Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studies were MRI-based, and two (15%) were CT-based. The sample size was <200 patients for all studies. There is significant heterogeneity in the literature, as evidenced by the relatively low RQS value (average score = 22.6%). There is not a homogeneous protocol used for MRI sequences among the different studies, although the highest predictive ability was always obtained in T2W-FS. Six articles (46%) reported on the potential application of the model in a clinical setting with a decision curve analysis (DCA). (4) Conclusions: Despite the variability in the radiomics method application, the similarity of results and conclusions observed is encouraging. Substantial limits were found; prospective and multicentric studies are needed to affirm the role of radiomics as a supporting tool.

Artificial intelligence in bone metastases: an MRI and CT imaging review / Faiella, Eliodoro; Santucci, Domiziana; Calabrese, Alessandro; Russo, Fabrizio; Vadalà, Gianluca; Zobel, Bruno Beomonte; Soda, Paolo; Iannello, Giulio; de Felice, Carlo; Denaro, Vincenzo. - In: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. - ISSN 1660-4601. - 19:3(2022). [10.3390/ijerph19031880]

Artificial intelligence in bone metastases: an MRI and CT imaging review

Calabrese, Alessandro
;
de Felice, Carlo
Penultimo
;
2022

Abstract

Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studies were MRI-based, and two (15%) were CT-based. The sample size was <200 patients for all studies. There is significant heterogeneity in the literature, as evidenced by the relatively low RQS value (average score = 22.6%). There is not a homogeneous protocol used for MRI sequences among the different studies, although the highest predictive ability was always obtained in T2W-FS. Six articles (46%) reported on the potential application of the model in a clinical setting with a decision curve analysis (DCA). (4) Conclusions: Despite the variability in the radiomics method application, the similarity of results and conclusions observed is encouraging. Substantial limits were found; prospective and multicentric studies are needed to affirm the role of radiomics as a supporting tool.
2022
CT; MRI; bone cancer; bone metastasis; lung cancer; machine learning; prostate cancer; radiomics; signature
01 Pubblicazione su rivista::01g Articolo di rassegna (Review)
Artificial intelligence in bone metastases: an MRI and CT imaging review / Faiella, Eliodoro; Santucci, Domiziana; Calabrese, Alessandro; Russo, Fabrizio; Vadalà, Gianluca; Zobel, Bruno Beomonte; Soda, Paolo; Iannello, Giulio; de Felice, Carlo; Denaro, Vincenzo. - In: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. - ISSN 1660-4601. - 19:3(2022). [10.3390/ijerph19031880]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1677883
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