Breast Cancer Clinical Trial
Official title:
Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization
NCT number | NCT05021055 |
Other study ID # | 6077 |
Secondary ID | 4927 |
Status | Not yet recruiting |
Phase | |
First received | |
Last updated | |
Start date | September 2021 |
Est. completion date | July 2022 |
The correct categorization of breast density is essential to adapt the diagnostic examination to the needs of each patient. Assessment of breast density is performed visually by radiologists. Some authors have detected that this method involves considerable intra and interobserver variability. On the other hand, automated systems for measuring breast density are becoming more and more frequent. Machine learning is a domain of Artificial Intelligence, which comprises the process of developing systems with the ability to learn and make predictions using data. These systems are designed to aid healthcare professional decision making. In the present work, the multicenter study of external validation of a tool based on deep learning for the categorization of mammographic breast density is proposed.
Status | Not yet recruiting |
Enrollment | 277 |
Est. completion date | July 2022 |
Est. primary completion date | April 2022 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | N/A and older |
Eligibility | Inclusion Criteria: - Mammograms included in the study should meet the following criteria: - Female patients of 40 years of age or more. - To have at least one screening mammography exam performed at Saint John's - Cancer Institute during the study period. These exams will be included regardless of the brand of the mammography equipment. - Mammograms should be performed with digital equipment. Exclusion Criteria: - Mammograms with the following criteria will be excluded from the study: - Patients with gigantomastia, defined by the need for more than one image of each mammographic view (mediolateral oblique and craniocaudal) to evaluate the entire breast volume. - Patients with breast implants. - Patients with a history of breast surgery. |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
---|---|
Hospital Italiano de Buenos Aires |
A L Mousa DS, Ryan EA, Mello-Thoms C, Brennan PC. What effect does mammographic breast density have on lesion detection in digital mammography? Clin Radiol. 2014 Apr;69(4):333-41. doi: 10.1016/j.crad.2013.11.014. Epub 2014 Jan 11. Review. — View Citation
Alikhassi A, Esmaili Gourabi H, Baikpour M. Comparison of inter- and intra-observer variability of breast density assessments using the fourth and fifth editions of Breast Imaging Reporting and Data System. Eur J Radiol Open. 2018 Apr 20;5:67-72. doi: 10.1016/j.ejro.2018.04.002. eCollection 2018. — View Citation
Alonzo-Proulx O, Jong RA, Yaffe MJ. Volumetric breast density characteristics as determined from digital mammograms. Phys Med Biol. 2012 Nov 21;57(22):7443-57. doi: 10.1088/0031-9155/57/22/7443. Epub 2012 Oct 24. — View Citation
Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, Jong RA, Hislop G, Chiarelli A, Minkin S, Yaffe MJ. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007 Jan 18;356(3):227-36. — View Citation
Carreira Gómez MC, Estrada Blan MC. What we need to know about dense breasts: implications for breast cancer screening. Radiologia. 2016 Nov - Dec;58(6):421-426. doi: 10.1016/j.rx.2016.08.002. Epub 2016 Oct 15. English, Spanish. — View Citation
Ciatto S, Bernardi D, Calabrese M, Durando M, Gentilini MA, Mariscotti G, Monetti F, Moriconi E, Pesce B, Roselli A, Stevanin C, Tapparelli M, Houssami N. A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification. Breast. 2012 Aug;21(4):503-6. doi: 10.1016/j.breast.2012.01.005. Epub 2012 Jan 27. — View Citation
Ciatto S, Visioli C, Paci E, Zappa M. Breast density as a determinant of interval cancer at mammographic screening. Br J Cancer. 2004 Jan 26;90(2):393-6. — View Citation
