Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02276443 (ARTEMIS), which is not on this map. Cited by 9 papers.
0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
NCT02276443 naactive not recruitingnot on this map
ARTEMIS: A Robust TNBC Evaluation FraMework to Improve Survival
TypeinterventionalSponsorM.D. Anderson Cancer CenterRan2015 to 2026Enrolled798ConditionsInvasive Breast Carcinoma, Stage I Breast Cancer AJCC v7, Stage IA Breast Cancer AJCC v7, Stage IB Breast Cancer AJCC v7ArmsChemotherapy, Immunotherapy, Laboratory Biomarker Analysis, Lymph Node Biopsy, Ultrasonography
Casey E StowersFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0002-7157-6248
Chengyue WuFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0002-3789-404X
Zhan XuFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0002-5892-0227
Sidharth KumarFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0001-8096-2469
Clinton YamFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.
Jong Bum SonFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0003-2548-5895
Jingfei MaFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0003-2827-8891
Jonathan I TamirFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0001-9113-9566
Gaiane M RauchFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0003-2079-5202
Thomas E YankeelovFrom the Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (C.E.S., C.W., J.I.T., T.E.Y.); Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Tex (S.K., J.I.T.); Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Tex (T.E.Y.); Departments of Imaging Physics (C.W., Z.X., J.B.S., J.M., T.E.Y.), Abdominal Imaging (G.M.R.), Breast Imaging (C.W., G.M.R.), Breast Medical Oncology (C.Y.), Biostatistics (C.W.), and Institute for Data Science in Oncology (C.W.), The University of Texas MD Anderson Cancer Center, Houston, Tex; and Departments of Biomedical Engineering (C.W., T.E.Y.), Diagnostic Medicine (J.I.T., T.E.Y.), and Oncology (T.E.Y.), The University of Texas at Austin, 107 W Dean Keeton St, Stop C0800, Austin, TX 78712.ORCID 0000-0001-6201-3913
Funding
Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant TherapyU01CA142565 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KARCZMAR, GREGORY S., NANDA, RITA · 2010 to 2019
$4.5M
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
National Science Foundation IFML 2019844NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA174706NCI NIH HHS U24 CA226110
6 · The paper itself
Abstract
Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC). Results The study included 118 female patients (median age, 51 years [range, 29-78 years]). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the concordance correlation coefficient values were 0.95 (95% CI: 0.90, 0.98) and 0.94 (95% CI: 0.87, 0.97), respectively. CNN-predicted TTC and TTV had an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.34, 0.94) and 0.72 (95% CI: 0.40, 0.95) for predicting tumor status at the time of surgery, respectively. Conclusion Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient's tumor during NAC using only pre-NAC MRI data.
Indexed as
Magnetic Resonance ImagingNeoadjuvant TherapyTriple Negative Breast NeoplasmsAdultAgedChemotherapy, AdjuvantClinical Trials as TopicDeep LearningFemaleHumansMiddle AgedNeural Networks, ComputerRetrospective StudiesTreatment OutcomeBiology-based Mathematical ModelConvolutional Neural NetworkNeoadjuvant ChemotherapyTriple-Negative Breast Cancer
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. · full record | OpenQuestion