Introduction to radiomics for a clinical audience

McCague, Cathal; Ramlee, Syafiq; Reinius, Marika; Selby, Ian; Hulse, David; Piyatissa, P; Bura, Vlad; Crispín-Ortuzar, Mireia; Sala, Evis; Woitek, Ramona

doi:10.1016/j.crad.2022.08.149

Cited by 52 publications

(24 citation statements)

References 224 publications

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“…In addition to providing proof of concept of 3D mould-based multi-sampling in a variety of ovarian lesions, this pilot study sought to test the feasibility of prospective implementation of the method in a busy clinical setting at a tertiary hospital. With patient care remaining the central priority throughout, clinical workflow constraints dictated the following absolute requirements for an ovarian 3D mould research pathway ( 1 ): to be compatible with standard clinical procedures without any interference or delay to patients’ standard of care treatment pathways ( 2 ); to be able to adapt to a variety of real-world timelines, including short treatment planning intervals for cases scheduled for elective surgery on an urgent basis ( 3 ); to have no or negligible impact on clinical workload ( 4 ); to maximise efficiency and effectiveness of communication between clinical and research team members from diverse disciplines.…”

Section: Resultsmentioning

confidence: 99%

“…Biomarkers that integrate routinely collected radiological data with molecular features may improve prediction of patient outcome and treatment response ( 3 , 4 ). However, radiogenomic studies to date predominantly rely on retrospective cohorts and a single tumour sample from a single site per case – thus introducing an unquantified risk of sampling bias, and offering limited insight into the spatial relationship between radiomic features ( 5 ) and molecular heterogeneity at the whole-tumour level.…”

Section: Introductionmentioning

confidence: 99%

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Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Delgado-Ortet

Reinius²,

McCague³

et al. 2023

Front. Oncol.

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BackgroundHigh-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.MethodsIn this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.ResultsFive patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.ConclusionsWe developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Delgado-Ortet

Reinius²,

McCague³

et al. 2023

Front. Oncol.

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“…Although radiomics can be used in a nononcologic context, most published articles in radiomics are oncologic studies. The top three topics in original radiomics research publications are hepatobiliary/gastrointestinal cancers, followed by respiratory/thoracic cancers and neurologic malignancies, with a total of $ 2,000 radiomics publications in the literature as of May 2022 29 and exponential growth since Lambin first coined the term "radiomics" in 2012. 22 MSK cancers comprise a comparatively small subset with 101 publications identified by McCague and colleagues.…”

Section: Application Of Radiomics In Musculoskeletal Sarcomasmentioning

confidence: 99%

“…22 MSK cancers comprise a comparatively small subset with 101 publications identified by McCague and colleagues. 29 Our own literature review in PubMed and Embase identified 134 original research publications focusing on radiomics in MSK sarcomas excluding hematopoietic malignancies, as of early April 2023.…”

Section: Application Of Radiomics In Musculoskeletal Sarcomasmentioning

confidence: 99%

Radiomics in Musculoskeletal Tumors

Brandenberger,

White

2024

Semin Musculoskelet Radiol

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Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

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“…Therefore it is capable of a more comprehensive and objective analysis of lesion information [2]. In addition, Radiomic has the advantages of being noninvasive, reproducible, rapid, economical and avoiding biopsy sampling errors [3]. Extracting and modeling tumor characteristics can assist doctors in making the most accurate diagnosis by combining the deep mining of a large amount of data.…”

Section: Introductionmentioning

confidence: 99%

Hot topics in global radiomic research: a Web of Science-based bibliometric analysis

Yang,

Kong,

Long

et al. 2023

Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023)

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Introduction to radiomics for a clinical audience

Cited by 52 publications

References 224 publications

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Radiomics in Musculoskeletal Tumors

Hot topics in global radiomic research: a Web of Science-based bibliometric analysis

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