Table Info

Table 4.

Effectiveness of the infrared-AI software and mammography in detecting BC cases

Screening techniques	Clinical validation		Sensitivity	Specificity	PPV	NPV

	Non-suspicious	BC^*
Mammography
BI-RADS 1,2	301	0	100.0%	97.10%	81.25%	100.0%
BI-RADS 3–5^*	9	39
Infrared-AI software
Th1,2 (non-suspicious)	224	2	94.87%	72.26%	30.08%	99.12%
Th3,4,5 (suspicious)	86	37

Patients with histopathological results

Supplementary material

The supplementary material for this article is available at: https://www.explorationpub.com/uploads/Article/file/1002135_sup_1.pdf.

Declarations

Acknowledgments

Thanks are due to FUCAM A.C. for facilitating communication with patients and for those little details given in this research, especially to the Department of Radiology.

Author contributions

EMDCM: Conceptualization, Methodology, Project administration, Writing—original draft. PASM: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing—original draft, Writing—review & editing. ERL: Writing—original draft, Writing—review & editing. FMLR: Data curation, Formal analysis. Writing—original draft. AHS: Conceptualization, Software, Formal analysis. JAJA: Software, Formal analysis. KYLC: Resources, Validation. LEHG: Conceptualization, Funding acquisition, Writing—review & editing. JAMG: Conceptualization, Funding acquisition. YGM: Investigation. All authors read and approved the submitted version.

Conflicts of interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: PASM, AHS, JAJA, LEHG, JAMG, and YGM report a relationship with Hearthcore SAPI de CV as associates, but declare to only have a scientific interest in this research. EMDCM reports a relationship with Hearthcore that includes non-financial support as a medical advisor. There are no further conflicts of interest to be declared.

Ethical approval

This research was conducted in accordance with the standards of the Research, Bioethics, and Biosafety Committee from FUCAM A.C. [PI 19-05], as well as with the 1964 Declaration of Helsinki and its later amendments.

Consent to participate

Informed consent to participate in the study was obtained from all participants.

Consent to publication

Informed consent to publication was obtained from relevant participants.

Availability of data and materials

The electronic patient record data used to support the findings of this research are restricted by FUCAM A.C. in order to protect patient’s privacy, also, the database of infrared images generated in this trial are property of Heathcore SAPI de CV. Requests for accessing the datasets should be directed to [Eva Ruvalcaba-Limon, evaruvalcaba@yahoo.com.mx] and [Pedro A. Sánchez-Méndez, pedro.sanchez@thermy.com.mx] if the researcher meets the criteria for access to confidential data.

Funding

This work was supported by the Social Responsibility Program of Avon Cosmetics S. de R.L. de C.V. [Cruzada Avon contra el Cáncer de Mama]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

References

Meneses-García A, Ramírez T, Ruiz-Godoy L, Chiquete E, Mohar A. Cost of breast cancer treatment prior to the introduction of immune-based therapy in Mexico. Rev Med Inst Mex Seguro Soc. 2012;50:19–24. [PubMed]

Mammography machines [Internet]. Paris: Organization for Economic Cooperation and Development; c2019 [cited 2021 May 21]. Available from: https://data.oecd.org/healtheqt/mammography-machines.htm

de Guadalupe Gómez Pérez M. Consejo mexicano de radiología e imagen, 40 aniversario. In: Entidades federativas actuales. Ciudad: Editorial Progreso; 2015.

Heinze-Martin G, Olmedo-Canchola VH, Bazán-Miranda G, Bernand-Fuentes NA, Guízar-Miranda P. Los médicos especialistas en México. Gac Med Mex. 2018;154:342–51. Spanish. [DOI] [PubMed]

Clinical radiology UK workforce census 2020 report [Internet]. London: The Royal College of Radiologists; c2022 [cited 2021 Oct 11]. Available from: https://www.rcr.ac.uk/publication/clinical-radiology-uk-workforce-census-2020-report

Garg T, Bajaj S, Dayan MJ, Makary MS, Smirniotopoulos JB, Silk M, et al. Temporal and geospatial variations among the interventional radiology physician workforce in the United States. Clin Imaging. 2021;78:105–9. [DOI] [PubMed]

CFR - code of federal regulations title 21 [Internet]. Maryland: U.S. Food & Drug Administration; c2021 [cited 2021 Jun 3]. Available from: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?fr=884.2980

Yao X, Wei W, Li J, Wang L, Xu Z, Wan Y, et al. A comparison of mammography, ultrasonography, and far-infrared thermography with pathological results in screening and early diagnosis of breast cancer. Asian Biomed. 2014;8:11–9. [DOI]

Sella T, Sklair-Levy M, Cohen M, Rozin M, Shapiro-Feinberg M, Allweis TM, et al. A novel functional infrared imaging system coupled with multiparametric computerised analysis for risk assessment of breast cancer. Eur Radiol. 2012;23:1191–8. [DOI] [PubMed]

10.

Keyserling JR, Ahlgren PD, Yu E, Belliveau N. Infrared imaging of the breast: initial reappraisal using high-resolution digital technology in 100 succesive cases of stage I and II breast cancer. Breast J. 1998;4:245–51. [DOI] [PubMed]

11.