Do S, Song KD, Chung JW. Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning. Korean J Radiol. 2020 Jan;21(1):33-41. doi: 10.3348/kjr.2019.0312. Review. — View Citation
Dontchos BN, Yala A, Barzilay R, Xiang J, Lehman CD. External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice. Acad Radiol. 2021 Apr;28(4):475-480. doi: 10.1016/j.acra.2019.12.012. Epub 2020 Feb 20. — View Citation
Eom HJ, Cha JH, Kang JW, Choi WJ, Kim HJ, Go E. Comparison of variability in breast density assessment by BI-RADS category according to the level of experience. Acta Radiol. 2018 May;59(5):527-532. doi: 10.1177/0284185117725369. Epub 2017 Aug 2. — View Citation
Gao J, Warren R, Warren-Forward H, Forbes JF. Reproducibility of visual assessment on mammographic density. Breast Cancer Res Treat. 2008 Mar;108(1):121-7. Epub 2007 Jul 7. — View Citation
Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS. Radiology. 2017 Feb;282(2):348-355. doi: 10.1148/radiol.2016152062. Epub 2016 Sep 5. — View Citation
Liu Y, Chen PC, Krause J, Peng L. How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. JAMA. 2019 Nov 12;322(18):1806-1816. doi: 10.1001/jama.2019.16489. — View Citation
Martínez Gómez I, Casals El Busto M, Antón Guirao J, Ruiz Perales F, Llobet Azpitarte R. Semiautomatic estimation of breast density with DM-Scan software. Radiologia. 2014 Sep-Oct;56(5):429-34. doi: 10.1016/j.rx.2012.11.007. Epub 2013 Mar 13. English, Spanish. — View Citation
McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006 Jun;15(6):1159-69. — View Citation
Melnikow J, Fenton JJ, Whitlock EP, Miglioretti DL, Weyrich MS, Thompson JH, Shah K. Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force. Ann Intern Med. 2016 Feb 16;164(4):268-78. doi: 10.7326/M15-1789. Epub 2016 Jan 12. Review. — View Citation
Pesce K, Tajerian M, Chico MJ, Swiecicki MP, Boietti B, Frangella MJ, Benitez S. Interobserver and intraobserver variability in determining breast density according to the fifth edition of the BI-RADS® Atlas. Radiologia (Engl Ed). 2020 Nov - Dec;62(6):481-486. doi: 10.1016/j.rx.2020.04.006. Epub 2020 May 31. English, Spanish. — View Citation
Sprague BL, Conant EF, Onega T, Garcia MP, Beaber EF, Herschorn SD, Lehman CD, Tosteson AN, Lacson R, Schnall MD, Kontos D, Haas JS, Weaver DL, Barlow WE; PROSPR Consortium. Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study. Ann Intern Med. 2016 Oct 4;165(7):457-464. doi: 10.7326/M15-2934. Epub 2016 Jul 19. — View Citation
Strand F, Azavedo E, Hellgren R, Humphreys K, Eriksson M, Shepherd J, Hall P, Czene K. Localized mammographic density is associated with interval cancer and large breast cancer: a nested case-control study. Breast Cancer Res. 2019 Jan 22;21(1):8. doi: 10.1186/s13058-019-1099-y. — View Citation
Swann CA, Kopans DB, McCarthy KA, White G, Hall DA. Mammographic density and physical assessment of the breast. AJR Am J Roentgenol. 1987 Mar;148(3):525-6. — View Citation
Wanders JOP, Holland K, Karssemeijer N, Peeters PHM, Veldhuis WB, Mann RM, van Gils CH. The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study. Breast Cancer Res. 2017 Jun 5;19(1):67. doi: 10.1186/s13058-017-0859-9. — View Citation
Winkler NS, Raza S, Mackesy M, Birdwell RL. Breast density: clinical implications and assessment methods. Radiographics. 2015 Mar-Apr;35(2):316-24. doi: 10.1148/rg.352140134. — View Citation
* Note: There are 22 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Agreement between the majority report and Artemisia´s categorization of dense breasts/non-dense breasts | The agreement between the CNN and the total of the professionals' categorizations will be calculated with the linear weighted kappa. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images. | 2 months | |
Primary | Agreement between the majority report and Artemisia in each one of the four breast density categories | For each one of the professionals involved in the study, the agreement with the CNN will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images. | 2 months | |
