Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review

Eliodoro Faiella, Domiziana Santucci, Alessandro Calabrese, Fabrizio Russo, Gianluca Vadalà, Bruno Beomonte Zobel, Paolo Soda, Giulio Iannello, Carlo de Felice and Vincenzo Denaro
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Eliodoro Faiella: Department of Radiology, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Domiziana Santucci: Department of Radiology, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Alessandro Calabrese: Department of Radiology, University of Rome “Sapienza”, Viale del Policlinico, 00161 Roma, Italy
Fabrizio Russo: Department of Orthopaedic and Trauma Surgery, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Gianluca Vadalà: Department of Orthopaedic and Trauma Surgery, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Bruno Beomonte Zobel: Department of Radiology, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Paolo Soda: Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Giulio Iannello: Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy
Carlo de Felice: Department of Radiology, University of Rome “Sapienza”, Viale del Policlinico, 00161 Roma, Italy
Vincenzo Denaro: Department of Orthopaedic and Trauma Surgery, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo, 00128 Roma, Italy

IJERPH, 2022, vol. 19, issue 3, 1-11

Abstract: (1) 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.

Keywords: MRI; CT; bone metastasis; bone cancer; lung cancer; prostate cancer; machine learning; radiomics; signature (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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