Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization

Breast Cancer Clinical Trial

Official title:

Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05021055
• Other study ID #: 6077
• Secondary ID: 4927
• Status: Not yet recruiting
• Start date: September 2021
• Est. completion date: July 2022

Study information

• Verified date: July 2021
• Source: Hospital Italiano de Buenos Aires
• Contact: Andrés Brandan
• Phone: +5493816212804
• Email: andres.brandan[@]hospitalitaliano.org.ar
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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. Consequently, in clinical practice, breast density is reported from the assessment carried out by specialists with the support of these systems. But there are few studies about the use, concordance and perception of usefulness of professionals on these tools. A study carried out at the Hospital Italiano de Buenos Aires reported a moderate to almost perfect inter- and intra-observer agreement among radiologists and a moderate concordance between the categorization carried out by experts and that carried out by commercial software of a digital mammography machine. 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. Once a system designed to aid healthcare professional decision making is developed, it must be validated. In 2019, an internal validation of a tool based on deep learning techniques was carried out for the automatic categorization of mammographic breast density. The tool reached a very good interobserver agreement, kappa = 0.64 (95% CI 0.58-0.69), when compared with the performance of the professionals. It reached a sensitivity of 83.2 (CI: 76.9-88.3) and a specificity of 88.4 (83.9-92.0.) 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. The evaluation of this tool will be carried out in two external institutions: Hospital Alemán and Fundación Científica del Sur.

Recruitment information / eligibility

• 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.

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Hospital Italiano de Buenos Aires

References & Publications (22)

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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

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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

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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

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* Note: There are 22 references in all — Click here to view all references

Outcome

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

External Links

•   Sickles EA, D'Orsi CJ, Bassett LW, Appleton CM, Berg WA, Burnside ES, et al. ACR BI-RADS® Atlas, Breast imaging reporting and data system. Reston, VA: American College of Radiology. 2013;39-48.