Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data
| العنوان: | Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data |
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| المؤلفون: | Cristina Campi, Bianca Bignotti, M. Piana, Michele Cea, Francesco Frassoni, Alida Dominietto, Sara Aquino, Lorenzo Torri, Federica Rossi, Daniela Schenone, Emanuele Angelucci, Alberto Tagliafico |
| المصدر: | Diagnostics, Vol 11, Iss 1759, p 1759 (2021) Diagnostics Volume 11 Issue 10 |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.medical_specialty, Medicine (General), Fuzzy clustering, business.industry, Clinical Biochemistry, pattern recognition, Plasma cell dyscrasia, Retrospective cohort study, medicine.disease, artificial intelligence, Article, Artificial intelligence, Computerized tomography, Image processing, Multiple myeloma, Pattern recognition, image processing, Correlation, multiple myeloma, R5-920, Principal component analysis, Pattern recognition (psychology), Medicine, Autologous transplantation, computerized tomography, Radiology, business |
| الوصف: | Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann–Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2075-4418 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b96ff5d02b6b3e6bceacc0a472a3c9a https://www.mdpi.com/2075-4418/11/10/1759 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....0b96ff5d02b6b3e6bceacc0a472a3c9a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20754418 |
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