10.1007/s11060-020-03626-1 · External reference
Cardio-oncology preventive care: racial and ethnic disparities
10.1007/s12170-020-00650-8 · 2020 · External reference
Use of bone resorption inhibitors in metastatic castration-resistant prostate cancer—20 years later, and the answer is still yes
10.1001/jamanetworkopen.2021.17159 · 2021 · External reference
Re: Clinical outcomes in men of diverse ethnic backgrounds with metastatic castration-resistant prostate cancer
10.1016/j.annonc.2020.05.010 · 2020 · External reference
10.3322/caac.21654
10.3322/caac.21654 · External reference
Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2018 · External reference
Clinical outcomes in men of diverse ethnic backgrounds with metastatic castration-resistant prostate cancer
10.1016/j.annonc.2020.03.309 · 2020 · External reference
Evaluating the clinical trends and benefits of low-dose computed tomography in lung cancer patients
10.1002/cam4.4229 · 2021 · External reference
10.1634/theoncologist.2018-0085
10.1634/theoncologist.2018-0085 · External reference
Disparities in care management during terminal hospitalization among adults with metastatic cancer from 2010 to 2017
10.1001/jamanetworkopen.2021.25328 · 2021 · External reference
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10.1001/jamanetworkopen.2020.31730 · External reference
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10.1093/nar/gkx1008 · External reference
Prevalence of missing data in the national cancer database and association with overall survival
10.1001/jamanetworkopen.2021.1793 · 2021 · External reference
10.1093/nar/30.1.207
10.1093/nar/30.1.207 · External reference
Image data sharing for biomedical research—meeting hipaa requirements for de-identification
10.1007/s10278-011-9422-x · 2012 · External reference
10.1038/s41586-019-1689-y
10.1038/s41586-019-1689-y · External reference
Risk factors of skeletal-related events in patients with bone metastatic castration-resistant prostate cancer undergoing treatment with zoledronate
2021 · External reference
Characteristics of available studies and dissemination of research using major clinical data sharing platforms
10.1177/17407745211038524 · 2021 · External reference
Real-world outcomes in first-line treatment of metastatic castration-resistant prostate cancer: the prostate cancer registry
10.1007/s11523-020-00720-2 · 2020 · External reference
10.1109/iccv.2017.74
10.1109/iccv.2017.74 · External reference
Medimlp: using grad-cam to extract crucial variables for lung cancer postoperative complication prediction
10.1109/jbhi.2019.2949601 · 2019 · External reference
10.1016/j.compbiomed.2022.105550
10.1016/j.compbiomed.2022.105550 · External reference
Grad-cam helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging
10.1016/j.jneumeth.2021.109098 · 2021 · External reference
A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images
10.1016/j.chaos.2020.110190 · 2020 · External reference
Numerical grad-cam based explainable convolutional neural network for brain tumor diagnosis
2022 · External reference
Arrhythmia detection model using modified densenet for comprehensible grad-cam visualization
10.1016/j.bspc.2021.103408 · 2022 · External reference
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10.1109/cvpr46437.2021.01128 · External reference
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10.1145/2939672.2939778
10.1145/2939672.2939778 · External reference
A unified approach to interpreting model predictions
2017 · External reference
10.1016/j.cjca.2021.09.004
10.1016/j.cjca.2021.09.004 · External reference
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
10.1038/s41591-022-02155-w · 2023 · External reference
Federated learning improves site performance in multicenter deep learning without data sharing
10.1093/jamia/ocaa341 · 2021 · External reference
Larynx cancer survival model developed through open-source federated learning
10.1016/j.radonc.2022.09.023 · 2022 · External reference
10.1038/s41467-020-18497-3
10.1038/s41467-020-18497-3 · External reference
Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer
10.21037/qims-20-525 · 2021 · External reference
Identification lymph node metastasis in esophageal squamous cell carcinoma using whole slide images and a hybrid network of multiple instance and transfer learning, Biomedical
10.1016/j.bspc.2023.104577 · 2023 · External reference
Computer-assisted diagnosis of lymph node metastases in colorectal cancers using transfer learning with an ensemble model
10.1016/j.modpat.2023.100118 · 2023 · External reference
Generalization in quantum machine learning from few training data
10.1038/s41467-022-32550-3 · 2022 · External reference
10.1038/s41467-021-22539-9
10.1038/s41467-021-22539-9 · External reference
10.1038/s43588-022-00311-3
10.1038/s43588-022-00311-3 · External reference