Secondary | Agreement between each observer and Artemisia´s categorization of dense breasts/non-dense breasts | To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images. | 2 months | |
Secondary | Agreement between each observer and Artemisia in each one of the four breast density categories | For each one of the professionals involved in the study, the agreement with the CNN will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by Artemisia for the same set of images. | 2 months | |
Secondary | Agreement between each observer and the majority report in the categorization of dense breasts/non-dense breasts | For each one of the professionals involved in the study, the agreement with the majority report will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by the majority report for the same set of images. | 2 months | |
Secondary | Agreement between each observer and the majority report in each one of the four breast density categories | For each one of the professionals involved in the study, the agreement with the majority report will be calculated with the linear weighted kappa coefficient. To this end, the categories assigned by the professionals will be considered as only one observer in each one of the studies and they will be compared to those assigned by the majority report for the same set of images. | 2 months |
Status | Clinical Trial | Phase | |
---|---|---|---|
Recruiting |
NCT04681911 -
Inetetamab Combined With Pyrotinib and Chemotherapy in the Treatment of HER2 Positive Metastatic Breast Cancer
|
Phase 2 | |
Terminated |
NCT04066790 -
Pyrotinib or Trastuzumab Plus Nab-paclitaxel as Neoadjuvant Therapy in HER2-positive Breast Cancer
|
Phase 2 | |
Completed |
NCT04890327 -
Web-based Family History Tool
|
N/A | |
Completed |
NCT03591848 -
Pilot Study of a Web-based Decision Aid for Young Women With Breast Cancer, During the Proposal for Preservation of Fertility
|
N/A | |
Recruiting |
NCT03954197 -
Evaluation of Priming Before in Vitro Maturation for Fertility Preservation in Breast Cancer Patients
|
N/A | |
Terminated |
NCT02202746 -
A Study to Assess the Safety and Efficacy of the VEGFR-FGFR-PDGFR Inhibitor, Lucitanib, Given to Patients With Metastatic Breast Cancer
|
Phase 2 | |
Active, not recruiting |
NCT01472094 -
The Hurria Older PatiEnts (HOPE) With Breast Cancer Study
|
||
Completed |
NCT06049446 -
Combining CEM and Magnetic Seed Localization of Non-Palpable Breast Tumors
|
||
Withdrawn |
NCT06057636 -
Hypnosis for Pain in Black Women With Advanced Breast Cancer: A Feasibility Study
|
N/A | |
Recruiting |
NCT05560334 -
A Single-Arm, Open, Exploratory Clinical Study of Pemigatinib in the Treatment of HER2-negative Advanced Breast Cancer Patients With FGFR Alterations
|
Phase 2 | |
Active, not recruiting |
NCT05501769 -
ARV-471 in Combination With Everolimus for the Treatment of Advanced or Metastatic ER+, HER2- Breast Cancer
|
Phase 1 | |
Recruiting |
NCT04631835 -
Phase I Study of the HS-10352 in Patients With Advanced Breast Cancer
|
Phase 1 | |
Completed |
NCT04307407 -
Exercise in Breast Cancer Survivors
|
N/A | |
Recruiting |
NCT03544762 -
Correlation of 16α-[18F]Fluoro-17β-estradiol PET Imaging With ESR1 Mutation
|
Phase 3 | |
Terminated |
NCT02482389 -
Study of Preoperative Boost Radiotherapy
|
N/A | |
Enrolling by invitation |
NCT00068003 -
Harvesting Cells for Experimental Cancer Treatments
|
||
Completed |
NCT00226967 -
Stress, Diurnal Cortisol, and Breast Cancer Survival
|
||
Recruiting |
NCT06019325 -
Rhomboid Intercostal Plane Block on Chronic Pain Incidence and Acute Pain Scores After Mastectomy
|
N/A | |
Recruiting |
NCT06006390 -
CEA Targeting Chimeric Antigen Receptor T Lymphocytes (CAR-T) in the Treatment of CEA Positive Advanced Solid Tumors
|
Phase 1/Phase 2 | |
Recruiting |
NCT06037954 -
A Study of Mental Health Care in People With Cancer
|
N/A |