Guidelines for breast thermology [Internet]. Greenville: American Academy of Thermology; c2021 [cited 2021 Jul 9]. Available from: https://aathermology.org/wp-content/uploads/2018/04/AAT-Breast-Guidelines-2021v2.pdf

12.

Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature. 2000;407:249–57. [DOI] [PubMed]

13.

Kandlikar SG, Perez-Raya I, Raghupathi PA, Gonzalez-Hernandez JL, Dabydeen D, Medeiros L, et al. Infrared imaging technology for breast cancer detection–current status, protocols and new directions. Int J Heat Mass Transf. 2017;108:2303–20. [DOI]

14.

American College of Radiology. Breast imaging reporting and data system: BI-RADS Atlas. 5th ed. Reston, VA: American College of Radiology; 2013.

15.

Antonini S, Kolarić D, Herceg Ž, Ferenčić Ž, Kuliš T, Borojević N, et al. Simplified description and interpretation of pathological thermography signs in malignant breast lesions. Period Biol. 2011;113:425–32.

16.

Pedraza RS, Raad JE. Aspectos sobre diseño y tamaño de muestra en estudios de prueba diagnóstica. Revista de la Facultad de Medicina: Universidad Nacional de Colombia. 2001;49:175–80. Spanish.

17.

SISA. Fisher 2 by 5 [software]. 1997 [cited 2021 Oct 18]. Available from: https://www.quantitativeskills.com/sisa/statistics/fiveby2.htm

18.

Duffy S, Vulkan D, Cuckle H, Parmar D, Sheikh S, Smith E, et al. Annual mammographic screening to reduce breast cancer mortality in women from age 40 years: long-term follow-up of the UK Age RCT. Health Technol Assess. 2020;24:1–23. [DOI] [PMC]

19.

Uscanga-Sánchez S, Torres-Mejía G, Ángeles-Llerenas A, Domínguez-Malpica R, Lazcano-Ponce E. Breast cancer screening process indicators in Mexico: a case study. Salud Publica Mex. 2014;56:528–37. Spanish. [PubMed]

20.

Ulloa-Pérez E, Mohar-Betancourt A, Reynoso-Noverón N. Estimation of the cost-effectiveness of breast cancer screening using mammography in Mexico through a simulation. Rev Invest Clin. 2016;68:184–91. [PubMed]

21.

Brandan ME, Navarro YV. Detección del cáncer de mama: estado de la mamografía en México. Cancerología. 2006;1:147–62. Spainish.

22.

Parisky YR, Sardi A, Hamm R, Hughes K, Esserman L, Rust S, et al. Efficacy of computerized infrared imaging analysis to evaluate mammographically suspicious lesions. AJR Am J Roentgenol. 2003;180:263–9. [DOI] [PubMed]

23.

Arora N, Martins D, Ruggerio D, Tousimis E, Swistel AJ, Osborne MP, et al. Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Am J Surg. 2008;196:523–6. [DOI] [PubMed]

24.

Gautherie M. Thermobiological assessment of benign and malignant breast diseases. Am J Obstet Gynecol. 1983;147:861–9. [DOI] [PubMed]

25.

Villarreal-Garza C, Aguila C, Magallanes-Hoyos MC, Mohar A, Bargalló E, Meneses A, et al. Breast cancer in young women in Latin America: an unmet, growing burden. Oncologist. 2013;18:26–34. [DOI] [PubMed]

26.

Sechopoulos I, Mann RM. Standalone artificial intelligence - the future of breast cancer screening? Breast. 2020;49:254–60. [DOI] [PubMed] [PMC]

27.

Gogoi UR, Bhowmlk MK, Bhattacharjee D, Ghosh AK. Singular value based characterization and analysis of thermal patches for early breast abnormality detection. Australas Phys Eng Sci Med. 2018;41:861–79. [DOI] [PubMed]

28.

Zuluaga-Gomez J, Al Masry Z, Benaggoune K, Meraghni S, Zerhouni N. A CNN-based methodology for breast cancer diagnosis using thermal images. Comput Methods Biomech Biomed Eng Imaging Vis. 2020;9:131–45. [DOI]

29.

Visual Lab DMR [Internet]. Niterói: Database of mastologic images; c2021 [cited 2021 Nov 28]. Available from: https://visual.ic.uff.br/dmi/

30.

Bhowmik MK, Gogoi UR, Majumdar G, Bhattacharjee D, Datta D, Ghosh AK. Designing of ground-truth-annotated DBT-TU-JU breast thermogram database toward early abnormality prediction. IEEE J Biomed Health Inform. 2018;22:1238–49. [DOI] [PubMed]

31.

Singh D, Singh AK. Role of image thermography in early breast cancer detection - past, present and future. Comput Methods Programs Biomed. 2020;183:105074. [DOI] [PubMed]

32.

Hakim A, Awale RN. Thermal imaging - an emerging modality for breast cancer detection: a comprehensive review. J Med Syst. 2020;44:136. [DOI] [PubMed]

33.

Rassiwala M, Mathur P, Mathur R, Farid K, Shukla S, Gupta PK, et al. Evaluation of digital infra-red imaging as an adjunctive screening method for breast carcinoma: a pilot study. Int J Surg. 2014;12:1439–43. [DOI] [PubMed]

34.

Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9:611–29. [DOI] [PubMed] [PMC]