CoChat
Sign Up
Cited by 18

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Advanced Tumor Imaging Approaches in Human Tumors

Nussbaum, Samuel, Shoukry, Mira, Ashary, Mohammed Ali, Kasbi, Ali Abbaszadeh, Baksh, Mizba, Gabriel, Emmanuel·2022·Cancers
DOI 10.3390/cancers14061549PMC PMC8945965
Open this paper in CoChat — free account

Abstract

The management of cancer has always relied heavily on the imaging modalities used to detect and monitor it. While many of these modalities have been around for decades, the technology surrounding them is always improving, and much has been discovered in recent years about the nature of tumors because of this. There have been several areas that have aided those discoveries. The use of artificial intelligence has already helped immensely in the quality of images taken but has not yet been widely implemented in clinical settings. Molecular imaging has proven to be useful in diagnosing different types of cancers based on the specificity of the probes/contrast agents used. Intravital imaging has already uncovered new information regarding the heterogeneity of the tumor vasculature. These three areas have provided a lot of useful information for the diagnosis and treatment of cancer, but further research and development in human trials is necessary to allow these techniques to fully utilize the information obtained thus far.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/cancers14061549

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Research works printed but not linked: 33

  1. Bi, 2019, Artificial intelligence in cancer imaging: Clinical challenges and applications, CA Cancer J. Clin., 69, 127, 10.3322/caac.21552 · DOI · Paper page
  2. Shimizu, 2020, Artificial intelligence in oncology, Cancer Sci., 111, 1452, 10.1111/cas.14377 · DOI · Paper page
  3. Cohen, M.E., Hudson, D.L., Banda, P.W., and Blois, M.S. (1991). Neural network approach to detection of metastatic melanoma from chromatographic analysis of urine. Annual Symposium on Computer Application in Medical Care Symposium on Computer Applications in Medical Care, Proceedings of the A Conference of the American Medical Informatics Association, Washington, DC, USA, 4–7 November 1990, IEEE Computer Society Press.
  4. Broeders, 2019, Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists, JNCI J. Natl. Cancer Inst., 111, 916, 10.1093/jnci/djy222 · DOI · Paper page
  5. Jiang, 1999, Improving breast cancer diagnosis with computer-aided diagnosis, Acad. Radiol., 6, 22, 10.1016/S1076-6332(99)80058-0 · DOI
  6. Kim, 2021, Artificial intelligence in breast ultrasonography, Ultrasonography, 40, 183, 10.14366/usg.20117 · DOI
  7. Tschandl, 2020, Human-computer collaboration for skin cancer recognition, Nat. Med., 26, 1229, 10.1038/s41591-020-0942-0 · DOI · Paper page
  8. Lin, 2019, Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma, Radiology, 291, 677, 10.1148/radiol.2019182012 · DOI
  9. Pantanowitz, 2020, An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: A blinded clinical validation and deployment study, Lancet Digit. Health, 2, e407, 10.1016/S2589-7500(20)30159-X · DOI
  10. Ma, 2022, Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer, PET Clin., 17, 137, 10.1016/j.cpet.2021.09.002 · DOI
  11. Gharavi, 2022, Clinical Application of Artificial Intelligence in PET Imaging of Head and Neck Cancer, PET Clin., 17, 65, 10.1016/j.cpet.2021.09.004 · DOI
  12. Le, 2019, Artificial intelligence in breast imaging, Clin. Radiol., 74, 357, 10.1016/j.crad.2019.02.006 · DOI
  13. Mondal, 2020, Repurposing Molecular Imaging and Sensing for Cancer Image-Guided Surgery, J. Nucl. Med., 61, 1113, 10.2967/jnumed.118.220426 · DOI
