Interdisciplinarity: an essential requirement for translation of radiomics research into clinical practice – a systematic review focused on thoracic oncology

Sollini, Martina; Gelardi, Fabrizia; Pini, Cristiano; Delgado-Bolton, Roberto; Chiti, Arturo; Kirienko, Margarita

doi:10.1016/j.remnie.2019.11.002

Cited by 12 publications

(17 citation statements)

References 28 publications

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“…The instability of radiomic features across different devices and acquisition protocols, especially for MRI images [ 67 ], could further limit the real application of radiomics to daily clinical practice. To this purpose, a collaboration between clinicians and medical imaging experts is pivotal, as interdisciplinarity correlates with the quality of the published research [ 68 ]. Cooperation between institutions is warranted to find methods capable of countering features variability and instability, based on the analysis of large databases [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

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Section: Discussionmentioning

confidence: 99%

Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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“…Radiomic and AI (i.e., image mining) (Sollini et al 2019a) research benefits from an interdisciplinary attitude. Overall, the chance to exchange knowledge between experts in different fields (physicians and imagers, physicists and statisticians, biologists, informatics, engineers, and data mining) remarkably impacts on methodology (i.e., quality) and confers robustness to results and ultimately promoting image mining toward clinical practice (Sollini et al 2020b).…”

Section: Interdisciplinaritymentioning

confidence: 99%

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Sollini

Bartoli

Marciano

et al. 2020

European J Hybrid Imaging

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Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer—being one of the most extensively malignancy studied by hybrid medical imaging—has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

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“…The 2 CTR features were assessed by compatibility ratios (>80%) Forest classification). 3 The combination of CT-and PET-based radiomics (with Sequential minimal optimization classification). 4 Other primary lung cancer types.…”

Section: Feature Selection and Normalizationmentioning

confidence: 99%

“…It can make clear histopathological diagnosis for the vast majority of cases, which is regarded as the final clinical diagnosis (1), but it is traumatic and costly. Radiomics is a cost-effective method to predict histological subtypes in lung cancer by using images features as the markers (2)(3)(4)(5).…”

Section: Introductionmentioning

confidence: 99%

“…The workflow of radiomics includes image acquisition, image preprocessing, volume of interest segmentation, feature extraction, feature selection, model building and validation. Sollini et al has comprehensively and clearly reported the methodological aspects of the radiomics workflow and possible pitfalls (2,3). In particular, for image types, different types of medical images have different advantages.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Yan

Wang

2020

Front. Oncol.

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Purpose: To develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET. Methods: Machine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases (Mts), adenocarcinoma (Adc), and squamous cell carcinoma (Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership. The performance metrics include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics. Results: The combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR fourclass classification model showed 91% Acc and 0.89 Kappa. Conclusion: The proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.

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Interdisciplinarity: an essential requirement for translation of radiomics research into clinical practice – a systematic review focused on thoracic oncology

Cited by 12 publications

References 28 publications

Radiomics of Liver Metastases: A Systematic Review

Radiomics of Liver Metastases: A Systematic Review

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

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