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Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery? Publisher Pubmed



Mohammadi A1 ; Mirzaaghazadehattari M2 ; Faeghi F3 ; Homayoun H4 ; Abolghasemi J5 ; Vogl TJ6 ; Bureau NJ7 ; Bakhshandeh M3 ; Acharya RU8, 9, 10 ; Abbasian Ardakani A3
Authors
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Authors Affiliations
  1. 1. Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran
  2. 2. Medical Radiation Sciences Research Group, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
  3. 3. Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
  6. 6. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
  7. 7. Department of Radiology, Centre Hospitalier de l'Universite de Montreal, Montreal, Canada
  8. 8. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
  9. 9. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
  10. 10. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan

Source: Journal of Ultrasound in Medicine Published:2022


Abstract

Objectives: The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning. Methods: A total of 229 breast lesions and 220 cervical lymph nodes were included in the study. A group of expert radiologists first performed medical imaging and segmented the lesions, after which a rectangular mask was drawn, encompassing all of the contouring. The mask was extended in each axis up to 50%, and 29 radiomics features were extracted from each mask. Radiomics features that showed a significant difference in each contour were used to develop a support vector machine (SVM) classifier for benign and malignant lesions in breast and lymph node images separately. Results: Single radiomics features extracted from extended contours outperformed radiologists' contours in both breast and lymph node lesions. Furthermore, when fed into the SVM model, the extended models also outperformed the radiologist's contour, achieving an area under the receiver operating characteristic curve of 0.887 and 0.970 in differentiating breast and lymph node lesions, respectively. Conclusions: Our results provide convincing evidence regarding the importance of the tumor periphery and TME in medical imaging diagnosis. We propose that the immediate tumor periphery should be considered for differentiating benign and malignant lesions in image quantification studies. © 2022 American Institute of Ultrasound in Medicine.
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