Measuring Tumor Aggressiveness Through Artificial Intelligence
Article Sidebar
Published:
Dec 20, 2023
Keywords:
Soft tissue sarcoma, artificial intelligence, histological grade, deep learning, automated machine learning, machine vision
Main Article Content
Abstract
Objective: To determine the degree of tumor aggressiveness by means of artificial intelligence techniques using magnetic resonance images of sarcomas with proven histological grade.
Materials and Methods: Two independent cohorts of patients with soft tissue sarcomas (STS) were retrospectively collected. For each patient in the two cohorts, three types of imaging sequences were acquired as indicated by the clinical protocols: T1-weighted (T1), fat-suppressed T2-weighted (FST2) and STIR. For the development of the artificial intelligence model, 134 images were used, both high-grade and low-grade T1 and T2 images, taking the most representative image of the tumor at any slice. This translated into more than 36 million pixels that were analyzed by the Landing AI program.
Results: To determine the degree of tumor aggressiveness by means of artificial intelligence techniques using magnetic resonance The model’s average accuracy was 84.3%, and its sensitivity was 73.3%, with a confidence threshold of 0.66, indicating that a good quality model was generated for predicting the grade of aggressiveness of an STS prior to biopsy using MRI scans.
Conclusions: A novel approach is presented to address a rare pathology using artificial intelligence techniques to determine the tumor grade based on nuclear magnetic resonance images. Based on the results of our model, it can be considered as a second expert opinion when performing imaging studies prior to biopsy.
Materials and Methods: Two independent cohorts of patients with soft tissue sarcomas (STS) were retrospectively collected. For each patient in the two cohorts, three types of imaging sequences were acquired as indicated by the clinical protocols: T1-weighted (T1), fat-suppressed T2-weighted (FST2) and STIR. For the development of the artificial intelligence model, 134 images were used, both high-grade and low-grade T1 and T2 images, taking the most representative image of the tumor at any slice. This translated into more than 36 million pixels that were analyzed by the Landing AI program.
Results: To determine the degree of tumor aggressiveness by means of artificial intelligence techniques using magnetic resonance The model’s average accuracy was 84.3%, and its sensitivity was 73.3%, with a confidence threshold of 0.66, indicating that a good quality model was generated for predicting the grade of aggressiveness of an STS prior to biopsy using MRI scans.
Conclusions: A novel approach is presented to address a rare pathology using artificial intelligence techniques to determine the tumor grade based on nuclear magnetic resonance images. Based on the results of our model, it can be considered as a second expert opinion when performing imaging studies prior to biopsy.
Downloads
Download data is not yet available.
Metrics
Metrics Loading ...
Article Details
How to Cite
Segura, F., Segura, P., & Segura, F. (2023). Measuring Tumor Aggressiveness Through Artificial Intelligence. Revista De La Asociación Argentina De Ortopedia Y Traumatología, 88(6), 653-661. https://doi.org/10.15417/issn.1852-7434.2023.88.6.1738
Issue
Section
Clinical Research
Copyright (c) 2023 Revista de la Asociación Argentina de Ortopedia y Traumatología
Manuscript acceptance by the Journal implies the simultaneous non-submission to any other journal or publishing house. The RAAOT is under the Licencia Creative Commnos Atribución-NoComercial-Compartir Obras Derivadas Igual 4.0 Internacional (CC-BY-NC.SA 4.0) (http://creativecommons.org/licences/by-nc-sa/4.0/deed.es). Articles can be shared, copied, distributed, modified, altered, transformed into a derivative work, executed and publicly communicated, provided a) the authors and the original publication (Journal, Publisher and URL) are mentioned, b) they are not used for commercial purposes, c) the same terms of the license are maintained.
In the event that the manuscript is approved for its next publication, the authors retain the copyright and will assign to the journal the rights of publication, edition, reproduction, distribution, exhibition and communication at a national and international level in the different databases. data, repositories and portals.
It is hereby stated that the mentioned manuscript has not been published and that it is not being printed in any other national or foreign journal.
The authors hereby accept the necessary modifications, suggested by the reviewers, in order to adapt the manuscript to the style and publication rules of this Journal.
References
1. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine 2006;27(4):12-4. https://doi.org/10.1609/aimag.v27i4.1904
2. Maxmen JS. The post-physician era: Medicine in the 21st century. Hoboken, New Jersey: Wiley; 1976, p. 2336-7.
3. Scarlat A. A machine learning primer for clinicians–part 1. HIStalk. 2018. Disponible en:
https://histalk2.com/2018/10/17/a-machine-learningprimer-for-clinicianspart-1/ [Consulta: 5 de agosto, 2019]
4. Chollet F. Deep learning with Python. Shelter Island, New York: Manning Publications; 2018.
5. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplasty 2018;33(8):2358-61. https://doi.org/10.1016/j.arth.2018.02.067
6. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69(1):7-34. https://doi.org/10.3322/caac.21551
7. Fletcher CDM, Bridge JA, Hogendoorn P, Mertens F. WHO Classification of Tumors of Soft Tissue and Bone. 4th ed., WHO Press; 2013, vol. 3.
