Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

Gatta, Roberto; Depeursinge, Adrien; Ratib, Osman; Michielin, Olivier; Leimgruber, A.

doi:10.1186/s41747-019-0143-0

Cited by 44 publications

(32 citation statements)

References 47 publications

(53 reference statements)

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“…Other innovative methods for quantification and image analysis derived from radiomics are expected to gradually translate into clinical medicine [ 125 , 126 , 127 , 128 ]. Using mathematical models for data characterization, radiomics allows us to extract a large number of features out of images, which might serve as prognostic parameters.…”

Section: Methodsmentioning

confidence: 99%

The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future

et al. 2020

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The NETTER-1 study has proven peptide receptor radionuclide therapy (PRRT) to be one of the most effective therapeutic options for metastatic neuroendocrine tumors (NETs), improving progression-free survival and overall survival. However, PRRT response assessment is challenging and no consensus on methods and timing has yet been reached among experts in the field. This issue is owed to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack of standardized parameters and timing of functional imaging and the heterogeneity of PRRT protocols in the literature. The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious PRRT.

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Section: Methodsmentioning

confidence: 99%

The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future

et al. 2020

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“…The gathering of genomic, radiomic, proteomic, clinical, immunohistochemical data, and their integration in predictive or prognostic models (Gatta et al 2020).…”

Section: Holomicsmentioning

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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“…Texture analysis (TA) can provide quantitative metrics extracted from routine clinical images that can be correlated to and/or predict multiple clinical endpoints 1–3 . Despite having the potential for wide applicability within clinical workflow for tasks such as objective whole lesion assessment and longitudinal disease monitoring, poor standardization of TA, limits its reliability, particularly in multicenter studies 4–8 …”

Section: Introductionmentioning

confidence: 99%

Identification of robust and reproducible CT‐texture metrics using a customized 3D‐printed texture phantom

Varghese

Hwang

Cen

et al. 2021

J Applied Clin Med Phys

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Objective The objective of this study was to evaluate the robustness and reproducibility of computed tomography‐based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three‐dimensional (3D) printed progressively increasing textural heterogeneity. Materials and Methods A custom‐built 3D‐printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray‐level Co‐occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two‐way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. Results A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray‐level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. Conclusion The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

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Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

Cited by 44 publications

References 47 publications

The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future

The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future

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

Identification of robust and reproducible CT‐texture metrics using a customized 3D‐printed texture phantom

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