Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets

Hecking, Thomas; Wang, Helen Y. C.; Varghese, A.J.; Donovan, Ellen M.; South, C.; Saxby, Helen; Nisbet, Andrew; Prakash, Vineet; Sasidharan, Balu Krishna; Pavamani, Simon; Devadhas, Devakumar; Mathew, Manu; Isiah, Rajesh; Evans, Philip

doi:10.3390/app13127291

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…There is a need for larger independent datasets to test models. The reproducibility of radiomics features and their relationship to biomedicine needs further study [ 38 ], and it is important that methodologies are reported in sufficient detail to enable others to reproduce results [ 39 ].…”

Section: Radiomics Applicationsmentioning

confidence: 99%

The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer

Tapper,

Carneiro,

Mikropoulos

et al. 2024

JPM

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Molecular imaging is a key tool in the diagnosis and treatment of prostate cancer (PCa). Magnetic Resonance (MR) plays a major role in this respect with nuclear medicine imaging, particularly, Prostate-Specific Membrane Antigen-based, (PSMA-based) positron emission tomography with computed tomography (PET/CT) also playing a major role of rapidly increasing importance. Another key technology finding growing application across medicine and specifically in molecular imaging is the use of machine learning (ML) and artificial intelligence (AI). Several authoritative reviews are available of the role of MR-based molecular imaging with a sparsity of reviews of the role of PET/CT. This review will focus on the use of AI for molecular imaging for PCa. It will aim to achieve two goals: firstly, to give the reader an introduction to the AI technologies available, and secondly, to provide an overview of AI applied to PET/CT in PCa. The clinical applications include diagnosis, staging, target volume definition for treatment planning, outcome prediction and outcome monitoring. ML and AL techniques discussed include radiomics, convolutional neural networks (CNN), generative adversarial networks (GAN) and training methods: supervised, unsupervised and semi-supervised learning.

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Section: Radiomics Applicationsmentioning

confidence: 99%

The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer

Tapper,

Carneiro,

Mikropoulos

et al. 2024

JPM

Self Cite

View full text Add to dashboard Cite

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Radiomics reproducibility in computed tomography through changes of ROI size, resolution, and hounsfield unit: A phantom study

Soleymani,

Valibeiglou,

Fazel Ghaziani

et al. 2024

Radiography

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Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study

Bodalal,

Hong,

Trebeschi

et al. 2024

Eur Radiol Exp

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Background Microsatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort. Methods Preoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC). Results We identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54−0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60−0.91, p = 0.002) and enhanced the reliability of the predictions. Conclusion Differences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models. Relevance statement Noninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies. Key Points Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm’s predictive performance. Graphical Abstract

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Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets

Cited by 3 publications

References 27 publications

The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer

The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer

Radiomics reproducibility in computed tomography through changes of ROI size, resolution, and hounsfield unit: A phantom study

Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study

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