8. Trojani M, Contesso G, Coindre JM, Rouesse J, Bui NB, de Mascarel A, et al. Soft tissue sarcomas in adults; study of pathological prognostic variables and definition of histopathological grading systems. Int J Cancer 1984;33(1):37-42. https://doi.org/10.1002/ijc.2910330108
9. Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture
features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol
2015;60(14):5471-96. https://doi.org/10.1088/0031-9155/60/14/5471
10. Refai F. The histopathological grading of soft tissue sarcomas: A review. Saudi J Pathol Microbiol 2019;4(8):581-5. https://doi.org/10.21276/sjpm.2019.4.8.2
11. Deyrup AT, Weiss SW. Grading of soft tissue sarcomas: the challenge of providing precise information in an imprecise world. Histopathology 2006;48(1):42-50. https://doi.org/10.1111/j.1365-2559.2005.02288.x
12. Amidi A, Amidi S. Hoja de referencia de consejos y trucos sobre Aprendizaje Automático. Métricas de clasificación. 2021. Disponible en: https://stanford.edu/~shervine/l/es/ teaching/cs-229/hoja-referencia-aprendizaje-automaEco-consejos-trucos
13. Navarro F, Dapper H, Asadpour R, Knebel C, Spraker MB, Schwarze V, et al. Development and external validation of deep-learning-based tumor grading models in soft-tissue sarcoma patients using MR imaging. Cancers (Basel) 2021;13(12):2866. https://doi.org/10.3390/cancers13122866
14. Coindre JM, Terrier P, Guillou L, Le Doussal V, Collin F, Ranchère D, et al. Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: a study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer 2001;91(10):1914-26.
https://doi.org/10.1002/1097-0142(20010515)91:10<1914::aid-cncr1214>3.0.co;2-3
2. Maxmen JS. The post-physician era: Medicine in the 21st century. Hoboken, New Jersey: Wiley; 1976, p. 2336-7.
3. Scarlat A. A machine learning primer for clinicians–part 1. HIStalk. 2018. Disponible en:
https://histalk2.com/2018/10/17/a-machine-learningprimer-for-clinicianspart-1/ [Consulta: 5 de agosto, 2019]
4. Chollet F. Deep learning with Python. Shelter Island, New York: Manning Publications; 2018.
5. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplasty 2018;33(8):2358-61. https://doi.org/10.1016/j.arth.2018.02.067
6. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69(1):7-34. https://doi.org/10.3322/caac.21551
7. Fletcher CDM, Bridge JA, Hogendoorn P, Mertens F. WHO Classification of Tumors of Soft Tissue and Bone. 4th ed., WHO Press; 2013, vol. 3.
8. Trojani M, Contesso G, Coindre JM, Rouesse J, Bui NB, de Mascarel A, et al. Soft tissue sarcomas in adults; study of pathological prognostic variables and definition of histopathological grading systems. Int J Cancer 1984;33(1):37-42. https://doi.org/10.1002/ijc.2910330108
9. Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture
features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol
2015;60(14):5471-96. https://doi.org/10.1088/0031-9155/60/14/5471
10. Refai F. The histopathological grading of soft tissue sarcomas: A review. Saudi J Pathol Microbiol 2019;4(8):581-5. https://doi.org/10.21276/sjpm.2019.4.8.2
11. Deyrup AT, Weiss SW. Grading of soft tissue sarcomas: the challenge of providing precise information in an imprecise world. Histopathology 2006;48(1):42-50. https://doi.org/10.1111/j.1365-2559.2005.02288.x
12. Amidi A, Amidi S. Hoja de referencia de consejos y trucos sobre Aprendizaje Automático. Métricas de clasificación. 2021. Disponible en: https://stanford.edu/~shervine/l/es/ teaching/cs-229/hoja-referencia-aprendizaje-automaEco-consejos-trucos
13. Navarro F, Dapper H, Asadpour R, Knebel C, Spraker MB, Schwarze V, et al. Development and external validation of deep-learning-based tumor grading models in soft-tissue sarcoma patients using MR imaging. Cancers (Basel) 2021;13(12):2866. https://doi.org/10.3390/cancers13122866
14. Coindre JM, Terrier P, Guillou L, Le Doussal V, Collin F, Ranchère D, et al. Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: a study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer 2001;91(10):1914-26.
https://doi.org/10.1002/1097-0142(20010515)91:10<1914::aid-cncr1214>3.0.co;2-3