  14. Zhou, 2017, Molecular imaging of the tumor microenvironment, Adv. Drug Deliv. Rev., 113, 24, 10.1016/j.addr.2016.07.012 · DOI · Paper page
  15. Sarabhai, 2018, Comparison of 18F-FDG PET/MRI and MRI for pre-therapeutic tumor staging of patients with primary cancer of the uterine cervix, Eur. J. Pediatr., 45, 67
  16. Boustani, 2018, Molecular imaging of prostate cancer, Br. J. Radiol., 91, 20170736, 10.1259/bjr.20170736 · DOI
  17. Ehlerding, 2018, Dual-Targeted Molecular Imaging of Cancer, J. Nucl. Med., 59, 390, 10.2967/jnumed.117.199877 · DOI
  18. Yang, 2015, Addressing challenges of heterogeneous tumor treatment through bispecific protein-mediated pretargeted drug delivery, J. Control. Release, 220, 715, 10.1016/j.jconrel.2015.09.040 · DOI
  19. Saeed, 2020, Molecular Imaging for Cancer Immunotherapy: Seeing Is Believing, Bioconjug. Chem., 31, 404, 10.1021/acs.bioconjchem.9b00851 · DOI
  20. Aubertin, 2017, Development and characterization of a handheld hyperspectral Raman imaging probe system for molecular characterization of tissue on mesoscopic scales, Med. Phys., 45, 328
  21. Hsu, 2020, Recent Advances in Molecular Imaging with Gold Nanoparticles, Bioconjug. Chem., 31, 303, 10.1021/acs.bioconjchem.9b00669 · DOI
  22. Gabriel, 2018, Intravital microscopy in the study of the tumor microenvironment: From bench to human application, Oncotarget, 9, 20165, 10.18632/oncotarget.24957 · DOI
  23. Miller, 2017, Imaging the pharmacology of nanomaterials by intravital microscopy: Toward understanding their biological behavior, Adv. Drug Deliv. Rev., 113, 61, 10.1016/j.addr.2016.05.023 · DOI
  24. Bayarmagnai, 2018, Intravital Imaging of Tumor Cell Motility in the Tumor Microenvironment Context, Methods Pharmacol. Toxicol., 1749, 175
  25. Trumbull, 2020, Intravital Microscopy (IVM) in Human Solid Tumors: Novel Protocol to Examine Tumor-Associated Vessels, JMIR Res. Protoc., 9, e15677, 10.2196/15677 · DOI
  26. Gaustad, 2021, Intravital microscopy of tumor vessel morphology and function using a standard fluorescence microscope, Eur. J. Pediatr., 48, 3089
  27. Boulch, 2019, Tumor Immunosurveillance and Immunotherapies: A Fresh Look from Intravital Imaging, Trends Immunol., 40, 1022, 10.1016/j.it.2019.09.002 · DOI
  28. Gabriel, 2021, A pilot trial of intravital microscopy in the study of the tumor vasculature of patients with peritoneal carcinomatosis, Sci. Rep., 11, 4946, 10.1038/s41598-021-84430-3 · DOI
  29. Lodygin, 2017, Intravital real-time analysis of T-cell activation in health and disease, Cell Calcium, 64, 118, 10.1016/j.ceca.2016.12.007 · DOI
  30. Herskovits, 2021, Artificial intelligence in molecular imaging, Ann. Transl. Med., 9, 824, 10.21037/atm-20-6191 · DOI
  31. Giampetraglia, 2021, Recent advances in intravital microscopy for preclinical research, Curr. Opin. Chem. Biol., 63, 200, 10.1016/j.cbpa.2021.05.010 · DOI
  32. Rowe, S.P., and Pomper, M.G. (2021). Molecular imaging in oncology: Current impact and future directions. CA Cancer J. Clin. · DOI
  33. Soulet, 2020, Multiphoton intravital microscopy in small animals: Motion artefact challenges and technical solutions, J. Microsc., 278, 3, 10.1111/jmi.12880 · DOI

Cited by

Sorted by citations

Metadata

Authors
Nussbaum, S., Shoukry, M., Ashary, M. A., Kasbi, A. A., Baksh, M., & Gabriel, E.
Venue
Cancers
Publisher
MDPI AG
Published
2022-03-18
Volume
14
Issue
6
Pages
1549
ISSN
2072-6694
DOI
10.3390/cancers14061549
PMC
PMC8945965
Cited by
18
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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