Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization
2021. augusztus 19. frissítette: Hospital Italiano de Buenos Aires
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.
A tanulmány áttekintése
Állapot
Még nincs toborzás
Körülmények
Részletes leírás
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.
Tanulmány típusa
Megfigyelő
Beiratkozás (Várható)
277
Kapcsolatok és helyek
Ez a rész a vizsgálatot végzők elérhetőségeit, valamint a vizsgálat lefolytatásának helyére vonatkozó információkat tartalmazza.
Tanulmányi kapcsolat
- Név: Andrés Brandan
- Telefonszám: +5493816212804
- E-mail: andres.brandan@hospitalitaliano.org.ar
Részvételi kritériumok
A kutatók olyan embereket keresnek, akik megfelelnek egy bizonyos leírásnak, az úgynevezett jogosultsági kritériumoknak. Néhány példa ezekre a kritériumokra a személy általános egészségi állapota vagy a korábbi kezelések.
Jogosultsági kritériumok
Tanulmányozható életkorok
- Gyermek
- Felnőtt
- Idősebb felnőtt
Egészséges önkénteseket fogad
Igen
Tanulmányozható nemek
Összes
Mintavételi módszer
Nem valószínűségi minta
Tanulmányi populáció
The unit of analysis will be the bilateral mammographic images with mediolateral oblique and craniocaudal views.
The images selected for this study will be screening mammograms performed at Saint John's Cancer Institute.
The institution will select the images according to the inclusion and exclusion criteria.
The images will be extracted from the institutional database retrospectively and will be anonymized without any personal data, except for the age of the patient.
These images will be stored in DICOM format, in a safe place with restricted access limited only to the investigation team.
Leírás
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.
Tanulási terv
Ez a rész a vizsgálati terv részleteit tartalmazza, beleértve a vizsgálat megtervezését és a vizsgálat mérését.
Hogyan készül a tanulmány?
Tervezési részletek
Mit mér a tanulmány?
Elsődleges eredményintézkedések
Eredménymérő |
Intézkedés leírása |
Időkeret |
|---|---|---|
|
Agreement between the majority report and Artemisia´s categorization of dense breasts/non-dense breasts
Időkeret: 2 months
|
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
|
|
Agreement between the majority report and Artemisia in each one of the four breast density categories
Időkeret: 2 months
|
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
|
Másodlagos eredményintézkedések
Eredménymérő |
Intézkedés leírása |
Időkeret |
|---|---|---|
|
Agreement between each observer and Artemisia´s categorization of dense breasts/non-dense breasts
Időkeret: 2 months
|
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
|
|
Agreement between each observer and Artemisia in each one of the four breast density categories
Időkeret: 2 months
|
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
|
|
Agreement between each observer and the majority report in the categorization of dense breasts/non-dense breasts
Időkeret: 2 months
|
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
|
|
Agreement between each observer and the majority report in each one of the four breast density categories
Időkeret: 2 months
|
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
|
Együttműködők és nyomozók
Itt találhatja meg a tanulmányban érintett személyeket és szervezeteket.
Szponzor
Nyomozók
- Kutatásvezető: Daniel R Luna, MD, Hospital Italiano de Buenos Aires
Publikációk és hasznos linkek
A vizsgálattal kapcsolatos információk beviteléért felelős személy önkéntesen bocsátja rendelkezésre ezeket a kiadványokat. Ezek bármiről szólhatnak, ami a tanulmányhoz kapcsolódik.
Általános kiadványok
- 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. doi: 10.1056/NEJMoa062790.
- 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.
- 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. doi: 10.1038/sj.bjc.6601548.
- 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.
- 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.
- 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. doi: 10.2214/ajr.148.3.525.
- 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.
- Carreira Gomez 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.
- 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. doi: 10.1158/1055-9965.EPI-06-0034.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Martinez Gomez I, Casals El Busto M, Anton 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.
- 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. doi: 10.1007/s10549-007-9581-0. Epub 2007 Jul 7.
- 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(R) 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.
- 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.
- 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.
- 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.
Tanulmányi rekorddátumok
Ezek a dátumok nyomon követik a ClinicalTrials.gov webhelyre benyújtott vizsgálati rekordok és összefoglaló eredmények benyújtásának folyamatát. A vizsgálati feljegyzéseket és a jelentett eredményeket a Nemzeti Orvostudományi Könyvtár (NLM) felülvizsgálja, hogy megbizonyosodjon arról, hogy megfelelnek-e az adott minőség-ellenőrzési szabványoknak, mielőtt közzéteszik őket a nyilvános weboldalon.
Tanulmány főbb dátumok
Tanulmány kezdete (Várható)
2021. szeptember 1.
Elsődleges befejezés (Várható)
2022. április 1.
A tanulmány befejezése (Várható)
2022. július 1.
Tanulmányi regisztráció dátumai
Először benyújtva
2021. augusztus 19.
Először nyújtották be, amely megfelel a minőségbiztosítási kritériumoknak
2021. augusztus 19.
Első közzététel (Tényleges)
2021. augusztus 25.
Tanulmányi rekordok frissítései
Utolsó frissítés közzétéve (Tényleges)
2021. augusztus 25.
Az utolsó frissítés elküldve, amely megfelel a minőségbiztosítási kritériumoknak
2021. augusztus 19.
Utolsó ellenőrzés
2021. július 1.
Több információ
A tanulmányhoz kapcsolódó kifejezések
További vonatkozó MeSH feltételek
Egyéb vizsgálati azonosító számok
- 6077
- 4927 (PRIISA)
Terv az egyéni résztvevői adatokhoz (IPD)
Tervezi megosztani az egyéni résztvevői adatokat (IPD)?
NEM
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Egy amerikai FDA által szabályozott gyógyszerkészítményt tanulmányoz
Nem
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Nem
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