An update in musculoskeletal tumors: from quantitative imaging to radiomics

Chianca, Vito; Albano, Domenico; Messina, Carmelo; Vincenzo, Gabriele; Rizzo, Stefania; Grande, Filippo Del; Sconfienza, Luca Maria

doi:10.1007/s11547-021-01368-2

Cited by 36 publications

(27 citation statements)

References 85 publications

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“…All 38 articles addressed an application of ML or DL with imaging data of MSK malignancies. Three review articles were found and excluded from statistical analysis [ 8 , 14 , 25 ]. 75.7% (28) of the studies were conducted retrospectively, 8.1% (3) were conducted prospectively and 16.2% (6) did not clearly state the study design.…”

Section: Resultsmentioning

confidence: 99%

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

et al. 2022

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Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. Key Points • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

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Section: Resultsmentioning

confidence: 99%

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

et al. 2022

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“…The data extracted from radiomicroscopy, when combined with other clinical data and correlated with outcome, can create accurate, robust, and evidence-based clinical decision support systems (CDSS) [ 62 , 119 ]. The rationale for radiomics is that quantitative variables based on individual voxels are more sensitive to various clinical endpoints than the qualitative radiologic, histopathologic, and clinical data routinely used in clinical practice [ 120 , 121 , 122 , 123 , 124 ].…”

Section: Radiomicsmentioning

confidence: 99%

“…Imaging is an important tool for tumor assessment in both preclinical and clinical settings [ 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. It provides data on the tumor’s size, location, and relationship to adjacent tissues.…”

Section: Introductionmentioning

confidence: 99%

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Bruno

Granata

Bellisari

et al. 2022

Cancers

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In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.

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“…Extra-abdominal DTs (EADTs) localization is more common in the head and neck region, where involvement of the airways or major vessels can present with hoarseness, dyspnea or, in extremely rare cases, be fatal [ 37 , 38 , 39 , 40 , 41 ]. Follow localizations at limbs, presenting as palpable masses, severe pain or muscle contractures and at the thoracic and abdominal wall [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Clinical Presentation and Treatmentmentioning

confidence: 99%

Multimodality Imaging Assessment of Desmoid Tumors: The Great Mime in the Era of Multidisciplinary Teams

Simonetti

Bruno

Fusco

et al. 2022

JPM

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Desmoid tumors (DTs), also known as desmoid fibromatosis or aggressive fibromatosis, are rare, locally invasive, non-metastatic soft tissue tumors. Although histological results represent the gold standard diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. Although histological analysis represents the gold standard for diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. DTs represent a challenge for the radiologist, being able to mimic different pathological conditions. A proper diagnosis is required to establish an adequate therapeutic approach. Multimodality imaging, including ultrasound (US), computed tomography (CT) and Magnetic Resonance Imaging (MRI), should be preferred. Different imaging techniques can also guide minimally invasive treatments and monitor their effectiveness. The purpose of this review is to describe the state-of-the-art multidisciplinary imaging of DTs; and its role in patient management.

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An update in musculoskeletal tumors: from quantitative imaging to radiomics

Cited by 36 publications

References 85 publications

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Multimodality Imaging Assessment of Desmoid Tumors: The Great Mime in the Era of Multidisciplinary Teams

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